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

Abstract

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.

Online measurement method for roundness of large aluminum alloy ring based on laser ranging principle

“…Finally, the optimal value is determined by comparison. 30–33 Compared with the GA and LS algorithms in roundness evaluation, it has the advantages of strong global search ability, fast convergence speed, and high accuracy. Assuming that the search space is D -dimensional, and there are m particles in the population, in order to ensure the diversity of the population and the search efficiency of the algorithm, the number of particles m is generally taken as 5–10 times of the dimension D of the particles.…”

“…Finally, the optimal value is determined by comparison. [30][31][32][33] Compared with the GA and LS algorithms in roundness evaluation, it has the advantages of strong global search ability, fast convergence speed, and high accuracy.…”

“…In equation ( 18), when particles are updated, the inertia weight w has a great influence on particle speed, 33 and it is usually a fixed value. However, the PSO algorithm has a high convergence speed at the initial optimizing stage while at the later optimizing stage, its convergence will slow down.…”

Study on optimization of process parameters in dressing of bonnet polishing tool

“…With the development of machine learning algorithms, process parameters optimization is also increasingly being applied based on experiments. [10][11][12] However, this experimental method is poor adaptability and requires extensive experimental data to determine the optimal process parameters for different dressing systems. Therefore, this study proposes a grain trajectory modeling method to achieve process parameter optimization.…”

The comprehensive improvement of surface integrity in whirlwind milling a large screw

“…In turn milling, Babu et al 17 used RSM to analyze the surface roughness, and obtain the minimal value under optimized cutting parameters. Chen et al 18 utilized RSM to achieve the most significant factors and improve the grinding shape accuracy. Alharbi 19 applied RSM to realize the multi-objective optimization of energy consumption, hardness, and surface roughness in ultrasonic shot peening.…”