Leveraging artificial intelligence for real-time indirect tool condition monitoring: From theoretical and technological progress to industrial applications

Abstract

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.