Tool health monitoring in lathe turning process by artificial intelligence techniques — a review

Abstract

Monitoring tool health is essential for maintaining efficiency, productivity, and quality in lathe turning operations. Traditional methods rely on manual assessments and subjective judgments, which can be time-consuming, inconsistent, and inadequate for detecting subtle tool wear. Therefore, this review discusses the literature review on predicting tool wear in the turning process, comprehensively examining the methods documenting for sensing and testing parameter design, image processing, and classification methods. The review outlines the use of vibration signals and images as datasets and advanced artificial intelligence techniques like machine learning, computer vision, deep learning, and expert systems to predict the accurate wear percentage in the tool. It also discusses the benefits and limitations of methods used in reviewed papers. To conclude, the performance of AI techniques from the reviewed papers, RNN from deep learning, gives more accuracy, with 97.04% predicting the tool wear. Within the Industry 4.0 framework, after a detailed review of the AI techniques, the combination of deep learning techniques that ensemble vibration signals and image information develops as a vital technology for evolving intelligent manufacturing.