Aiming at the joint flexibility and wear state existing in the process of driving mechanical parts, this paper first proposes a stiffness and position decoupling control method for variable stiffness joints, which realizes the joint position control and the unity of joint compliance. The joint stiffness model was obtained by using the static relationship between the Jacobian matrix and the model, and the nonlinear equations composed of the mechanical model and the stiffness model of the variable stiffness device were solved by the optimization method to realize the nonlinear decoupling of the stiffness and position of the variable stiffness joint. Secondly, this paper proposes an online monitoring method of wear state in the machining process based on machine tool information. In this method, OPC-UA communication technology was used to collect and store the information of CNC machine tools online, and the internal process information related to the wear of the machine tools was obtained. Based on such information and the corresponding wear information, a wear state recognition model is established by using a convolutional neural network. The feasibility and effectiveness of the proposed compliance control scheme and the performance of online monitoring of wear condition are analysed and verified by simulation experiments.

Titanium alloy Ti6Al4V is widely used in aerospace, shipbuilding, petrochemical, and other industrial fields. Low-speed wire-cut electrical discharge machining (LS-WEDM) has the advantage of high machining accuracy. However, there are few research studies on the comprehensive effects of the electrical parameters of TC4 (Ti6Al4V) made by LS-WEDM on the machining surface. This paper focuses on LS-WEDM machining of titanium alloy TC4 and investigates the effects of electrical parameters on the surface roughness, kerf width, and cutting speed of TC4 specimens based on orthogonal tests. Five electrical parameters are optimized using the grey correlation method and the response surface method. The surface roughness of 1.744 μm, the kerf width of 215.432 μm, and the cutting speed of 24.759 mm2·min−1 are found to be the best process indicators, with errors of 3.3, 3.2, and 12.5% compared with the predicted values. The optimized results show that the surface roughness value is reduced by 50.9%, the kerf width is reduced by 29.4%, and the cutting speed is increased by 23%, which proves the accuracy of the optimized method.