WO2019070644A2

WO2019070644A2 - Systems and methods for utilizing multicriteria optimization in additive manufacture

Info

Publication number: WO2019070644A2
Application number: PCT/US2018/053872
Authority: WO
Inventors: Nitin V. HATTANGADY; Sergio Butkewitsch CHOZE; Haresh G. MALKANI; Kyle A. CRUM
Original assignee: Arconic Inc.
Priority date: 2017-10-02
Filing date: 2018-10-02
Publication date: 2019-04-11
Also published as: WO2019070644A3

Abstract

Disclosed are various embodiments of systems and methods for specifically relates to systems and methods for utilizing multicriteria optimization in simulating various parameters in additive manufacture to generate build instructions for an Additive Manufacture ("AM") machine. An embodiment of the present disclosure provides a method that at least includes: receiving a physical measurement during an AM build process of building a current AM part by the AM machine; determining a plurality of conflicting objectives for building the current AM part based at least in part on a digital twin of the current AM part and the physical measurement; determining an optimized solution that meets the plurality of conflicting objectives; updating, based on the optimal solution, the digital twin to obtain an updated digital twin; and building, by the AM machine, the current AM part based on the updated digital twin, suitable to certify, without a physical inspection, a compliance of the current AM part.

Description

SYSTEMS AND METHODS FOR UTILIZING MULTICRITERIA OPTIMIZATION IN

ADDITIVE MANUFACTURE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority from U.S. Provisional Patent Application No. 62/567, 132 filed October 2, 2017, and entitled "COMPUTER-DRIVEN SYSTEMS AND COMPUTER-IMPLEMENTED METHODS CONFIGURED FOR UTILIZING MULTICRITERIA OPTFMIZATION IN ADDITIVE MANUFACTURE," which is incorporated herein by reference in its entirety for all purposes.

FIELD OF TECHNOLOGY

[0002] The subject matter herein generally relates to additive manufacture ("AM"), and specifically relates to systems and methods for utilizing multicriteria optimization in simulating various parameters in additive manufacture to generate build instructions for an AM machine.

BACKGROUND OF TECHNOLOGY

[0003] 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

[0004] The present disclosure provides systems and methods for utilizing multicriteria optimization in simulating various parameters in additive manufacture to generate build instructions for an AM machine. An embodiment of the present disclosure provides a method that at least includes the steps of: (A) receiving, by a processor, at least one physical measurement during an Additive Manufacture (AM) build process of building a current AM part by an AM machine, where the at least one physical measurement is related to one of: i) at least one portion of the current AM part, or ii) at least one portion of a previously-built AM part; (B) determining, by a processor, a plurality of conflicting objectives for the AM process of the current AM part, where each conflicting objective is defined based at least in part on one or more simulation models contained in a digital twin of the current AM part and the at least one physical measurement; (C) determining, by the processor, a plurality of candidate solutions based at least in part on: 1) the digital twin of the current AM part 2) the at least one physical measurement, and 3) the plurality of conflicting objectives; where each candidate solution is distinct from another candidate solution in the plurality of candidate solutions; (D) determining, by the processor, a plurality of AM parameters to be used to identify an optimal solution from the plurality of candidate solutions; (E) for each candidate solution: i) ranking, by the processor, based on a ranking of the plurality of conflicting objectives, the plurality of AM parameters to obtain an AM parameter ranking; ii) assigning, by the processor, based on the AM parameter ranking, weights to the plurality of AM parameters; iii) determining, , by the processor, for each AM parameter, a discrete weighted value by multiplying a respective value of each of the plurality of AM parameters by a respective assigned weight; and iv) determining, by the processor, a discrete value score for each candidate solution; (F) determining, by the processor, the optimized solution that meets the plurality of conflicting objectives based on the determined discrete value score for each of the plurality of candidate solutions; (G) updating, by the processor, based on the optimal solution, the digital twin to obtain an updated digital twin; (H) transmitting, by the processor, based on the updated digital twin, at least one AM part build instruction to the AM machine to build the current AM part; (I) building, by the AM machine, the current AM part based on the at least one AM part build instruction; and where the updated digital twin is suitable to certify, without a physical inspection of the current AM part, a compliance of the current AM part to the conflicting objectives. [0005] An embodiment of the present disclosure provides a system that includes at least the following components: an AM machine, configured to build a current AM part during an AM build process based at least in part on a digital twin of the current AM part; at least one processor; and a non-transitory computer readable storage medium storing thereon program logic, where, when executing the program logic, the at least one processor is configured to: (A) receive at least one physical measurement of the current AM part, where the at least one physical measurement is related to one of: i) at least one portion of the current AM part, or ii) at least one portion of a previously-built AM part; (B) determine a plurality of conflicting objectives for the AM process of the current AM part, where each conflicting objective is defined based at least in part on one or more simulation models contained in the digital twin of the current AM part and the at least one physical measurement; (C) determine a plurality of candidate solutions based at least in part on: 1) the digital twin of the current AM part 2) the at least one physical measurement, and 3) the plurality of conflicting objectives; where each candidate solution is distinct from another candidate solution in the plurality of candidate solutions; (D) determine a plurality of AM parameters to be used to identify an optimal solution from the plurality of candidate solutions; (E) for each candidate solution: i) rank, based on a ranking of the plurality of conflicting objectives, the plurality of AM parameters to obtain an AM parameter ranking; ii) assign, based on the AM parameter ranking, weights to the plurality of AM parameters; iii) determine, for each AM parameter, a discrete weighted value by multiplying a respective value of each of the plurality of AM parameters by a respective assigned weight; and iv) determine a discrete value score for each candidate solution; (F) determine the optimized solution that meets the plurality of conflicting objectives based on the determined discrete value score for each of the plurality of candidate solutions; (G) update, based on the optimal solution, the digital twin to obtain an updated digital twin; (H) transmit, based on the updated digital twin, at least one AM part build instruction to the AM machine to build the current AM part; and where by the AM machine is configured to use the at least one AM part build instruction to build the current AM part; and where the updated digital twin is suitable to certify, without a physical inspection of the current AM part, a compliance of the current AM part to the conflicting objectives.

[0006] In some embodiments, the exemplary method may further include determining, by the processor, a lack of the optimized solution; and transmitting, by the processor, based on the lack of optimized solution, at least one AM part discard instruction to the AM machine to stop building the current AM part.

[0007] In some embodiments, the exemplary method may further include obtaining, by an at least one in-situ monitoring sensor, the at least one physical measurement.

[0008] In some embodiments, the plurality of conflicting objectives includes at least two of: i) a first conflicting objective, identifying a desired build speed, ii) a second conflicting objective, identifying a desired material usage, iii) a third conflicting objective, identifying a desired value of at least one surface property of the current AM part, iv) a fourth conflicting objective, identifying a desired value of at least one engineering material property of the current AM part, v) a fifth conflicting objective, identifying a desired weight of the current AM part, or vi) a six conflicting objective, identifying a desired cost to manufacture the current AM part.

[0009] In some embodiments, the determining the plurality of candidate solutions includes: searching a design space, by using an AM material parameter as a discrete decision variable, to identify one or more candidate solutions. [00010] In some embodiments, the optimized solution is a solution in which a further improvement in at least one higher-ranked AM parameter compromises at least one conflicting objective of the plurality of conflicting objectives.

[00011] In some embodiments, the at least one higher-ranked AM parameter is a material composition.

[00012] In some embodiments, the at least one higher-ranked AM parameter is one of a laser power and a raster pattern.

[00013] In some embodiments, where the assigning, based on the AM parameter ranking, the weights to the plurality of AM parameters includes: applying a Pareto solution to determine an optimal value for each respective weight based on one or more trade-off scenario.

BRIEF DESCRIPTION OF THE DRAWINGS

[00014] 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.

[00015] FIG. 1 is a schematic illustration of an overall architecture of that may occur within an exemplary inventive computer-based AM systems and related methods according to one or more embodiments of the present disclosure;

[00016] FIG. 2 is a schematic representation of an exemplary inventive computer-based

AM system according to an embodiment of the present disclosure; [00017] Fig. 3 shows an illustrative example of a block diagram according to an embodiment of the present disclosure;

[00018] Fig. 4 shows an illustrative example of a diagram according to an embodiment of the present disclosure;

[00019] Fig. 5 illustrates an exemplary diagram according to an embodiment of the present disclosure;

[00020] Fig. 6 illustrates an exemplary diagram according to an embodiment of the present disclosure;

[00021] Fig. 7 illustrates an exemplary diagram according to an embodiment of the present disclosure; and

[00022] Fig. 8 illustrates an exemplary diagram according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[00023] 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.

[00024] 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.

[00025] 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 affect such feature, structure, or characteristic in connection with other implementations whether or not explicitly described herein.

[00026] 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."

[00027] 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 "realtime processing," "real-time computation," and "real-time execution" 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 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. 10.1109/PADS.2002.1004212.

[00028] 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.

[00029] 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

[00030] 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.

[00031] In one approach, the AM parts metal alloys described herein are in the form of an additive manufacturing feedstock.

[00032] 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)).

[00033] 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.

[00034] 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).

[00035] 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.

[00036] 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.

[00037] 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.

[00038] 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).

[00039] 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.

[00040] 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 nonremovable 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.

[00041] 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).

[00042] 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.

[00043] 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), wire□ line type connections, wireless type connections, cellular or any combination thereof. Likewise, sub□ 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.

[00044] 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.).

[00045] 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.

[00046] 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.

[00047] 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.

[00048] 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.

[00049] 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).

[00050] 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).

[00051] 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.

[00052] 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.

[00053] 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).

[00054] 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.

[00055] 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 realtime 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.

[00056] 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).

[00057] 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)).

[00058] 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.

[00059] 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.

[00060] 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 116, and/or iii) external conditions associated with the exemplary AM machine (e.g., environmental conditions (e.g., surrounding temperature, atmospheric pressure, humidity, etc.)).

[00061] 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 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.

[00062] 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.

[00063] 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.

[00064] 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 116) 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).

[00065] 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 110) (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, 110, and 112 of Fig. 1 until quality metrics identified in item 116 meet the specification. Consequently, in at least some embodiments, the in-situ monitoring (item 116) 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.

[00066] 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 118) may include non-destructive testing, destructive testing (completed on parts), or both.

[00067] 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).

[00068] 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.

[00069] In some embodiment, the digital twin 138 may be stored according to a predetermined data model and/or schema and include, for example without limitation, 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.

Production and Processing

[00070] 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. [00071] 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

[00072] The resulting AM part/products made 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.

[00073] 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 (ΒΓνΥ) automotive product.

[00074] 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. [00075] 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 that are 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(ies) in the ensuing description, and the term "3D print farm" is analogous to the term "3D printing bureau."

[00076] 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.

[00077] 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.

[00078] 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 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.

[00079] 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.

[00080] 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 112 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).

[00081] In some embodiments, the present disclosure is directed to simulating complex multicriteria AM processes and utilizing the multicriteria simulations to dynamically adjust the actual AM processes to achieve optimal manufacturing parameters. In some embodiments, the exemplary inventive computer-based AM system may be configured to dynamical determine one or more optimum trade-off solution when there are conflicting criteria such as but not limited to adjustment criteria, overall performance criteria, and other similarly suitable criteria being outputted by an exemplary simulation conducted in accordance with one or more methodologies detailed herein. For example, the exemplary inventive computer-based AM system may be configured to address an exemplary specific problem of determining an optimum material selection for an AM manufacture part (item 108 of Fig. 1) by mapping one or more optimum trade-offs between conflicting criteria, such as, but not limited to, build speed, surface properties (e.g., roughness), engineering material properties (e.g., yield strength, elongation, crack resistance), weight, cost, and other similarly suitable criteria.

[00082] Fig. 3 shows an illustrative example of a block diagram depicting one example of the multi-criteria decision making process that may be employed by the exemplary inventive computer-based AM system in accordance with at least some embodiments and principles of the present disclosure. For example, the exemplary inventive computer-based AM system may be configured to begin the multi criteria optimization of conflicting criteria at step 302 to compute and/or measure one or more conflicting objectives, such as, but not limited to, build speed and cost. In some embodiments, conflicting objective may be defined based at least in part on values related to the simulation model(s) contained in the Digital Twin and/or physical (in-situ/after build) measurements. For example, the exemplary inventive computer-based AM system may be configured to perform the step 302 by utilizing one or more simulation models whose inputs may be determined via a suitable combination of assumed and/or measured parameters. Examples of such simulation models may be, but not limited to, the part build simulation (item 110 of Fig. 1). In some embodiments, the computation of the possible conflicting objectives is performed by one or more simulation models and/or software codes programmed in accordance with one or more principles described here. In some embodiments, values of the conflicting objectives may be supplied either by the simulation models contained in the digital twin and/or physical (in- situ/after build) measurements.

[00083] In one embodiment, the exemplary inventive part build simulation code may include structural scale where finite element, finite volume and/or 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. 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 code(s) of other process(es) that the AM part may undergo after the AM build would be completed. In some embodiments, the other process may include one or more of machining, extrusion, rolling, sheet forming, stamping, casting, welding, and other suitable parameter(s) may be simulated and determined.

[00084] In one embodiment, the exemplary inventive part build simulation code may include final product modelling/simulations wherein one or more of the following criteria may be simulated and determined: performance, impact, fatigue, corrosion, and suitably others. In one embodiment, the exemplary inventive part build simulation code 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 code may include a mesoscale simulation based on at least one continuum level formulation that may be used with discrete quantities at multiple micrometer scale.

[00085] In some embodiments, exemplary inventive part build simulation codes 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 simulation codes 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 simulation codes 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 simulation codes 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.

[00086] In some embodiments, examples of inventive part build simulation models/codes 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/codes may include, but not limited to, CALPHAD computational thermodynamics codes/models that predict free energy as a function of composition.

[00087] In some embodiments, the exemplary inventive part build simulation codes 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® (Project House ICMEaix, RWTH Aachen University, Aachen, Germany). [00088] 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.

[00089] Then, at step 304, the exemplary inventive computer-based AM system may be configured to apply one or more suitable optimization techniques (e.g., mathematical optimization routines) by using material as a discrete decision variable to search a design space for at least one feasible/candidate solution, if any, that would comply with one or more conflicting criteria. Exemplary mathematical optimization routines include but not limited to Simplex method, maximum ascent/descent method, quasi-newton methods (DFP/BFGS), Newton-Methods, Levenberg-Marquadt, Genetic Algorithms, Simulated Annealing, Particle Swarm, Ensemble methods, or any combination of the above. The method for searching the design space may vary according to a particular optimization method. Some non-limiting examples of methods for searching the design space are described in Xu Wang, "Method of Steepest Descent and its Applications," Department of Engineering, University of Tennessee, Knoxville, TN (2008); and Hicken, Jason and Alonso, Juan, "Chapter 3 : Gradient-Based Optimization," AA222: MDO Lecture, 6^th Apr. 2012, Stanford University.

[00090] At step 306, the exemplary inventive computer-based AM system may be configured to then determine if one or more feasible/candidate solutions exist. At step 308, if the exemplary inventive computer-based AM system has determined that no feasible/candidate solution exists, the exemplary inventive computer-based AM system may be configured to end the process. At step 310, if the exemplary inventive computer-based AM system has determined that one or more feasible/candidate solutions exist, the exemplary inventive computer-based AM system may be configured to determine a plurality of AM parameters for each candidate solution to be used to identify an optimal solution from the plurality of feasible/candidate solutions. The plurality of AM parameters include, but not limited to, build speed, surface properties, engineering material properties, weight, and/or cost.

[00091] In one embodiment, the exemplary inventive computer-based AM system identifies the optimal solution from the plurality of feasible/candidate solutions by applying an optimizer to select at least one solution out of the plurality of feasible/candidate solutions that would provide the most favorable outcome such as, but not limited to, an increase in a value of a first parameter that positively effects, for example, at least one of cost, material usage, and/or build time of an exemplary AM build; and/or a decrease in a value of a second parameter that negatively effects, for example, at least one of cost, material usage, and/or build time of the exemplary AM build. Some non-limiting examples of suitable optimizers can be optimizers included with Matlab Optimization Toolboxes (MathWorks, Natick, MA), VisualDOC (Vanderplaats Research & Development, Inc., Novi, MI), and modeFrontier (ESTECO, Trieste, Italy).

[00092] In some embodiments, a particular solution ("Pareto solution") may be selected when, for example, the material composition may have been chosen such that it would be no longer possible to improve any single performance criterion without compromising at least one of the other variables/criteria. In one embodiment, the material composition may be chosen as the discrete decision variable alone or in combination with other decision variables such as, but not limited to, laser power and/or raster pattern. [00093] In some embodiment, the exemplary inventive computer-based AM system may be further configured to apply a brute force and/or greedy algorithm to exhaustively search for material selection(s) within a certain resolution. In some embodiments, for the brute force adjustment analysis, the exemplary inventive computer-based AM system may be configured to calculate parameter(s) including, but not limited to, build speed, surface properties, engineering material properties, weight, and/or cost.

[00094] Still referring to Fig. 3, at step 312, in some embodiment, the exemplary inventive computer-based AM system may be configured to rank the plurality of conflicting objectives. At step 312, in some embodiment, for each candidate solution, the exemplary inventive computer- based AM system may be further configured to rank the plurality of AM parameters to obtain an AM parameter ranking based on a ranking of the plurality of conflicting objectives. In some embodiment, the above ranking of the plurality of AM parameters is reflective of a ranking of outputs of interest. In some embodiments, the exemplary inventive computer-based AM system may be configured to rank the outputs of interest separately.

[00095] At step 316, in one embodiment, the exemplary inventive computer-based AM system may be further configured to determine when the rankings of the plurality of AM parameters reveal conflicts among objectives, and/or when there might be inherently conflicting objectives, such as speed and cost, among various other similar combinations. At step 318, when the exemplary inventive computer-based AM system determines that there would be no disagreement and/or a conflicting objective, the exemplary inventive computer-based AM system may be configured to finish the respective inventive multicriteria optimization. At step 320, when the exemplary inventive computer-based AM system determines that at least some of the plurality of AM parameters/outputs of interest are in different positions across many rankings (e.g., one for each criterion), in some embodiments, such outputs of interest would be designated as conflicting objectives and the exemplary inventive computer-based AM system may be configured to assign weights to the plurality of AM parameters. In some embodiments, the weights may be assigned on an ad-hoc basis. In one embodiment, the Pareto solution determines the optimal values of the weights for each trade-off scenario.

[00096] Upon assigning the weights to the conflicting objectives, at step 322, the exemplary inventive computer-based AM system may be configured to determine a discrete weighted value for each candidate solution by multiplying a respective value of each of the plurality of AM parameters by a respective assigned weight. Finally, the exemplary inventive computer-based AM system may perform, based on at least the discrete weighted value, the inventive optimization to achieve an optimum trade-off across conflicting objectives.

[00097] In some embodiment, the exemplary inventive computer-based AM system may be configured to utilize a differentiated approach during the inventive optimization. In some embodiment, the differentiated approach may include using a population based on an optimization technique and executing a respective simulation model to calculate values of the conflicting objectives to gradually generate non-dominated design(s). Examples of the population-based adjustment (optimization) techniques are, but not limited to, genetic algorithms, simulated annealing, ant colony, and/or its various possible ensembles. In some embodiment, the term "non-dominated design" is directed to a design for which it would be no longer possible to improve one objective by not degrading at least another objective.

[00098] In some embodiments, the exemplary adjustment (optimization) techniques such as those listed above are configured to progress towards non-dominated points, which may gather around a geometric locus called the Pareto Front, as depicted schematically in Fig. 4 (creative commons on the Pareto Front).

[00099] Fig. 4 shows an illustrative example of a diagram depicting one example of a Pareto progression (the Pareto solution) during an exemplary inventive multicriteria simulation that may be executed by the 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. In the exemplary simulation, criteria such as two quantities fl and f2 may be calculated by the simulation code executed by exemplary inventive computer-based AM system. For example, if the criteria fl and f2 are build speed and the cost respectively, the exemplary inventive computer-based AM system may be configured to calculate, at point A, the fastest possible build speed constrained by costs not exceeding a certain value. For example, the exemplary inventive computer-based AM system is configured to calculate, at point B, a possibility to reduce the cost by a certain amount. Further, in some embodiments, the exemplary inventive computer-based AM system may be configured to quantify the loss in build speed by calculating the difference between f 1 (A) and f 1 (B). For the points that are not connected by the Pareto Front (or "Pareto Curve" in 2D cases, e.g. point C), would be the dominated ones, which would be of less or no practical usage because their configurations still allow for improvement in at least one objective without necessarily degrading any of the others.

[000100] Although, the description detailed above addresses cases with only 2 objectives, the exemplary inventive computer-based AM system may be configured to execute the inventive multicriteria optimization of the present disclosure for any number of conflicting objectives. In some embodiments, values of the conflicting objectives may be provided by calculations executed every time there would be a change in a state of the respective digital twin or one or more components of the exemplary inventive computer-based AM system of Fig. 1 (e.g., the digital twin may be updated based on a real-time in-situ measurement (item 116 of Fig. 1)). In some embodiment, the exemplary inventive computer-based AM system may be configured to change the material selection of an AM build part after the AM build process (item 114 of Fig. 1) has been initiated.

Pareto Figures and Simulation

Example 1: Upskin Surface Roughness vs. Laser Velocity

[000101] Fig. 5 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. In one embodiment, a desired solution may be when the AM part is built to have a sufficiently lower surface roughness/surface finish with a sufficiently higher scan speed (e.g., sufficiently faster build rate). In such example, there may be a trade-off between scan speed and surface roughness/surface finish. At high scan speeds, such as 502 of Fig. 5, there tends to be more partially melted powder (via smaller melt pools), resulting in rougher surfaces (e.g., sufficiently higher surface roughness). At lower scan speeds, such as 504 of Fig. 5, a fuller melting is achieved (via larger melt pools), resulting in smoother surfaces (e.g., the sufficiently lower surface roughness).

[000102] In this illustrative example, the 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 RaL = Longitudinal Roughness, RaT = Transverse Roughness, RaAVG = Average Roughness, P = Laser Power V = Scan Velocity, H = Hatch Spacing, LR = Layer Reference.

[000103] In one embodiment, the illustrative equations shown above may be used in developing the figures 502 and 504 of Fig. 5 for Material 2, specifically for upskin surfaces. For the above exemplary inventive surrogate model, a Design of Experiment ("DoX") was employed using suitable variables affecting surface (e.g., power, velocity, hatch spacing, layer reference). For example, every sample in the DoX was then measured for surface roughness (Ra) to predict and optimize AM processing parameters.

Example 2: LOF Porosity vs. Build Rate

[000104] Fig. 6 illustrates an exemplary diagram of Lack-of-Fusion Porosity v. build speed trade-off 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, a desired simulation solution would be to have the highest build speed possible (e.g., faster build rate) with little to no lack-of-fusion. In such example, there may be a trade-off between the build rate (e.g., faster build rate) and lack-of- fusion porosity. For example, at a sufficiently faster build rate, such as 602 of Fig. 6, melt pools tend to be smaller and thus result in unmelted powder creating lack-of-fusion porosity. At a sufficiently lower build rate, such as 604 of Fig. 6, the melt pools tend to be larger resulting in less lack-of-fusion porosity.

[000105] In this illustrative example, the 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 Λ = 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.

[000106] The equations above associated with LOF porosity and melt pool dimensions are used in developing the figures 602 and 604 of Fig. 6 and are derived for Material 2.

[000107] For example, the illustrative equations for melt pool width (W) and depth (D) are derived via, for example, two separate methods. In one example, a first method was based on 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. In one embodiment, the melt pool width and depth were then measured to develop the exemplary inventive statistical models for W and D. In one embodiment, the second exemplary method used may be based at least in part on computational fluid dynamics (CFD) modelling (see image at bottom right) to simulate a single melt track DoX with varying P and V. For example, for the pictured simulation (Fig. 6), the melt pool dimensions were extracted from the exemplary simulation results and were used to produce statistical models for W and D so that the information on melt pool length and thermal parameters can be predicted and modelled.

[000108] In one embodiment, 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 may be then scaled by a shape ratio to develop a closed form function. In one embodiment, the volumetric build rate may be a standard definition. Example 3: Residual Stress vs. Distortion

[000109] Fig. 7 illustrates an exemplary residual stress v. distortion trade-off 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 such example, a desired solution may be when the distortion and residual stress are as sufficiently low. In such example, there may be a trade-off between the residual stress and the distortion. For example, at an exemplary, sufficiently lower distortion, such as 702 of Fig. 7, the AM part is more constrained and would build up a sufficiently higher residual stress as illustrated by 704 of Fig. 7. In such example, the sufficiently higher residual stress may lead to issues during the AM process such as cracking. On the other hand, the sufficiently lower residual stress, such as 706 of Fig. 7, may indicate that the AM part has distorted significantly enough to relieve the residual stress, illustrated by 708 of Fig. 7, however, the relief in the residual stress may lead to issues in maintaining the AM part's tolerances.

[000110] Fig. 7 illustrates an example for varying base plate thickness. On the bottom left, the exemplary base plate has a first thickness (10mm) which results in more distortion (708 of Fig. 7) but less residual stress (706 of Fig. 7). On the top right, the exemplary plate used in simulation has a second thickness (50mm) which results a reduced distortion (702 of Fig. 7) but an increased residual stress (704 of Fig. 7).

[000111] In one embodiments, the results in Fig. 7 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; and/or 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.

[000112] In some embodiments, there may be other parameters including but not limited to base plate size, base plate clamping method (boundary condition), and part geometry and supports (if applicable).

[000113] In some embodiments, 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 Simufact (MSC Coftware Copr., Newport Beach, CA). In some embodiments, the geometry may be determined on the layer-by-layer basis, with predictions of the residual stress and/or the distortion may be 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 embodiments, the exemplary simulation may be performed using Material 2 as the AM material.

Example 4: Surface Roughness vs. Gas Porosity

[000114] Fig. 8 illustrates an exemplary diagram of an exemplary surface roughness v. gas porosity trade-off 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, a desired simulation solution would have a sufficiently lower surface roughness/surface finish with a sufficiently lower or no gas porosity. In such example, there may be a trade-off between the surface roughness and gas porosity. For example, the sufficiently lower gas porosity levels, such as 802 of Fig. 8, may require a sufficiently lower power and/or a sufficiently higher velocity (e.g., sufficiently faster build rate) to avoid keyholing. However, this combination results in very poor surface finish (high roughness). In one embodiment, to achieve the sufficiently lower surface roughness/surface finish, an exemplary stimulation solution may include the AM process conditions of operating at a sufficiently higher power and at a sufficiently lower build rate. In one example, the exemplary stimulation solution of having the sufficiently higher power and the sufficiently lower build rate may result in creating the gas porosity, for example, via keyholing.

[0001 15] In this illustrative example, the equations for representing the upskin surface roughness are:

RaL = (P,V,H,LR) (9) RaT = (P,V,H,LR) (10) RaAVG = (RaL+ RaT)l2 (1 1)

[0001 16] For gas porosity, the relations is:

GP = (P ,L) (12) wherein RaL = Longitudinal Roughness, RaT = Transverse Roughness, RaAVG = Average

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

= Gas Porosity, L = Layer Thickness. [000117] In some embodiments, the illustrative surface roughness equations shown above are used in developing the figures 802 and 804 of Fig. 8 and are, for example, derived for Material 2. In this illustrative example, the illustrative surface roughness equations have been developed using an empirical and statistics-based approach. For example, a DoX was employed using suitable variables affecting surface (e.g., power, velocity, hatch spacing, layer reference). In one embodiment, every sample in the DoX was then measured for surface roughness (Ra) and to develop the exemplary inventive statistical surrogate model.

[000118] In one embodiment, an exemplary gas porosity model may be developed using a similar approach. For example, a DoX approach may be employed using a range of input parameters. For example, samples may be then analysed and porosity may be categorized as gas vs. lack-of-fusion to produce an exemplary statistical model to predict gas porosity.

[000119] In one embodiment, various inventive models described herein may be applied as part of a process parameter prediction tool to predict and/or optimize the AM process parameters based on a multitude of these models.

[000120] Aspects of the invention will now be described with reference to the following numbered clauses.

1. A method, comprising:

(A) receiving, by a processor, at least one physical measurement during an Additive Manufacture (AM) build process of building a current AM part by an AM machine, wherein the at least one physical measurement is related to one of:

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part; (B) determining, by a processor, a plurality of conflicting objectives for the AM process of the current AM part, wherein each conflicting objective is defined based at least in part on one or more simulation models contained in a digital twin of the current AM part and the at least one physical measurement;

(C) determining, by the processor, a plurality of candidate solutions based at least in part on:

1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives;

wherein each candidate solution is distinct from another candidate solution in the plurality of candidate solutions;

(D) determining, by the processor, a plurality of AM parameters to be used to identify an optimal solution from the plurality of candidate solutions;

(E) for each candidate solution:

i) ranking, by the processor, based on a ranking of the plurality of conflicting objectives, the plurality of AM parameters to obtain an AM parameter ranking; ii) assigning, by the processor, based on the AM parameter ranking, weights to the plurality of AM parameters;

iii) determining, by the processor, for each AM parameter, a discrete weighted value by multiplying a respective value of each of the plurality of AM parameters by a respective assigned weight; and

iv) determining, by the processor, a discrete value score for each candidate solution; (F) determining, by the processor, the optimized solution that meets the plurality of conflicting objectives based on the determined discrete value score for each of the plurality of candidate solutions;

(G) updating, by the processor, based on the optimal solution, the digital twin to obtain an updated digital twin;

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

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

wherein the updated digital twin is suitable to certify, without a physical inspection of the current AM part, a compliance of the current AM part to the conflicting objectives.

2. A system, comprising:

an Additive Manufacturing (AM) machine, configured to build a current AM part during an AM build process based at least in part on a digital twin of the current AM part;

at least one processor; and

a non-transitory computer readable storage medium storing thereon program logic, wherein, when executing the program logic, the at least one processor is configured to:

(A) receive at least one physical measurement of the current AM part, wherein the at least one physical measurement is related to one of:

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part;

(B) determine a plurality of conflicting objectives for the AM process of the current AM part, wherein each conflicting objective is defined based at least in part on one or more simulation models contained in the digital twin of the current AM part and the at least one physical measurement;

(C) determine a plurality of candidate solutions based at least in part on:

1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives;

(D) determine a plurality of AM parameters to be used to identify an optimal solution from the plurality of candidate solutions;

(E) for each candidate solution:

i) rank, based on a ranking of the plurality of conflicting objectives, the plurality of AM parameters to obtain an AM parameter ranking;

ii) assign, based on the AM parameter ranking, weights to the plurality of AM parameters;

iii) determine, for each AM parameter, a discrete weighted value by multiplying a respective value of each of the plurality of AM parameters by a respective

assigned weight; and

iv) determine a discrete value score for each candidate solution;

(F) determine the optimized solution that meets the plurality of conflicting objectives based on the determined discrete value score for each of the plurality of candidate solutions;

(G) update, based on the optimal solution, the digital twin to obtain an updated digital twin; (H) transmit, based on the updated digital twin, at least one AM part build instruction to the AM machine to build the current AM part; and

wherein by the AM machine is configured to use the at least one AM part build instruction to build the current AM part; and

3. The method of Clause 1, further comprising:

determining, by the processor, a lack of the optimized solution; and

transmitting, by the processor, based on the lack of optimized solution, at least one AM part discard instruction to the AM machine to stop building the current AM part.

4. The system of Clause 2, wherein the at least one processor is further configured to:

determine a lack of the optimized solution; and

transmit, based on the lack of optimized solution, at least one AM part discard instruction to the AM machine to stop building the current AM part.

5. The method of Clause 1, further comprising:

obtaining, by an at least one in-situ monitoring sensor, the at least one physical measurement.

6. The system of Claim 2, further comprising:

at least one in-situ monitoring sensor that is configured to obtain the at least one physical measurement.

7. The method of Clause 1 or the system of Clause 2, wherein the plurality of conflicting objectives comprises at least two of:

i) a first conflicting objective, identifying a desired build speed, ii) a second conflicting objective, identifying a desired material usage,

iii) a third conflicting objective, identifying a desired value of at least one surface property of the current AM part,

iv) a fourth conflicting objective, identifying a desired value of at least one engineering material property of the current AM part,

v) a fifth conflicting objective, identifying a desired weight of the current AM part, or vi) a six conflicting objective, identifying a desired cost to manufacture the current AM part.

8. The method of Clause 1, wherein the determining the plurality of candidate solutions comprises:

searching a design space, by using an AM material parameter as a discrete decision variable, to identify one or more candidate solutions.

9. The system of Clause 2, wherein, to determine the plurality of candidate solutions, the at least one processor is further configured to search a design space, by using an AM material parameter as a discrete decision variable, to identify one or more candidate solutions.

10. The method of Clause 1 or the system of Clause 2, wherein the optimized solution is a solution in which a further improvement in at least one higher-ranked AM parameter compromises at least one conflicting objective of the plurality of conflicting objectives.

11. The method of Clause 1 or the system of Clause 2, wherein the at least one higher-ranked AM parameter is a material composition.

12. The method of Clause 1 or the system of Clause 2, wherein the at least one higher-ranked AM parameter is one of a laser power and a raster pattern. 13. The method of Clause 1, wherein the assigning, based on the AM parameter ranking, the weights to the plurality of AM parameters comprises:

applying a Pareto solution to determine an optimal value for each respective weight based on one or more trade-off scenario.

14. The system of Clause 2, wherein, to assign the weights to the plurality of AM parameters, the at least one processor is further configured to apply a Pareto solution to determine an optimal value for each respective weight based on one or more trade-off scenario.

15. A method, comprising:

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part;

(B) determining, by a processor, a plurality of conflicting objectives for the AM process of the current AM part, wherein each conflicting objective is defined based at least in part on one or more simulation models contained in a digital twin of the current AM part and the at least one physical measurement;

1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives; wherein each candidate solution is distinct from another candidate solution in the plurality of candidate solutions;

(E) determining, by the processor, the optimized solution that meets the plurality of conflicting objectives based on a respective discrete value score for each of the plurality of candidate solutions;

(F) updating, by the processor, based on the optimal solution, the digital twin to obtain an updated digital twin;

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

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

16. A method, comprising:

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part;

1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives;

(D) determining, by the processor, an optimized solution that meets the plurality of conflicting objectives based on a respective discrete value score for each of the plurality of candidate solutions;

(E) updating, by the processor, based on the optimal solution, the digital twin to obtain an updated digital twin;

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

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

[000121] 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:

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part;

1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives;

(E) for each candidate solution: i) ranking, by the processor, based on a ranking of the plurality of conflicting objectives, the plurality of AM parameters to obtain an AM parameter ranking; ii) assigning, by the processor, based on the AM parameter ranking, weights to the plurality of AM parameters;

iv) determining, by the processor, a discrete value score for each candidate solution;

(F) determining, by the processor, the optimized solution that meets the plurality of conflicting objectives based on the determined discrete value score for each of the plurality of candidate solutions;

2. The method of claim 1, further comprising:

determining, by the processor, a lack of the optimized solution; and transmitting, by the processor, based on the lack of optimized solution, at least one AM part discard instruction to the AM machine to stop building the current AM part.

3. The method of claim 1, further comprising:

4. The method of claim 1, wherein the plurality of conflicting objectives comprises at least two of:

i) a first conflicting objective, identifying a desired build speed,

ii) a second conflicting objective, identifying a desired material usage,

5. The method of claim 1, wherein the determining the plurality of candidate solutions comprises:

6. The method of claim 1, wherein the optimized solution is a solution in which a further improvement in at least one higher-ranked AM parameter compromises at least one conflicting objective of the plurality of conflicting objectives.

7. The method of claim 6, wherein the at least one higher-ranked AM parameter is a material composition.

8. The method of claim 6, wherein the at least one higher-ranked AM parameter is one of a laser power and a raster pattern.

9. The method of claim 1, wherein the assigning, based on the AM parameter ranking, the weights to the plurality of AM parameters comprises:

10. A system, comprising:

at least one processor; and

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part;

(C) determine a plurality of candidate solutions based at least in part on: 1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives;

(E) for each candidate solution:

assigned weight; and

iv) determine a discrete value score for each candidate solution;

(G) update, based on the optimal solution, the digital twin to obtain an updated digital twin;

(H) transmit, based on the updated digital twin, at least one AM part build instruction to the AM machine to build the current AM part; and wherein by the AM machine is configured to use the at least one AM part build instruction to build the current AM part; and

11. The system of Claim 10, wherein the at least one processor is further configured to:

determine a lack of the optimized solution; and

12. The system of Claim 10, further comprising:

13. The system of Claim 10, wherein the plurality of conflicting objectives comprises at least two of:

i) a first conflicting objective, identifying a desired build speed,

ii) a second conflicting objective, identifying a desired material usage,

14. The system of Claim 10, wherein, to determine the plurality of candidate solutions, the at least one processor is further configured to search a design space, by using an AM material parameter as a discrete decision variable, to identify one or more candidate solutions.

15. The system of Claim 10, wherein the optimized solution is a solution in which a further improvement in at least one higher-ranked AM parameter compromises at least one conflicting objective of the plurality of conflicting objectives.

16. The system of Claim 15, wherein the at least one higher-ranked AM parameter is a material composition.

17. The system of Claim 15, wherein the at least one higher-ranked AM parameter is one of a laser power and a raster pattern.

18. The system of Claim 15, wherein, to assign the weights to the plurality of AM parameters, the at least one processor is further configured to apply a Pareto solution to determine an optimal value for each respective weight based on one or more trade-off scenario.

19. A method, comprising:

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part;

(B) determining, by a processor, a plurality of conflicting objectives for the AM process of the current AM part, wherein each conflicting objective is defined based at least in part on one or more simulation models contained in a digital twin of the current AM part and the at least one physical measurement; (C) determining, by the processor, a plurality of candidate solutions based at least in part on:

1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives;

building, by the AM machine, the current AM part based on the at least one AM part build instruction; and

20. A system, comprising:

at least one processor; and

a non-transitory computer readable storage medium storing thereon program logic, wherein, when executing the program logic, the at least one processor is configured to: (A) receive at least one physical measurement of the current AM part, wherein the at least one physical measurement is related to one of:

i) at least one portion of the current AM part, or

ii) at least one portion of a previously-built AM part;

(C) determine a plurality of candidate solutions based at least in part on:

1) the digital twin of the current AM part

2) the at least one physical measurement, and

3) the plurality of conflicting objectives;

(D) determining an optimized solution that meets the plurality of conflicting objectives based on a respective discrete value score for each of the plurality of candidate solutions;

(E) updating, based on the optimal solution, the digital twin to obtain an updated digital twin;

(F) transmitting, based on the updated digital twin, at least one AM part build instruction to the AM machine to build the current AM part;

wherein by the AM machine is configured to use the at least one AM part build instruction to build the current AM part; and wherein the updated digital twin is suitable to certify, without a physical inspection of the current AM part, a compliance of the current AM part to the conflicting objectives.