Machine learning and IoT-based approach for tool condition monitoring: A review and future prospects

Abstract

In the “Industry 4.0” era, autonomous and self-adaptive industrial machining attracts significant attention in professional manufacturing. This trend originates from the rising demands for machining quantity and part quality, as well as the need for optimization of the overall operational cost. Under such circumstances, tool condition monitoring (TCM) systems contribute to the mentioned goals by monitoring the cutting tool operation states, which detect signs of tool failures such as cracks, chippings, fractures, and estimate the remaining tool life for the ultimate goal of optimizing the performance of machining processes. This article presents the latest advancements in each stage of TCM systems, namely fusion sensors methods, modern data acquisition systems (DAQ), virtual machining, and lightweight TCM models, wherein artificial intelligence (AI) and Internet of Things (IoT) technologies demonstrate promising operational efficiency and accuracy while showing their potential in practical applications. Moreover, emphasis is paid to the current blank spots and limitations of implementing machine learning, deep learning, and IoT technologies in tool-embedded TCM, namely big data handling, generalization of Machine Learning models, as well as cloud computing latency, which suggests corresponding solutions to enable the practicality of mass implementation of Industrial Internet of Things (IIoT) for TCM systems, such as cloud migration, custom TCM network, and shared knowledge databases between multiple factories. In addition to a comprehensive review of state-of-the-art innovations for TCM systems, the future prospects of intelligent TCM systems are also presented for smart manufacturing.