Hardness-guided machine learning for tungsten alloy strength prediction

Abstract

Yield strength determines the material's resistance to permanent deformation. However, the traditional estimation method based on the triple relationship of Vickers hardness exhibits limited accuracy when applied to tungsten heavy alloys (WHA) with two-phase structure. This study presents a novel approach using machine learning and hardness data to predict the yield strength of tungsten heavy alloy efficiently and accurately. Among the eight machine learning models used, Gradient boosting decision tree model (GBDT) was the most effective model. The results demonstrate a strong agreement between the GBDT-based predictions and experimental results, with an average error of 6.7 %. We also gave the hardness versus yield strength relationship for the 90WNiFe alloy system. This finding highlights the potential of the GBDT regression model in conjunction with hardness measurements for predicting yield strength with high precision.