Artificial Intelligence for Tool Chatter Detection and Monitoring: A Review
doi.org/10.63339/FME20260305
Keywords:
Tool Chatter Detection, Artificial Intelligence, Machine Learning, Deep Learning, Smart ManufacturingAbstract
Chatter of tools is the major drawback of machining processes which causes poor surface finish, reduced tool life and low productivity. Traditional methods of chatter detection, which are mainly founded on signal processing and analytical models, are not usually flexible with different cutting conditions. Artificial intelligence (AI) has emerged as the way of providing efficient solution to strong and real-time chatter observation in the recent past. In this review, the AI-based tool chatter detection methods are briefly discussed in a short discussion including machine learning methods such as support vector machines, decision trees, and k-nearest neighbors, and deep-learning-based methods such as convolutional and recurrent neural networks. Vibration, acoustic emission and force signal are also other sensor input presented in feature extraction and model development. In the paper, the latest evolution about the intelligent monitoring systems and the key issues of the system such as data dependency, generalization and complications of computation are also observed. The future trends are on explainable AI and adaptive learning to maintain stable machining performance.
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