Vaibhav Joshi

In modern manufacturing, milling and micromilling processes play a central role in precision production. However, rapid wear of cutting tools often leads to sudden tool breakage, unplanned downtime, and part rejection. Maintenance is therefore essential to ensure efficiency, safety, and cost-effectiveness across industries. Traditional maintenance strategies have gradually evolved into predictive maintenance approaches supported by advanced technologies, creating a strong industrial demand for accurate and reliable predictive solutions. This review systematically analyzes studies from the past decade on predictive maintenance in milling and micromilling processes, with a particular focus on the performance of machine learning and deep learning algorithms integrated with computer vision techniques. The study evaluates model performance, prediction accuracy, and industrial applicability to identify key strengths and existing research gaps. The findings indicate that multi-sensor data fusion, deep learning methods, and hybrid models achieve the highest performance in tool wear monitoring and remaining useful life prediction of cutting tools.