Algorithm Development in Materials Science and Engineering: Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS Functional Materials Division, TMS Structural Materials Division, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee, TMS: Phase Transformations Committee, TMS: Solidification Committee, TMS: Chemistry and Physics of Materials Committee
Program Organizers: Adrian Sabau, Oak Ridge National Laboratory; Ebrahim Asadi, University of Memphis; Enrique Martinez Saez, Clemson University; Garritt Tucker, Colorado School of Mines; Hojun Lim, Sandia National Laboratories; Vimal Ramanuj, Oak Ridge National Laboratory

Tuesday 5:30 PM
March 21, 2023
Room: Exhibit Hall G
Location: SDCC


M-14: Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing: Loc Truong1; WoongJo Choi1; Colby Wight1; Elizabeth Coda1; Tegan Emerson1; Keerti Kappagantula1; Henry Kvinge1; 1PNNL
    Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques are nascent and emerging comparatively, making designing and running experiments largely Edisonian. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to use pattern matching capability for data-driven experimental design. DPC takes two possible experiment conditions and outputs a prediction of which will produce a more desirable performance. We demonstrate the success of DPC on AA7075 tube process design and mechanical property data. We show that by focusing on the need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.