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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
Presentation Title Autonomous High-Throughput Compression Testing Enabled by Machine Vision and AI Agents
Author(s) Janith C. Wanni, Avanish Mishra, William Kunkel, Dan Thoma, Saryu Fensin
On-Site Speaker (Planned) Janith C. Wanni
Abstract Scope Mechanical characterization is a major bottleneck in high-throughput materials development due to the manual effort required for specimen handling, testing, and data analysis. We present an autonomous compression testing platform capable of evaluating up to 100 specimens in approximately two hours with minimal user intervention. The system integrates automated specimen handling, autonomous dimensional measurement, real-time strain tracking, machine vision for experiment monitoring, and an AI agent that independently executes experiments and generates analyses based on natural language user requests. Users simply load specimens into the sample tray and specify the desired testing or analysis objectives. Machine vision continuously detects experimental anomalies enabling reliable unattended operation. The platform is demonstrated by characterizing a large library of additively manufactured alloy specimens produced across a broad processing window, illustrating its potential to accelerate mechanical characterization and materials process optimization.
Proceedings Inclusion? Planned:
Keywords Characterization, Machine Learning, Mechanical Properties

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