Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling

Abstract

This study proposes a framework to estimate the metal temperature from an optical micrograph of metals by using a machine learning approach. Specifically, 38 image statistical parameters such as area, contour, and circularity are calculated for the precipitate region determined through optical microscopy. Sparse modeling is then conducted to build a statistical model to estimate the Larson-Miller parameter (LMP), which is generally used in the evaluation of creep strength. This allows for the prediction of the metal temperature from the optical micrographs. The prediction performance of the proposed method is analyzed by applying it to KA-SUS304J1HTB (18Cr-9Ni-3Cu-Nb-N steel), reported in the NIMS Creep Data Sheets No. 56A and No. M-11. Consequently, temperature prediction is successfully achieved for unknown data with an error within ± 10°C.

Keywords

• stainless steel
• creep
• microstructural features
• larson-miller parameter
• regression analysis
• sparse modeling