Machine learning for predicting high-temperature strength of austenitic stainless steels based on composition and deformation parameters
Source abstract
Abstract
This study uses a machine learning (ML) approach to investigate the effect of compositions and deformation parameters on the yield stress (YS) and ultimate tensile strength (UTS) for austenitic stainless steels at high temperatures. The input variables for the 10 ML models, used in the present investigation, are Chemical composition (wt.%), initial grain size (µm), strain rate (1/s), and temperature (¿) The dataset consists of 568 data points and 24 variables. The gradient boosting regressor (GBR) has emerged as the best model for predicting YS and UTS, with R- squared scores of 0.95 and 0.96, respectively. The SHAP plot showed that the five variables delivering maximum impact for YS were temperature, strain rate, iron, initial grain size, and molybdenum, respectively. In contrast, temperature, strain rate, iron, nitrogen, and molybdenum were the first five features for UTS. The SHAP plot, which indicates the effect of each parameter on YS and UTS, has been constructed. According to the SHAP, strain rates increase is accompanied by an increase in Ni, N, and Mo percent for both YS and UTS. Conversely, higher temperatures, coarser grains, and a high value of Fe lead to a decrease in them. The experimental 2 results also corroborated the GBR model and established its acceptability for predicting YS and UTS in austenitic stainless steels at high temperature.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · High-Velocity Impact and Material Behavior · Machine Learning in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (59)
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