Abstract Scope |
Image based property predictions enable quick and relatively noninvasive sample testing of metals for a variety of applications. This study tries to predict physical properties, processing temperatures, and additive compositions of nominally 0.28% carbon steel using scanning electron micrographs. The image processing pipeline began with feature extraction, which used convolutional neural networks which employed transfer learning. Dimensional reduction was then conducted on the feature vectors to minimize computational overhead. Finally, a variety of regressions and classifications models were used to predict properties. Physical and processing properties were effectively classified and regressed upon by multiple different predictive models, while chemical properties were universally a challenge. The interaction between processing, microstructure, and physical properties is well understood, particularly compared to composition's impact on microstructure. The different compositions of additives lead to nonlinear and complex changes within microstructures, which lead to difficulties in modeling. |