| Scope |
Processing defines microstructure, defects, and interfaces across metals, alloys, composites, and functional materials, playing a decisive role in shaping phase morphologies and governing reliability, manufacturability, and performance. Yet most current artificial intelligence (AI) and machine learning (ML) efforts still focus primarily on composition, highlighting a clear need for AI/ML frameworks that explicitly incorporate processing as a primary design axis. With advances in rapid manufacturing, characterization, accelerated testing, process monitoring, and data-informed methods, there is a growing opportunity to integrate AI and ML with both established and emerging experimental processing routes. This symposium focuses on integrating processing science with data-driven and physics-informed approaches to advance process-aware materials design. Our goal is to encourage discussion and emphasize how AI and ML can complement experimental methods across thermomechanical processing, heat treatment, casting, additive manufacturing, surface modification, and thin-film or vapor deposition.
Sessions will highlight strategies that encode processing histories into predictive models of microstructures and properties, alongside surrogate models for complex processing routes, uncertainty-aware and few-shot learning approaches for sparse datasets, Bayesian optimization for process tuning, and closed-loop workflows that couple synthesis, processing, characterization, testing, and iterative model refinement. The goal of this symposium is to bring together materials processors, metallurgists, experimentalists, data scientists, and device engineers to elevate processing as a primary axis in AI-driven materials design and to accelerate the development of robust, manufacturable, and scalable materials and components.
Topics of interest include, but are not limited to:
- Integrating AI/ML and data-informed tools with experimental processing for microstructure and property control, optimization, and scale-up
- High throughput and data-aware experimental design and analysis for processing innovation
- Structure-property-processing relationships supported by advanced and accelerated characterization, testing, and real-time process monitoring
- Applications in structural alloys, biocompatible alloys, additively manufactured components, coatings, batteries, and other functional materials
- Experimental advances in casting, rolling, extrusion, forging, heat treatment, additive manufacturing, and deposition-based processes |