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Exploiting Advanced Manufacturing Capabilities: Topology Optimization in Design Proceedings of a Workshop (2022) / Chapter Skim
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6 Discussion
Pages 52-56

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From page 52...
... In addition to gaps and concerns related to knowledge, data, and testing, participants discussed specific struggles in the context of additive manufacturing and multi-physics designs. STRUGGLES WITH ADDITIVE MANUFACTURING Joseph DeSimone, Carbon, Inc., agreed that manufacturability remains a key constraint for topology optimization.
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From page 53...
... For example, he said, existing technologies have failed to realize the promise of composites, and it is important to understand and fix these issues to expand the design space. Alicia Kim, University of California, San Diego, suggested that integrated computational materials engineering may have faced similar problems that the topology optimization field can learn from.
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From page 54...
... She noted that metal-based additive manufacturing is moving into higher technology readiness levels, but she suggested that what is needed is more support for fundamental research to enhance understanding of how characteristics like surface roughness and porosity can be predicted and managed in a multi-physics, multi-materials environment. Kimberly Saviers, United Technologies Research Center, suggested that pursu ing global optima and minima may not be a productive path, and Manoj Kolel Veetil, Naval Research Laboratory, suggested incorporating artificial intelligence and machine learning into this space, which works well even with small amounts of data and as long as the laws of physics are still respected.
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From page 55...
... At the macro level, technological tools can consolidate and test multiple parts at once, but she stressed that it is important to include designers and structural analysts in the entire process to establish qualifications and create confidence in the designs, especially for new technology. KNOWLEDGE AND DATA GAPS Ole Sigmund, DTU Technical University of Denmark, stated that optimization is only possible if it is also possible to quantify quantifiability, and King replied that a key gap lies in determining how to quantify this.
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From page 56...
... Claus Pedersen, Dassault Systèmes Simulia Corp, agreed, adding that reprogramming simulations to accommodate non­linearities also creates challenges. The company has incorporated non­linearities into its optimization software, he said, but it has been difficult and sometimes requires various optimization attempts by the user.
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Key Terms

  • topology optimization
  • existing tools
  • topology optimizations
  • modeling packages
  • optimized designs

  • geometric features
  • physical modeling
  • combine multiple
  • additively manufactured
  • future failures

  • partners prefer
  • testing gradient
  • fear data
  • flow transfer
  • productive path

  • sensitivity analysis
  • viscous resin
  • multiple physical
  • reinhard radermacher
  • include designers

  • various optimization
  • william benard
  • physics package
  • creates challenges
  • technical university

  • test multiple
  • manufacturing joseph
  • single package
  • avoid common
  • zheng noted


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