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Abstract Tool wear monitoring in machining operations is vital for maintaining product quality and minimizing downtime. Traditional methods, like optical microscopy, are often time consuming and offline. However, advancements in digital image processing, particularly machine vision, have made online tool wear monitoring more feasible. This systematic literature review investigates the application of artificial intelligence (AI) techniques in tool wear monitoring over the past two decades. The review reveals a growing interest in AI-based approaches, particularly Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), for stable turning operations and online prediction. Key trends in input selection, preprocessing techniques, and output considerations across various AI models are identified, providing valuable insights into the evolving landscape of tool wear monitoring methodologies. Looking ahead, the future of tool wear monitoring appears promising, with continued advancements in AI technologies. Challenges remain, including the variable evolution of tool degradation and underutilization of CNC data. Addressing these challenges will require interdisciplinary collaboration and innovative solutions. In conclusion, AI-driven tool wear monitoring represents a promising approach to enhance productivity and quality in the metal cutting industry.
Agrawal et al. (Thu,) studied this question.