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About this Abstract
Meeting	MS&T21: Materials Science & Technology
Symposium	Accelerating Materials Science with Big Data and Machine Learning
Presentation Title	Machine Learning to Predict Mechanical Properties of Steel Alloys Based on Chemical Composition and Heat Treatment Process
Author(s)	Yutao Wang
On-Site Speaker (Planned)	Yutao Wang
Abstract Scope	As-cast steel alloys often have a non-homogeneous microstructure, which includes large grains and other unwanted microstructures. Post-heat treatment is needed to adjust the mechanical properties of the as-cast parts. In this study, Artificial Neural Network (ANN) models are created to predict the mechanical properties of wide steel grades. The independent variables of the ANN model are the chemical compositions of the steel alloys and the corresponding heat-treatment process. Over 30,000 data entries of processing, composition, and property of different steel alloys are collected and included by SFSA from steel foundries all over the United States and are particularly useful for understanding non-standard steel alloys. The ANN prediction results are compared with traditional linear regression results. High accuracy of predictions on Hardness, Yield strength, ultimate tensile strength, and elongation is demonstrated. The prediction results are found to be in good correspondence with the microstructure development during heat treatment.

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