Large shafts, such as metallurgical shafts, large motor rotors, and large curved rollers, are widely used in industry. Multi-pass grinding is employed to remove the excess material on the shaft to ensure the high requirements of dimensional accuracy and surface quality. Due to the influence of the grinding wheel width, there is a stepped dimensional error in the grinding process. In this paper, a grinding wheel profile optimization method is proposed to decrease the dimensional error caused by wheel width. An error calculation model is firstly established to describe the dimensional error caused by wheel width, and the gradient descent method is further developed to optimize the wheel profile to improve dimensional accuracy. The experimental results show that after optimization, the total error decreases from 16.9 mm to 11.1 mm and the mean error decreases from 11.2 mm to 6.3 mm, respectively, which proves that the proposed method is effective to reduce the dimensional error for complex large shaft curve grinding.

Dimensional accuracy is one of the most important quality indicators of large shaft grinding, which directly affects the shaft service performance. Overcut/undercut caused by grinding wheel width and wear are the main factors affecting dimensional accuracy of large shaft grinding, which can be described by establishing physical models, and the physical models would be further used for compensation. However, the residual error always exists due to modeling uncertainty, and the residual error has nonlinear relation with compensation value, which is hard to completely eliminate. In order to solve the problem, this paper proposes a dimensional accuracy compensation method of large shaft grinding via residual error iteration with fuzzy approach. Two physical models are firstly established by considering the grinding wheel width and wear, respectively. The residual error after using these two models is further dealt with iteration, and the fuzzy approach is applied to dynamically calculate the compensation coefficients to improve dimensional accuracy while ensuring con-vergence. The experimental results show that the mean dimensional error is reduced 83% by using the pro-posed method, which is much better than other compensation methods.