Energy optimization method for variable curvature contour machining

Energy saving and consumption reduction is one of the current important research in the field of green and sustainable manufacturing. Products or components containing variable curvature contouring are widely used in the automotive, medical, aerospace, and mold industries, while there is a lack of methods to model the energy consumption ratio for variable curvature contouring and improve its energy efficiency. A method for modeling the specific energy consumption of variable curvature contouring and energy consumption optimization is proposed for this problem. Firstly, the components of energy consumption in processing of the CNC machine tool machining are analyzed, and the relationship between curvature characteristics and material removal rate is investigated from the geometric perspective. Secondly, orthogonal experiments with different curvatures of straight lines, convex arcs, and concave arcs are designed to collect energy consumption data. Based on the experimental data, the Dueling Deep Q-Network optimization support vector regression (Dueling DQN-SVR) was used to establish the specific energy consumption model considering the curvature. Finally, a multi-objective optimization model is constructed when considering specific energy consumption, efficiency, and quality, and the Pareto solution set is solved using a multi-objective Gray Wolf optimization algorithm (MOGWO). The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to select the optimal combination of machining parameters. The experimental results show that the accuracy of the established model is more than 95