To improve the surface quality and fatigue resistance performance of 42CrMo steel parts,the orthogonal experiments were de-signed to analyze the significance of ultrasonic rolling extrusion processing parameters and their influence laws on surface performance in-dexes.Based on experimental data,the BP neural network and exponential regression prediction models were established to verify the accu-racy of the model.The prediction model was optimized by using multi-objective whale algorithm(MOWOA)to perform three-objective and two-objective optimization,and the sets of processing parameters and surface performance optimal parameters were obtained,and the trade-off relationship between surface performance indicators was analyzed.The results show that the exponential prediction model has higher accu-racy and the optimal set of processing parameters is:rotation speed of 210-250 r·min-1,feeding speed of 12-16 mm·min-1,amplitude of 25-28 μm,static pressure of 517-630 N.The optimal set of surface performance parameters is:surface roughness of 0.466-0.507 μm,residual compressive stress of 1002-1110 MPa,microhardness of 709-720 HV.The accuracy of the algorithm was verified by experiments.
ultrasonic rolling extrusionBP neural networkexponential modelMOWOAsurface performance
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