Review of advances in tool condition monitoring techniques in the milling process

Abstract

Abstract Milling is an extremely adaptable process that can be utilized to fabricate a wide range of shapes and intricate 3D geometries. The versatility of the milling process renders it useful for the production of a diverse range of components and products in several industries, including aerospace, automotive, electronics, and medical equipment. Monitoring tool conditions is essential for maintaining product quality, minimizing production downtime, and maximizing tool life. Advances in this field have been driven by the need for increased productivity, reduced tool wear, and improved process efficiency. Tool condition monitoring (TCM) in the milling process is a critical aspect of machining operations. TCM involves assessing the health and performance of cutting tools used in milling machines. As technology evolves, staying updated with the latest developments in this field is essential for manufacturers seeking to optimize their milling operations. However, addressing the challenges associated with sensor integration, data analysis, and cost-effectiveness remains crucial. To fill this research gap, this paper provides an overview of the extensive literature on monitoring milling tool conditions. It summarizes the key focus areas, including tool wear sensors and the application of various machine learning and deep learning algorithms. It also discusses the potential applications of TCM beyond wear detection, such as predicting tool breakage, tool wear, the cutting tool’s remaining lifetime, and the challenges faced by TCMs. This review also provides suggestions for potential future research endeavors and is anticipated to offer valuable insights for the development of advanced TCMs in terms of tool wear monitoring and predicting remaining useful life.