InTech

NOV-DEC 2018

Issue link: http://intechdigitalxp.isa.org/i/1058858

Contents of this Issue

Navigation

Page 21 of 56

22 INTECH NOVEMBER/DECEMBER 2018 WWW.ISA.ORG By Subham Sett and Jing Bi A dditive manufacturing (AM) has wit- nessed tremendous growth as its focus has shifted from prototypes to end-use func- tional parts. However, the industry has a set of criti- cal production challenges, including build repeat- ability, process stability, yield rates (and, perhaps, failure for critical components), and the ability to deploy in-service. Digital tools are helping resolve some of these issues: generative design, functional lattices, build planning with hardware integration, thermal distortions, and shape compensation. Some of these tools are very specialized for certain tasks while relying on others to complete the entire additive process. As a result, an organization often relies on a disparate set of applications connected together in a file-based approach. Such a system can lead to lost productivity as users work through multiple software packages. This makes it a chal- lenge for production processes where version con- trol, traceability, and data accuracy are critical. A model-based approach (as opposed to a file-based one) that integrates design, materials, manufacturing, and production is paramount to the successful evolution of additive from a lab en vironment to a production environment. This is being addressed by software platforms that enable end-to-end digitalization of additive manufactur- ing while connecting additive to the rest of an orga- nization's industrial processes. Physics-based simulations of the additive pro- cess are crucial in assessing the finished part's quality. Much of the attention has been on pow - der bed metal processes, as industries—primarily aerospace, defense, and medical—work to bring certified parts to market. Mostly based on finite element methods, these simulations either rely on precalibrated libraries (based on scanning strat - egies) or thermal strains that serve as inputs to relatively fast computations of the part distor - tions. These methods are fairly simple to use and do not require the user to dive deep into the physics of the solutions. Additive manufacturing simulations Any machine, any process, any material

Articles in this issue

Links on this page

Archives of this issue

view archives of InTech - NOV-DEC 2018