PI: Henry Kvinge

Advances in materials science can help address many of the critical challenges facing the modern world, from improved energy efficiency to increased compute capacity. As in many other scientific domains, methods from artificial intelligence and machine learning (AI/ML) promise to accelerate research and progress in this field. Unfortunately, the most recent advances in AI/ML (e.g., deep learning) have only recently started affecting advanced manufacturing, where classical methods still dominate. The purpose of AI4AM is to leverage mathematical frameworks to develop ML solutions to a range of advanced manufacturing problems. This includes working across a wide range of modalities, from images to time series to tabular data. Our goal is to advance ML by bringing in techniques from topology, algebra, and geometry to find more effective ways of representing data, framing learning problems, and extracting features.

The initial manufacturing process that we targeted was Shear Assisted Processing and Extrusion (ShAPE), a solid phase processing method developed at Pacific Northwest National Laboratory. We focused on Aluminum 7075, a material for which a substantial number of ShAPE experiments already exist. Despite this initial concentration on ShAPE, the project’s goal has always been to match problems in materials science and advanced manufacturing with novel ML tools and methods. Thus, the tools that AI4AM has produced have been largely manufacturing process/material agnostic or at least flexible enough to be transferred to other settings without extensive modification. The number of tools developed on AI4AM that are now being used on different projects with different materials or different manufacturing processes confirms our success in this regard.

In 2023 our team shifted to examining Topological Materials of interest to future computing, energy, and sensing applications.