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<p>In green manufacturing, CNC gear grinding plays a pivotal role in the precision machining of gears, necessitating a comprehensive examination of the relationship between energy consumption and processing quality. This study develops an energy consumption model and introduces the concept of specific grinding energy (SEG). Using a multi-functional gear grinding machine, orthogonal and complete factorial experiments were conducted to investigate power consumption and surface roughness. A predictive model for <i>SEG</i> and <i>Ra</i> was established through nonlinear fitting. Additionally, multi-objective particle swarm optimization (PSO) was employed to optimize the grinding parameters. The experimental results demonstrate that the optimization model of <i>SEG</i> and <i>R</i><sub><i>a</i></sub> closely align with the actual machining conditions, with relative errors of less than 8.3% for <i>SEG</i> and 9.9% for <i>R</i><sub><i>a</i></sub>, respectively. The multi-solution set of PSO algorithms provides valuable theoretical insights for production management, energy conservation, and the enhancement of machine tool efficiency.</p>

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Optimization of heavy gear grinding parameters based on the specific grinding energy and surface roughness

  • 期刊论文

  • 发表日期：

    2025年6月10日

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  • Wenzheng Ding,
  • Bo Xing,
  • Erkuo Guo,
  • Boxiang Wang

摘要

In green manufacturing, CNC gear grinding plays a pivotal role in the precision machining of gears, necessitating a comprehensive examination of the relationship between energy consumption and processing quality. This study develops an energy consumption model and introduces the concept of specific grinding energy (SEG). Using a multi-functional gear grinding machine, orthogonal and complete factorial experiments were conducted to investigate power consumption and surface roughness. A predictive model for SEG and Ra was established through nonlinear fitting. Additionally, multi-objective particle swarm optimization (PSO) was employed to optimize the grinding parameters. The experimental results demonstrate that the optimization model of SEG and Ra closely align with the actual machining conditions, with relative errors of less than 8.3% for SEG and 9.9% for Ra, respectively. The multi-solution set of PSO algorithms provides valuable theoretical insights for production management, energy conservation, and the enhancement of machine tool efficiency.