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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 Enabling Autonomous Research Workflow Design, Scheduling, and Optimization at a Self-Driving Facility
Author(s) Aaron Stebner
On-Site Speaker (Planned) Aaron Stebner
Abstract Scope Recent work to enable ensembles of artificial intelligence (AI) agents to cooperate to design, recommend, optimize, and schedule materials discovery research and development workflows will be presented. The general rubric, algorithms, and constraints will be verified in test and verification of initial deployments at Georgia Tech’s Advanced Manufacturing Pilot Facility (AMPF). AMPF is a national innovation hub for transforming technologies and workforce for materials and manufacturing through collaborations of artificial intelligence (AI) and people from industry, academia, and government. Within the facility, fifteen integrated, self-driving labs facilitate research that translates scientific discoveries to industrial deployments. They are complemented by three support (non-self-driving) laboratories. The autonomous research workflow AI agent developments are part of a larger collection of efforts to enable cooperations of different AI agents to make the facility easier to access and use and to assist people in optimizing research workflows will also be presented.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Benchtop Self-Driving Laboratory for Aqueous Chemistry Built on the CubXL Platform
A Cost-Aware Contextual Bandit Framework for Autonomous Optimization of Titanium Leaching from Iron-Depleted Red Mud Slag
A Deep Learning-Based Framework for Three-Dimensional Temperature Field Prediction and Intelligent Secondary Cooling Optimization in Continuous Casting
A Domain-Grounded Agentic AI Framework for Autonomous Manufacturing Process Data Analysis and Quality Prediction
A physics informed digital twin for powder bed fusion additive manufacturing via data assimilation and AI surrogates
A Processing-Aware Multimodal Data Pipeline for Porosity Quantification in High-Pressure Die Casting
A Programmable Powder Doser with 15+ Reservoirs and Automated Auger Swapping for AI-Enabled Alloy-Development Workflows
AlloyBot Automated SEM–XRD Pipeline for High-Throughput Microstructure Characterization
AlloyBot Self-Driving Laboratory to Develop High Temperature Alloys 100x Faster
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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