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NOV-DEC 2018

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26 INTECH NOVEMBER/DECEMBER 2018 WWW.ISA.ORG FACTORY AUTOMATION Seamless deployment Making this simulation framework in- tegral to the model-based digital thread allows for seamless deployment to the end users within an organization. A part designer can quickly evaluate part dis- tortions and compensate for the shape (with the tedium of file export and im- port), so that the final part is within man- ufacturing tolerance. The machine operator and build plan- ner can then determine optimal part ori- entations or quickly verify the entire build plate by running the fast simulations, al ready preconfigured by the experts. They can change support strategies, and scan strategies and the simulation models are updated automatically, with a full history of updates stored—so they can revert to old scenarios with ease. The analyst, who is responsible for sign- ing off on the part's functional quality, can do so by incorporating the additively manu- factured part—let's say a bracket—in her product configuration and running it across multiple loads and fatigue scenarios, since the residual stresses and material behav- iors are inherently part of the part model she is provided with. If yields are being affected, the researcher can diagnose the issues off of the same digital thread, build- ing highly detailed models to investigate defects, porosities, or crack propagation. All of the above feed into a data lake that continually enhances an organization's intellectual competence. It decreases its time to market while learning from past experi- ences to accelerate future production targets. A final thought on simulations: it is imperative that simulations be val- idated with reality. As control settings for tests lead to variances in exper- imental data, the same applies to simulations. Mod- eling assumptions, physics approximations, and boundary settings all impact the simulation out- come, so verification of what you do is key. Furthermore, establishing wide, un- biased benchmarks is critical. The entire simulation community should come to- gether and validate their methods against a common set of tests. The AM-Bench from the National Institute of Standards and Technology is an excellent step in that direction, as it will build trust in the addi- tive manufacturing community of the role simulation has in helping them move ad- ditive from a niche manufacturing tech- nique to a mainstream one (figure 4). For more information on additive manufacturing, visit http://go.3ds.com/ print2perform. n ABOUT THE AUTHORS Subham Sett is director of additive manu- facturing and materials, Dassault Systèmes SIMULIA. Sett leads a team responsible for the roll out of roles and applications for additive manufacturing and materials. He has more than 15 years of engineering simulation experience, including structural and multiphysics solutions. Previously, he was a MEMS product development engi- neer with Coventor Inc. (now Land) and earned several patents in capacitive switch designs for RF communications. Sett has an MS from the University of Colorado, Boulder, and a B.Tech. from the Indian In stitute of Technology, Kharagpur. Jing Bi, senior consultant, additive manu- facturing and materials, Dassault Sys- tèmes SIMULIA, focuses on solutions us- ing additive manufacturing and materials technologies for production use. She has been with Dassault Systèmes for seven years and spent three of those years as a support engineer for BMW. Her simula- tion specializations include crashworthi- ness, multiscale materials, composites, and additive manufacturing. She holds a BS from Huazhong University of Science and Technology, a BE from Wuhan Uni- versity, and an MS and a PhD from Uni- versity of North Carolina at Charlotte. Please send any questions or comments to: 3ds@racepointglobal.com. View the online version at www.isa.org/intech/20181203. Figure 4. TWI and Dassault Systèmes won first place in best mod - eling results predicting the residual stresses within an as-built IN625 bridge structure in the NIST AM Benchmark Challenge (www.nist.gov/ambench/awards). Multi-jet fusion (MJFTM) process simulation Fradl et al. (2017, 2018). Science in the Age of Experience

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