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<p>The selection of machining parameters for superalloy honeycomb ice fixation clamping significantly affects the surface quality and machining efficiency of the honeycomb core. In current honeycomb core machining, machining quality and efficiency are inherently conflicting, making it difficult to identify parameters that ensure both. To address this issue, this study proposes a multiobjective optimization method based on the NSGA-II algorithm to determine parameter sets that balance both machining quality and efficiency. A series of machining parameters and outcomes were obtained through single-factor experiments, and a multiobjective optimization model was constructed. The validity of the model was verified through analysis of variance (ANOVA). The model was then optimized using the NSGA-II algorithm, yielding a set of Pareto optimal solutions. The results indicate that when the spindle speed is 7000&#xa0;rpm, feed rate is 4000&#xa0;mm/min, and cutting width is 0.5&#xa0;mm, increasing the cutting depth from 0.5 to 1.5&#xa0;mm significantly enhances Machining efficiency while Maintaining good quality. Analysis of the effect of machining parameters on defect quantity revealed that keeping the cutting depth below 1.5&#xa0;mm effectively reduces defects. This optimization method provides a more efficient and suitable parameter selection range for the ice fixation clamping machining of superalloy honeycomb cores.</p>

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Cutting parameter optimization and experimental analysis of superalloy honeycomb core with ice fixation clamping

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Daomian Sun,
  • Shaowei Jiang,
  • Yishun Cheng,
  • Jianming Li,
  • Huiwen Long,
  • Kuo Liu,
  • Haibo Liu,
  • Yongqing Wang

摘要

The selection of machining parameters for superalloy honeycomb ice fixation clamping significantly affects the surface quality and machining efficiency of the honeycomb core. In current honeycomb core machining, machining quality and efficiency are inherently conflicting, making it difficult to identify parameters that ensure both. To address this issue, this study proposes a multiobjective optimization method based on the NSGA-II algorithm to determine parameter sets that balance both machining quality and efficiency. A series of machining parameters and outcomes were obtained through single-factor experiments, and a multiobjective optimization model was constructed. The validity of the model was verified through analysis of variance (ANOVA). The model was then optimized using the NSGA-II algorithm, yielding a set of Pareto optimal solutions. The results indicate that when the spindle speed is 7000 rpm, feed rate is 4000 mm/min, and cutting width is 0.5 mm, increasing the cutting depth from 0.5 to 1.5 mm significantly enhances Machining efficiency while Maintaining good quality. Analysis of the effect of machining parameters on defect quantity revealed that keeping the cutting depth below 1.5 mm effectively reduces defects. This optimization method provides a more efficient and suitable parameter selection range for the ice fixation clamping machining of superalloy honeycomb cores.