Mechanical Property Prediction of Metal Cutting Based on Numerical Simulation and Decision Tree Regression

CHENG Yijin; FENG Zhiqiang; LI Yan

doi:10.21656/1000-0887.460102

Volume 47 Issue 1

Jan. 2026

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CHENG Yijin, FENG Zhiqiang, LI Yan. Mechanical Property Prediction of Metal Cutting Based on Numerical Simulation and Decision Tree Regression[J]. Applied Mathematics and Mechanics, 2026, 47(1): 32-45. doi: 10.21656/1000-0887.460102

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Mechanical Property Prediction of Metal Cutting Based on Numerical Simulation and Decision Tree Regression

doi: 10.21656/1000-0887.460102

School of Mechanics and Aerospace Engineering, Southwest Jiaotong University, Chengdu 611756, P.R.China

Funds:

The National Science Foundation of China(12372142

12572232)

Received Date: 2025-05-20
Rev Recd Date: 2025-07-30

Available Online: 2026-01-21

Publish Date: 2026-01-01

Abstract

Abstract

Rapid prediction of mechanical properties of metal cutting is critical to optimal design and productivity improvement of industrial manufacturing. Current prediction models often require expensive and time-consuming 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 prediction model can quickly predict multi-mechanical properties under different simulation parameters, and the appropriate pruning strategy can improve the accuracy, generalization ability and stability of the prediction model.
- metal cutting,
- mechanical property prediction,
- numerical simulation,
- adaptive smoothed particle hydrodynamics,
- decision tree regression

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References(36)

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