An adaptive parameters adjustment and planning method for robotic belt grinding using modified quality model

As a kind of flexible manufacturing system, the machining quality of a robotic belt grinding system is related to a variety of factors with strong time variation, which easily leads to process fluctuations and affects the final quality. Therefore, it is a great challenge to control the quality precisely during the whole grinding procedure. Focusing on this problem, an adaptive parameters adjustment and planning method for robotic belt grinding using the modified quality model is proposed in this paper. Firstly, the correlation analysis method of grinding parameters in time domain is presented based on an improved-Mahalanobis distance. The response surface methodology (RSM) is utilized to construct the quality prediction model, and furtherly the parameter sensitivity function, which can characterize the influence degree of different parameters on the grinding quality, is introduced to calculate the Mahalanobis distance for improving the accuracy of the correlation analysis method. Secondly, based on the correlation analysis, a conversion method from the old samples into the new samples space is presented using vector field smoothing algorithm (VFS), then the modified grinding quality model can be re-established adopting the new samples. Furtherly, taking the problem of poor robotic response rate into consideration, a multi-parameters collaborative planning method under the smoothness constraint is developed using particle swarm optimization (PSO) algorithm, which can avoid the parameter mutation and improve the process stability. Finally, belt grinding experiments on a curved surface were carried out based on the robotic grinding platform. The results show that the approach can improve the grinding shape accuracy, which verify the effectiveness of the proposed methods.