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<p>In the field of mechanical processing, the thermal error problem of grinding motorized spindles severely restricts the improvement of machining accuracy. Traditional solutions have many deficiencies when dealing with this complex issue. This research innovatively combines multi-objective optimization with intelligent algorithms to improve the prediction accuracy of thermal errors. By building a specialized experimental platform to collect data, applying data-driven modeling techniques such as principal component analysis (PCA) combined with support vector machines (SVM), and using intelligent optimization algorithms like the whale optimization algorithm-genetic algorithm (WOA-GA), the optimal layout and selection of temperature measurement points have been successfully achieved. The experimental results strongly demonstrate that the identified key measurement points can significantly enhance the prediction accuracy of thermal errors, providing a practical solution for improving the accuracy of grinding motorized spindles and being of great significance for promoting the quality and efficiency of mechanical processing.</p>

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Research on identifying key measurement points and improvement of thermal error prediction accuracy for grinding motorized spindles based on multi-objective optimization and intelligent algorithms

  • 期刊论文

  • 发表日期：

    2025年10月10日

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  • Quanhui Wu,
  • Jiawei Lv,
  • BaiSong Pan,
  • Ning Li,
  • Shaojun Xie,
  • Hailin Luo,
  • Xinhua Yao

摘要

In the field of mechanical processing, the thermal error problem of grinding motorized spindles severely restricts the improvement of machining accuracy. Traditional solutions have many deficiencies when dealing with this complex issue. This research innovatively combines multi-objective optimization with intelligent algorithms to improve the prediction accuracy of thermal errors. By building a specialized experimental platform to collect data, applying data-driven modeling techniques such as principal component analysis (PCA) combined with support vector machines (SVM), and using intelligent optimization algorithms like the whale optimization algorithm-genetic algorithm (WOA-GA), the optimal layout and selection of temperature measurement points have been successfully achieved. The experimental results strongly demonstrate that the identified key measurement points can significantly enhance the prediction accuracy of thermal errors, providing a practical solution for improving the accuracy of grinding motorized spindles and being of great significance for promoting the quality and efficiency of mechanical processing.