Conference Logo ProgramMaster Logo
Conference Tools for 2027 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract
Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
Presentation Title An Autonomous Platform for Machine Learning-Guided Electrodeposition
Author(s) Spencer Newman, Jason Taylor, Matthew Roop, Tylan Watkins, Ryan Katona
On-Site Speaker (Planned) Spencer Newman
Abstract Scope Electrodeposition often requires iterative, operator-intensive tuning to achieve target coating thicknesses because efficiency varies with sample condition, bath chemistry, and operating parameters such as temperature. We present a closed-loop automated platform integrating a Mecademic Meca500 six-axis robot, BioLogic SP-300 potentiostat, and Mettler Toledo analytical balance under Python control to learn and execute thickness-targeted deposition. The system weighs a sample, electrodeposits at prescribed time and current, reweighs it, and estimates coating thickness from mass change. If the measured thickness misses the target, a probabilistic machine learning algorithm autonomously schedules additional deposition steps while recording process histories and outcomes. These data train a model to predict deposition time needed for specified target thicknesses, enabling progressively improved control over repeated trials and demonstrating a path toward autonomous, data-driven electrochemical processing for reproducible coating fabrication. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. This is document SAND2026-23040A.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Surface Modification and Coatings,

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
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement