WO2019055538A1

WO2019055538A1 - Systems and methods for additive manufacture

Info

Publication number: WO2019055538A1
Application number: PCT/US2018/050713
Authority: WO
Inventors: Haresh G. MALKANI; Serigio BUTKEWITSCH CHOZE; Kyle A. CRUM
Original assignee: Arconic Inc.
Priority date: 2017-09-12
Filing date: 2018-09-12
Publication date: 2019-03-21

Abstract

Disclosed are various embodiments of systems and methods of performing part build simulation for Additive Manufacture ("AM") parts applying various optimization methods in creation of three-dimensional volume quality models of AM parts during an AM build. An embodiment of the present disclosure provides a method including applying at least a fidelity model to design data for an AM part to estimate one or more AM part parameters; determining one or more part-specific AM build parameters; generating and optimizing a digital twin of the AM part; validating one or more optimization constraints and one or more materials to be used in the AM build process to achieve the one or more corresponding material properties of the AM part; updating the digital twin; verifying that the digital twin meets a geometry prescribed by the design data of the AM part; and transmitting AM part build instructions for building the AM part.

Description

SYSTEMS AND METHODS FOR ADDITIVE MANUFACTURE CROSS-REFERENCE TO RELATED APPLICATIONS

[001] The present application claims priority from U.S. Provisional Patent Application No. 62/557,568 filed September 12, 2017, and entitled "COMPUTER-DRIVEN SYSTEMS AND COMPUTER-IMPLEMENTED METHODS CONFIGURED FOR CONDUCTING PART BUILD SIMULATIONS IN ADDITIVE MANUFACTURE," which is incorporated herein by reference in its entirety for all purposes.

FIELD OF TECHNOLOGY

[002] The subject matter herein generally relates to additive manufacturing ("AM"), and specifically relates to systems and methods of performing part build simulation for AM parts applying various optimization methods in creation of three-dimensional volume quality models of additively manufactured parts (e.g. AM parts) during the AM build.

BACKGROUND OF TECHNOLOGY

[003] Additive manufacturing may be used to build, via computer control, successive layers of an AM part. Defects in the AM part may occur due to errors in parameters of the AM process.

SUMMARY OF THE INVENTION

[004] The present disclosure provides systems and methods for performing part build simulation for AM parts applying various optimization methods in creation of three- dimensional volume quality models of AM parts during the AM build. An embodiment of the present disclosure provides a method including (A) applying, by a processor, at least one first fidelity model to design data for at least one Additive Manufacture ("AM") part to generate a first set of simulation results to estimate one or more AM part parameters associated with at least one AM part to be built by an AM build process; wherein the design data is representative of a desired design of the at least one AM part; wherein the one or more AM part parameters are representative of one or more corresponding material properties of the at least one AM part; (B) determining, by the processor, based on the one or more AM part parameters, one or more part-specific AM build parameters for the AM build process to build the at least one AM part; (C) generating, by the processor, a digital twin of the at least one AM part; wherein the digital twin comprises: i) the one or more AM part parameters of the at least one AM part; and ii) the one or more part-specific AM build parameters for the AM build process to build the at least one AM part; (D) determining, by the processor, that the digital twin requires to be optimized by at least one optimization method to meet or improve upon the desired design; (E) optimizing, by the processor, the digital twin by: i) assigning one or more code numbers to the one or more AM part parameters; ii) performing, based on the first set of simulation results, a numerical part build optimization for each parameter of the one or more AM part parameters to generate material-related optimization choice variables for the at least one AM part; and iii) obtaining, based on the material-related optimization choice variables, discrete material-related optimization choice variables for the at least one AM part; wherein the discrete material-related optimization choice variables have discrete values; iv) generating, based on the discrete material-related optimization choice variables, at least one of one or more optimization targets or one or more optimization constraints; (F) validating, by the processor, based on the one or more optimization targets, the one or more optimization constraints, or both, one or more materials to be used in the AM build process to achieve the one or more corresponding material properties of the at least one AM part; wherein the validating results in one of: i) identifying one or more acceptable materials to be used in the AM build process to build the at least one AM part, or ii) repeating the optimization of the digital twin to obtain one or more updated optimization targets, one or more updated optimization constraints, or both; wherein the repeating of the optimization of the digital twin is based on a second set of simulation results generated by applying at least one second fidelity model to the design data of the at least one AM part; (G) updating, by the processor, based on the one or more acceptable materials, the digital twin to obtain an updated digital twin; (H) verifying, by the processor, that the updated digital twin meets a geometry prescribed by the design data of the at least one AM part; (I) transmitting, by the processor, based on the updated digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and (J) building, by the AM machine, the at least one AM part based on the at least one AM part build instruction; wherein the updated digital twin is suitable to certify, without a physical inspection of the at least one AM part, a compliance of the at least one AM part to the desired design.

[005] In some embodiments, the at least one first fidelity model may be a low-fidelity simulation model.

[006] In some embodiments, the at least one first fidelity model may be a high-fidelity simulation model.

[007] In some embodiments, the one or more AM part parameters representative of one or more corresponding material properties of the at least one AM part comprise at least one of displacement, strain, distortion, stress, temperature, or gradient.

[008] In some embodiments, the method further including (K) determining, by the processor, that all simulation results allow for at least one of the validation and verification, by utilizing the simulation results in an open loop mode.

[009] In some embodiments, the numerical build optimization is based on solving an inverse mathematical problem, wherein the first set of simulation results is at least one of a set of optimization targets or a set of optimization constraints, and wherein process conditions or inputs are the material-related optimization choice variables.

[0010] In some embodiments, the obtaining the discrete material-related optimization choice variables is performed by one of: A) a second optimization method that operates directly with the discrete material-related optimization choice variables, B) an "branch and bound" method, or C) a method of rounding the results of the optimization run.

[0011] In some embodiments, the at least one first fidelity model defines inter-relationships between at least one of composition, microstructure or properties of the at least one AM part.

[0012] In some embodiments, applying the at least one first fidelity model to design data for the at least one AM part to generate the first set of simulation results utilizes multiscale modelling to evaluate at least one of one or more material properties or one or more material behaviors.

[0013] In some embodiments, the multiscale modelling utilizes at least one Integrated Computational Materials Engineering (iCME) technique.

[0014] In an embodiment of the method of the present disclosure, the method includes (A) applying, by a processor, at least one first fidelity model to design data for at least one Additive Manufacture ("AM") part to generate a first set of simulation results to estimate one or more AM part parameters associated with at least one AM part to be built by an AM build process; wherein the design data is representative of a desired design of the at least one AM part; wherein the one or more AM part parameters are representative of one or more corresponding material properties of the at least one AM part; (B) determining, by the processor, based on the one or more AM part parameters, one or more part-specific AM build parameters for the AM build process to build the at least one AM part; (C) generating, by the processor, a digital twin of the at least one AM part; (D) optimizing, by the processor, the digital twin; (E) transmitting, by the processor, based on the digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and (F) building, by the AM machine, the at least one AM part based on the at least one AM part build instruction; wherein the digital twin is suitable to certify, without a physical inspection of the at least one AM part, a compliance of the at least one AM part to the desired design. [0015] In an embodiment of the method of the present disclosure, the method includes (A) applying, by a processor, at least one first fidelity model to design data for at least one Additive Manufacture ("AM") part to generate a first set of simulation results to estimate one or more AM part parameters associated with at least one AM part to be built by an AM build process; wherein the design data is representative of a desired design of the at least one AM part; wherein the one or more AM part parameters are representative of one or more corresponding material properties of the at least one AM part; (B) determining, by the processor, based on the one or more AM part parameters, one or more part-specific AM build parameters for the AM build process to build the at least one AM part; (C) generating, by the processor, a digital twin of the at least one AM part; (D) optimizing, by the processor, the digital twin by: i) assigning one or more code numbers to the one or more AM part parameters; ii) performing, based on the first set of simulation results, a numerical part build optimization for each parameter of the one or more AM part parameters to generate material- related optimization choice variables for the at least one AM part; and iii) obtaining, based on the material-related optimization choice variables, discrete material-related optimization choice variables for the at least one AM part; wherein the discrete material-related optimization choice variables have discrete values; and iv) generating, based on the discrete material-related optimization choice variables, at least one of one or more optimization targets or one or more optimization constraints; (E) transmitting, by the processor, based on the digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and (F) building, by the AM machine, the at least one AM part based on the at least one AM part build instruction; wherein the digital twin is suitable to certify, without a physical inspection of the at least one AM part, a compliance of the at least one AM part to the desired design. [0016] In an embodiment of a system of the present disclosure, the system includes (1) a processor; (2) a non-transitory computer readable storage medium storing thereon program logic for execution by the processor, wherein, when executing the program logic, the processor is configured to: (A) apply at least one first fidelity model to design data for at least one Additive Manufacture ("AM") part to generate a first set of simulation results to estimate one or more AM part parameters associated with at least one AM part to be built by an AM build process; wherein the design data is representative of a desired design of the at least one AM part; wherein the one or more AM part parameters are representative of one or more corresponding material properties of the at least one AM part; (B) determine, based on the one or more AM part parameters, one or more part-specific AM build parameters for the AM build process to build the at least one AM part; (C) generate a digital twin of the at least one AM part; wherein the digital twin comprises: i) the one or more AM part parameters of the at least one AM part; and ii) the one or more part-specific AM build parameters for the AM build process to build the at least one AM part; (D) determine that the digital twin requires to be optimized by at least one optimization method to meet or improve upon the desired design; (E) optimize the digital twin by: i) assigning one or more code numbers to the one or more AM part parameters; ii) performing, based on the first set of simulation results, a numerical part build optimization for each parameter of the one or more AM part parameters to generate material-related optimization choice variables for the at least one AM part; and iii) obtaining, based on the material-related optimization choice variables, discrete material- related optimization choice variables for the at least one AM part; wherein the discrete material-related optimization choice variables have discrete values; iv) generating, based on the discrete material-related optimization choice variables, at least one of one or more optimization targets or one or more optimization constraints; (F) validate, based on the one or more optimization targets, the one or more optimization constraints, or both, one or more materials to be used in the AM build process to achieve the one or more corresponding material properties of the at least one AM part; wherein the validating results in one of: i) identifying one or more acceptable materials to be used in the AM build process to build the at least one AM part, or ii) repeating the optimization of the digital twin to obtain one or more updated optimization targets, one or more updated optimization constraints, or both; wherein the repeating of the optimization of the digital twin is based on a second set of simulation results generated by applying at least one second fidelity model to the design data of the at least one AM part; (G) update, based on the one or more acceptable materials, the digital twin to obtain an updated digital twin; (H) verify that the updated digital twin meets a geometry prescribed by the design data of the at least one AM part; and (I) transmit, based on the updated digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and (3) the at least one AM machine configured to: receive the at least one AM part build instruction; (J) build the at least one AM part based on the at least one AM part build instruction; wherein the updated digital twin is suitable to certify, without a physical inspection of the at least one AM part, a compliance of the at least one AM part to the desired design.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present disclosure can be further explained with reference to the attached drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure. [0018] FIG. 1 is a schematic illustration of an overall architecture of various activities that may occur within an exemplary inventive computer-based AM systems and related methods according to one or more embodiments of the present disclosure;

[0019] FIG. 2 is a schematic representation of an exemplary inventive computer-based AM system according to an embodiment of the present disclosure;

[0020] FIG. 3 is representation of an exemplary inventive simulation process according to one or more embodiments of the present disclosure;

[0021] FIG. 4 is a schematic workflow of an exemplary inventive part build simulation process according to one or more embodiments of the present disclosure;

[0022] FIG. 5 is a schematic workflow of a process depicting one example of an exemplary inventive discrete optimization process according to one or more embodiments of the present disclosure;

[0023] Fig. 6A-6D show illustrative examples of the numerical build optimization that may occur within an exemplary inventive computer-based AM system according to an embodiment of the present disclosure;

[0024] Fig. 7 illustrates an exemplary diagram of cost v. mechanical properties tradeoff in the exemplary inventive material selection optimization that may occur within an exemplary inventive computer-based AM system according to an embodiment of the present disclosure;

[0025] Fig. 8 illustrates an exemplary diagram of surface roughness v. scan speed tradeoff that may occur within an exemplary inventive computer-based AM system according to an embodiment of the present disclosure;

[0026] Fig. 9 illustrates an exemplary diagram of Lack-of-Fusion Porosity v. build speed tradeoff that may occur within an exemplary inventive computer-based AM system according to an embodiment of the present disclosure; [0027] Fig. 10 illustrates an exemplary residual stress v. distortion tradeoff that may occur within an exemplary inventive computer-based AM system according to an embodiment of the present disclosure; and

[0028] Fig. 11 illustrates an exemplary diagram of surface roughness v. gas porosity tradeoff that may occur within an exemplary inventive computer-based AM system according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[0029] The present disclosure can be further explained with reference to the included drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[0030] Among those benefits and improvements that have been disclosed, other objects and advantages of this invention can become apparent from the following description taken in conjunction with the accompanying figures. Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative of the invention that may be embodied in various forms. In addition, each of the examples given in connection with the various embodiments of the present disclosure is intended to be illustrative, and not restrictive.

[0031] Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases "in one embodiment" and "in some embodiments" as used herein do not necessarily refer to the same embodiment(s), though they may. Furthermore, the phrases "in another embodiment" and "in some other embodiments" as used herein do not necessarily refer to a different embodiment, although they may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. Further, when a particular feature, structure, or characteristic is described in connection with an implementation, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other implementations whether or not explicitly described herein.

[0032] The term "based on" is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of "a," "an," and "the" include plural references. The meaning of "in" includes "in" and "on. "

[0033] It is understood that at least one aspect/functionality of various embodiments described herein can be performed in real-time, faster-than-real-time, and/or dynamically. As used herein, the term "real-time" is directed to an event/action that can occur instantaneously or almost instantaneously in time when another event/action has occurred. For example, the "real-time processing," "real-time computation," and "real-time execution" all pertain to the performance of a computation prior to an actual time that the related physical process or physical transformation occurs (e.g., adding a build layer to an Additive Manufacture (AM) part), so that results of the real-time computation (e.g., a simulated dynamics model of the AM part being built) can be used in guiding the physical process (e.g., AM process). As used herein, the term "faster-than-real-time" is directed to simulations in which advancement of simulation time may occur faster than real world time. For example, some of the "faster- than-real-time" simulations of the present disclosure may be configured in accordance with one or more principles detailed in D. Anagnostopoulos, 2002, "Experiment scheduling in faster-than-real-time simulation," 148-156. 10.1 109/P ADS.2002.1004212. [0034] As used herein, the term "dynamically" means that events and/or actions can be triggered and/or occur without any human intervention. In some embodiments, events and/or actions in accordance with the present disclosure can be in real-time and/or based on a predetermined periodicity of at least one of: nanosecond, several nanoseconds, millisecond, several milliseconds, second, several seconds, minute, several minutes, hourly, several hours, daily, several days, weekly, monthly, etc.

[0035] As used herein, the term "runtime" corresponds to any behavior that is dynamically determined during an execution of a software application or at least a portion of software application.

Additive Manufacturing

[0036] As used herein, "additive manufacturing" means "a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies", as defined in ASTM F2792-12a entitled "Standard Terminology for Additively Manufacturing Technologies". The AM parts described herein may be manufactured via any appropriate additive manufacturing technique described in this ASTM standard, such as binder jetting, directed energy deposition, material extrusion, material jetting, powder bed fusion, or sheet lamination, among others. In one embodiment, an additive manufacturing process includes depositing successive layers of one or more materials (e.g., powders of materials) and then selectively melting and/or sintering the materials to create, layer-by-layer, an AM part/product. In one embodiment, an additive manufacturing processes uses one or more of Selective Laser Sintering (SLS), Selective Laser Melting (SLM), and Electron Beam Melting (EBM), among others. In one embodiment, an additive manufacturing process uses an EOSINT M 280 Direct Metal Laser Sintering (DMLS) additive manufacturing system, or comparable system, available from EOS GmbH (Robert-Stirling-Ring 1, 82152 Krailling/Munich, Germany). Additive manufacturing techniques (e.g. when utilizing metallic feedstocks) may facilitate the selective heating of materials above the liquidus temperature of the particular alloy, thereby forming a molten pool followed by rapid solidification of the molten pool. Non-limiting examples of additive manufacturing processes useful in producing AM products include, for instance, DMLS (direct metal laser sintering), SLM (selective laser melting), SLS (selective laser sintering), and EBM (electron beam melting), among others. Any suitable feedstocks may be used, including one or more materials, one or more wires, and combinations thereof. In various embodiments, AM is configurable to utilize various feedstocks - e.g. metallic feedstocks (e.g. with additives to promote various properties, e.g. grain refiners and/or ceramic materials), plastic feedstocks, and polymeric feedstocks (or reagent-based feedstock materials which form polymeric AM builds/ AM parts), to name a few. In some embodiments the additive manufacturing feedstock is comprised of one or more materials. Shavings are types of particles. In some embodiments, the additive manufacturing feedstock is comprised of one or more wires. A ribbon is a type of wire.

[0037] In one approach, the AM parts metal alloys described herein are in the form of an additive manufacturing feedstock.

[0038] As noted above, additive manufacturing may be used to create, layer-by -layer, an AM part/product. In one embodiment, a powder bed is used to create an AM part/product (e.g., a tailored alloy product and/or a unique structure unachievable through traditional manufacturing techniques (e.g. without excessive post-processing machining)).

[0039] In one approach, a method comprises (a) dispersing an AM feedstock (e.g. metal alloy powder in a bed), (b) selectively heating a portion of the material (e.g., via an energy source or laser) to a temperature above the liquidus temperature of the particular AM part/product to be formed, (c) forming a molten pool and (d) cooling the molten pool at a cooling rate of at least 1000°C per second. In one embodiment, the cooling rate is at least 10,000°C per second. In another embodiment, the cooling rate is at least 100,000°C per second. In another embodiment, the cooling rate is at least 1,000,000°C per second. Steps (a)-(d) may be repeated as necessary until the AM part/product is completed.

[0040] In another approach, a method comprises (a) dispersing a feedstock (e.g. AM material powder) in a bed, (b) selectively binder jetting the AM material powder, and (c) repeating steps (a)-(b), thereby producing a final additively manufactured product (e.g. including optionally heating to burn off binder and form a green form, followed by sintering to form the AM part).

[0041] In another approach, electron beam (EB) or plasma arc techniques are utilized to produce at least a portion of the AM part/product. Electron beam techniques may facilitate production of larger parts than readily produced via laser additive manufacturing techniques. An illustrative example provides feeding a to the wire feeder portion of an electron beam gun. The wire may comprise a metal feedstock (e.g. metal alloy including titanium, cobalt, iron, nickel, aluminum, or chromium alloys to name a few). The electron beam heats the wire or tube, as the case may be, above the liquidus point of the alloy to be formed, followed by rapid solidification of the molten pool to form the deposited material.

[0042] The alloy may be, for instance, an aluminum-based alloy, a titanium-based alloy (including titanium aluminides), a nickel-based alloy, an iron-based alloy (including steels), a cobalt-based alloy, or a chromium-based alloy, among others.

[0043] Any suitable alloy composition may be used with the techniques described above to produce AM part/product. Some non-limiting examples of alloys that may be utilized are described below. However, other alloys may also be used, including copper-based, zinc- based, silver-based, magnesium-based, tin-based, gold-based, platinum-based, molybdenum- based, tungsten-based, and zirconium-based alloys, among others. [0044] As used herein, "aluminum alloy" means a metal alloy having aluminum as the predominant alloying element. Similar definitions apply to the other corresponding alloys referenced herein (e.g. titanium alloy means a titanium alloy having titanium as the predominant alloying element, and so on).

[0045] In some embodiments, the inventive specially programmed computing systems with associated devices are configured to operate in the distributed network environment, communicating over a suitable data communication network (e.g., the Internet, etc.) and utilizing at least one suitable data communication protocol (e.g., IPX/SPX, X.25, AX.25, AppleTalk(TM), TCP/IP (e.g., HTTP), etc.). Of note, the embodiments described herein may, of course, be implemented using any appropriate hardware and/or computing software languages. In this regard, those of ordinary skill in the art are well versed in the type of computer hardware that may be used, the type of computer programming techniques that may be used (e.g., object-oriented programming), and the type of computer programming languages that may be used (e.g., C++, Objective-C, Swift, Java, Javascript). The aforementioned examples are, of course, illustrative and not restrictive.

[0046] The material disclosed herein may be implemented in software or firmware or a combination of them or as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. As used herein, the machine-readable medium may include any medium and/or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). By way of example, and not limitation, the machine-readable medium may comprise computer readable storage media, for tangible or fixed storage of data, or communication media for transient interpretation of code-containing signals. Machine-readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable and non-removable media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data. Machine-readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, flash memory storage, or any other physical or material medium which can be used to tangibly store the desired information or data or instructions, including but not limited to electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and which can be accessed by a computer or processor.

[0047] In another form, a non-transitory article, such as non-volatile and non-removable computer readable media, may be used with any of the examples mentioned above or other examples except that it does not include a transitory signal per se. It does include those elements other than a signal per se that may hold data temporarily in a "transitory" fashion such as RAM and so forth. In some embodiments, the present disclosure may rely on one or more distributed and/or centralized databases (e.g., data center).

[0048] As used herein, the term "server" should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term "server" can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Servers may vary widely in configuration or capabilities, but generally a server may include one or more central processing units and memory. A server may also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input output interfaces, or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

[0049] As used herein, a "network" should be understood to refer to a network that may couple devices so that communications may be exchanged, such as between a server and a client device or other types of devices, including between wireless devices coupled via a wireless network, for example. A network may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), or other forms of computer or machine readable media, for example. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wireD line type connections, wireless type connections, cellular or any combination thereof. Likewise, subD networks, which may employ differing architectures or may be compliant or compatible with differing protocols, may interoperate within a larger network. Various types of devices may, for example, be made available to provide an interoperable capability for differing architectures or protocols. As one illustrative example, a router may provide a link between otherwise separate and independent LANs.

[0050] As used herein, the terms "computer engine" and "engine" identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, etc.).

[0051] Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual- core mobile processor(s), and so forth.

[0052] Software may refer to 1) libraries; and/or 2) software that runs over the internet or whose execution occurs within any type of network. Examples of software may include, but are not limited to, software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[0053] One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as "IP cores" may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that actually make the logic or processor. [0054] In some embodiments, the exemplary inventive AM processes of the present disclosure may include one or more steps detailed, without limitation, in U.S. Patent Pub. No. 2016/0224017 which is hereby incorporated herein by reference. For example, the AM process may be a process of joining materials to make objects from 3D model data, usually layer upon layer. In some embodiments, additive manufacturing includes building successive layers of an AM material (e.g., aluminium alloy powder) by depositing a feed stock powder of the AM material (e.g., metal powder) and then selectively melted and/or sintered (e.g. with a laser or other heat source) to create, layer-by-layer, an AM part (e.g., an aluminium alloy product, a titanium alloy product, a nickel alloy product). Additive build processes utilizing a powder feedstock that can employ one or more of the embodiments of the instant disclosure include: direct metal laser sintering (e.g. a powder bed fusion process used to make metal AM parts directly from metal powders without intermediate "green" or "brown" parts); directed energy deposition (e.g., an AM process in which focused thermal energy is used to fuse materials by melting as they are being deposited); powder bed fusion (e.g. an AM process in which thermal energy selectively fuses regions of a powder bed); or laser sintering (e.g., a powder bed fusion process used to produce objects from powdered materials using one or more lasers to selective fuse or melt the particles at the surface, layer by layer, in an enclosed chamber) to name a few. Some non-limiting examples of suitable additive manufacturing systems include the EOSINT M 280 Direct Metal Laser Sintering (DMLS) additive manufacturing system, available from EOS GmbH (Robert-Stirling-Ring 1, 82152 Krailling/Munich, Germany). Other suitable additive manufacturing systems include Selective Laser Sintering (SLS) systems, Selective Laser Melting (SLM) systems, and Electron Beam Melting (EBM) systems, among others.

[0055] Fig. 1 shows an illustrative example of an overall architecture 100 of various activities that may occur within an exemplary inventive computer-based AM system 102 that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. While some activities identified in Fig. 1 are detailed herein as occurring in sequential order, such description is done for purposes of convenience and should not be viewed as being limited since, as a skilled practitioner would readily recognize, at least some activities may occur concurrently, in reverse order, or not occur at al under certain condition(s).

[0056] Referring to item 104 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may receive/obtain electronical data describing one or more parts to be manufactured ("part data"). In some embodiments, the exemplary inventive computer-based AM system may analyze the part data to determine one or more functions that are desired for each AM part. In some embodiments, when an AM part may be constructed from a plurality of sub-parts and/or when the AM part may be intended to be combined with at least one other part, which may or may not be manufactured utilizing an AM process, to perform its intended function, the exemplary inventive computer-based AM system may further determine one or more characteristics that may influence how the AM part would perform for its intended purpose(s).

[0057] In some embodiments, any individual part manufactured via AM may be subject to one or more additional processes, such as machining for finishing purposes and/or forging for inducing desired microstructural properties. In some embodiments, at least one sub-part may not be manufactured via AM. In some embodiments, the exemplary inventive computer- based AM system may be configured to perform such analysis/determination as part of preparation for generating software instructions and/or software model(s) that may direct how the AM part is created during the additive manufacturing process. In some embodiments, the exemplary inventive computer-based AM system may be configured to perform the above analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination performed during the activity of item 104 to influence, in real time, how an exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[0058] Referring to, for example, item 106 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may analyze/determine how a proposed (initial) design of the AM part in the part data received/obtained by the exemplary inventive computer-based AM system would be suitable/fit to perform its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to analyze/determine how the design of the AM part would influence the overall performance of the exemplary inventive computer-based AM system. In some embodiments, during a part of the activity of item 106, the exemplary inventive computer-based AM system may be configured to dynamically alter the material composition of the initial design of the AM part to improve performance of the exemplary inventive computer-based AM system during one or more subsequent activities without sacrificing and/or improving how the AM part would perform for its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination during the activity of item 106 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[0059] Referring to item 108 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may select at least one of: i) feedstock (e.g., usable material) processing paths, ii) material composition(s) from one or more pre-determined material compositions that would be sufficiently suitable to the intended function(s) of the AM part, and/or iii) AM processing path(s).

[0060] In some embodiments, the exemplary inventive computer-based AM system may be configured to analyze how the material composition of the AM part would influence the overall performance of the exemplary inventive computer-based AM system. For example, the exemplary inventive computer-based AM system may be configured to analyze life expectancy, cost, weight, corrosion resistance, and other parameter(s) of AM build part.

[0061] In some embodiments, a part of the activity of item 108, the exemplary inventive computer-based AM system may be configured to select, from one or more pre-determined material compositions, an initial material composition of the AM part, and processing path in the part data to improve performance of the exemplary inventive computer-based AM system during one or more subsequent activities without sacrificing and/or improving how the AM part would perform for its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination during the activity of item 108 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[0062] Referring to item 110 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may run one or more part build simulations to analyze/test how, for example without limitation, one or more characteristics of the AM part would influence and/or be influenced by one or more subsequent activities of the exemplary inventive computer-based AM system. In some embodiments, as a part of the activity 110, the exemplary inventive computer-based AM system may be configured to dynamically alter, in real-time, the one or more part build simulation parameters based, at least in part, on one or more real-time characteristics of the exemplary inventive computer-based AM system and/or one or more real-time internal and/or external conditions associated with the exemplary inventive computer-based AM system (e.g., a temperature inside of an AM machine). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback control mechanism that may be configured to utilize the one or more AM part build simulations developed during the activity of item 110 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1. In some embodiments, the one or more AM part build simulations may be based, at least in part, on at least in part, any given simulation of any given part, may be influenced by and compared to simulation(s) of other sufficiently similar AM part(s).

[0063] In some embodiments, during the activity of item 110, the exemplary inventive computer-based AM system may be configured to generate a dynamically adjustable digital representation ("digital twin") 138 of the AM part that would be manufactured. In some embodiments, the digital twin 138 includes current and/or historical data related to function(s) of the AM part; the design of the AM part, and/or the material composition of the AM part (the part-centered data such as design data 128 and material data 130). In some embodiments, in addition to the part-centered data, the digital twin 138 may include AM process parameter(s) associated with the exemplary AM process to be employed to manufacture the AM part and/or code instructions that are configured to direct an exemplary AM machine to build the AM part (the build-centered data such as simulation data 132 and process data 134). In some embodiments, the build-centered data may include historical error data generated during the additive manufacturing of other similar AM part(s) (i.e., digital twin(s) of previously manufactured other similar AM part(s)).

[0064] In some embodiments, in addition to the part-centered data and the build-centered data, the digital twin 138 may include certification requirement data (e.g., defect determination parameter(s)) that may be employed to certify that the AM part would be fit for its intended function(s) in connection with in-situ monitoring (item 116) and post-build inspection (item 118) (the certification-centered data such as inspection data 136). In some embodiments, the digital twin 138 may be configured to be self-contained, self-adjustable, and/or self-executing computer entity that is agnostic to a type of an AM machine that may be employed to build the AM part.

[0065] Referring to item 112 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may be configured to utilize the digital twin 138 to determine one or more settings for the exemplary AM machine for building the AM part (AM machine setting data). In some embodiments, the exemplary inventive computer-based AM system may be configured, during the activity of item 112 to incorporate the AM machine setting data into the digital twin 138. In some embodiments, the AM machine setting data may include data that cause the exemplary machine to calibrate itself in a particular way prior to building the AM part (AM machine calibration data). In some embodiments, as a part of the activity of item 112, the exemplary inventive computer-based AM system may be configured to utilize the monitoring data collected, in real-time, about the exemplary AM machine, while the exemplary AM machine builds other AM part(s), to dynamically adjust the AM machine setting data in the digital twin 138 of the AM part to account, without limitation, for machine-to-machine parameter variability.

[0066] In some embodiments, the monitoring data may include at least one of: i) operational parameter(s) of the exemplary AM machine, ii) internal (in-situ) conditions of the exemplary AM machine (e.g., temperature within a build chamber, 02 concentration, etc.), which may be generated, for example without limitation, during activity of item "6", and/or iii) external conditions associated with the exemplary AM machine (e.g., environmental conditions (e.g., surrounding temperature, atmospheric pressure, humidity, etc.)).

[0067] Referring to item 114 of Fig. 1, the exemplary inventive computer-based AM system may be configured to execute the digital twin 138 so that the AM machine may be instructed to build the AM part in accordance with the corresponding digital twin 138. For example, based on the digital twin 138, the AM machine may be instructed to deposit a an initial layer of AM part based on an estimated build position, extract actual coordinates of the build layer, compare the coordinates of the initial build layer with the estimated coordinates sent to the AM machine, and determining a deviation, if any, between an ideal (estimated) build of the digital twin 138 and the actual build layer.

[0068] In some embodiments, as a part of activity of item 114, based on the digital twin 138, if particular point(s) in the build portion of the AM part is/are determined to deviate from a threshold condition to a tolerable degree (e.g., an out-of-compliance-but-reparable condition), the exemplary inventive computer-based AM system may be configured to mitigate such noncompliance by adjusting build instruction(s) for next build layer(s) and/or build portion(s) of the same layer in which the noncompliance has been determined. In some embodiments, the out-of-compliance-but-reparable condition may be a condition in which repair would not be needed. In one embodiment, the out-of-compliance-but-reparable condition may be a condition that would be within tolerances without need to repair. In one embodiment, the out- of-compliance-but-reparable condition may be a condition that would be outside of tolerances but still repairable.

[0069] In some embodiments, as a part of activity of item 114, based on the digital twin 138, if particular point(s) in the build portion of the AM part is/are determined to deviate from a threshold condition to a non-tolerable degree (unrepairable condition), the exemplary inventive computer-based AM system may be configured to cause the exemplary AM machine to stop the build process. In such case, the defective intermediate may be discarded, avoiding deposition of additional layers which would save cost associated with material for those layers and the time to complete them.

[0070] In some embodiments, the exemplary inventive computer-based AM system may be configured to execute an active feedback control mechanism (item 126 of Fig. 1) which may be triggered based, at least in part, on the in-situ monitoring data (item 1 16) whenever there is/are discrepancy(ies)/deviation(s) within at least one of: i) definitions determined during the material selection activity (item 108), with or without executing the iterative adjustment of build material selection (item 122 of Fig. 1); ii) definitions determined during the part build simulation activity (item 110), with or without the interposition of the optimization step 124); and/or iii) definitions determined during the AM machine's set points determination (item 112).

[0071] In some embodiments, independently of the discrepancies identified during the material selection activity (item 108) (optionally influenced by item 122), during the part build simulation activity (item 1 10) (optionally influenced by item 124) and during the AM machine's set points determination (item 112) being known or quantifiable, the inventive active feedback control mechanism (item 126) may be configured to either interrupt the build process of the AM part and/or re-run the iterative adjustments (items 122 and/or 124) to affect values of items 108, 1 10, and 112 of Fig. 1 until quality metrics identified in item 1 16 meet the specification. Consequently, in at least some embodiments, the in-situ monitoring (item 1 16) drives the inventive active feedback control mechanism (item 126) to dynamically specify machine set points that result in a successful completion of the build or in sufficiently earlier stop of the build process to minimize the waste of material and/or time. In some embodiments, the inventive active feedback control mechanism (item 126) may be configured as at least one of suitable control strategies such as, without limitation, classical Proportional-Integral-Derivative (PID) control, adaptive control, optimal control, and combinations thereof, etc.

[0072] Referring to item 118 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may be configured to generate a final state of the digital twin 138 after the physical AM part (item 120: the physical twin) has passed the post- build inspection (item 118) so that the final state of the digital twin 138 is utilized to certify a subsequently built AM part as being fit for its intended function(s) (e.g., compliance with the certification requirements and other desired requirement(s)) without actual/physical evaluation of the subsequent AM part itself. In some embodiments, the post-build inspection (item 1 18) may include non-destructive testing, destructive testing (completed on parts), or both.

[0073] Referring to at least activities of items 114 and 116 of Fig. 1 , the exemplary inventive computer-based AM system may be configured to dynamically adjust, in real-time, the digital twin 138 and/or the AM build process based, at least in part, on and may include at least one of: i) the part design data (item 128 of Fig. 1), ii) the material composition data (item 130 of Fig. 1), iii) the part-build simulation data (item 132 of Fig. 1), iv) the AM process data (item 134 of Fig. 1), and/or v) the inspection/certification data (item 136 of Fig. 1).

[0074] In some embodiment, the AM process data may be the process data collected during the production and/or certification of similar AM part(s), which may be then used to complete one or more of i) the part design data 128, ii) the material composition data 130, and/or iii) the part-build simulation data 132. In some embodiment, the inspection/certification data 136 is used to adjust one or more of i) the part design data 128, ii) the material composition data 130, and/or iii) the part-build simulation data 132. [0075] In some embodiment, the digital twin 138 may be stored according to a predetermined data model and/or schema and include all of data items 128-136. In some embodiment, the digital twin 138 may include data that describes the machine setup changes resulting from the inventive active feedback control mechanism (item 126). In some embodiments, the digital twin 138 of AM process parts may be configured to be processed by applying at least one of suitable analytical techniques such as, without limitation, machine learning algorithms, neural networks, and/or predictive modelling techniques.

[0076] Fig. 2 shows an illustrative example of an overview of a distributed computer network system 200 including an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. In some embodiments, the exemplary inventive AM system may include several different entities, such as an AM operator's terminal 208 and customers 204 all operatively communicable via a shared communication network 206, such that data, such as the inventive AM digital twin files, may be transferred between any one of the aforementioned connected entities 202 and 204. The customer logical environment 204 may include an authentication server that may be arranged to authenticate if a customer entity is authorized to access a relevant data file, such as a particular AM digital twin. In some embodiments, the shared communication network 206 may relate to the Internet, a LAN, a WAN, or any other suitable computer network. In some embodiments, the AM process logic environment 202 may effectively be a print farm, comprising one or more different operatively connected AM Machines/3D printers 210. Accordingly, the terms "AM machines" and "3D print farm" may be used interchangeably to refer to the same physical entity/entities in the ensuing description, and the term "3D print farm" is analogous to the term "3D printing bureau." [0077] The customer environment 204 may include a server 218 operatively connected to the communication network 206, enabling direct data connections and communication with the attached terminal 208 and the 3D print farm 202. In addition, the server 218 may host a website through which a user using any one of the different operatively connected terminals 202 and 208, may interact with the customer environment 204 using standard web browsers.

[0078] In some embodiments, the server 218 may be operatively connected to a database 220, which may be stored in a storage device local to the server 218, or in an external storage unit (not shown). In some embodiments, the exemplary inventive AM system may be configured so that the customer environment 204 provides several different functions. For example, it provides a centralized network peer, which is entrusted with managing access rights to proprietary information included in the inventive AM digital twin file. It may also provide a centralized networked means for advertising and accessing content, such as the inventive AM digital twin files, AM parameter settings, and for securely distributing content between different networked terminals. Such content may also relate to CAD software made available by a software developer who can be the AM operator.

[0079] In some embodiments, the exemplary inventive AM system may be configured so that access to information included in an exemplary inventive AM digital twin file may be controlled via the customer environment 204, using a combination of unique identifier(s) and data encryption. By unique identifiers is intended any electronically verifiable identifier. For example, the unique identifier associated with a 3D printer may relate to the printer's serial number. The database 220 maintains a record of all parties registered to use the 3D printers (AM machines). Such parties may include, but are not limited to, registered AM operators 208. This information may be stored as one or more records and/or tables within the database 220. [0080] In some embodiments, the exemplary inventive AM system may be configured to require a registration capability in order for each operatively connected entity to be uniquely identifiable by the customer environment 204, to thereby enable the customer environment 204 to manage access rights to encrypted content. For example, to manage access rights to the encrypted content of exemplary inventive AM digital twin files. In some embodiments, the exemplary inventive AM system may be configured so that the exemplary 3D print farm 202 may include a server 212, which is operatively connected to the shared communication network 206. The server 212 may itself be operatively connected to one or more different AM machines/3D printers 210. In some embodiments, the function of the server 212 is to execute one or more activities identified in Fig. 1 such as dynamically instructing an appropriate AM machine 210 to AM produce an exemplary AM part based on exemplary inventive AM digital twin.

[0081] In some embodiments, an exemplary inventive part build simulation may be designed to evaluate one or more geometrical and functional parameters of an exemplary AM part. For example, the exemplary inventive part build simulation may evaluate, without limitation, geometric parameters such as but not limited to orientations, functional parameters such as cooling rates and residual stresses, and/or process related parameters such as supports, and other similarly suitable parameters.

[0082] In one embodiment, the exemplary inventive computer-based AM system may be configured to execute simulations, such as the part build simulation (item 110 of Fig. 1) in combination with material selection optimization (item 122 of Fig. 1) and/or build optimization (item 124 of Fig. 1) as described above. In some embodiments, the exemplary inventive computer-based AM system may be configured to include the part build simulation data (item 132 of Fig. 1) as part of the digital twin of the AM part as described above (item 112 of Fig. 1). In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize the part build simulation data to define the operational set points that may define all necessary and sufficient configurations for the AM equipment to build the AM part.

[0083] An exemplary simulation process performed by the exemplary inventive computer- based AM system is shown in Fig. 3. In the embodiment as shown in Fig. 3, at 302, the part build simulation data/input data (item 132 of Fig. 1) including but not limited to geometry, material, and boundary conditions related to an AM part to be simulated are obtained by the exemplary inventive computer-based AM system. The exemplary inventive computer-based AM system proceeds to step 304 to pre-process the input data. Then at step 306, the exemplary inventive computer-based AM system discretizes the information related to the processed input data associated with the AM part to be simulated. At step 308, the exemplary inventive computer-based AM system generates an input deck with the discretized information. At step 310, based on the input deck, the exemplary inventive computer-based AM system proceeds to perform the exemplary inventive part build simulation related to the AM part. Then at step 312, the exemplary inventive computer-based AM system postprocesses the data after the simulation.

[0084] In some embodiments, the exemplary inventive part build simulation may include at least one of:

i) iterative adjustment of build material selection (item 122 of Fig. 1), and

ii) iterative adjustment of the AM machine's setup parameters (item 124 of Fig. 1).

[0085] In some embodiment, individual setup parameters of a particular AM machine (item 124 of Fig. 1) may be determined based on an execution of a calibration routine utilized at step 1 12 of Fig. 1. In some embodiments, the setup parameters of each AM machine may be stored in a database, such as item 214 of Fig. 2, associated with the AM machine's corresponding serial number, such as item 216 of Fig. 2. An exemplary general calibration routine performed by the exemplary inventive computer-based AM system is shown at section 4 of J. Palomo, et al, Journal of Statistical Software, "SAVE: An R Package for the Statistical Analysis of Computer Models" (2015).

[0086] In some embodiments, the part build simulation of the exemplary inventive computer- based AM system may include the application of modelling and simulation tools into a sequence of steps necessary and sufficient to certify the engineering properties of an additively manufactured part. Examples of the modelling and simulation tools include but not limited to MSC-Nastran, MSC-Simufact, Abaqus, Ansys, LS-Dyna, DEFORM, Matlab- Simulink, and/or R.

[0087] Fig. 4 shows an illustrative example of a block diagram depicting one example of a part build simulation process 400 implemented by combination of one or more of i) part build simulation 402 (item 110 of Fig. 1), ii) material selection optimization 412 (item 122 of Fig. 1) and iii) build optimization 408 (item 124 of Fig. 1) that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. In one embodiment, the exemplary inventive part build simulation process 400 includes both an open loop mode, where simulation is used to define the results arising from a set of operating/input conditions, or in closed loop, where simulation predictions guide a numerical optimizer to define optimal values for operating/input conditions of the build. In some embodiments, the exemplary inventive part build simulation provides functionality, based on numerical simulation, for step 406 validation of engineering material properties and verification of adherence to the geometry prescribed by the design.

[0088] In some embodiments, the ability to steer the build in real-time by combining simulation, optimization, physical measurements and/or active feedback control mechanisms may allow to reduce significant amounts of variability. In some embodiments, exemplary mathematical optimizers of the exemplary inventive computer-based AM system are shown in G. N. Vanderplats, 2007, "Multidiscipline Design Optimization", VRAND Inc. and Z. Giirdal, R. T. Haftka and P. Hajela, Wiley-Interscience, 1 st Ed.1999, "Design and Optimization of Laminated Composite Materials." In particular, the handling of discrete decision variables is described within the chapter entitled "Integer Programming Techniques" of the above reference. In some embodiments, the above exemplary mathematical optimizers of the exemplary inventive computer-based AM system may be used for material selection when operating with discrete decision variables, reflecting the discrete choice of one and only one material option.

[0089] In some embodiments, the exemplary inventive part build simulation process includes active optimization, based on numerical optimization, such that a design space is exhausted for the determination of operating/input conditions that allows for the successful validation of engineering material properties and verification of adherence to the geometry prescribed by the part design (item 106 of Fig. 1). Exemplary numerical optimizations of the exemplary inventive computer-based AM system are shown in B. Frederickson, "An Interactive Tutorial on Numerical Optimization," http://www.benfrederickson.cori^numerical-optirnization/ and Section 1-3 (General Problem Statement) of http://www2.mae. ufi.edu/haftka/eoed/V anderplaats-chapl .pdf

[0090] In some embodiments, the part build simulation process of the exemplary inventive computer-based AM system may begin at step 402 for simulation. The simulation process may use physics-based representations of the AM process at various fidelities, i.e., from semi-empirical equations (e.g. low-fidelity simulation models) all the way through fully discretized solutions (e.g. high-fidelity simulation models). These variable/multiple fidelity models may provide estimates for quantities such as, but not limited to, displacement (e.g. plus the derived quantities of strain and distortion), stresses (e.g. including residual stresses), temperatures, as well as gradients of these (i.e. temperatures, stresses, displacements) and other similarly suitable parameters.

[0091] In some embodiment, the variable/multiple fidelity models may provide estimates for one of the parameters of displacement, strain, distortion, stress, temperature, or gradient thereof. In some embodiment, the fidelity models may provide estimates for displacement and strain, or gradient thereof. In some embodiment, the fidelity models may provide estimates for strain and distortion, or gradient thereof. In some embodiment, the fidelity models may provide estimates for distortion and stress, or gradient thereof. In some embodiment, the fidelity models may provide estimates for stress and temperature, or gradient thereof. In some embodiment, the fidelity models may provide estimates for displacement and distortion, or gradient thereof. In some embodiment, the fidelity models may provide estimates for displacement and stress, or gradient thereof. In some embodiment, the fidelity models may provide estimates for displacement and temperature, or gradient thereof. In some embodiment, the fidelity models may provide estimates for strain and stress, or gradient thereof. In some embodiment, the fidelity models may provide estimates for strain and temperature, or gradient thereof. In some embodiment, the fidelity models may provide estimates for distortion and temperature, or gradient thereof.

[0092] In some embodiment, the fidelity models may provide estimates for displacement, strain, and distortion, or gradient thereof. In some embodiment, the fidelity models may provide estimates for strain, distortion, and stress, or gradient thereof. In some embodiment, the fidelity models may provide estimates for distortion, stress, and temperature, or gradient thereof. In some embodiment, the fidelity models may provide estimates for displacement, strain, distortion, and stress, or gradient thereof. In some embodiment, the fidelity models may provide estimates for strain, distortion, stress, and temperature, or gradient thereof. In some embodiment, the fidelity models may provide estimates for strain, distortion, stress, and temperature, or gradient thereof. In some embodiment, the fidelity models may provide estimates for displacement, strain, distortion, stress, and temperature, or gradient thereof.

[0093] Based on the information of estimated parameters, the illustrative inventive part build simulation models may be used to determine the AM specific parameters of interest, including but not limited to, melt pool size, resulting orientation, and other similarly suitable parameters. In some embodiments, the exemplary inventive computer-based AM system may be configured to validate engineering material properties at step 404 and verify the adherence to the geometry prescribed by the part design (item 106 of Fig. 1) so that to be guaranteed by the set of simulation results, or performance improvements sought for. In some embodiments, if the set of simulation results are to be guaranteed and no performance improvements are to be sought, the exemplary inventive computer-based AM system may proceed to verify and validate at step 406. In some embodiments, if all simulation results allow for at least one of the validation and verification of step 406, the utilization of these simulation resources is utilized in an open loop mode.

[0094] In some embodiments, if at step 404 the validation and verification are not granted by the set of simulation results, or if performance improvements are to be sought for even if they are expected, the exemplary inventive computer-based AM system may be configured to perform a numerical build optimization 408 by solving the inverse mathematical problem, where the simulation outputs are the optimization targets/constraints 410 and the process conditions/inputs are the decision variables. An exemplary numerical build optimization 408 is described herein in connection with Fig. 6A-6D.

[0095] With the outputs of 410, the exemplary inventive computer-based AM system then performs the validation and verification at step 406. In one embodiment, the user would then be able to formulate several variations of optimization problems, and/or execute re-defined ones, such that various measures of performance, validation of engineering material properties and verification of adherence to the geometry prescribed by the design are addressed when feasible solutions are available.

[0096] In some embodiments, the exemplary inventive computer-based AM system may be configured to further utilize a material selection optimization 412 in the build optimization 408 whenever the factor of materials may be considered among optimization decision variable(s). An exemplary inventive material selection optimization 412 is described herein in connection with in Fig. 8.

[0097] In some embodiments, the exemplary inventive material selection optimization 412 can be used for a new material selection (different from original material provided by, for example, a customer), which may then be pointed out by the optimizer over the course of the optimization. In some embodiments, this way, the part build simulation enables optimization of all the build parameters, including materials as needed during the inventive part build simulation process (item 110 of Fig. 1) of the exemplary inventive computer-based AM system. In some embodiments, including the inventive material selection optimization 412 during the inventive part build simulation (item 110 of Fig. 1) is that the models underlying the simulation process may define inter-relationships between composition, microstructure and/or properties of the exemplary AM part.

[0098] In some embodiments, the exemplary inventive part build simulation process (item 110 of Fig. 1) may utilize multiscale modelling to evaluate material propert (ies) and/or behavior(s). For example, in some embodiments, the exemplary inventive part build simulation process (item 110 of Fig. 1) may include, but not limited to, one or more of the combination of the following scales, in one embodiment, the exemplary inventive part build simulation may include structural scale where finite element, finite volume and finite difference partial differential equation may be solvers used to simulate structural responses such as, without limitations, solid mechanics and/or transport phenomena at large scales. [0099] in some embodiments, an exemplary inventive multiscale modelling utilizes at least one integrated Computational Materials Engineering (ICME) technique in the exemplary inventive AM process. In one embodiment, besides the AM process simulation, the exemplary inventive part build simulation may further include simulation(s) of other process(es) that the AM part may undergo after the AM build would be completed.

[00100] In some embodiments, other process may include at least one of machining, sheet forming, stamping, casting, welding, surface finishing, and any other similarly suitable process. In some embodiments, other process may include at least one of surface coating, residual stress mitigating techniques (e.g., cold work by forging or stretching, etc.), residual stress inducing techniques (e.g., burnishing, shot peening), thermal treatments (e.g., annealing, heat treating, aging), forging, HIPing (hot-isostatic-pressing), and any other similarly suitable process.

Production and Processing

[00101] In some embodiments, the AM part/product may be subject to any appropriate dissolving (e.g. includes homogenization), working and/or precipitation hardening steps. If employed, the dissolving and/or the working steps may be conducted on an intermediate form of the additively manufactured body and/or may be conducted on a final form of the additively manufactured body. If employed, the precipitation hardening step is generally conducted relative to the final form of the AM part product.

[00102] After or during production, an AM part/product may be deformed (e.g., by one or more of rolling, extruding, forging, stretching, compressing). The final deformed product may realize, for instance, improved properties due to the tailored regions and thermo- mechanical processing of the final deformed AM part product. Thus, in some embodiments, the final product is a wrought AM part/product, the word "wrought" referring to the working (hot working and/or cold working) of the AM part/product, wherein the working occurs relative to an intermediate and/or final form of the AM part/product. In other approaches, the final product is a non- wrought product, i.e., is not worked during or after the additive manufacturing process. In these non-wrought product embodiments, any appropriate number of dissolving and precipitating steps may still be utilized.

Product Applications

[00103] The resulting AM part/productsmade in accordance with the systems and methods described herein may be used in a variety of product applications. In one embodiment, the AM parts (e.g. metal alloy parts) are utilized in an elevated temperature application, such as in an aerospace or automotive vehicle. In one embodiment, an AM part or product is utilized as an engine component in an aerospace vehicle (e.g., in the form of a blade, such as a compressor blade incorporated into the engine). In another embodiment, the AM part or product is used as a heat exchanger for the engine of the aerospace vehicle. The aerospace vehicle including the engine component / heat exchanger may subsequently be operated. In one embodiment, the AM part or product is an automotive engine component. The automotive vehicle including an automotive component (e.g. engine component) may subsequently be operated. For instance, the AM part or product may be used as a turbo charger component (e.g., a compressor wheel of a turbo charger, where elevated temperatures may be realized due to recycling engine exhaust back through the turbo charger), and the automotive vehicle including the turbo charger component may be operated. In another embodiment, an AM part or product may be used as a blade in a land based (stationary) turbine for electrical power generation, and the land-based turbine included the AM part or product may be operated to facilitate electrical power generation. In some embodiments, the AM part or products are utilized in defense applications, such as in body armor, and armed vehicles (e.g., armor plating). In other embodiments, the AM part or products are utilized in consumer electronic applications, such as in consumer electronics, such as, laptop computer cases, battery cases, cell phones, cameras, mobile music players, handheld devices, computers, televisions, microwaves, cookware, washers/dryers, refrigerators, and sporting goods, among others.

[00104] In another aspect, the AM part or products are utilized in a structural application. In one embodiment, the AM part or products are utilized in an aerospace structural application. For instance, the AM part or products may be formed into various aerospace structural components, including floor beams, seat rails, fuselage framing, bulkheads, spars, ribs, longerons, and brackets, among others. In another embodiment, the AM part or products are utilized in an automotive structural application. For instance, the AM part or AM part or products may be formed into various automotive structural components including nodes of space frames, shock towers, and subframes, among others. In one embodiment, the AM part or product is a body-in-white (BIW) automotive product.

[00105] In another aspect, the AM part or products are utilized in an industrial engineering application. For instance, the AM part or products may be formed into various industrial engineering products, such as tread-plate, tool boxes, bolting decks, bridge decks, and ramps, among others.

[00106] In one embodiment, the exemplary inventive part build simulation may include final product modelling/simulations wherein one or more of the following criteria may be simulated and determined: performance, impact, fatigue, corrosion, (to name a few) and suitably others. In one embodiment, the exemplary inventive part build simulation may include macroscale simulation based on: constitutive (rheology) equation(s) that may be used at the continuum level in solid mechanics and transport phenomena at millimeter scales. In one embodiment, the exemplary inventive part build simulation may include a mesoscale simulation based on at least one continuum level formulation that may be used with discrete quantities at multiple micrometer scale. [00107] In some embodiments, exemplary inventive part build simulations may be directed to, without limitations, crystal plasticity for metals, Eshelby solutions for any materials, homogenization methods, and/or unit cell methods. In one embodiment, exemplary inventive part build simulations may include microscale simulation techniques such as dislocation dynamics codes for metals and phase field models for multiphase materials. In one embodiment, exemplary inventive part build simulations may include nanoscale simulation(s) techniques wherein semi-empirical atomistic methods may be used such as Lennard- Jones, Brenner potentials, embedded atom method (EAM) potentials, and modified embedded atom potentials (MEAM) in molecular dynamics (MD), molecular statics (MS), Monte Carlo (MC), and/or kinetic Monte Carlo (KMC) formulations. In one embodiment, exemplary inventive part build simulations may include electronic scale simulation(s) wherein Schroedinger equations may be used in computational framework as density functional theory (DFT) models of electron orbitals and bonding on angstrom to nanometer scales.

[00108] In some embodiments, examples of inventive part build simulation models that may be used to determine inter-relationships between composition, microstructure, and/or properties of the exemplary AM part may include, but not limited to, small scale models configured to calculate material properties, or relationships between properties and parameters (e.g., yield strength vs. temperature, for use in continuum models). In one embodiment, the exemplary models may include, but not limited to, CALPHAD computational thermodynamics software/models that predict free energy as a function of composition.

[00109] In some embodiments, the exemplary inventive part build simulation may include initial and boundary conditions for modelling/simulating microstructure evolution. For example, the boundary conditions may be taken e.g. from the simulation of the actual process. For example, the initial conditions (e.g., initial microstructure entering into the actual AM process step) may involve entire integrated process history data starting from the homogeneous, isotropic and stress-free melt. In some embodiments, to determine the initial conditions, the exemplary inventive AM system may be configured to utilize a modular, standardized simulation platform that may include, but not limited to, Aachen Virtual Platform for Materials Processing, AixViPMaP®.

[001 10] In one embodiment, the exemplary inventive part simulation models may include, but not limited to, process models that calculate spatial distribution of structure features (e.g., fiber density and orientation in a composite material); small-scale models that then determine relationships between structure and properties, for use in a continuum models of overall part and/or system behavior.

[001 11] In some embodiments, the simulation and optimization steps 402, 404, and

406 of the exemplary inventive part build simulation process may utilize actual data collected, in real-time, from the in-situ monitoring and/or post-build inspection (e.g. respectively, items 1 16 and 118 of Fig. 1). In some embodiments, information captured in items 116 and 1 18 of Fig. 1 may be translated into updated geometry, material distributions and/or boundary conditions other than those prescribed during the part design phase (e.g. item 106 of Fig. 1), serving as inputs for the simulated responses that feed the inventive active feedback control mechanism (e.g. item 126 of Fig. 1) to improve/heal the part based on its actual condition.

[001 12] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize one or more of the following in-situ AM build monitoring techniques to generate data to be utilized by the inventive part build simulation engine of the present disclosure, such as, without limitation, techniques described in A. Sharma et al, 2006, "Apparatus and Method for Z-Height Measurement and Control for a Material Deposition Based Additive Manufacturing Process"; Proc. National Seminar on Non- Destructive Evaluation Dec. 7 - 9, 2006, Hyderabad, Indian Society for Non-Destructive Testing Hyderabad Chapter D. N.Trushnikov et al, 2012, "Online Monitoring of Electron Beam Welding of TI6AL4V Alloy Through Acoustic Emission"; and Mat.-wiss. u. Werkstofftech. 2012, 43, No. 10, (DOI 10.1002/mawe.201200933), "Secondary-Emission signal for weld formation monitoring and control in electron beam welding (EBW)."

[001 13] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize one or more of the following in-situ AM build monitoring techniques that may include, but are not limited to:

i) measuring the build layer temperature (e.g., via contact thermal couples);

ii) measuring the laser/electron beam power (e.g., per the control system and/or calculated wattage from some feedback on the system);

iii) performing the visual / thermal imaging (e.g., from images or video obtained during the build);

iv) acquiring sound recordings (e.g., via acoustic emission sensors located on or around the build plate);

v) performing the layer by layer optical topography;

vi) measuring gas flows (e.g., via gas flow meters / sensors placed on the entry into (e.g., pressure sensor feedback) and/or within the chamber);

vii) monitoring the molten pool size (e.g., via a camera and/or optical sensor);

viii) monitoring the powder distribution (e.g., via a camera and/or acoustic emission sensor); and

ix) monitoring the powder contamination (e.g., via a camera and/or optical sensor and/or communicated from the control system with inputs/information from the feedstock source material supply). [00114] In some embodiments, when one or more of the systems or methods of the present disclosure are directed towards an AM process utilizing a wire feedstock and/or wire- based feedstock (e.g. hybrid type wires like entwined fibers of differing materials and/or wire tube with core (e.g. powder core) of different material), the exemplary inventive computer- based AM system may be configured to utilize one or more of the following in-situ AM build monitoring techniques that may include, but are not limited to one or more of those set out in the above portion, including: wire deposition rate, z height (height between deposition and surface (e.g. build layer or substrate); bead orientation, feedstock thickness, or others.

[00115] In some embodiments, the present disclosure provides an inventive computer- based AM system with at least the capabilities and benefits for inspection capabilities, including but not limited to non-contact dimensional scanning and contact dimensional inspection; fluorescent penetrant, visual surface grain, x-ray diffraction; automated ultrasonic with crystallographic compensation; and digital and traditional radiography and computer tomography.

[00116] Fig. 5 shows an illustrative example of a process depicting one example of the material selection optimization 412 as described above, which is a discrete optimization process 500 applied for materials selection of the exemplary inventive part build simulation process. At step 502, the exemplary inventive part build simulation process begins with assigning code numbers to one or more properties of individual materials. In one embodiment, more than one code number may be assigned to each individual material to reflect more than one property, and the numbering sequence would reflect the relative position of each material in a rank of each property. At step 504, the exemplary part build simulation process may, based on the simulation results from process 400, perform the optimization for each property at a time or for all of the properties at once. In the former case, in some embodiments, the exemplary inventive AM system may be configured to reconcile all properties in, at least, ad-hoc manner. In one embodiment, the exemplary inventive AM system may be configured to utilize multicriteria optimization methods (Pareto, Hypervolume, etc.) to perform the optimization for all of the properties at once.

[001 17] Upon performing the optimization for each property, at step 506, the exemplary part build simulation process may be configured to handle the discrete nature of the material related variables. In one embodiment, the handling of the variables may include, but not limited to, Option A which may involve using optimization methods that operate directly with discrete variables (e.g., simplex and/or genetic algorithms).

[001 18] In another embodiment, the handling of the variables may include, but not limited to, Option B which may involve using "branch and bound" methods that post-process the results of an optimization run. In some embodiments, the branch and bound (also referred to as: BB, B&B, or BnB) is an algorithm design paradigm for discrete and combinatorial optimization problems, as well as mathematical optimization. In one embodiment, the exemplary part build simulation process may utilize a branch-and-bound algorithm that consists of a systematic enumeration of candidate solutions by way of state space search: the set of candidate solutions is thought of as forming a rooted tree with the full set at the root. In one embodiment, the BnB algorithm may be configured to explore branches of this tree, which represent subsets of the solution set. Before enumerating the candidate solutions of a branch, the branch is checked against upper and lower estimated bounds on the optimal solution, and is discarded if it cannot produce a better solution than the best one found so far by the algorithm.

[001 19] In one embodiment, an exemplary skeleton of an exemplary branch and bound algorithm for minimizing an arbitrary objective function f that may be utilized in performing one or more inventive part build simulation is detailed in the illustrative example of Table 1. Table 1. 1. Using a heuristic, find a solution x_/, to the optimization problem. Store its value, B

(If no heuristic is available, set B to infinity.) B will denote the best solution found so far, and will be used as an upper bound on candidate solutions.

2. Initialize a queue to hold a partial solution with none of the variables of the problem assigned.

3. Loop until the queue is empty:

1. Take a node N off the queue.

2. If N represents a single candidate solution x and flx) < B, then x is the best solution so far. Record it and set B <—flx).

3. Else, branch on Nto produce new nodes N,. For each of these:

1. If g(N,) > B, do nothing; since the lower bound on this node is greater than the upper bound of the problem, it will never lead to the optimal solution, and can be discarded.

2. Else, store N, on the queue.

[00120] In another embodiment, the handling of the variables may include, but not limited to, Option C that may include rounding the results of an exemplary optimization run. Ν. Gould, "An introduction to algorithms for continuous optimization," Oxford University Computing Laboratory and Rutherford Appleton Laboratory, http://www.numerical.rl.ac.uk/people/nimg/course/lectures/paper/paper.pdf, provides an exemplary optimization run performed by the exemplary inventive computer-based AM system. Other exemplary optimization runs performed by the exemplary inventive computer- based AM system include but not limited to Genetic Algorithms, Simulated Annealing and Particle Swarm. In some embodiments, alternative Option C of handling the discrete nature of the material related variables may have the poorest performance among the three options. While it is case dependent whether or not Option A or B of handling the discrete nature of the material related variables would be the best, it is certain that Option A would be more time consuming if genetic algorithms (or any other population-based method) may be chosen. In some embodiments, exemplary but non-limiting decision rules may be: 1) if the quality/performance of the optimum solution prevails the time requirement in the optimization, Option A is chosen; and 2) if the time requirement prevails the quality/performance in the optimization, i.e., a quicker evaluation is more important, Option B is chosen.

[00121] In some embodiment, the handling of the variables may include both Options

A and B. In some embodiment, the handling of the variables may include both Options B and C. In some embodiment, the handling of the variables may include both Options A and C. In some embodiment, the handling of the variables may include all Options A, B, and C.

[00122] In some embodiments, data cubes and n-D arrays may be possible computer implementations of the data structures in a more comprehensive embodiment. Examples of data schema include but not limited to a NIST data schema with a visual representation shown at page 8 of Y. Lu et al, "AMMD- An Open Database for Additive Manufacturing Analytics," Engineering Lab, National Institute of Standards and Technology, RAPID 2017, May 9, 2017; and/or GRANTA schema (Granta Design, Materials Park, OH).

[00123] Fig. 6A-6D show some examples of the numerical build optimization that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Specifically, Fig. 6A shows a representation of an exemplary inventive AM orient optimization to minimize support area according to some embodiment. Fig. 6B shows a representation of an exemplary inventive AM orient optimization to minimize build height and/or time according to some embodiment. Fig. 6C shows a representation of an exemplary inventive AM orient optimization to minimize support area with equality constraints according to some embodiment. Fig. 6D shows a representation of an exemplary inventive AM orient optimization to minimize support area with side constraints according to some embodiment.

Pareto Figures and Simulation

Example 1: Cost vs. Mechanical Properties

[00124] Fig. 7 shows an exemplary diagram of cost v. mechanical properties tradeoff in the exemplary inventive material selection optimization 412 of Fig. 4 that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. The exemplary inventive computer-based AM system considers significant differences between 2 materials, both in terms of cost and mechanical properties. Trade-offs for two materials Material 1 and Material 2are that one has to pay more to improve mechanical properties.

[00125] On the other hand, there could be a third material with an in-between tradeoff, i.e., such material does not have as good performance as Material 1, but also is not as costly. In addition, performance of such material is not as low as Material 2 (for certain applications), but it is more expensive than Material 2.

[00126] Because the Pareto Front between these 2 materials Material 1 and Material 2 is discontinuous (expressed by the dashed line), this hypothetical third trade-off depends on a discrete material choice.

Example 2: Upskin Surface Roughness vs. Laser Velocity

[00127] Fig. 8 illustrates an exemplary diagram of surface roughness v. scan speed tradeoff that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Ideally, it is desired that the lowest surface roughness possible (best finish) combined with the highest scan speed possible. However, there is a trade-off between scan speed and surface roughness. At high scan speeds, such as 802 of Fig. 8, there tends to be more partially melted powder (via smaller melt pools), resulting in rougher surfaces. At lower scan speeds, such as 804 of Fig. 8, a more full melting is achieved (via larger melt pools), resulting in smoother surfaces.

[00128] The governing equations for representing the upskin surface roughness are:

RaL=(P,V,H,LR) (1) RaT=(P,V,H,LR) (2) RaAVG=(RaL+ RaT)/2 (3) wherein R_aL = Longitudinal Roughness, R_ar = Transverse Roughness, R_OAVG = Average Roughness, P = Laser Power V= Scan Velocity, H = Hatch Spacing, LR = Layer Reference.

[00129] The equations as shown above are used in developing the figures 802 and 804 of Fig. 8 and are derived for Material 2, specifically for upskin surfaces. For the above surrogate model, a Design of Experiment ("DoX") was employed using key variables affecting surface (power, velocity, hatch spacing, layer reference). Every sample in the DoX was then measured for surface roughness (Ra) to predict and optimize AM processing parameters.

Example 3: LOF Porosity vs. Build Rate

[00130] Fig. 9 illustrates an exemplary diagram of Lack-of-Fusion Porosity v. build speed tradeoff that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Ideally, it is desired that the highest build speed possible with little to no lack-of-fusion. However, there is a trade-off between build rate and lack-of-fusion porosity. At high build rates (higher velocity), such as 902 of Fig. 9, melt pools tend to be smaller and thus result in unmelted powder creating lack-of-fusion porosity. At lower build rates (lower velocity), such as 904 of Fig. 9, the melt pools tend to be larger resulting in less lack-of-fusion porosity.

[00131] The governing equations for representing the LOF porosity are:

A=(H,W,L,D) (4)

LOF=(A) (5)

W=(P,V) (6)

D=(P,V) (7)

BR=VHL (8) wherein A = Shape Ratio, H = Hatch Spacing, W = Melt Pool Width, L = Layer Thickness, D = Melt Pool Depth, LOF = Lack-of-Fusion Porosity, P = Laser Power, V= Scan Velocity, BR = Build Rate.

[00132] The equations above associated with LOF porosity and melt pool dimensions are used in developing the figures 902 and 904 of Fig. 9 and are derived forMaterial 2.

[00133] The equation for melt pool width (W) and depth (D) are derived via two different methods. The first method was an empirical and statistical approach. In this approach a DoX producing single bead melt pool tracks was performed across a wide range of P and V. The melt pool width and depth were then measured. From this data, statistical models were developed for W and D. The second method used computational fluid dynamics (CFD) modeling (see image at bottom right) to simulate a single melt track DoX with varying P and V. The pictured simulation is from the commercial software, STAR CCM+. The melt pool dimensions were extracted from simulation results and similarly used to produce statistical models for W and D. This also allowed information on melt pool length and thermal parameters to be predicted and modeled. [00134] The above exemplary LOF model is a geometric based model which used melt pool dimensions and layer-by-layer laser rotation to identify regions the laser would miss, leaving unmelted powder. This geometric model is then scaled by a shape ratio to develop a closed form function. In addition, the volumetric build rate is a standard definition.

Example 4: Residual Stress vs. Distortion

[00135] Fig. 10 illustrates an exemplary residual stress v. distortion tradeoff that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Ideally, it is desired to have both the distortion and residual stress as low as possible. However, there is a trade-off between residual stress and distortion. Low distortion, such as 1002 of Fig. 10, generally means the part is more constrained and builds up more residual stress as, such as 1004 of Fig. 10, a result. This can lead to issues during the build such as cracking. On the other hand, low residual stress, such as 1006 of Fig. 10, generally indicates that the part has distorted significantly enough to relieve that residual stress, such as 1008 of Fig. 10. However, this can lead to issues in maintaining part tolerances.

[00136] The example as shown in Fig. 10 is for varying base plate thickness. On the bottom left the base plate is much thinner (10mm), results in more distortion (1008 of Fig. 10), but less residual stress (1006 of Fig. 10). On the top right the plate used in simulation is thicker (50mm), resulting in much less distortion (1002 of Fig. 10), but increased residual stress (1004 of Fig. 10).

[00137] The results from Fig. 10 may be obtained by using finite element analysis to provide predictions of residual stress and distortion. During the simulation as implemented by the exemplary inventive computer-based AM system, model inputs include but not limited to: i) Material Properties including but not limited to thermal conductivity (as function of temperature), specific heat (as function of temperature), thermal expansion coefficient (as function of temperature), heat transfer coefficient, absorption, elastic modulus (as function of temperature), Poisson's Ratio (as function of temperature), yield strength (as function of temperature), flow stress (as function of temperature), and/or inherent strain values.

ii) Process Parameters including but not limited to laser power, laser velocity, hatch spacing, layer thickness, scanning pattern, base plate temperature, initial powder temperature, and ambient temperature.

[00138] There are other parameters including but not limited to base plate size, base plate clamping method (boundary condition), and part geometry and supports (if applicable).

[00139] In some embodiment, the simulation and models as described above in connection with Fig. 10 are implemented by the exemplary inventive computer-based AM system that is configured to use the commercial software such as MSC Simufact AM.

[00140] Within this approach, the geometry is built layer-by-layer, with predictions of residual stress and distortion made as each layer is built. In the embodiment described above, a generic rectangular geometry may be used with varying base plate thickness to demonstrate the very common relationship between residual stress and distortion. In some embodiment, the simulation may be performed using Material 2 as the material.

Example 4: Surface Roughness vs. Gas Porosity

[00141] Fig. 1 1 illustrates an exemplary diagram of surface roughness v. gas porosity tradeoff that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Ideally, it is desired to have the lowest surface roughness possible (best finish) combined with little to no gas porosity. However, there is a trade-off between surface roughness and gas porosity. Low gas porosity levels, such as 1102 of Fig. 11, require lower power and higher velocity to avoid keyholing. However, this combination results in very poor surface finish (high roughness). To achieve better surface finish, a combination of higher power and lower velocity is preferred. However, this combination results in the creation of gas porosity, often via keyholing.

[00142] The governing equations for representing the upskin surface roughness are:

RaL=(P,V,H,LR) (1) RaT=(P,V,H,LR) (2) RaAVG=(RaL+ RaT)/2 (3)

[00143] For gas porosity, the relations is:

GP=(P,V,L) (9) wherein R_AL = Longitudinal Roughness, R_ar = Transverse Roughness, R_OAVG = Average

Roughness, P = Laser Power, V = Scan Velocity, H = Hatch Spacing, LR = Layer Reference,

GP = Gas Porosity, L = Layer Thickness.

[00144] The surface roughness equations shown above are used in developing the figures 1102 and 1104 of Fig. 11 and are, for example, derived for Material 2. This was developed using an empirical and statistics-based approach. A DoX was employed using key variables affecting surface (e.g. power, velocity, hatch spacing, layer reference). Every sample in the DoX was then measured for surface roughness (Ra). Finally, these results were used to develop a statistical surrogate model which has been employed here.

[00145] The gas porosity model may be developed using a similar approach. A DoX was employed using a range of input parameters. Samples may be then analyzed and porosity was categorized as gas vs. lack-of-fusion. With data compiled, a statistical model was developed to predict gas porosity. [00146] These models are deployed as part of a process parameter prediction tool which helps predict and optimize AM processing parameters based on a multitude of these models.

[00147] Various embodiments of the present disclosure have been described with reference to the following numbered clauses:

1. A method, comprising:

(A) applying, by a processor, at least one first fidelity model to design data for at least one Additive Manufacture ("AM") part to generate a first set of simulation results to estimate one or more AM part parameters associated with at least one AM part to be built by an AM build process; wherein the design data is representative of a desired design of the at least one AM part; wherein the one or more AM part parameters are representative of one or more corresponding material properties of the at least one AM part;

(B) determining, by the processor, based on the one or more AM part parameters, one or more part-specific AM build parameters for the AM build process to build the at least one AM part;

(C) generating, by the processor, a digital twin of the at least one AM part;

wherein the digital twin comprises:

i) the one or more AM part parameters of the at least one AM part; and ii) the one or more part-specific AM build parameters for the AM build process to build the at least one AM part;

(D) determining, by the processor, that the digital twin requires to be optimized by at least one optimization method to meet or improve upon the desired design;

(E) optimizing, by the processor, the digital twin by:

i) assigning one or more code numbers to the one or more AM part parameters; ii) performing, based on the first set of simulation results, a numerical part build optimization for each parameter of the one or more AM part parameters to generate material-related optimization choice variables for the at least one AM part; and

iii) obtaining, based on the material-related optimization choice variables, discrete material-related optimization choice variables for the at least one AM part; wherein the discrete material-related optimization choice variables have discrete values;

iv) generating, based on the discrete material-related optimization choice variables, at least one of one or more optimization targets or one or more optimization constraints;

(F) validating, by the processor, based on the one or more optimization targets, the one or more optimization constraints, or both, one or more materials to be used in the AM build process to achieve the one or more corresponding material properties of the at least one AM part; wherein the validating results in one of:

i) identifying one or more acceptable materials to be used in the AM build process to build the at least one AM part, or

ii) repeating the optimization of the digital twin to obtain one or more updated optimization targets, one or more updated optimization constraints, or both; wherein the repeating of the optimization of the digital twin is based on a second set of simulation results generated by applying at least one second fidelity model to the design data of the at least one AM part;

(G) updating, by the processor, based on the one or more acceptable materials, the digital twin to obtain an updated digital twin; (H) verifying, by the processor, that the updated digital twin meets a geometry prescribed by the design data of the at least one AM part;

(I) transmitting, by the processor, based on the updated digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and

(J) building, by the AM machine, the at least one AM part based on the at least one AM part build instruction;

wherein the updated digital twin is suitable to certify, without a physical inspection of the at least one AM part, a compliance of the at least one AM part to the desired design.

2. The method of Clause 1, wherein the at least one first fidelity model may be a low-fidelity simulation model.

3. The method of Clauses 1 or 2, wherein the at least one first fidelity model may be a high- fidelity simulation model.

4. The method of Clauses 2-3, wherein the one or more AM part parameters representative of one or more corresponding material properties of the at least one AM part comprise at least one of displacement, strain, distortion, stress, temperature, or gradient.

5. The method of Clauses 1-4, further comprising:

(K) determining, by the processor, that all simulation results allow for at least one of the validation and verification, by utilizing the simulation results in an open loop mode.

6. The method of Clauses 1-5, wherein the numerical build optimization is based on solving an inverse mathematical problem, wherein the first set of simulation results is at least one of a set of optimization targets or a set of optimization constraints, and wherein process conditions or inputs are the material-related optimization choice variables.

7. The method of Clauses 1-6, wherein the obtaining the discrete material-related optimization choice variables is performed by one of: A) a second optimization method that operates directly with the discrete material-related optimization choice variables, B) an "branch and bound" method, or C) a method of rounding the results of the optimization run.

8. The method of Clauses 1-7, wherein the at least one first fidelity model defines interrelationships between at least one of composition, microstructure or properties of the at least one AM part.

9. The method of Clauses 1-8, wherein applying the at least one first fidelity model to design data for the at least one AM part to generate the first set of simulation results utilizes multiscale modelling to evaluate at least one of one or more material properties or one or more material behaviors.

10. The method of Clause 9, wherein the multiscale modelling utilizes at least one integrated Computational Materials Engineering (ICME) technique.

11. A system comprising:

(1) a processor;

(2) a non-transitory computer readable storage medium storing thereon program logic for execution by the processor, wherein, when executing the program logic, the processor is configured to:

(A) apply at least one first fidelity model to design data for at least one Additive Manufacture ("AM") part to generate a first set of simulation results to estimate one or more AM part parameters associated with at least one AM part to be built by an AM build process; wherein the design data is representative of a desired design of the at least one AM part; wherein the one or more AM part parameters are representative of one or more corresponding material properties of the at least one AM part;

(B) determine, based on the one or more AM part parameters, one or more part- specific AM build parameters for the AM build process to build the at least one AM part;

(C) generate a digital twin of the at least one AM part; wherein the digital twin comprises:

(D) determine that the digital twin requires to be optimized by at least one optimization method to meet or improve upon the desired design;

(E) optimize the digital twin by:

i) assigning one or more code numbers to the one or more AM part parameters;

ii) performing, based on the first set of simulation results, a numerical part build optimization for each parameter of the one or more AM part parameters to generate material-related optimization choice variables for the at least one AM part; and

(F) validate, based on the one or more optimization targets, the one or more optimization constraints, or both, one or more materials to be used in the AM build process to achieve the one or more corresponding material properties of the at least one AM part; wherein the validating results in one of: i) identifying one or more acceptable materials to be used in the AM build process to build the at least one AM part, or

(G) update, based on the one or more acceptable materials, the digital twin to obtain an updated digital twin;

(H) verify that the updated digital twin meets a geometry prescribed by the design data of the at least one AM part; and

(I) transmit, based on the updated digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and

(3) the at least one AM machine configured to:

receive the at least one AM part build instruction;

(J) build the at least one AM part based on the at least one AM part build instruction; wherein the updated digital twin is suitable to certify, without a physical inspection of the at least one AM part, a compliance of the at least one AM part to the desired design.

12. The system of Clause 11, wherein the at least one first fidelity model may be a low- fidelity simulation model.

13. The system of Clauses 11 or 12, wherein the at least one first fidelity model may be a high-fidelity simulation model.

14. The system of Clauses 11-13, wherein the one or more AM part parameters representative of one or more corresponding material properties of the at least one AM part comprise at least one of displacement, strain, distortion, stress, temperature, or gradient. 15. The system of Clauses 12-14, wherein, when executing the program logic, the processor is further configured to:

(K) determine that all simulation results allow for at least one of the validation and verification, by utilizing the simulation results in an open loop mode.

16. The system of Clauses 11-15, wherein the numerical build optimization is based on solving an inverse mathematical problem, wherein the first set of simulation results is at least one of a set of optimization targets or a set of optimization constraints, and wherein process conditions or inputs are the material-related optimization choice variables.

17. The system of Clauses 11-16, wherein the obtaining the discrete material-related optimization choice variables is performed by one of: A) a second optimization method that operates directly with the discrete material-related optimization choice variables, B) an "branch and bound" method, or C) a method of rounding the results of the optimization run.

18. The system of Clauses 11-17, wherein the at least one first fidelity model defines interrelationships between at least one of composition, microstructure or properties of the at least one AM part.

19. The system of Clauses 11-18, wherein applying the at least one first fidelity model to design data for the at least one AM part to generate the first set of simulation results utilizes multiscale modelling to evaluate at least one of one or more material properties or one or more material behaviors.

20. The system of Clause 19, wherein the multiscale modelling utilizes at least one Integrated Computational Materials Engineering (ICME) technique.

21. A method, comprising:

(C) generating, by the processor, a digital twin of the at least one AM part;

(D) optimizing, by the processor, the digital twin;

(E) transmitting, by the processor, based on the digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and

(F) building, by the AM machine, the at least one AM part based on the at least one AM part build instruction;

wherein the digital twin is suitable to certify, without a physical inspection of the at least one AM part, a compliance of the at least one AM part to the desired design.

22. A method, comprising:

(C) generating, by the processor, a digital twin of the at least one AM part; (D) optimizing, by the processor, the digital twin by:

i) assigning one or more code numbers to the one or more AM part parameters;

iii) obtaining, based on the material-related optimization choice variables, discrete material-related optimization choice variables for the at least one AM part; wherein the discrete material-related optimization choice variables have discrete values; and

(iv) generating, based on the discrete material-related optimization choice variables, at least one of one or more optimization targets or one or more optimization constraints;

[00148] All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same.

Claims

CLAIMS; What is claimed is

1. A method, comprising:

(C) generating, by the processor, a digital twin of the at least one AM part;

wherein the digital twin comprises:

(E) optimizing, by the processor, the digital twin by:

i) assigning one or more code numbers to the one or more AM part parameters;

(G) updating, by the processor, based on the one or more acceptable materials, the digital twin to obtain an updated digital twin;

(H) verifying, by the processor, that the updated digital twin meets a geometry prescribed by the design data of the at least one AM part; (I) transmitting, by the processor, based on the updated digital twin, at least one AM part build instruction to at least one AM machine to build the at least one AM part; and

2. The method of claim 1 , wherein the at least one first fidelity model may be a low-fidelity simulation model.

3. The method of claim 1 , wherein the at least one first fidelity model may be a high-fidelity simulation model.

4. The method of claim 1, wherein the one or more AM part parameters representative of one or more corresponding material properties of the at least one AM part comprise at least one of displacement, strain, distortion, stress, temperature, or gradient.

5. The method of claim 1 , further comprising:

6. The method of claim 1 , wherein the numerical build optimization is based on solving an inverse mathematical problem, wherein the first set of simulation results is at least one of a set of optimization targets or a set of optimization constraints, and wherein process conditions or inputs are the material-related optimization choice variables.

7. The method of claim 1 , wherein the obtaining the discrete material -related optimization choice variables is performed by one of: A) a second optimization method that operates directly with the discrete material-related optimization choice variables, B) an "branch and bound" method, or C) a method of rounding the results of the optimization run.

8. The method of claim 1, wherein the at least one first fidelity model defines interrelationships between at least one of composition, microstructure or properties of the at least one AM part.

9. The method of claim 1, wherein applying the at least one first fidelity model to design data for the at least one AM part to generate the first set of simulation results utilizes multiscale modelling to evaluate at least one of one or more material properties or one or more material behaviors.

10. The method of claim 9, wherein the multiscale modelling utilizes at least one Integrated Computational Materials Engineering (ICME) technique.

11. A system comprising:

(1) a processor;

(C) generate a digital twin of the at least one AM part;

wherein the digital twin comprises:

(E) optimize the digital twin by:

i) assigning one or more code numbers to the one or more AM part parameters;

(F) validate, based on the one or more optimization targets, the one or more optimization constraints, or both, one or more materials to be used in the AM build process to achieve the one or more corresponding material properties of the at least one AM part; wherein the validating results in one of:

i) identifying one or more acceptable materials to be used in the AM build process to build the at least one AM part, or ii) repeating the optimization of the digital twin to obtain one or more updated optimization targets, one or more updated optimization constraints, or both; wherein the repeating of the optimization of the digital twin is based on a second set of simulation results generated by applying at least one second fidelity model to the design data of the at least one AM part;

(3) the at least one AM machine configured to:

receive the at least one AM part build instruction;

12. The system of claim 11, wherein the at least one first fidelity model may be a low-fidelity simulation model.

13. The system of claim 11, wherein the at least one first fidelity model may be a high- fidelity simulation model.

14. The system of claim 11, wherein the one or more AM part parameters representative of one or more corresponding material properties of the at least one AM part comprise at least one of displacement, strain, distortion, stress, temperature, or gradient.

15. The system of claim 11, wherein, when executing the program logic, the processor is further configured to:

16. The system of claim 11, wherein the numerical build optimization is based on solving an inverse mathematical problem, wherein the first set of simulation results is at least one of a set of optimization targets or a set of optimization constraints, and wherein process conditions or inputs are the material-related optimization choice variables.

17. The system of claim 11, wherein the obtaining the discrete material-related optimization choice variables is performed by one of: A) a second optimization method that operates directly with the discrete material-related optimization choice variables, B) an "branch and bound" method, or C) a method of rounding the results of the optimization run.

18. The system of claim 11, wherein the at least one first fidelity model defines interrelationships between at least one of composition, microstructure or properties of the at least one AM part.

19. The system of claim 11, wherein applying the at least one first fidelity model to design data for the at least one AM part to generate the first set of simulation results utilizes multiscale modelling to evaluate at least one of one or more material properties or one or more material behaviors.

20. The system of claim 19, wherein the multiscale modelling utilizes at least one Integrated Computational Materials Engineering (ICME) technique.