基于数值模拟和决策树回归的金属切削力学性能预测OA

Rapid prediction of mechanical properties of metal cutting is critical to optimal design and productiv-ity improvement of industrial manufacturing.Current prediction models often require expensive and time-consu-ming experimental and analytical processes.A prediction model based on metal cutting simulation and decision tree regression was constructed to obtain mechanical properties under different cutting conditions.Firstly,the adaptive smoothed particle hydrodynamics(ASPH)was used to simulate the metal cutting process,capture a variety of mechanical properties under different simulation parameters,and form a simulation dataset of 2 000 cutting conditions.Secondly,the decision tree regression(DTR)was used to learn the simulation data set,train and construct the metal cutting prediction model,and evaluate the effect of the prediction model under different pruning strategies by cross-validation and grid search.The results show that,the established predic-tion model can quickly predict multi-mechanical properties under different simulation parameters,and the ap-propriate pruning strategy can improve the accuracy,generalization ability and stability of the prediction model.