Application of Machine Learning in Constitutive Relationship Prediction of Porous Titanium Materials for Artificial Bone

Abstract

Artificial bone porous titanium materials are widely used in orthopedic implants. However, the traditional constitutive model is often limited by the complexity and accuracy of the model, and it is difficult to accurately and efficiently describe the constitutive relationship of porous titanium materials. In this study, structured data were established based on experimental data from published papers, and goodness of fit (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to evaluate the model. The prediction effect of random forest (RF), multi-layer perceptron (MLPR) and support vector machine (SVR) on the constitutive relationship of porous titanium materials was discussed. Through comprehensive comparison, it can be seen that the RF model with max_depth of 24 and n_estimators of 160 has the best performance in prediction, and the average absolute percentage error is less than 4.4%, which means it can accurately predict the temperature sensitivity and strain rate sensitivity of porous titanium materials. And its predictive ability is better than that of the traditional constitutive model, which provides a new idea and method for the constitutive modeling of porous titanium materials.