Identification and adjustment method of axis deviation for crankshaft grinding clamping

Abstract During large-scale crankshaft grinding, axis deformation occurs due to steady rest misalignment and clamping forces, adversely affecting machining accuracy. The current practice involves adjusting the height of the grinding machine’s steady rests to compensate for crankshaft deformation. However, the nonlinear interactions among multiple steady rests complicate the adjustment process. To improve the precision and efficiency of deformation compensation, this study proposes a novel method for identifying and correcting crankshaft axis deviation during grinding. Finite element simulations were conducted using ABAQUS to analyze various steady rest support configurations, obtaining data on post-deformation axis deviation and (CWD). A deep neural network (DNN) was employed to model the nonlinear relationship between axis deviation and CWD, while particle swarm optimization (PSO) was applied to determine the current axis deviation and compute optimal adjustment values for each steady rest position. Experimental results demonstrate that the proposed method maintains adjustment accuracy while significantly enhancing operational efficiency.