Prediction and optimization of metal alloy strength and wear resistance using machine learning

With the continuous development of manufacturing industry, alloying metals that improve metal properties have become a research hotspot in the field of materials science. In order to accurately predict and optimize the strength and wear resista nce of metal alloys, a machine learning based metal alloy performance prediction model was constructed using the random forest algorithm, and an improved Stacking algorithm was used to optimize the model on this basis. The results showed that the optimized Stacking algorithm model controlled the prediction error within 5%, and the machine learning based prediction model achieved a strength prediction accuracy of 93.87% for high entropy alloys. In summary, the use of machine learning for predicting and optimizing the strength and wear resistance of metal alloys has effectively improved prediction accuracy, providing important support and reference for predicting the properties of metal alloys in the manufacturing field.