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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 AlloyBot Self-Driving Laboratory to Develop High Temperature Alloys 100x Faster
Author(s) Vignesh Selvaraj, Ming En Pek, Theo Evers, Tianhong Tan, Anagh Dutta, Ben Neuman, Joel Paulson, Sebastian A. Kube
On-Site Speaker (Planned) Sebastian A. Kube
Abstract Scope We present our AlloyBot self-driving laboratory currently being built at UW-Madison to accelerate the development of high temperature alloys by 100x. AlloyBot uses robotics, automation, and AI to experimentally test and optimize new alloy materials with minimal human assistance. As first building block, we previously presented the automatic arc-melting synthesis module, which can produce over 150 bulk alloys per week. Here, we present subsequent modules which enable automatic sample preparation, processing, microstructure analysis, and performance testing. Artificial Intelligence is integrated to enable real-time data processing and closed-loop alloy design.
Proceedings Inclusion? Planned:
Keywords High-Temperature Materials, Machine Learning, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Benchtop Self-Driving Laboratory for Aqueous Chemistry Built on the CubXL Platform
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A Deep Learning-Based Framework for Three-Dimensional Temperature Field Prediction and Intelligent Secondary Cooling Optimization in Continuous Casting
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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
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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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