Real-Time Monitoring and Evaluation of Cutting Quality in Miniature Saw Machines Based on Convolutional Neural Networks

In light of the growing demand for precision manufacturing, the real-time monitoring of cutting quality in micro sawing machines assumes great significance, with the objective of ensuring high cutting accuracy and efficiency. In this study, an innovative model based on Convolutional Neural Networks (CNNs) is proposed for the real-time monitoring and evaluation of cutting quality in micro sawing machines. Based on the existing ResNet architecture, the model introduces an attention mechanism to focus on the key features in the cutting mode and multi-scale feature fusion to enhance the detection ability of subtle defects. Furthermore, a temporal convolution module has been developed to capture the dynamic behaviour of the cutting process, thereby enabling the processing of video streams and the evaluation of quality trends in real time. The experimental results demonstrate that the proposed model exhibits an evaluation accuracy of over 95% on the cutting dataset of an industrial micro sawing machine. ()