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<p>Grinding is a crucial process in modern manufacturing, where optimizing productivity, ensuring product quality, and reducing costs are essential. However, the complexity of grinding processes, influenced by factors like material properties, tool wear, and environmental conditions, makes achieving consistent results challenging. Traditional modeling methods have persistent difficulty in accurately predicting grinding forces, temperatures, and surface quality, leading to inefficiencies. With the rise of Industry 4.0, advanced techniques such as artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), genetic algorithms (GA), and machine learning (ML) have been increasingly applied to enhance prediction and monitoring in grinding. This paper reviews the recent literature on artificial intelligence in intelligent modeling and detection of the grinding process, and summarizes its remarkable effectiveness in optimizing grinding force, temperature and surface quality prediction, as well as in detecting grinding defects, surface quality, process parameters, and tool wear. These intelligent methods have proven effective in enhancing machining precision and enabling real-time detection and optimization. Future efforts will focus on improving model fidelity and integrating advanced AI technologies to accelerate the intelligent transformation of grinding and boost overall manufacturing performance.</p>

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Intelligent modeling and detection in grinding: a review of advances, challenges, and prospects

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Zhenzhong Zhang,
  • Jiancheng Li,
  • Laixiao Lu,
  • Zhen Wang,
  • Xiaoliang Liang,
  • Ying Zhang

摘要

Grinding is a crucial process in modern manufacturing, where optimizing productivity, ensuring product quality, and reducing costs are essential. However, the complexity of grinding processes, influenced by factors like material properties, tool wear, and environmental conditions, makes achieving consistent results challenging. Traditional modeling methods have persistent difficulty in accurately predicting grinding forces, temperatures, and surface quality, leading to inefficiencies. With the rise of Industry 4.0, advanced techniques such as artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), genetic algorithms (GA), and machine learning (ML) have been increasingly applied to enhance prediction and monitoring in grinding. This paper reviews the recent literature on artificial intelligence in intelligent modeling and detection of the grinding process, and summarizes its remarkable effectiveness in optimizing grinding force, temperature and surface quality prediction, as well as in detecting grinding defects, surface quality, process parameters, and tool wear. These intelligent methods have proven effective in enhancing machining precision and enabling real-time detection and optimization. Future efforts will focus on improving model fidelity and integrating advanced AI technologies to accelerate the intelligent transformation of grinding and boost overall manufacturing performance.