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Beyond Fingerprinting: AI Approaches to Unearthing Process-Structure-Property Correlations in Additive Manufacturing

Jonas Actor, Sandia National Laboratories
Elise Walker, Sandia National Laboratories
With the synthesis of new high-throughput methods, materials R&D is readying for the discovery, characterization, and design of robust materials and manufacturing processes through the development and implementation of multimodal, physics-informed machine learning algorithms. The fusion of human expert materials knowledge with multimodal, physically constrained, machine learning algorithms can aid in detection of ""fingerprints"" critical in materials behavior, prognose component performance, and adapt manufacturing strategies.
This minisymposium convenes world-class researchers in advanced manufacturing, materials characterization, data science, modeling/simulation, and hardware engineering to showcase works that detect critical features that govern material behavior. This minisymposium will discuss:
  • Hybrid, physics informed machine learning methods to understand process-structure mappings
  • Surrogate models using multimodal data streams combining experiments and simulations
  • Machine learning guided process optimization