Modeling and effect analysis of machining parameters for surface roughness and specific energy consumption during TC18 machining using deep reinforcement learning and neural networks

Under the impetus of green manufacturing and a low-carbon economy, the critical challenge lies in reducing energy consumption while maintaining machining quality. Against this background, this paper presents the method of modeling and effect analysis for surface roughness and specific energy consumption during TC18 machining using Deep Reinforcement Learning and Neural Networks. In this method, to reduce the experiment cost, multilayer-layer design (MLD) for computer simulation is applied to design a physical experiment, and to improve modeling accuracy, backpropagation neural network (BPNN) optimized by Double deep Q network algorithm (DDQN) is utilized to develop the prediction models of surface roughness (Ra) and specific energy consumption of cutting (E_sec). Finaly, the synergistic influence of cutting parameters on Ra and E_sec is analyzed based on the prediction models of Ra and E_sec built by MLD and DDQN-BPNN. The effectiveness and low cost of MLD and the excellent prediction performance of DDQN-BPNN are verified by comparisons of optimized BPNNs using common heuristic optimization algorithms through the milling experiment of TC18. These technologies provide effective solutions for modeling and factor impact of target features in machining field, and research results provides an effective guidance for the selection of milling parameters of TC18 to reduce the specific energy consumption of cutting under ensuring or improving machining quality.