A self-adaptive machining parameters adjustment method for stabilizing the machining-induced surface roughness

To maintain a qualified product, it is necessary to control the final machined quality approximately. To this end, massive research has been devoted to modeling and controlling the machining-induced surface roughness. However, a generalized surface roughness prediction model is hard to develop due to the complex modeling process and insufficient data. And a feasible surface roughness stabilization method is often missing in the existing studies. To this end, this paper proposed a novel self-adaptive machining parameters adjustment method for stabilizing the machining-induced surface roughness. In the proposed method, a physical surface roughness prediction model is developed at first. Then, a CNN-LSTM is employed to realize spatial-temporal feature extraction. Next, the MMD-MSE-based method is employed to realize the transfer learning process. Finally, a self-adaptive process parameter tuning system using the gradient descent method is developed, based on the surface prediction method. Experiments are conducted on a milling machine, and results indicate that the proposed method can realize a high accuracy and generalization prediction of surface roughness. In terms of the machined surface roughness, the proposed method effectively maintains the surface roughness under 1.6 mu m.