Tool condition monitoring (TCM) during mechanical cutting is critical for maximising the utilisation of cutting tools and minimising the risk of equipment damage and personnel injury. The demand for highly efficient and sustainable machining in modern industries has led to the development of new processes operating under specific conditions. Real-world datasets obtained under harsh cutting conditions often suffer from intense interference, making the anti-interference capabilities of TCM methods crucial for effective industrial applications. Previous literature reviews on TCM have focused on theoretical methods for monitoring tool wear and breakage. However, reviews of the scientific methodologies and technologies employed in TCM for industrial production are limited. The lack of scientific understanding relevant to the monitoring of cutting tools in industrial production should be addressed urgently. The current data processing, feature dimensionality reduction, and decision-making methods utilised in TCM may not adequately fulfil the real-time and anti-interference demands. The TCM methods also face the challenges of small sample sizes and imbalanced data during real-world dataset processing. Therefore, this study conducts a systematic review of TCM methods to overcome these limitations. First, the theoretical guidelines for the application of TCM methods in industrial production are provided. The sensing system, signal processing, feature dimensionality reduction, and decision-making methods for TCM methods are comprehensively discussed in terms of both their advantages and limitations for applications in industrial production. Considering the effects of real-world datasets with small samples and imbalanced data caused by the harsh environment of a real factory, a systematic presentation is proposed at the data, feature, and decision levels. Finally, the challenges and potential research directions of TCM methods for industrial applications are discussed. A research route for smart factory-oriented machining system management is proposed based on published literature. This review bridges the gap between theoretical research and the industrial application of TCM techniques in industrial production. Prospective research and further development of TCM systems will provide the groundwork for establishing smart factories.

中文翻译：

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利用人工智能进行实时间接刀具状态监测：从理论和技术进步到工业应用


机械切削过程中的刀具状态监测 (TCM) 对于最大限度地提高切削刀具的利用率并最大限度地降低设备损坏和人员伤害的风险至关重要。现代工业对高效和可持续加工的需求导致了在特定条件下运行的新工艺的开发。在恶劣切削条件下获得的真实数据集经常受到强烈干扰，这使得TCM方法的抗干扰能力对于有效的工业应用至关重要。之前关于 TCM 的文献综述主要集中在监测刀具磨损和破损的理论方法上。然而，对中医药工业化生产所采用的科学方法和技术的评论却很有限。对工业生产中切削刀具监测缺乏科学认识的问题亟待解决。目前TCM中使用的数据处理、特征降维和决策方法可能不足以满足实时性和抗干扰的要求。 TCM方法在现实数据集处理过程中还面临样本量小和数据不平衡的挑战。因此，本研究对中医方法进行系统回顾，以克服这些局限性。首先，为中医方法在工业生产中的应用提供理论指导。全面讨论了中医方法的传感系统、信号处理、特征降维和决策方法在工业生产中应用的优点和局限性。 考虑到真实工厂恶劣环境造成的小样本和数据不平衡的现实数据集的影响，在数据、特征和决策层面提出了系统的呈现。最后，讨论了中医方法工业化应用面临的挑战和潜在的研究方向。基于已发表的文献，提出了面向智能工厂的加工系统管理的研究路线。本综述弥补了中医药技术在工业生产中的理论研究与工业应用之间的差距。 TCM系统的前瞻性研究和进一步开发将为建立智能工厂奠定基础。