About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Improving Autonomous Data Collection by Iterative Learning Control as Applied to a Robomet.3D Mechanical Serial-sectioning System |
Author(s) |
Damian L. Gallegos-Patterson, Claus Danielson, Jonathan Madison |
On-Site Speaker (Planned) |
Damian L. Gallegos-Patterson |
Abstract Scope |
Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency, and which can require frequent user interaction to monitor and maintain. Operating upon a RoboMET.3D automated mechanical serial-sectioning system, an iterative learning control algorithm has been developed to accelerate data collection and reduce data inconsistency. Using historical data amassed over a decade of experiments, weighted influence for two system variables were determined and employed to demonstrate how experimental setups, system variables and produced data are received as inputs and optimal iterative variable changes, are provided as outputs. Three example cases will be shown with quantitative metrics reported for the algorithm’s suggested modifications and the benefits realized. |
Proceedings Inclusion? |
Planned: |