Data-Driven Quantification of Temperature-Induced Mechanical Property Variations in 5Cr–0.5Mo Steel Using Artificial Neural Networks

This study presents the quantitative estimation of the effect of temperature on the mechanical properties of 5Cr–0.5Mo steels using an artificial neural network (ANN) model. The developed ANN model predicts yield strength (YS, MPa), ultimate tensile strength (UTS, MPa), elongation (El, %), and reduction in area (RA, %) at different service temperatures. Predictions were validated against experimental data at critical temperatures of 450 °C and 700 °C and found to show high accuracy. Predicted results show minimal errors of 3.84%, 2.3%, 2.2%, and 0.42% for YS, UTS, El, and RA, respectively at 450 °C, and 3.7%, 0.45%, 1.88%, and 0.19%, respectively at 700 °C. Furthermore, ten-fold cross-validation confirmed the generalization capability of the developed model, yielding high coefficients of determination and correlation coefficients together with low normalized prediction errors across all output variables. Despite the absence of explicit metallurgical descriptors, the ANN model successfully quantified the influence of temperature from 25 to 700 °C, demonstrating its effectiveness as a predictive tool for high-temperature Cr–Mo steels. Furthermore, a user-friendly graphical interface was developed to facilitate rapid property estimation, demonstrating the potential of the framework as a supportive tool for the preliminary assessment of high-temperature Cr–Mo steels.