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<p>Grinding response has a significant impact on the fatigue life of materials and components. Developing a predictive model for the grinding response is of great value in optimizing process parameters and enhancing the fatigue life of components. The influences of wheel diameter, grain size, and process parameters on critical outcomes such as grinding force, surface residual stress, and grinding temperature were analyzed for 18CrNiMo7-6 gear steel in this work. A multi-grain grinding wheel was employed, accompanied by finite element simulation and dimensional analysis, to develop a mathematical model that accurately predicts grinding response. Comparative analysis with experimental data from surface grinding of 18CrNiMo7-6 gear steel yielded maximum relative errors of 10.3% and 11.5% respectively for grinding force and residual stress, with corresponding absolute errors peaking at 0.136&#xa0;N and 23.5&#xa0;MPa. These results confirm the practicality of the model in guiding parameter selection to achieve optimal machining quality and enhance service performance.</p>

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Modeling and simulation of surface grinding of 18CrNiMo7-6 gear steel considering process and wheel parameters

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Minghao Zhao,
  • Luhang Sun,
  • Yunlong Zhang,
  • Yazhou Lu,
  • Lubing Shi,
  • Bingbing Wang,
  • Jianwei Zhang

摘要

Grinding response has a significant impact on the fatigue life of materials and components. Developing a predictive model for the grinding response is of great value in optimizing process parameters and enhancing the fatigue life of components. The influences of wheel diameter, grain size, and process parameters on critical outcomes such as grinding force, surface residual stress, and grinding temperature were analyzed for 18CrNiMo7-6 gear steel in this work. A multi-grain grinding wheel was employed, accompanied by finite element simulation and dimensional analysis, to develop a mathematical model that accurately predicts grinding response. Comparative analysis with experimental data from surface grinding of 18CrNiMo7-6 gear steel yielded maximum relative errors of 10.3% and 11.5% respectively for grinding force and residual stress, with corresponding absolute errors peaking at 0.136 N and 23.5 MPa. These results confirm the practicality of the model in guiding parameter selection to achieve optimal machining quality and enhance service performance.