Abstract
The concavity and convexity of complex helical surface makes it the characteristic of variable feed speed and load processing, which leads to long machining time, high energy consumption power for material removal. The concavity and convexity of complex surfaces exacerbate spindle vibration during milling, resulting in poor machining quality and high energy consumption. For the high quality and low energy consumption machining of helical surface, this paper proposes a method of optimising the energy consumption of process parameters of CNC milling machine for helical groove considering spindle vibration. Firstly, based on the orthogonal test data, the influence of process parameters on spindle vibration, etc. is analysed, and the empirical model of spindle vibration is established through process parameters. Aiming at the prediction error caused by the multicollinearity problem of the energy consumption mechanism model due to the regression fitting method, it is proposed to construct an energy consumption error compensation model based on the data-driven by improved stochastic configuration network algorithm through the use of process parameters, and to combine the mechanism model with the error compensation model, so as to improve the prediction accuracy of the machining energy consumption. Finally, the multi-objective optimisation problem of the helical surface milling is solved by the improved non-dominated sorting algorithm, which is verified by actual machining comparison with expeeience process parameters. The results show that the optimised machining energy consumption is reduced by 17.52% and the surface roughness value is reduced by 6.07%, which proves that the method proposed in this paper can provide certain theoretical support for the machine tool industry to achieve green manufacturing.