Honda Motor Co., Ltd.

University of Tennessee Research Foundation

Trustees of the Colorado School of Mines

Imagars LLC

Xuesong Fan

Jianxun Hu

Baldur Andrew Steingrimsson

Anand Kulkarni

Zhenzhen Yu

Abdelrahman Abdelmotagaly

Peter K Liaw

Chanho Lee

John William Bohling

Carl D Lundin

Peter Liaw

Justin Brown

Duckbong Kim

Peiyong Chen

This invention presents an innovative framework for the application of machine learning for identification of alloys or composites with desired properties of interest. For each output property of interest, we identify the corresponding driving (input) factors. These input factors may include the material composition, heat treatment, process, microstructure, temperature, strain rate, environment or testing mode. Our framework assumes selection of optimization technique suitable for the application at hand and data available, starting with simple linear, or quadratic, regression analysis. We present a physics-based model for predicting the ultimate tensile strength, a model that accounts for physical dependencies, and factors in the underlying physics as a priori information. In case an artificial neural network is deemed suitable, we suggest employing custom kernel functions consistent with the underlying physics, for the purpose of attaining tighter coupling, better prediction, and extracting the most out of the—usually limited—input data available.

Machine learning to accelerate alloy design

A multi-material component joined by a high entropy alloy is provided, as well as methods of making a multi-material component by joining dissimilar materials with high entropy alloys.

Multi component solid solution high-entropy alloys

Growing community of inventors

Knoxville, TN, United States of America

Xuesong Fan