Hybrid Deep Learning And Metaheuristic Optimization For Cnc Machining Parameter Prediction

Abstract

It is important to accurately predict and optimize the machining parameters in the dynamic working conditions to enhance the surface quality, energy saving, and the productivity of the manufacturing process in CNC machining. In this study, a novel integrated intelligent CNC machining parameters prediction framework is proposed, which integrates an advanced data preprocessing method, hybrid metaheuristic optimization, and a hybrid deep learning model (HLK-Net). A preprocessing process of ARIMA-ZKF filtering and KNN imputing is applied to enhance the quality of sensor data, and a hybrid Fox Optimization–Snow Geese Algorithm (FO–SGA) is proposed for an effective balance between global exploration and local exploitation in optimizing the machining parameters. Moreover, the proposed HLK-Net combines HRNet, LSTM, and Kernel Extreme Learning Machine (KELM), which has the capability of capturing spatial, temporal, and nonlinear relationships in machining data. The superiority of the proposed framework over the existing deep learning models is demonstrated by experimental evaluation on a set of CNC Milling and CNC Turning data. The proposed framework was experimentally evaluated using CNC milling and turning data and it was verified that the proposed framework obtained 0.172 MAE, 0.231 RMSE, 0.947 R 2 , and 0.0387 MPL. The proposed model, HLK-Net, resulted in a 13.13% reduction in MAE, a 13.48% reduction in RMSE, a 25.57% decrease in prediction latency and a 2.82% increase in the R 2 compared to the conventional LSTM model. In addition, the number of iterations of convergence of FO–SGA was 39 compared to 54.12% less iterations than GA. The optimized machining parameters also reduced normalized energy consumption and surface roughness, yet another indication of the practical benefits of the proposed framework. In summary, the proposed framework can accurately, robustly, and efficiently predict and optimize the real-time intelligent CNC machining and smart manufacturing.