Roughness prediction method for precision magnetic abrasive finishing of the inner surface of aeroengine bent tubes based on the SVM-IPSO model

Addressing the issues of insufficient parameter coupling characterization, susceptibility to local optima, and slow convergence speed in the existing optimization model for magnetic abrasive finishing process parameters of bent pipes, this paper proposes a prediction method for the internal surface roughness of aeroengine bent pipes based on the combination of Support Vector Machine (SVM) and Improved Particle Swarm Optimization (IPSO). By deeply analyzing the nonlinear mapping relationship between the multi-parameter coupling effect of magnetic abrasive finishing process and surface quality, a key influencing factor system is established, and the morphology data before and after processing are collected. Subsequently, a normalization operator is introduced to eliminate the influence of dimensions. The optimal magnetic abrasive finishing (MAF) process parameters for tube bending are obtained as follows: magnetic field rotational speed of 996.86 r/min, feed rate of 0.98 mm/s, abrasive grain size of 97.32 mesh, and processing time of 57.24 min. The constructed SVM-IPSO model integrates the robustness of SVM in small-sample regression with the efficient global optimization potential of IPSO. By dynamically iterating and optimizing the model weights and process variables, it effectively overcomes the blindness of manual trial and error methods and significantly reduces experimental costs. It provided theoretical basis and technical support for the optimization of magnetic abrasive finishing process on the inner surface of aeroengine bends.