|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||High Performance Steels
||Using Computer Vision to Predict Mechanical Properties of High Temperature Alloys
||Nan Gao, Zongrui Pei, Youhai Wen, Michael Gao, Elizabeth Holm
|On-Site Speaker (Planned)
Microstructures governed by their chemical compositions and processing methods affect materials properties. Exploring the linkage between microstructure and properties is especially important to material design of high performance alloys. Microstructures are usually characterized by visual inspection and metallographic measurements. Although morphology information can be captured and observed, the rich, multiscale microstructural feature data contained in a typical micrograph is rarely fully quantified or exploited. In this research, pre-trained convolutional deep neural networks (CNNs) are used to extract information from the microstructures, and regression models are optimized to predict mechanical properties based on characteristic features that exist at a hierarchy of length scales. The yield stress of steel alloys is predicted with good fidelity, and links to microstructural features that influence mechanical response are made. We find that computer vision and machine learning are promising tools for connecting microstructure to properties.
||Iron and Steel, Machine Learning, Mechanical Properties