Hexagon MI MSC AM Ebook A4 W

Additive Manufacturing 2020
Smarter 3D Printing
First Time Right by Design
Authors:
Hendrik Schafstall
Roger Assaker
Volker Mensing
Bridge the Gap Between
Design and Manufacturing
MSC Apex Generative Design is a radically new, fully automated
generative design solution built on the most intuitive CAE
environment in the world, MSC Apex. It exploits all the easy-to-use
and easy-to-learn features of MSC Apex while employing the most
innovative generative design engine in the background.
The software delivers a new and innovative approach for design

optimization which overcomes the constraints of classical topology
optimization techniques and dramatically decreases the effort
required in the design optimization workflow by up to ten-fold.
Learn More or Request a Quote

mscsoftware.com/generativedesign
92 | Engineering Reality Magazine

Table of
contents
Foreword 04
Bridging the Gap Between Design and 05 Optimize the Product Part, Not Just the 26
Additive Manufacturing Using Smart Geometry- A Real World End2End
Generative Design Additive Manufacturing Solution
- Hendrik Schafstall and Raj Dua - Dr. Hendrik Schafstall
Fast and Accurate Additive 09 Development of an Additive 31

Manufacturability Analysis Manufacturing Quality System for Gas
- Coriolis Composites Turbine Engine Part Production
- Samara University
Flexibility Through Additive 12

Manufacturing: How Simulation Robert Bosch India Use Simufact 36
Supports 3D Prototyping Additive to Digitally Lightweight a
- MBFZ Toolcraft Fixture Tool and Save 70% in Mass
- Bosch India
Use of MaterialCenter for Metals Additive 14

Manufacturing Data Management Sinterline Prototyping by Solvay 39
- US Army - Sylvain Mathieu & Dominique Giannotta
Use of Simulation in Additive 20 Simulating Effects of Warpage 41

Manufacturing Process Chain of - Bender Kutub & Olivier Lietaer
Thin-walled Automotive Parts
- Ampower
Author Profiles 44
Simufact Additive: Collaborative 23
Simultaneous Engineering Tool for
Additive Manufacturing
- Safran
Foreword
For more than 30 years, dozens of Additive Manufacturing (AM) technologies have
been used to realize prototyping applications. AM is now becoming increasingly
widespread across industry sectors. We are starting to see additive manufacturing
being used in the serial production of high-tech parts, particularly in aeronautics,
space, and medical industries.
This e-book features some insightful commentary on the state of the additive
manufacturing industry and some of the dominant trends. In addition, it also
includes some compelling case studies that demonstrate the scope and range of
applications for simulation in additive manufacturing.
Today’s AM technology offers some major advantages, such as geometry design freedom that allows the
creation of optimized shapes according to the targeted function. Another key benefit of using 3D printing
technologies is the ability to reduce the weight, cost, and complexity of parts production without sacrificing
the reliability and durability of materials. AM affords the advantage of small production runs with less
material waste, significant energy cost savings, and the possibility to produce functional, high performance
parts that simply can’t be subtractively manufactured, cast or formed.
You can read about how engineers in Robert Bosch India are employing the Simufact Additive product from
MSC Software to model the additive manufacturing (AM) metal build process and subsequent
post-processing steps to help eliminate design errors before committing to AM. Similarly, there are use cases
from MBFZ Toolcraft, Ampower, Safran, Samara University, and Solvay, on various facets of additive
manufacturing. [each article can have separate three bullet points] on pages after foreword, or a short
summary which I have already included here].
The articles in this e-book also touch upon the concept of Generative Design that helps customers engineer
concepts unimaginable by the human mind and how this plays a role in enhancing the potential of additive
manufacturing.
As Additive Manufacturing becomes increasingly mainstream, this e-book intends to serve as a useful
compendium of useful insights on the role of simulation in additive manufacturing.
Dr. Hendrik Schafstall

Vice President, Virtual Manufacturing & Costing, MSC Software
Bridging the Gap Between
Design and Additive Manufacturing
Using Smart Generative Design
By Hendrik Schafstall, CEO Simufact

Raj Dua, Product Manager,
MSC Apex Generative Design
Additive Manufacturing | mscsoftware.com | 05

For MSC Software and Hexagon,
Generative Design is an initiative to
provide a tool that will help customers
design concepts unimaginable by the
human mind.
W
ith the release of MSC Apex Generative Design, MSC Software is
now offering an entire digital solution from the design to the final
validated part for all materials. Connecting design solutions like MSC
Apex Generative Design to virtual manufacturing simulation with What is
Digimat AM or Simufact Additive, the design can account for the Generative Design?
engineering and production phase challenges earlier in the product development phase.
As a digital twin, the virtual manufacturing simulation is used to identify the best printing Simply stated, Generative Design is a
process and to optimize the orientation of the part and the build process. Furthermore, process of automatically generating
the outcome of the additive manufacturing process chain can be used for the validation several design concepts that satisfy
of the “real” geometry, while accounting for the residual stress distribution and the local a set of user defined objectives,
deformation under real load conditions using MSC Software’s design validation solutions criteria, and constraints. Generative
such as MSC Nastran or Marc. The end-to-end process enables engineers to make sure Design can be accomplished in
their optimized designs are validated for manufacturability and performance. many ways depending on what
criteria and constraints have been
defined by the user. For example,
if a user defines a set of structural
loads and boundary conditions
that a part must withstand as
criteria, an upper stress limit as
a constraint, and an objective of
MSC Apex minimizing mass, a method known
Generative Design as Topology Optimization (which
many of our MSC Nastran users
are very familiar with) can be used
to generate a number of design
concepts that satisfy the given
criteria and constraints. However,
Generative Design is more than just
Topology Optimization. For instance,
a user may want to know what the
best way is to package a number
of electronic components in a given
space in order to minimize the
gap between all the components.
Generative Design can help answer
that question. For MSC Software
and Hexagon, Generative Design
is an initiative to provide a tool to
our design customers that will truly
act as a companion and help them
think of design concepts that are
unimaginable by human mind.

Volume X - Winter 2019 | mscsoftware.com | 31
Why Do We Need are seen as two major constraints in wide Each optimization always leads to a
Generative Design? adoption of AM for mass production. geometrical and mechanical correct design
Therefore, there is a need to account and that can be used for manufacturing. In
optimize for the total manufacturing costs addition to the geometric side, the user
and print time while designing parts for AM. must also understand the cost and
With MSC Apex Generative Design, we are feasibility of using AM for this design
focusing not only on optimizing the parts for candidate. With MSC Apex Generative
AM, but also optimizing the process for AM. Design, our goal is to allow users to
We believe that it is only after we bridge the specify manufacturing related constraints.
gap between design and manufacturing For example, if the goal is to minimize
that we can see AM become a sustainable the cost of 3D printing, then MSC Apex
manufacturing method. Generative Design will automatically check
each design candidate for: (a) amount of
material required for the part, (b) volume
Bridging The Gap of support structure required for support
and heat dissipation in the AM machine, (c)
MSC Apex Generative Design is being cost of removal of support structures and
developed as a first-of-its-breed tool machining for desired surface roughness,
The first release of MSC Apex to bridge the gap between design and (d) costs related to maximizing the number
Generative Design has been manufacturing. Our goal is to automate of parts printed at one time on a build plate,
released to assist design engineers the process of Generative Design with etc. These checks are performed in the
create organic topologies that can user intervention only required for defining background using MSC’s Simufact Additive
be manufactured using 3D printing, the objective, criteria, and constraints technology for metal parts and Digimat AM
i.e. Laser Powder Bed Additive for design space exploration. MSC Apex technology for polymers. At the end of the
Manufacturing. Technologies Generative Design will then account optimization routine, MSC Apex Generative
such as Topology Optimization for how the part fits within the overall Design selects the candidates that meet
are being reinvigorated thanks assembly, how it redirects loads to other the specified criteria and summarizes
to advancements in Additive parts of the assembly as its stiffness them. The design engineer can then export
Manufacturing. It is widely accepted changes, and most importantly MSC the selected design(s) in CAD format and
that Additive Manufacturing has Apex Generative Design accounts for perform further checks, for example, for
the ability to manufacture virtually manufacturability – all automatically while buckling, fatigue, and nonlinear for part
any topology. As a result, the generating several design candidates that performance or decide for one design
industry has seen a rise in the all meet the user’s defined expectations. based on additional reasons such as of
number of tools that allow creation Many Generative Design tools in the dirt problems or just aesthetic ones. The
of organic topologies via concepts market today allow users to minimize design engineer may also choose to send
such as Topology Optimization the mass subject to a stress constraint. the part to a manufacturing engineer to
and Generative Design. However, The tool then solves a mathematical perform further checks on manufacturability
if you have ever tried to 3D optimization problem and produces one or via Simufact Additive and/or Digimat AM.
print any “organic” topology more design candidates. Although these Users will be able to perform any geometry
that resulted from the Topology candidates often are more ideas for a modifications needed using the geometry
Optimization algorithm, you have part and visualizations of how the forces editing tools in MSC Apex. Eventually, MSC
probably realized that AM is not flow through the design are. A proper Apex Generative Design is able to perform
very forgiving, and an unrevised Generative Design tool needs to produce these checks during the optimization
Topology Optimization result often is directly printable designs that can be used process automatically as well.
far from feasible. Despite its unique without any need for manual rework of
ability to manufacture virtually geometry defects. This is what MSC Apex
any topology, AM still has many Generative Design will deliver. Speed Is Crucial
limitations today. Issues such as
shrink lines, cracking, overheated In order to evaluate several design
zones, etc. have kept AM from candidates in a time effective manner,
replacing other manufacturing it is necessary to have a finite element
methods. These issues were not solver and an optimization engine
as pervasive when 3D printing was that can take advantage of the latest
only used for prototyping. However, computing technologies for extremely fast
they become prevalent when performance. With MSC Apex Generative
using AM for production parts, Design, we have done exactly that. We
especially primary or secondary have completely rewritten the FE solver
structural parts for Aerospace or and the optimization engine to scale on
Automotive industry. Today, cost of Figure 2: Topology Optimization of a GE Engine multiple GPUs and CPUs. The ability to
manufacturing and time of printing Bracket using MSC Apex Generative Design explore design space in a time efficient

Figure 3: Design for Additive Manufacturing using MSC Apex Generative Design
manner ensures that the design process is as possible while keeping into account the full potential and benefits of AM, users
not a bottleneck and thus allows our users the boundary conditions, constraints and need to be able to produce designs that
to make decisions solely based on design optimization goal. Thus, several design are specifically validated for AM. With MSC
criteria. Only a complete examination of the candidates are produced and directly Apex Generative Design, we are developing
design space with a variety of results, and in verified in the background using Simufact technologies that validate manufacturability
a short time, leads to the best results. Additive for metals or Digimat AM for in the Generative Design process. As such,
plastic products. While selecting the right the optimization engine only produces
candidate and iterating the manufacturing geometry candidates that have been
Demonstrating The Potential simulation, the perfect design in terms validated for AM.
of manufacturability, weight and costs is
To bring evidence on the potential of MSC selected. As a last step in the virtual world, Finally, after printing the part with your 3D
Apex Generative Design and to show this design finally gets a last validation printer of choice, Hexagon metrology’s
its usability, a wheel carrier of a formula with MSC Nastran for FE qualification state-of-the-art scanners can verify the
student team is considered to demonstrate and back again in Adams to ensure the accuracy of the simulations and compare
a use case. Due to its very complex load correct stiffness and behavior in the overall the “as-built” part to the “as-designed”
cases and a high demand on lightweight assembly. Thus, an optimal design was part. This allows for genuine “First Time
design, it is the perfect fit for demonstration. found that was printed and successfully Right” 3D printing. Time and cost are two
Furthermore, there is a lot of experience used in this year’s formula student season. of the major constraints in wide adoption of
in optimizing this part, as this race series AM today. Typically, with MSC Software’s
officially is an engineering competition that Generative Design solution we find that we
requires to develop a new race car each Summary and Conclusions can cut the time and cost of simulations
year. Other MSC tools such as Adams and by x10. Furthermore, most importantly,
MSC Nastran have been used for this part MSC Software’s MSC Apex Generative with our bridge to manufacturing, we find
in the past for optimization. Design is bridging the gap between design that we can get closer to “First Time Right”
and additive manufacturing. Additive 3D printing. MSC Apex Generative Design
As shown in Figure 3, the development Manufacturing has come a long way technology is here to make the design and
process starts with retrieving the loads by since its inception and is changing the development for AM smarter and more
a multi body simulation based on Adams manufacturing landscape. In order to realize sustainable.
Car. Hereby, the overall suspension is
engineered, including all coordinates for
the connection points, as well as the acting Read an overview in our
forces. This information is used to set
up the optimization model and define its
goals. Therefore, a “design space” as big
Read an Overview in our Previous Issue for More
as possible is added (shown as translucent
Information on Hexagon and MSC’s End-to-End Solution:
material). In this case the overall inner space
www.mscsoftware.com/Engineering-Reality-Summer-2019
of the rim minus the installation space for
wishbones and braking system is selected.
Running the optimization, this material
in the design space is reduced as much

M AT ERIALS
Fast and Accurate

Additive Manufacturability Analysis
By Yvan Blanchard, Coriolis Composites

Anthony Cheruet, e-Xstream Engineering
T
his article focuses on the design optimization of complex 3D composites
structures made by additive manufacturing processes.
There are commercial CAD-CAM software solutions for detailed offline

path programming, but there is a growing need for innovative tools and
methodologies for trade off studies very early in the design stage. A new
innovative solution has been developed on top of the CATFIBER© software, allowing both
designers and stress engineers to quickly analyze complex double-curved geometries. It
also includes a variable stiffness approach with tow-steering, and structural analysis of the
manufacturing defects using Digimat© software.
Design Analysis Framework Description
Today, more and more CFRP structures are manufactured by automated processes such
as fiber placement robotic systems (Figure 1).
The design trade-off analysis can be done on a simplified quasi-isotropic laminate (with full
plies), in order to just analyze the surface curvature impact, independently of plies shape.
But thickness effects, material excess, staggering rule, part productivity rate, could not be
correctly estimated.
The engineering and manufacturing requirements may quickly interfere, and a difficult
compromise between feasibility, strength and cost needs to be found, especially with
double-curved layup surfaces.

them test many combinations, such Structural Strength Analysis
as material width, maximum number
of tows by course, and maximum To verify the structural strength of the
fiber deviation angle. The solution optimized Layup design proposal, it
should be able to analyze complex and is important to have a quick and easy
representative laminates such as large transfer of all the as-manufactured
aerospace panels with double curvature composite properties onto a structural
and hundreds of plies. mesh used for sizing purposes. This
mapping is done using the Digimat
An automatic ply splicing algorithm, Platform© and concerns the transfer of
based on both engineering and
manufacturing requirements, allows
to quickly and easily generate a
manufacturable design proposal. This
algorithm also uses a patented rosette
transfer feature, allowing steered-path
propagation and then variable-stiffness
modeling (Figure 2).
Ply course centerlines and splice cuts

are first computed, then the ply boundary
is filled with (tow) strip surfaces. This
allows us to capture all the process and
material specificities, such as triangle
Figure 2: Steered-paths on wing skin surface.
of gaps, tow overlaps, minimum course
length (MCL), and minimum distance
between tow cuts (Figure 3).
The design analysis system is also

able to compute and output several
Figure 1: Automated Fiber Placement robotic manufacturing cost indicators, to help
system (Coriolis Composites) designers sort the manufactural design
proposals (number of courses by
sequence, number of tow cuts and drops,
buy-to-fly ratio).
Moreover, such analysis systems are not
able to reach a good compromise between The design analysis tools were
performances, level of details and results implemented on top of Coriolis
accuracy. The expected computation Composites CATFIBER© Offline
time is a few seconds to a few minutes so Programming solution, through a
that several analysis runs can be done in dedicated infrastructure made of scripts
parallel within a few hours only. (Python or Visual Basic) and a Microsoft
Excel© spreadsheet. This allows us to
Figure 3: Plies build-up with tape
Designers and stress engineers need easily launch several background runs courses covering.
robust tools and methodologies to help from a very light user interface.

the as-manufactured fiber orientation, References
the exact location of the gap and
Wu KC. “Design and analysis of tow-steered
resin-rich area. Using Digimat, such
composite shells using fiber placement”.
information can be transferred In: Proceedings of the American Society for
automatically to the Finite Element Composites 23rd Technical Conference.
Model used for sizing activities by Memphis, TN, USA. 2008, doi: 10.4271/2012-
01-0082
Stress Engineers. In this case, Digimat
Fayazbakhsh, K., Arian Nik, M.,Pasini, D.,
can generate the composite layup Lessard, L., Defect layer method to capture
command cards with the local as- effect of gaps and overlaps in variable
manufactured fiber orientation for stiffness laminates made by Automated Fiber
Placement, doi: 10.4271/2012-01-0082
each ply. In addition, the effect of the
G. Gonzalez Lozano, A. Tiwari, C. Turner
gaps on the local stiffness can be A design algorithm to model fibre paths
handled in two ways, depending on the for manufacturing of structurally optimized
reconsolidation process. The first one composite laminates. In: Composite Structures
(2018), doi:10.1016/2018.07.088
considers that the gaps have an effect
on the local thickness while the second
one considers that the gaps are filled by
resin and affects the local fiber volume
fraction of the composite. Using a
micro-mechanical model of the material,
this local variation of the fiber content
Digimat can is computed at each Gauss point of the
generate the FE Model (Figures 4 & 5).
composite layup
command cards Figure 4: Thickness map analysis
(quasi-isotropic laminate) with tow gaps
with the local as- capture with Digimat© software.
manufactured
fiber orientation
for each ply.
Figure 5: Local stress field per ply and effect of gaps computed by Digimat©
For Details About CADFiber Standalone©, or CATFiber©

for CATIA Solutions: www.coriolis-composites.com
For Details About Digimat©:

www.mscsoftware.com/product/digimat

AD D I TIV E MANUFA C TU R I N G
Flexibility
Through Additive Manufacturing:
How Simulation Supports
3D Prototyping
Simufact and its technology partner toolcraft shows

in a best practice case how additive manufacturing helps
to save time and money in the production of prototypes.
M
BFZ toolcraft GmbH require the component to have a long service process suitable for series production.
from Georgensgmünd life and high wear resistance so that it can However, before a new blade geometry
in Middle Franconia withstand mechanical and thermal loads. can be used with the required properties,
has optimized together many tests are required for which
with its software partner prototypes or small batches of blades are
Simufact Engineering from Hamburg the From Prototype to Series / required. In exceptional cases - depending
additive production of a turbine wheel Take a View On Manufacturing on the number of parts required - the
from ABB Turbo Systems AG. Typically, and Its Challenges in Serial turbine blades required for testing can
these components can be found in drive Manufacturing also be produced by casting in very small
units of heavy machines and vehicles, series. In general, these processes are
such as diesel locomotives, off-highway Filigree blade geometries are typically very time-consuming and cost-intensive
trucks or dump trucks. Depending on the produced by casting processes as and therefore not much more than two
application, manufacturers an economical and robust production prototypes are available to develop the

CHALLENGE: Transfer prototyping SOLUTION: Generate variant diversity
into serial manufacturing. Using the with the help of additive manufacturing.
example of a filigree blade geometry we This technique helps you save time
consider the challenges of traditional and money. Reach the first-time-right
manufacturing. approach through simulation.
USED PRODUCTS: Simufact Additive USER: MBFZ toolcraft GmbH
final product for use in series turbines. Due to component geometry and thermal
At this point, additive manufacturing has stress, high stresses occur during the
become a key technology that saves time building process. This is due to the special
and money. Furthermore, the technology features of the geometry, which on the one
offers a maximum flexibility, one of the hand has a solid core with a lot of material
most important requirements in the field and volume, while on the other hand the
of prototyping. With the help of this blades are very filigree. As a result, there
innovative manufacturing process, a are large cross-sectional changes in the
variety of turbine blades can be produced component, which favour the residual
in a very short time, which ultimately stresses during the manufacturing
leads to a better product. This is where process. These in turn result in a high
MBFZ toolcraft‘s high manufacturing susceptibility to distortion.
competence throughout the entire value-
added chain in turbine blade production MBFZ toolcraft solves this problem with
proves its worth. Within the framework of a careful simulation-based as-is analysis
the cooperation between MBFZ toolcraft in which critical areas are identified. From
and ABB Turbo Systems AG, the products this, the necessary measures can then
can be designed and implemented as 3D be derived to counteract the distortion
printing right from the start. problem. This includes the development
of suitable support structures that
generally minimize distortion and thus
Simulation Provides Reliable ensure a safe construction process. But
Information on Distortion and the ideal alignment of the components
Stresses in the Component to be printed on the base plate can also
be very helpful in individual cases. The
For MBFZ toolcraft, the greatest challenge last step is an automated compensation
in manufacturing prototypes is maintaining of the remaining distortion based on a
the required tolerances and dimensional quantitative distortion analysis, with which
Image 1: Simulation helps to reduce component
accuracy. The decisive factor here is the the remaining distortion is determined. The distortion and thus to keep tight tolerances.
component distortion caused by the AM results obtained in this way can be used
process. In order to keep the distortions as to derive the print preparation. Thanks to
low as possible, MBFZ toolcraft relies on the simulation, MBFZ toolcraft achieves a
Simufact Additive. By using the user-friendly low-distortion component structure and
and process-oriented simulation solution, can thus remain to its “ first-time-right“
MBFZ toolcraft makes it possible to approach - to fulfil all requirements on
significantly minimize distortions by means the component with the first print. The
of suitable process parameters and to use of additive manufacturing enables
compensate where they cannot be avoided. MBFZ toolcraft to react flexibly and quickly
In this way, MBFZ toolcraft can meet all to customer requests, such as design
required tolerances, thus eliminating the changes, and thus to significantly reduce
need for time-consuming reworking. project lead times. The virtual engineering
offered by the powerful simulation solution
enables significantly tighter processes in
Problems and Challenges in the process development of 3D printing
the Building Process projects. This approach can be realized
through the reliable software Simufact
Image 2: From design to simulation to the
A closer look at the building process clearly Additive. finished component – less distortions thanks
reveals the challenges and problems. to Simufact Additive.

M ATERIA LS
US Army Use of
MaterialCenter for
Metals Additive
Manufacturing Data
Management
Based on an Interview with the United States

Army Combat Capabilities Development
Command Armaments Center
T
he United States Army Combat Capabilities Development Command (CCDC)
Armaments Center is the US Army’s primary research and development arm
for armament and munitions systems. It is a leading defense facility for Additive
Manufacturing (AM) of Metals and is located in New Jersey. Armaments Center
has been investigating AM for a number of years now with programs aimed at
exploiting the novel capabilities of additive manufacturing. The facility has a number of AM
systems at their disposal including a laser powder bed fusion EOS M290 machine that prints
in Steel (4340/4140/17-4), Inconel, and Cobalt Chrome; and an E-Beam system, an ARCAM
A2X machine that prints in Titanium, Inconel, and Cobalt Chrome. In addition, there is access
to a wide range of support and testing equipment for powder synthesis (Plasma Reactors,
High Energy Mills), post processing (HIP, Heat Treatment, Surface Finishing), machining in
a full machine shop (EDM, CNC, etc.), testing (Tensile, Charpy Impact, Hardness), and part
characterization (Scanning Electron Microscopy, Particle Size Analysis, X-Ray Fluorescence &
Diffraction, Oxygen/Nitrogen Analysis).
Armaments Center is interested in using AM equipment to prototype, develop, and fabricate

metal parts via a layer by layer powder bed laser sintering process. AM has the potential to
provide a wide range of design flexibility over traditional manufacturing methods allowing for
rapid prototyping, part weight reduction, novel part design, reduced time to product, and
overall manufacturing flexibility. The benefits of AM include a reduced logistics footprint and
time-to-field for replacement parts, manufacturing options to reduce single point failures, and
creation of novel and improved part designs for reduced weight while meeting or exceeding
performance requirements. In turn AM results in a manufacturing process for providing parts
on rapid response, and on-demand basis.

Armaments Center has identified six qualified for part acceptance for use
practical areas of interest for additive in armament systems meaning design
manufacturing technologies in the US and manufacturing process data
Army (see Figure 1): required to support repeatable additive
manufacturing production must be
1. Novel Materials: Novel powder defined. Several examples of additively
synthesis for Non-COTS materials, manufactured armaments produced are
2. Rapid Prototyping: Multiple build shown in Figure 2.
iterations on the same build plate for
design optimization. Small runs for
prototype testing,
3. Replacement Parts: Investigating
component replacements which
match properties but can be delivered
in an accelerated timeframe,
4. Novel Designs: Investigating novel
weapons systems components with
designs difficult or impossible with
traditional machining,
5. Rapid Fielding: Investigating Additive
Technologies to overcome the
challenges of bringing metals additive
to the field, and
6. Process Monitoring: Working to
develop custom In-Situ Monitoring
Hardware which can be retrofit on
existing equipment.
For AM benefits to be fully realized, Figure 1: Additive Manufacturing Areas of

processes must be developed and Interest to the US Army
Figure 2: Metals AM Build Examples for the US Army at CCDC Armaments Center

What is the L-PBF and the constraints for AM. Today,
Quality Strategy? widespread adoption of the AM process
is limited due to a combination of these
CCDC Armaments Center at Picatinny, NJ, many challenges. Finally, with respect
have focused on additive manufacturing to utilization of Digital Product Data in
process development for AM materials and AM, a system for controlled electronic
parameters along with the development data management and sharing must be
of Quality Assurance provisions and implemented - software types used and
requirements to develop manufacturing digital file control must be set prior to
guidelines for robust and reliable new manufacturing initiation.
build L-PBF components. To do this, test
components for new build demonstration
and testing were selected. The team CCDC Armaments Center
wanted to establish a process for Additive Manufacturing
qualification and certification of AM Benchmark Demonstration
components, then transition the process
on to internal government facilities and the A CCDC Armaments Center goal is to
AM industry with a manufacturing guide. qualify powder bed fusion AM technologies
In addition, the US Army wanted to share as a viable alternative manufacturing
knowledge of the additive manufacturing process to fabricate armament systems
process and create a knowledge base of components. To do this, multiple areas
AM products aligning to their roadmap. in the total manufacturing process need
developmental efforts addressed to them to
be able to produce an accepted additively
What are the Challenges to the manufactured component. An additive
Figure 4: Selection of some of our 325 AM
Benchmark samples and parts Use of Additive Manufacturing manufacturing benchmark testing method
in the Military? was devised using 4340 Steel Powder
due to its chemistry, particle size, and flow
First and foremost, part acceptance for US characteristics. AM processing parameters
DoD (Department of Defense) applications were developed focusing on energy density
relies on a process for qualification and ranges, and a DoE (Design of Experiment)
certification. However, the relationship was established that looked at the following
between AM materials properties, parameters over 325 samples that were
processing parameters, and component fabricated (see Figure 4):
performance are extremely complex,
and complicated further by unique part 1. Laser Power
geometries. There is also an extremely large 2. Scan Speed
pool of materials and AM equipment to 3. Hatch Distance
choose from, raw materials must be readily 4. Energy Density Range
available and trusted to manufacturer or
internal specifications, processing condition The resultant AM parts were evaluated
windows must be defined to ensure part based on microstructure, density,
quality, In-Situ Monitoring technology must porosity, and hardness. Their mechanical
be utilized and improved upon, and a properties were compared to wrought
recognition that technology advancement steel after stress relief, quench and
might introduce previously unforeseen temper heat treatment.
manufacturing variables.
For each mechanical test specimen, four
It is fair to say that AM standards are still identical consecutive builds were printed
in development. There is a clear need to assess process reliability. The team
for continuing collaboration between focused on variations in location and
academia, industry, government agencies, orientation within and across builds, while
and others to push standards adoption. collecting tensile, hardness, density, and
Moreover, with respect to design for toughness data. The team normalized
Figure 5: Benchmark Steel 3d Printing Test AM there is a need to educate and samples with heat treatments per the
showing Locations of Parts of the Base Plate inform part designers of new principles AMS 2759 standard.

Figure 6: Benchmark AM Printing Sample Mechanical Property Results for the different print Figure 7: Six AM Printer Machine results for the
locations in Figure 5 same set of parts being printed the same way
AM Benchmark Results and • Equipment chosen included an will occur. Complex data sets can be
Lessons Learned EOSM290, ProX320, SLM, and the generated from even a single build.
EOSM280 Hence, data storage solutions are needed
The tests indicated that parts printed • 4340 steel powder was procured from where process monitoring solutions
in the XY direction had 12% higher a single lot to minimize variance require large file storage spaces and
elongation values than parts built in the Z • A manufacturing guide was written bandwidth. In effect, AM processing
direction. Ultimate Tensile Strength (UTS), and disseminated to all participants pedigrees are required. There is a need
Density, and Hardness values matched outlining all major aspects of the for historical records of print builds to
wrought steel properties. The parts manufacturing process exist for data tracking and analysis to
printed at Location 2 (top left of Figure • The aim of this round robin test relate back to field performance without
5) had the lowest mechanical properties was to observe variance in material duplicating efforts, allowing teams to learn
(~9% less) of all builds. Build locations 2 properties as a function of orientation from mistakes or successes. To do all this
and 4 had Z oriented tensile data with the and plate location across equivalent raises big questions over IT infrastructure
lowest values (see table in Figure 6). This and different equipment types, with issues. If there is no uniform software and
was because gas flow worsened when the same or equivalent process network system across different branches
the machine’s filters were nearly full. In parameters. and centers, then it will be difficult for
addition, many AM process conditions approvals and data sharing to happen
needed to be taken into account such as with additively manufactured parts.
powder coverage, build plate material/ AM Engineering Simulation
condition, recirculating gas filtration, Digital Data Storage Challenges
gas flow rates, part orientation, and part MSC’s MaterialCenter as the
location on the underlying build plate and In particular, given the sensitive nature of AM Data Management Solution
these parameters must be controlled military parts, data security is critical in AM at CCDC Armaments Center
for consistent AM part mechanical – how is digital data adequately protected
properties. Hence, a manufacturing in additive manufacturing? How is data To overcome digital data challenges of
plan with defined operating windows is sharing implemented especially if different additive manufacturing, a software solution
needed to ensure parts are consistently network security protocols exist, where is necessary for traceability, storage,
made to specification. cloud-based solutions are not widely and analysis of simulation material data.
adopted? Moreover, in terms of data Armaments Center used MSC Software’s
classification where data aggregation could MaterialCenter (Figure 8) and developed
The Effect on the AM raise the classification, there is a need for an additive manufacturing schema to
Benchmark Tests on Using a controlled system. Invariably, different enable the storage of all printer machine
Different Machine Types formats occur across a wide variety of parameters along with corresponding
OEM machines for metals AM with no material properties. It utilizes M/S Excel
To check for the effect of different additive standardized software or file format. integration in order to map and import
machines, six AM commercial machines custom templates. The data collected is:
were chosen to print the same parts in a In terms of data organization, a unified file
“round robin” demonstration of variability structure does not in general exist. With • Machine Information
(see Figure 7): AM, large amounts of data generation • Part Data (CAD/STL/MAGICS Files)

• Starting powder properties
• Machine Build Parameters
• Build Layout and Orientation
• Laser Parameters
• Post Processing
• Metallographic Analysis
• Mechanical Testing Data
The data stored using MSC Software’s

MaterialCenter involves a flexible schema
for different applications, an automated
method for input of material process
information, data analysis to compare
and contrast properties and understand
how to optimize them, and finally allowing
traceability of test data. Figure 8: MSC MaterialCenter in the center of the entire AM Material Lifecycle workflow
Capturing the Entire AM at CCDC Armaments Center - this is

Material Lifecycle shown schematically in Figure 11 where
the integration between Windchill and
Figure 8 shows the data tracking process MaterialCenter for additive manufacturing
for additive manufacturing and Figure 9 is shown pictorially. The benefits of
depicts the two parts of the AM process: this system are that it is always up-
to-date for version control tracking; it
1. “Left of Test” where manufacturing leads to less duplication of efforts and
inputs that are used to create a part therefore reduced costs; it is a common
or specimen are captured. This side file system for traceability, file security, This additive manufacturing ePDM
of the test leverages MaterialCenter’s historical storage, etc; it provides for workflow helps to deal with the
Work Request, Pedigree and a better collaborative environment in management of data issue and can
Process features. MSC Software’s order to coordinate efforts; it allows for become a US Army Standardized
MaterialCenter tracks the test quicker fabrication of hard to replace Tool for Lifecycle support when
specimen from raw material through parts with standardized file systems printing a component. It provides
the complete specimen build process and organization; it yields common data confidence through validation to
(Figure 10). The team tracked the models for standardization and validation; the end user of part performance.
materials & the environment, Batch/ it is a single source of data for linkage Everything is in the chain from raw
Specimen numbers and the Part between systems; and it provides true material, files, machines, and post
Inspection. lifecycle configuration management for treatment and it is validated to
2. “Right of Test” where Material products additive manufacturing. In short, for perform as designed. In terms of
are tracked from test to export. us to make Additive Manufacturing as Data Capture, automated processes
“available” as traditional manufacturing to feed into the data management
Finally, PTC Windchill was chosen as the techniques, materials and process data, system were enabled. In terms of
ePDM system for this application and it must be linked to part data using this On-Demand Manufacturing, the
the overall central data system for AM enterprise ePDM approach. team has qualified and authorized
personnel with access to the data.
In terms of Predictive Modeling,
knowing how a part will perform
before printing is invaluable. In terms
of Cooperation & Data Sharing, it
will lead to the saving of money and
time by building on the most up-
to-date work. And, lastly, in terms
of Data Analysis, the team can
optimize process parameters via
statistical modeling and understand
the relationship between key AM
process parameters.
Figure 9: Data management applied to additive manufacturing process

Future Focus of Additive
Manufacturing in the US Army
The US Army CCDC Armaments Center

is aiming at integrating material data
management with other enterprise
software, eg. PLM, and collaboration with
other services such as the US Air Force,
US Navy, etc. Ultimately, this approach
can be expanded to other manufacturing
processes and the capture of legacy
manufacturing data with the creation
and storage of new data libraries. It is
also looking at new materials systems
(functionally graded materials, novel
alloys, hybrid materials), the fielding of AM Figure 10: Overall Flowchart of Additive Manufacturing Data
parts and AM systems for on-demand

Battlefield manufacturing, a wide range
of qualification & certification of materials,
processes and parts via additive Reference
manufacturing, and advanced fabrication
“Army Efforts in Metals Additive Manufacturing & Data Management”, R. Carpenter, SME Smart
integration with sensors and electronics.
Manufacturing Series – Additive Manufacturing, 07 June 2018
Figure 11: PTC Windchill and MSC MaterialCenter integration ePDM system for Additive Manufacturing Data

V
Use of Simulation in
Process Chain of Thin-walled
Automotive Parts
By Dr. Maximilian Munsch,
Ampower GmbH & Co KG
For more than 30 years, dozens of Additive Identifying automotive PBF-L applications becomes
Manufacturing (AM) technologies have been used for challenging when taking the industry’s high demands
realizing prototyping applications. Over the past few regarding cost, quality and time into account.
years, AM was increasingly adopted for serial Because of the cost per volume of AM parts,
applications throughout industry, such as medical or currently only high priced, low volume vehicles or
aviation. The process of powder bed fusion with racing sports cars are targeted for application
laser beam (PBF-L) of metals has the largest impact. screening. In automotive production for mass
It offers the highest degree of freedom of design and markets, cost per part dominates the final decision
flexibility as well as excellent material properties. on whether they will be manufactured additively or

Figure 1 Additive Manufacturing process chain
conventionally such as forging or casting. the engine’s performance to the customer’s eye.
Manufacturers of high performance sports cars with Conventionally, those blends are manufactured from
limited quantitities up to approximately 5,000 units stainless steel or titanium alloys. Two metal sheets
per year will be early adopters of AM. Ampower formed by deep drawing are joined by a welding
expects the largest potential in automotive seam. Requirements for the mechanical properties
applications to be in the power and drive train as well are driven by vibration and corrosion which put high
as the suspension system. stress on the welding seam. Additionally, tail pipes
are subject to major design iterations. This leads to
To analyze the status quo, Ampower conducted a remanufacturing of deep drawing tools at extremely
study on Additive Manufacturing of a high-end high cost and typical lead times of over 12 months.
automotive application - a tail pipe blend from a
Porsche GT2 RS sports car and analyzed the AM rarely make sense without exploiting the
complete AM process chain. Tail pipe blends are the potential of redesign. A redesign has to consider
visible part of the engine exhaustsystem. Optical not only specific parts but also all surrounding
requirements are high since the component reflects components, functions and assembly steps. For the
present application, realized redesign advantages
are short time to market due to tool-free
manufacturing, increase of quality due to
homogenous material properties, reduction of
number of parts – and thus less assembly steps
– and potential for customized design.
Figure 2 Re-designed, printed and post-processed tail pipe Figure 3 Results of simulation with Simufact Additive and
blend of sports car Porsche GT2 RS computer tomography measurement of printed part

The AM process chain used for production of the tail
pipe blend is displayed in Figure 1. The final part
manufactured with PBF-L using titanium alloy
Ti-Al6-4V is shown in Figure 2 .
For complex free-form surfaces, optical 3D scanning,

e. g. with Hexagon metrology devices, and computer
tomography (CT) imaging are well suited methods to
accurately measure the resulting geometry. In this
study, the results of CT imaging were used to assess
Figure 4 Detection of shrink lines with the new function the feasibility of simulation tools that allow prediction
inside Simufact Additive
and compensation of stress-induced deformations.
The overall accuracy is mostly affected by distortion
and part shrinkage from residual stress formed
during the PBF-L process, where material cools at
rates of several thousand Kelvin per second.
The simulation of the PBF-L process was

conducted with Simufact Additive using the inherent
strain model. The voxel size for discretizing the CAD
data was set to 2 mm – the range of the wall
thickness of the part. The simulation yielded a
stress distribution and a prediction of the shape
About Ampower: deformation. The comparison of the results of the
simulation and the CT measurement are displayed
Ampower is the leading consultancy in in Figure 3. The conducted simulation shows a
the field of industrial Additive good match of absolute range of distortion, and the
Manufacturing. Ampower advises their deviation is represented quite well.
clients on strategic decisions by
developing and analyzing market Further analysis was done in collaboration with
scenarios as well as compiling technology Simufact headquarters in Hamburg employing a
studies. On operational level, Ampower brandnew function to detect specific part defects -
supports the introduction of Additive so-called ‘shrink lines’. Such shrink lines are formed
Manufacturing through targeted training in layers where manufactured areas grow together,
program as well as identification and shrink during solidification and leave visible marks on
development of components suitable for the surface. These defects were visible at the upper
production. Further services include the region on the tail pipe blend after production as
setup of quality management and support displayed in Figure 4. The part defects were correctly
in qualification of internal and external predicted by the simulation software and will allow
machine capacity. The company is based for future compensation.
in Hamburg, Germany. More about
Ampower at am-power.de. In conclusion, the study revelaed the feasibility for
use of PBF-L process of thin-walled automotive
Contact: parts. However, the relative high cost of the
process will limit the use to high end applications
Ampower GmbH & Co. KG with low volume. Simufact Additive predicted the
ZAL TechCenter deformation and shrinkage correctly and will allow
Hein-Saß-Weg 22 improved process chains by enabling first time
21129 Hamburg, Germany
right production.
Dr. Maximilian Munsch

munsch@am-power.de
+49 175 8787870

Simufact Additive: Collaborative
Simultaneous Engineering Tool
for Additive Manufacturing
By Clara Moriconi, Head of Safran Additive Manufacturing’s Methods,
Tools and Application Team, France
A
dditive manufacturing is a process that has been used Additive manufacturing of metal components is becoming
for some years in Safran’s production centers. Safran more and more widespread in all sectors of industry. A major
Additive Manufacturing - a technology platform advantage of this technology is the geometry design freedom
attached to Safran Tech, Safran’s dedicated research center - that allows the creation of optimized shapes according to the
is aiming to support the widespread use of the additive targeted function. It now starts to be used for the serial
manufacturing technology within the Group: first by production of high-tech parts, particularly in the aeronautics
recommending tools and standards while evaluating and and space industry. Another key benefit of using 3D printing
validating the solutions through use cases, then by technologies is the ability to reduce the weight, cost and
accompanying the companies of the Group in the deployment complexity of parts production without sacrificing the reliability
and use of these tools. and durability of materials.

The Additive Manufacturing Challenge
Although some applications are already in production, many

are still at the proof-of-concept stage. Thus, in order to expand
the use of additive manufacturing and make the most of this
technology, it is essential to accelerate the capability to model
additive manufacturing processes in detail - and more broadly,
to improve the understanding of the technology by the relevant
employees within our Group.
The Methods, Tools and Application team of Safran Additive

Manufacturing is operating in this context. The objective of the
team is to evaluate and qualify additive manufacturing process
simulation solutions, and then facilitate their deployment within
the various Safran operating units.
Figure 1: Example of the effect of a 3D printer scraper / workpiece

collision on the powder bed
Figure 2: Example of macro-cracks on LBM parts that appeared during the manufacturing process due to part distortion and Simufact Additive stress
predictions of the parts (Red is high, blue is low)

Simulation of the Additive and post-treatment operations, risk of collision with the
Manufacturing Process recoater, as well as the possible risks of failure of the part itself
or the supporting structure attached to the part. Figures 1 and
One of the manufacturing processes in which Safran Additive 2 illustrate two types of failures that can happen in additive
Manufacturing is more specifically interested in, is the Laser manufacturing: 3d printer scraper / workpiece collision in the
Beam Melting (LBM) process. The simulation of this process powder bed during the manufacturing process, and large scale
aims at identifying issues associated with part distortion during crack formation during the 3d printing process due to inherent
the manufacturing process, as well as the potential risks of stresses in the part during the manufacturing process
failure of the part and its supporting structure.
The Benefits of Simufact Additive to Safran
Safran called on MSC Software, which offers a solution that
uniquely covers the entire manufacturing process, from the The use of Simufact Additive has enabled us to save
initial melting step of the part to the completion of a final HIP considerable time in production preparation thanks to the
treatment (Hot Isostatic Pressing), including all post-processing predictive nature of the software, which limits development by
operations such as a stress-relaxation heat treatment, manufacturing iterations by using virtual development
baseplate cutting and supports removal. This solution is upstream, but also during the part design phase, by enabling
Simufact Additive. us to anticipate the effects and limitations of the process at the
product design level.
Safran Additive Manufacturing uses the software iteratively as
part of our feasibility studies for the following two applications: One of the added values of the Simufact Additive solution is
that it allows us to bring together two activities: engineering
• For production support: to virtually develop and validate and production. On the one hand, people from engineering
the process, in order to reduce physical iterations on who design parts with a strong focus on part performance in
the machine; service, and, on the other hand, the methods office who
• Further upstream, in the product design phase: to check master the industrial processes and their associated
the manufacturability of parts and to take into account the constraints. Simufact Additive is a solution well adapted to
specific constraints linked to the process during the simultaneous engineering that facilitates dialogue between
product design phase. the different business activities involved in the same project.
In addition, the software is easy to use, with an intuitive,
Simufact Additive allows for the identification of potential issues business-oriented interface that allows for quick and easy
due to deformation of parts during the manufacturing phase appropriation/ownership.
Conclusions
Safran Additive Manufacturing has taken full advantage of the added value of the Simufact Additive solution in order to secure the
integration of the additive manufacturing processes into its “product-process” development processes, both upstream during product
design and downstream for the production launch.
Safran Additive Manufacturing is now focusing on extending the use of the Simufact Additive solution to different types of parts and
different grades of material, in order to improve the design process for additive manufacturing as a whole. MSC Software supports Safran
Additive Manufacturing and the Group in achieving this objective through this solution that integrates into the global additive manufacturing
value chain, ensuring a quality and open digital continuity.
Try Simufact free for 30 days! Learn how: www.mscsoftware.com/simufact

Optimize the Product Part,
Not Just the Geometry
- A Real World End2End
Solution
By Dr. Hendrik Schafstall, Vice President,
Virtual Manufacturing & Costing, MSC Software
W
ith the continuing rapid adoption and development
of additive manufacturing techniques and
technologies in multiple industries led by
can be obtained in companies. This includes the huge potential

for lightweighting, small production runs with less material
produce functional, high performance parts that simply can’t

be subtractively manufactured, cast or formed. One of the
challenges is a full automation and to minimize the physical
try-outs. This can only be achieved with a full digital
With the acquisition of MSC Software in 2017, and its

manufacturing oriented and material focused business units of
Simufact and e-Xstream in particular; Hexagon’s Manufacturing
Intelligence Division now has in its portfolio a unique
combination of tools including cutting edge CAD/CAM
production software plus existing market leading metrology
solutions. The smart factory solution Xalt from Hexagon is
offering the needed framework for the connecting of all data
(from real and virtual sources) to enable a fully connected Figure 1: End2End Workflow for Virtual Simulation, Printing and
Scanning in Additive Manufacturing

Upper Fork (Metal) Bike Saddle (Polymer)
Figure 2: Concept of a folding bicycle with 3d printed Metal and Polymer parts
These technologies within Hexagon allows the development of a metal or polymer parts in a straightforward way so that they are
compelling solution for the challenges of the additive manufacturing ‘First Time Right’ printed. Let us unwrap that statement a bit by way
industry where unit costs can be high and errors can be costly. It is of first outlining a typical End2End Additive Manufacturing workflow
important to not just optimize the 3D CAD geometry during 3D (see Figure 1). And secondly, using an example for a new innovative
printing, but also, to optimize the end product part. There is a need lightweight folding bicycle concept (see Figure 2). We choose two
\Jewel Deb\Kenscio Office
for real world solutions thatCreatives\MSC
are fast, accurate andSoftware\2020\Jan-2020\MSC-UX580-EBOOK
robust than cover the
typical parts from the bike, to demonstrate pages and
principles fordemo
the pages for
alternative PLM and CAD-based methods. In effect, with this available solutions for metals and polymer. The first is a handlebar
combination of technologies I believe it is now possible to plan, upper fork as a 3D printed metallic part and the second one a bike
optimize, validate and replicate high quality additively manufactured saddle as a polymer part.
Figure 3: Process of Reverse Engineering the Arena Seat Saddle using a 3D Scanner

sources. As an example for a ‘reverse engineered’ part, we
used the bike saddle. The geometry was created out of a 3D
scanned point cloud, see Figure 1: 5 o’clock (Figure 3), where
we used a Hexagon Absolute Arm 7-Axis machine.
For the handlebar upper fork, we carried out a topology

optimization of the part, Figure 1: 7 to 8 o’clock, in a suitable
CAD-centric tool like MSC Apex (Figure 4 shows the fork part).
Used in early design, MSC Apex allows users to obtain geometries
that will withstand the loads on the component and minimize its
weight (by as much as 70%). Topology optimization therefore can
Figure 4: MSC Apex topology optimization of the folding bike be used to redesign existing components and account for
fork geometry manufacturing constraints early on. After this optimization step, the
user needs to be able to evaluate the strength and stress of the
optimized design by predicting its distorted geometry on full loading
to see if it fits within allowable tolerances.
The wheel in figure 1 shows the overall workflow and data flow.
You can start in any of the segments depending on the Once a suitable geometry has been designed, like all computer-
requirements and parts you want to produce. The geometry for aided engineer simulation predictions, the build process need to be
the part, which we want to print, can come from different qualified, critical areas to be identified and at the end the whole
D\ERM_Winter 2019 Package\Links
Figure 5: Schematic representation of polymeric materials in MaterialsCenter
Gold = initial Final geometry with

Blue = distortion compenstion Optimized support structure
Figure 6: Comparison of simulated and optimized 3D printed metal Fork part in Simufact Additive

Volume IX - Summer 2019 | mscsoftware.com | 61
Comparison of “as-built”
to “as-designed” part
Figure 7: Comparison of the final ‘as-built’ 3D printed polymer seat Figure 9: Hexagon EdgeCAM’s knowledge of machine control in
part to the ‘as-designed’ part in Digimat Additive Additive DED machines
process chain needs to optimized, so that we will get the right whole process will become more transparent and the process
shape with the required part performance. The final part can be made more robust to ensure that all errors are
performance is the outcome of the used process and print eliminated before the designs are committed to in the printers.
parameter. The data can be taken for all materials from an open
and flexible material data management tool like MaterialCenter from In the shown folding bike scenario, we worked with a Hexagon
MSC Software (Figure 5). This solution was adjusted dedicated to partner organization, NIAR, at Wichita State University in America
AM, to be able to handle all experimental data, to calculate the to use their 3D printers to additively manufacture both the fork and
needed parameter out of it for the material models for the simulation the saddle (Figure 8). This part of the process is represented by
and finally, also to control all material properties during the the segment in Figure 1 at 1 o’clock where you go through the 3D
production process. The final material properties need to be printing process based on the optimized designs from the CAE
documented and stored for sensitive parts in AM. MaterialCenter is software predictions at Figure 1: 11 o’clock. We want to thank
the perfect solution to be used besides the production and for the NIAR for their collaboration in this project.
virtual manufacturing simulation as a digital twin.
30% of the costs are incurred directly through the post-
But let us go back to the manufacturing simulation (Figure 1: processing step for machining of the printed part. The used
11 o’clock). MSC Software offers best in class technologies orientation of the part during the process and therefore the
with Simufact for metals (Figure 6) and Digimat for polymers needed support structures etc. are directly influencing the effort
(Figure 7). The simulation will predict the distortion and for the machining stage. So there is a need also to take this
behavior of the parts (fork and saddle) during the whole manufacturing step into account to be able to optimize the
process chain and will detect critical areas or possible whole process chain with all the main influencing steps. It also
problems. This enables the user to optimize the whole process has an impact on the predefined design and can be used to
steps and minimize the risk for manufacturing problems. The minimize the total costs. That is why MSC is developing an
Figure 8: NIAR facilities for 3D printing and the machines used in the bike saddle and fork printing

had NIAR print. The simulation results stand for quality and the
use of the software tools for productivity.
Summary and Conclusions
Hexagon’s virtual predictive design & engineering simulation

software from MSC Software, production simulation software,
and 3D metrology measurement workflow for additive
Figure 10: Distortion of a DED additive manufactured part after the
machining process manufacturing captures the entire process chain (Figure 1)
through a printed part’s final ‘as-built’ performance. Virtual
printing stress analysis simulation (either by Simufact for metals
or Digimat for polymers) allows users to optimize the 3D print
process via these innovative simulation tools, thus saving time
and material cost. We have illustrated this by way of two
components from an innovative folding bike design.
Good 3D CAD geometry topology optimization (under

development via MSC Apex, our modern CAE preprocessing
tool) allows for fast identification of the part geometry design
parameters in order to minimize material cost and printer time.
It will be directly linked to the manufacturing simulation and will
take the manufacturing constrains into account. This enables
Figure 11: Final inspection of the 3D printed polymer seat part the designer to optimize a good printable part with the right
Hexagon Absolute Arm part performance for the loads.
The data management of all important material data, from test

through simulation up to the production and process parameter,
End2End solution with a closed feedback loop. It further allows requires a good data management solution. MSC has developed
supporting better hybrid machines in the future. a solution dedicated for AM based on MaterialCenter. Support of
good material properties ensures the most accurate simulations
Hexagon MI has dedicated production software tools for predictions for either metals or polymers. Finally, after printing the
machining operation, which are simulating the toolpath and part with your 3D printer of choice, Hexagon metrology’s
machine behavior that will be connected with the manufacturing state-of-the-art scanners can verify the accuracy of the
simulation to predict and optimize the tool path and machining simulations and compare the ‘as-built’ part to the ‘as-designed’
and printing strategy. This can be used also for direct energy part. This allows for genuine ‘First Time Right’ 3D printing.
deposition processes (DED) based on blown powder or Typically, with this workflow, we find that we can get useful
wire-feed for large structures or repaired parts (figure 9). Figure engineering simulation results for additive manufacturing in
10 shows the final distortion of an Airframe after a DED process minutes and hours versus hours and days for alternatives.
and machining simulation. This solution from Simufact is still
under development and should demonstrate the ongoing The virtual and real world with all different sources can be
activities from MSC in the field of Additive manufacturing connected via Xalt and linked to PLM systems. With Hexagon
processes and how we take advantage out of being part of technology the users will be able to make the design and
Hexagon for the most benefit for the customer, to connect development smarter and at the end have a smart virtual and
production software and manufacturing simulation. real factory.
Finally, we validate all simulation results with the real measured

data (Figure 1: 4 o’clock), by using a Hexagon Absolute arm to
scan the 3d printed plastic seat by acquiring a point cloud MSC Software and Hexagon’s Unique
using the physical dimensions of the ‘as-built’ part to inspect Additive Manufacturing Solutions:
the final part for quality assurance. The simulation results were www.mscsoftware.com/additive
proofed and it was found to be very close to what was required
in the specification, as indeed was the metal handlebar fork we

Development of an
Quality System
for Gas Turbine Engine
Part Production
By A.I Khaimovich, V.V. Kokareva, V.G. Smelov, A.V. Agapovichev, A.V. Sotov
Department of Aircrafts Engines’ Construction Technologies, Samara
National Research University, Moskovskoye Shosse 34, Samara,
443086, Russian Federation

Introduction roughness, its residual stresses and part deformations. An
SLM quality system for gas turbine engine parts production
S
elective laser melting (SLM) is a powder bed fusion should be based on an interaction model of the technological
additive manufacturing (AM) process which occurs at a factors affecting the quality of the final fabricated parts.
high metal melting temperature. High local temperature
gradients and brief cooling effects can cause residual stresses There are three main methods for predicting the temperature
and part deformation during 3d printing, the consequences of distribution and residual stress during the SLM process:
which can be additional surface treatment and reduced
productivity for the process. To understand how to control the 1. Simulation methods,
formation of AM residual stresses and part form deformation, a 2. Experimental work, and
reliable method to investigate influences between technological 3. Combined simulation and experimental approach
parameters and quality behaviours is required. There are basic
physical mechanisms of the selective laser melting process that Since it is difficult to predict part distortion in micro detail due
can lead to part distortion and cracking: high temperature to enormous computational resources being required, a SLM
gradients, high viscosity and surface tension of the molten process for a practical part can be divided into three scales;
powder zone, un-melted powder and oxidized particles. micro scale, meso scale and macro scale. With this type of
approach, the temperature history and residual stress fields
The following variables of the SLM process can be established during the SLM process can be predicted. Thermal information
as the most important: has to be transferred through micro scale laser scanning, meso
scale layer hatching, and macro scale additive part build-up.
1. Powder, composition, size distribution, shape, and
thickness of the melting layer; Description of our SLM Model
2. Laser, power, spot size, beam spatial distribution, scanning
velocity and protective gas atmosphere; and The laboratory of additive technology at Samara National
3. Strategy of additive manufacturing Research University developed a model of influences on the
SLM process parameters of quality by way of an Ishikawa
The main target of our research was to find and control the diagram. The quality of the final additive manufactured part can
optimum SLM process parameters to minimize printed part be decided by powder properties, process parameters, SLM
S L M equipment P owder
P article size Density
S ervice
Type Mixing Time of mixing
Vergin
C alibration
Used
Maintenance Melting Flowability
Monitoring
temperature
and control
S orting Flowability
S L M proc es s quality
P arameters P arameters
G eometry
Type Weight
P arts orientation Technology type
S upport
P arts preparation C oating
R oughness
S L M proc es s P arts F inis hing
Figure 1. Ishikawa diagram of a SLM process’ quality

equipment characteristics, finishing and detail behaviours as database in the PDM system, Teamcenter Manufacturing. The
shown in Figure 1. input technological parameters were all the influences on part
quality: scan speed and laser power, the powder layer
SLM equipment characteristics are determined by the type of thickness, the hatching distance, the hatching angle.
3d printer, the monitoring system, kind of technologies used,
and its frequency of service and maintenance. In order to Development of the SLM Quality System
ensure technological accuracy, it is recommended to calibrate
the production system and to build in every month test In our study (reference 1), an effort to better understand the
samples as the benchmark for complex shapes. Then it is factors influencing part quality resulted in us developing an
necessary to check weight (density), dimensions, tolerances, evaluation method. Technological parameters were divided into
and surface roughness under different part orientations. Quality two types: those controlled by the operator of the additive
maintenance requires keeping the equipment’s daybook machine (inputs) and those defined by the final part’s functional
rigorously where all actions are recorded: powder changing, use (limiting conditions).
cleaning, stopping, optic system controlling, and parts
replacement. Powder analysis includes understanding of the The input SLM parameters were:
particle size distribution and particle shape using scanning
electron microscope. Furthermore, it is necessary to evaluate 1. Gas atmosphere concentration (percentage of oxygen);
powder ‘flowability’ and its apparent density. A SLM quality 2. Powder layer thickness; and
system should therefore include registration of the qualitative 3. Set of 3d printer process conditions: scan speed, laser
and quantitative parameters of powders especially the power, hatching.
proportion of mixed powders. In addition, the main material
quality parameter is the rate of sieved and reused powder in a The limiting SLM conditions were:
subsequent process powder.
1. Powder behaviours,
It is clear that an additive part quality is therefore dependent on 2. Geometry accuracy, and
SLM process parameters which should be controlled and 3. Powder grain size.
managed. In order to determine the optimal AM built
parameters with the aspired objectives and technical The input parameters influenced the SLM process by the way of
requirements, there is a need to consider many factors, such the layer thickness increasing effort on the bed fusion while the
as cost, time, part quality, batch quantity all together. For density of melting material is decreasing. Another example of the
simplifying this task, we developed a database of SLM input parameters’ influence is if we increase the oxygen
technological parameters for domestic powders: aluminium, concentration in the building camera a melting material becomes
titanium, heat resistant steel, stainless steel. We produced this more crack-sensitive. We therefore proposed to use a SLM
Input parameters Limiting conditions
Material and Equipment

geometry
Gas atmosphere
parameters
Evaluation Deviation
SLM
Process Build strategy
Management
Output parameters
Selective laser
melting process
Set of process conditions Part quality
Making decision algorithm
Figure 2. Schematic of the SLM quality system

Powder particle melting
SLM parameters
• Laser power
• Scan speed
• Hatching
• Layer thickness
• The temperature gradient

• Internal stress
Distortion evaluation
Input parameters Comparison MSC Simufact Additive

• Quality parameters
Limiting conditions
Experimental specimens
Figure 3. Our proposed SLM quality system
quality system which is based on managing and controlling of needed an engineering simulation model of the SLM process
input parameters taking into account the limiting conditions. The for better understanding of the link between input and output
main blocks of the proposed SLM quality system are shown in parameters under different limiting conditions. We achieved this
figure 2. In order to select the appropriate set of technological by employing the predictive simulation tool, Simufact Additive,
parameters, the system uses a making-decision algorithm, and from MSC Software.
selection of input parameters depends on the link between part
requirements (accuracy, geometry, surface) and building regimes Simulation techniques have been widely used to predict
for corresponding material and mechanical behaviors. The main residual stresses and part distortions in the SLM processes.
idea of this quality system is that decision and denoting of SLM But they are only suitable for analyzing the thermal-mechanical
parameters are based on experience, and our statistical model to predict residual stresses and distortions of a sintered
database is included in the making-decision algorithm. After specimen. For an original SLM part, it is difficult to predict part
each part is manufactured ‘successfully’, its database record’s distortion due to requiring millions of micro-scale laser scans
input parameters with certain limiting conditions are recorded as which will increase the computational hardware requirement
meaning that all quality requirements are satisfied. prohibitively. However, Simufact Additive allowed us to
compare numerical and experimental results and to develop a
The making-decision algorithm should include not only the multi scale approach to achieve acceptable accuracy of part
statistical database, but a method of quality prediction. The distortion and internal stress. As already mentioned, if we
prediction of accuracy and surface behaviors found in the divide a SLM process for a practical part into three scales such
physical process during SLM: temperature gradients and as micro scale, meso scale and macro scale; with this
distortions, internal stresses and deformations. For this approach, the temperature history and residual stress fields
approach we needed the ability to both monitor the SLM during the SLM process can be predicted. Thermal information
process and to manage this process. Such a system is the key can be transferred through micro scale laser scanning, meso
step to achieving digital manufacturing transformation scale layer hatching, and macro scale part build-up. The aim of
according to the well-known Industry 4.0 concept. our research was to develop a perspective quality system for
the SLM process based on a making-decision algorithm and
Figure 3 illustrates the developed additive manufacturing quality predicting the part quality by SLM process simulation in
system we devised for SLM. It should be noted that we consideration of the temperature distribution and internal stress

Support structure
CAD-model Mesh generation Workflow process Stress simulation and calculation
modelling
Build parameters and SLM fabricated specimens and deformation evaluation

strategy
Printing preparation Results stress and deviation Distortion model correction Materials properties correlations
Figure 4. SLM distortion prediction by Simufact Additive for a Gas Turbine printed part
in the workpiece. For developing the SLM quality system, a the SLM process. For getting the required part quality influence
conceptual model was established. We chose to simulate the factors correct, factors must be considered such as limiting
entire metal SLM process of a gas turbine engine part including conditions (material properties, equipment specifications), and
Simufact Additive predictions: build, baseplate cutting and input parameters (building conditions and process parameters).
support removal process (see figure.4). Simufact Additive However, during the SLM process, the localized increased
allowed us to predict the distortion and residual stresses in the compression and tension caused by large temperature
turbine blade part and guided the quality system in how to gradients and fast cooling of the 3d printing process can lead
pre-compensate to ensure a quality part was printed the first to significant internal stresses in the workpiece and consequent
time right. Process control variables were selected in Simufact shape deformation. Simufact Additive was a major predictive
Additive to optimize this SLM process to reduce printing time simulation tool to avoid this and for the success of our
and material waste successfully. proposed SLM quality process.
Summary and Conclusions Reference
We developed a model of all the influences of additively 1. “Development of SLM quality system for gas turbines engines parts
manufactured SLM process parameters for a gas turbine part production”, V V Kokareva, V G Smelov, A V Agapovichev, A V
Sotov, and V S Sufiiarov, ISPCIET’2018, IOP Conf. Series: Materials
based on quality and influencing parameters as described by Science and Engineering, 441 (2018) 012024
an Ishikawa diagram. The SLM quality system includes
technical-organizational methods of managing and controlling

Robert Bosch India Use
Simufact Additive to Digitally
Lightweight a Fixture Tool
and Save 70% in Mass
By Radhakrishnaiah Bathina, Technical
Specialist Electric Drives, Bosch India
R
obert Bosch Engineering and Business Solutions costs and time, the idea was put forward to produce the fixture
Private Limited is a 100% owned subsidiary of Robert tool by additive manufacturing in a single part with the goal of
Bosch GmbH, one of the world’s leading automotive removing as much weight as possible without compromising
Tier 1 suppliers of technology and services with 400,000 the part’s mechanical strength.
employees and $100Bn annual revenues. Bosch in India offers
end-to-end Engineering, IT and Business Solutions and Bosch engineers decided to employ the Simufact Additive
employs over 19,000 associates. It has the largest software product from MSC Software to model the additive manufacturing
development center outside of Bosch Germany and is a (AM) metal build process and subsequent post-processing steps
Technology Powerhouse with a global footprint and presence to help eliminate design errors before expensive AM was
in the US, Europe and the Asia Pacific region. committed to. Simufact Additive is very powerful at predicting the
magnitude and distribution of residual stresses in an additive
In making the rotor parts of motors, Bosch employs an IRIS manufacturing situation taking into account variables such as
fixture tool (figure 1). Each year typically 200 units of this IRIS process type, build rate, build sequence, amount of constraints,
tool needs to be produced for assembling various types of etc. Highly localized heating and cooling during the AM process
motors. Until recently, the IRIS tool used to be manufactured typically produces non-uniform thermal expansion and contraction
by a conventional casting process as two parts. To save tooling in the part, which results in a complicated distribution of residual

stresses in the heat affected zones and unexpected distortion in distortion reduction of 70% to 1.067 mm after 1 pre-
across the entire structure. Moreover, these residual stresses may compensation run by ensuring a more uniform metal particle
promote fractures and fatigue in the AM part, and induce melting temperature of 1399°C throughout the simulation process
unpredictable buckling during the service of the printed part. in order to avoid thermal-stress issues. Effective metal maximum
Hence, it is vital to predict the behavior of the AM process and to
optimize the design/manufacturing parameters before committing
to 3D printing. Simufact Additive is able to predict the influence of
several components in the AM build space, determine the best
build orientation by performing sensitivity studies, reduce the
number of physical iterations and yield high design productivity
benefits because it leads to a reduction of total time for AM.
A first Simufact Additive prediction (Case 1) for the part being

considered for replacement without precompensation of the part
(Figure 2) identified severe manufacturing issues due to high local
temperatures in the 3D printed part, final part distortions with
tolerances exceeding 3.5 mm, and final part effective stresses
exceeding 1,260 MPa if this part was additively manufactured.
Using Topology Optimization methods, Bosch engineers iterated

to a Simufact Additive prediction (Case 2) where they were able to
integrate the formerly two-part fixture into just one part and to
result in a reduction of the component’s overall weight by 70% Figure 1: Traditionally manufactured cast metal IRIS tool (cream and
(figure 3). In Case 2, Simufact Additive delivered a shape deviation maroon parts) inside its assembly
Temperature [°C] Temperature [°C]
Melting temp of MS1 powder is 1399 °C
Shape deviation [mm] Effective stress [N/mm2]
Distorted
geometry
Initial geometry Maximum stress > 1260MPa
Figure 2: Additively Manufactured IRIS Fixture Tool prediction that has not been topology optimized showing non-uniform melting temperatures of
1399°C, part distortions of up to 3.5 mm and final part effective stresses exceeding 1,260 MPa (Case 1)

Temprature [°C] Temprature [°C]
Melting temp of MS1 powder is 1399 °C
Shape deviation [mm] Effective stress [N/mm2]
Distorted
geometry
Maximum stress < 1260MPa
Initial geometry
Figure 3: Additively Manufactured IRIS Fixture Tool prediction that was been topology optimized showing constant melting temperatures of 1399°C,
part distortions of up to 1.07 mm and final part effective stresses less than 1,260 MPa (Case 2)
stresses in the AM part were kept below the yield strength limit of
1260 MPa. For the optimization of this AM build process, they
used the Simufact Additive pre-compensation method which
aimed at a part geometry within acceptable distortion tolerances.
In addition, Simufact Additive optimization methods for the build
process (e.g. support structure optimization) and post-processing
(e.g. cutting strategies, support removal strategies) were also used
to improve this manufacturing process.
By applying topology optimization methods to Simufact Additive

predictions, Bosch engineers were able in this study to re-design
the IRIS tool parts with the objective of developing a lighter
single part with adequate stiffness, lower material usage and
thus AM power consumption, and ultimately yielding a process
cost saving (as well as a mass reduction) – see Figure 3.
Summary
Bosch India used Simufact Additive to replace costly low-volume

tool production (casting) by tool-less additive manufacturing for a
motor IRIS fixture tool. By re-design and topology optimization,
Bosch engineers managed to integrate the functionality of what
was once two cast parts into a single AM metallic part with similar
70% reduction in the weight
(After topology optimization)
mechanical characteristics while at the same time reducing the
part’s weight by 70%. AM process simulation with Simufact
Additive therefore helped Bosch engineers to overcome additive
Figure 4: IRIS Fixture Tool from a traditional cast part (top) and fully manufacturing issues (distortion, residual stresses) and to
topology optimized AM part (bottom) establish a new manufacturing process “first time right”.

Sinterline ®
Prototyping
by Solvay
Ultimate strength prediction of a plenum under
pressure produced by selective laser sintering
Sylvain Mathieu Dominique Giannotta

Software Dev.Engineer, e-Xstream Project Director, Solvay EP
S
olvay, a global leader in direction, its ultimate strength is reduced
advanced polyamide
solutions, is the principal
in the stacking direction.This issue
is inherent to additive manufacturing
The designed plenum
material sponsor for the processes, as successively deposited should sustain the
Polimotor project. It aims layers are not perfectly bound together.
to open the way for a technological The impact of the produced part
working load conditions
breakthrough in the automotive sector
by replacing up to 10 metal parts by
orientation in the build chamber of SLS and may be redesigned
devices, and AM processes in general,
plastic materials in the engine Polimotor must not be neglected and this new by topology optimization
2 engine.
Among the manufactured plastic parts,
parameter influence must be evaluated.
in order to lighten the
In the image below, the plenum has been
the Polimotor 2 engine will feature a
printed in a peculiar direction due to structure while taking
3D printed plenum chamber produced
through selective laser sintering (SLS) by
the limited space available in a building
chamber: this will be taken into account
advantage of the 3D
using a Sinterline® Technyl® polyamide 6
(PA6) powder grade reinforced with a 40
while predicting the ultimate pressure printing technology.
load it can sustain.
percent loading of glass beads.
The target is to demonstrate that the
plenum plastic part (manufactured with
this technology and material) can perform
with the same reliability as its injection-
molded counterpart.
Challenge
Due to the fact that parts are built of layer
superposition without the need of support
materials, laser sintering can quickly
produce components that integrate
complex internal features and functions.
However, the direction in which the part
is built greatly affects the printed part
strength. Although the printed material
Polimotor 2 Plenum printed with Sinterline®
behavior is not affected by the building
Solution • Perform a coupled MSC/Digimat AM Results Validation
• Create and calibrate the material calculation to establish the ultimate The maximum pressure load sustainable
behavior using the appropriate pressure load the part is able to has been numerically predicted to
constitutive law. The glass beads are withstand. 9.1 bars, whereas 3 bars has been
modelled using an elastic law while experimentally applied without failure
the pressure-dependent Drucker-
Results/Benefits in the same environmental conditions.
Prager model is well suited to catch • Precise description of the material The designed plenum should sustain
the matrix behavior. behavior and failure surface the working load conditions and may
• Study sensitivity of the part strength be redesigned by topology optimization
• Fully characterize the failure surface
to its orientation in the build chamber in order to lighten the structure while
using the appropriate failure criterion.
taking advantage of the 3D printing
The failure surface shape, specific • Avoid producing parts that do not
technology. u
to 3D printed material, can be well meet the strength requirements by
fitted with a generalized version of taking into account the specificity of
the Tsai-Wu transversely isotropic 3D printing processes
failure criterion.
Manufacturing direction vs. various tensile samples Manufacturing direction of the plenum

Simulating
Effects of
Warpage
Bender Kutub Olivier Lietaer

Senior Additive Manufacturing Business Development Engineer,
Research Engineer, Stratasys e-Xstream
F
or more than 25 years,
Stratasys has been a defining Challenge
force and dominant player To unlock the full value additive
in additive manufacturing – manufacturing has to offer, simulation
notably inventing the Fused
tools are needed to predict and mitigate
Deposition Modeling (FDM) Technology.
part warpage as well as realize the impact
The company’s solutions provide
of design decisions on the manufacturing
customers with unmatched design
process before the part is printed.
freedom and manufacturing flexibility –
Several challenges face the development
reducing time-to-market and lowering
of this process simulation:
development and manufacturing costs.
FDM® (fused deposition modeling) is • The complex thermomechanical
becoming the technology of choice for loadings that occur during the
rapid production of high-temperature layer-by-layer deposition of the Virtual printing of the composite
(> 177 ° C), low-volume, composite material and the successive tooling in Digimat-AM
lay-up and repair tools, as well as cooling of the part
for moderate-temperature (<163 °C) • Additive manufacturing is a true
production sacrificial tooling. Relative to multi-scale challenge: the position
traditional tooling materials and methods, of bead deposition creates specific
FDM offers significant advantages microstructures based on the • The thermal history of the material
in terms of lead time, tool cost and printing toolpath pattern, which deposition generates differential
simplification of tool design, fabrication drives the macroscopic mechanical shrinkage between adjacent beads
and use, while enabling increased behavior – typically inducing or layers that affects the end
functionality and geometric complexity. anisotropy. tolerances of the part.

For engineers to unlock Solution
Stratasys is working with e-Xstream
the design freedom that to create FDM process simulation via
additive manufacturing a multiscale approach as a function of
process setup and material choice:
offers, they need tools • Solve a fully coupled
for accurate and thermomechanical problem of the
deposition process to identify the
effective analysis. warpage behavior of the printed
material accounting for thermal
Working with e-Xstream, exchanges inside the printer build
we’re enabling 3D (conduction, convection and
radiation)
printing to become • Load the toolpath issued from the
a high performance manufacturing processing software
and extract information about the Warpage prediction after geometry compensation
in Digimat-AM. Left: Superposition of the
production technology. deposition sequence as-printed (red) and as-design
• Model via micromechanics
the heterogeneous material
microstructure as a function of
Scott Sevcik, the toolpath (e.g., porosity volume Optimize the manufacturing process
Head of Aerospace, fraction and orientation) Quickly explore at virtually zero marginal
Defense & Automotive, • Predict the resulting warpage cost the sensitivity of process parameters
Stratasys induced by the printing process on the process quality and part fidelity
• Iterate the design and optimize the
Work with an efficient and
manufacturing process parameters
user-friendly GUI
to minimize the warpage.
Designed to follow the printing workflow
and accessible for non FEA experts
Results/Benefits
Working with Digimat AM, Stratasys
Results/Correlation to Test Data
Engineers were able to: The warpage prediction has been
compared to 3D-scan measurement of
Print it right the first time a physically printed composite tool.
Iterate designs and parameters through Given the different modeling assumptions,
simulation rather than wasting time and
the comparison shows a good general
materials with iterating through printing
correlation with similar deformation
Save time & material pattern and amplitude. The warpage
Comparison between measured warpage on a
Anticipate printing issue with simulation compensation procedure decreases
physically printed part (RMS signed distance, (e.g., evaluate the impact of the printing significantly the maximum deviation
left) and Digimat-AM warpage prediction (X direction and location on results)
displacements, right) between the reference geometry and
Minimize warpage in only two steps! the as-printed part (0.5 mm to less than
Thanks to a predeformed geometry 0.1 mm). u
Digimat-AM simulation approach for optimal printing

Image Source: Materialise
Print right the first time

Additive manufacturing simulation for
plastics & metals
Award- winning simulation solutions for metal, polymer, and composite parts-
delivering a unique combination of material engineering, process simulation and
structural analysis solutions. Optimize your AM process chain by reducing final
part distortion, minimizing residual stress and optimizing build-up orientation &
support structures.
| Visit mscsoftware.com/additive
www.mscsoftware.com/additive-manufacturing
Dr. Hendrik Schafstall
Vice President, Virtual Manufacturing & Costing, MSC Software
Dr. Hendrik Schaftall is Vice President, Virtual Manufacturing & Costing, MSC Software. Together with his partner Michael
Wohlmuth, Hendrik Schafstall founded FEMUTEC engineering in 1995 – today´s Simufact Engineering GmbH.
Prior to the company founding, he was employed as a research associate at the Helmut-Schmidt-University of Hamburg,
obtaining his doctorate researching friction models in cold massive forming. He was a mechanical engineering student at
the Leibniz University of Hannover and received a diploma as a graduate engineer.
Dr. Roger Assaker

Chief Customer Engagement Officer, MSC Software
Dr. Roger Assaker is the Chief Customer Engagement Officer, MSC Software. He is also the Co-Founder & CEO of
e-Xstream engineering, a high-tech company 100% focused on advanced material modelling. Roger holds a PhD and MS
in Aerospace Engineering with a strong focus on nonlinear computational mechanics where he totals more than 20 years
of experience. Roger complemented his engineering education with an MBA in International Business and several
advanced technology, business and entrepreneurship courses from prestigious universities such as MIT. In parallel to
growing e-Xstream to be the world leader in advanced composite modelling, Roger is the Vice Chairman of NAFEMS
Composite Working Group and active member of other technical material associations such as SPE and SAMPE.
Volker Mensing
Global Director of Business Solutions Marketing, MSC Software
Volker Mensing is the Global Director of Business Solutions Marketing at MSC Software. He is responsible for driving
cross-product global marketing campaigns for MSC & Hexagon, such as Additive Manufacturing, Autonomous Driving,
etc. With a background in journalism and public relations specialising in manufacturing technology and Information
Technology, he has built a 20-year marketing career in the software and IT sector.
Copyright © 2020 Hexagon AB and/or its subsidiaries. All rights reserved. Hexagon, the Hexagon logo, and other logos, product and service names of Hexagon
and its subsidiaries are trademarks or registered trademarks of Hexagon AB and/or its subsidiaries in the United States and/or other countries. All other trademarks
belong to their respective owners. Information contained herein is subject to change without notice. Hexagon shall not be liable for errors contained herein.
www.mscsoftware.com

Hexagon MI MSC AM Ebook A4 W

Uploaded by

Copyright:

Available Formats

Hexagon MI MSC AM Ebook A4 W

Uploaded by

Document Information

Original Description:

Original Title

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

Hexagon MI MSC AM Ebook A4 W

Uploaded by

Copyright:

Available Formats

Additive Manufacturing 2020

The software delivers a new and innovative approach for design

Learn More or Request a Quote

92 | Engineering Reality Magazine

Fast and Accurate Additive 09 Development of an Additive 31

Flexibility Through Additive 12

Use of MaterialCenter for Metals Additive 14

Use of Simulation in Additive 20 Simulating Effects of Warpage 41

Dr. Hendrik Schafstall

By Hendrik Schafstall, CEO Simufact

Additive Manufacturing | mscsoftware.com | 05

Additive Manufacturing | mscsoftware.com | 06

Additive Manufacturing | mscsoftware.com | 07

Additive Manufacturing | mscsoftware.com | 08

Fast and Accurate

By Yvan Blanchard, Coriolis Composites

There are commercial CAD-CAM software solutions for detailed offline

Design Analysis Framework Description

Additive Manufacturing | mscsoftware.com | 09

Ply course centerlines and splice cuts

The design analysis system is also

Additive Manufacturing | mscsoftware.com | 10

generate the FE Model (Figures 4 & 5).

with the local as- capture with Digimat© software.

For Details About CADFiber Standalone©, or CATFiber©

For Details About Digimat©:

Additive Manufacturing | mscsoftware.com | 11

Simufact and its technology partner toolcraft shows

Additive Manufacturing | mscsoftware.com | 12

USED PRODUCTS: Simufact Additive USER: MBFZ toolcraft GmbH

Additive Manufacturing | mscsoftware.com | 13

Based on an Interview with the United States

Armaments Center is interested in using AM equipment to prototype, develop, and fabricate

Additive Manufacturing | mscsoftware.com | 14

For AM benefits to be fully realized, Figure 1: Additive Manufacturing Areas of

Additive Manufacturing | mscsoftware.com | 15

Additive Manufacturing | mscsoftware.com | 16

Additive Manufacturing | mscsoftware.com | 17

The data stored using MSC Software’s

Capturing the Entire AM at CCDC Armaments Center - this is

Additive Manufacturing | mscsoftware.com | 18

The US Army CCDC Armaments Center

parts and AM systems for on-demand

Additive Manufacturing | mscsoftware.com | 19

Additive Manufacturing | mscsoftware.com | 20

Additive Manufacturing | mscsoftware.com | 21

For complex free-form surfaces, optical 3D scanning,

The simulation of the PBF-L process was

Dr. Maximilian Munsch

Additive Manufacturing | mscsoftware.com | 22

Additive Manufacturing | mscsoftware.com | 23

Although some applications are already in production, many

The Methods, Tools and Application team of Safran Additive

Figure 1: Example of the effect of a 3D printer scraper / workpiece

Additive Manufacturing | mscsoftware.com | 24

Try Simufact free for 30 days! Learn how: www.mscsoftware.com/simufact

Additive Manufacturing | mscsoftware.com | 25

can be obtained in companies. This includes the huge potential