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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 The Agility Forge as a Tool for Shaping, Material Optimization and Accelerated Experimentation
Author(s) Glenn S. Daehn
On-Site Speaker (Planned) Glenn S. Daehn
Abstract Scope The Agility Forge is a robotic blacksmith: a programmable, incremental forging platform that shapes metal while locally controlling strain, strain rate, and temperature, to tailor microstructure and properties. This talk describes three complementary roles. As a shaping tool, it forms complex geometries through, sensor-monitored strokes. As a material-optimization tool, it exploits per-stroke control of the local thermomechanical path to drive interior structure toward target properties—both in non-heat-treated surrogate whose structure and strength are set directly by deformation, and for heat-treatment-dominated alloys, where the forge delivers a sound, well-conditioned precursor with assured heat-treat response. As an accelerated-experimentation engine, its instrumentation enables rapid data assimilation: each stroke becomes a controlled experiment, building history-resolved datasets that calibrate and validate machine-learned models of structure-property evolution. Together these capabilities position the Agility Forge for autonomous, property-controlled manufacturing and discovery. This work is core to the NSF HAMMER-ERC. Several collaborators will be noted in this presentation.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Shaping and Forming, Process Technology

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AlloyBot Automated SEM–XRD Pipeline for High-Throughput Microstructure Characterization
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AlloyBot SmartMelting: Closed-Loop Process Control for Reliable Automatic Arc-Melting Synthesis
An Autonomous Agentic System Enabled by Harness Engineering for Active Learning in a Self-Driving Laboratory for Materials Spallation
An Autonomous Platform for Machine Learning-Guided Electrodeposition
Asynchronous, Autonomous Materials Aging Characterization
Automating Rietveld Refinement like a Human Expert in GSAS-II
Autonomous Arc Melting for High-Throughput Alloy Synthesis
Autonomous High-Throughput Compression Testing Enabled by Machine Vision and AI Agents
Causal Intervention Mapping from Process Intensification to Pellet Architecture Design in Hydrogen Based Direct Reduction for Green Steelmaking
Closed-Loop Bayesian Optimization of Multi-Material 3D-Printed Tensegrity-Inspired Energy Absorbers
Closed-loop machine learning guided exploration of processing pathways reveals optimal microstructures
Data-Driven Multi-Objective Optimization of Ultrashort-Pulse Laser Cutting of Magnesium Alloy Microtubes for Bioabsorbable Stent Manufacturing
Deep Learning-Based Segmentation for Porosity and Dendrite Quantification in Aluminum-Silicon Castings
Development of an automated heat-treatment and evaluation system for autonomous exploration of process–structure–property relationships in structural metallic materials
Diffusion-Based Discovery of Ordered Superconductors
Enabling Autonomous Research Workflow Design, Scheduling, and Optimization at a Self-Driving Facility
Framework Towards Design of Workflows for Self-driving Laboratories (Application in Additive Manufacturing)
From Automated Materials Experiments to Trusted Autonomous Discovery in AIMD-L
From process observation to parameter selection in Directed Energy Deposition: An Expertise-Guided, Bead-to-Build Methodology
Low-Cost Open-Source Camera Modules for Continuous Monitoring of Self-Driving Laboratories
Machine Learning for Thermographic Feature Extraction in Multi-Material Directed-Energy Deposition
Machine learning in sintering and ceramic processing: from gradient descent optimizations to hybrid methods with finite element simulations.
Online Wire-arc Additive Manufacturing Process Optimization Using Active Learning
Optimizing Bead Geometry during Laser Wire-Powder Directed Energy Deposition using a Multi-objective Machine Learning Approach
Real-time digital twins for open-die cold forging using machine learning surrogate models
Reexamining Hall–Petch and the Tabor Relation in Concentrated FCC Alloys via BO-Driven Alloy Development
Resolving hidden subgroups in heterogeneous alloy datasets
The Agility Forge as a Tool for Shaping, Material Optimization and Accelerated Experimentation
The Optimization of Tar Sand Digestion by a Locally Fabricated Tar Sand Digester
Uncertainty-Aware Processing and Heat Treatment Design of Laser Powder Additive Manufactured Alloys
Uncertainty-Propagating Active Learning Accelerates High-Entropy Oxide Discovery via Pulsed Electrodeposition for Hydrogen Evolution
Using Machine Learning for Alloys Design and Optimisation in Additive Manufacturing

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