Resumen de Prediction of surface quality in end milling based on modified convolutional recurrentneural network

Wen Guan, Zhangjie Liu-, Ayman Al domoor

• The quality of the milled surface affects the performance of the affiliated workpiece, since it plays a vital role in determiningthe precision of the geometry and duration of service time. In this paper, a modified convolution recurrent neural network(CRNN) is proposed to effectively predict the surface quality of the end milling workpiece. First, the validated featuresof milling force data in the machining process are extracted based on the proposed artificial network model. Second, amodified CRNN model is constructed by merging residual neural network with the help of bidirectional long- and short-term memory as well as attention mechanism. Third, the model’s weight is optimised according to the changes in theloss function and directional propagation principle, which significantly improves the effectiveness of the proposed model.Finally, the actual experiment is carried out on a 5-axis milling centre to validate our model. Also, the surface qualitypredicted by the CRNN model is in good accordance with the experimental result. In our experiment, an accuracy of98.35% is achieved, which is a significant improvement compared to the classic CRNN method