Journal of Engineering Research

In intelligent CNC machining systems, tool wear monitoring is crucial for ensuring machining quality and efficiency. Traditional monitoring methods suffer from limitations such as low accuracy and poor real-time performance, which fail to meet the demands of modern high-efficiency machining. Deep learning technology, with its powerful feature extraction and pattern recognition capabilities, offers new approaches for tool wear monitoring. By constructing deep learning models and validating them with multi-source data training, high-precision real-time monitoring can be achieved, significantly enhancing the intelligence level of the machining process. In the future, as technology continues to advance, the application of deep learning in tool wear monitoring will become more widespread, and is expected to further promote the intelligent transformation of the manufacturing industry.

Deep learning; Intelligent CNC machining; Tool wear; Real-Time monitoring; Model optimization