No abstract

Surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall polishing system, which makes it an ever-increasing concern in industries and academia nowadays. In this article, from microscopic point of view, based on the statistics analysis, and by the use of the elastic contact theory and the plastic contact theory, the model of the maximum cutting depth of abrasive grains is developed. Then based on back-propagation neural network, taking the maximum cutting depth of abrasive grains, the rotation speed of belt and the feed rate of workpiece as the input parameters, a prediction model of surface roughness in belt polishing is presented. The prediction model fully takes the characteristics of polishing tool and workpiece into consideration which makes the model more comprehensive. Compared with the model that takes the polishing force as the input parameter, the model in this article needs fewer experiment samples which will save the experiment cost and time. Moreover, it has a wider range of uses and is suitable for different polishing situations such as different workpieces and polishing tools. The results indicate a good agreement between the predicted values and experimental values which verify the 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.

No abstract

This paper proposes a modified tangential contact stiffness model considering friction’s effect, which is the first key step to establish the dynamic model of the fixture-workpiece system, and this is the foundation of vibration suppression for the manufacturing process of aerospace blades. According to Love’s elastic deformation, the model’s derivation process starts with the potential function in each coordinate axis’s direction respectively. The generalized Hertz contact theory is employed to calculate the contact forces in this model. The symmetrical characteristic of the contact area has simplified the derivation process to obtain the eventual tangential contact stiffness model. A validation experiment focusing on a tangential stiffness measuring is achieved by putting two spherical objects in contact together to get the tangential contact stiffness. Based on the data collected in this experiment, a comparison with a most similar existed model is carried out, and the result shows that the relative error of this modified model are all less than 10%, while the original model’s (the most similar model) relative error exceeding 50% captures more than 3/4 of the 30 data sets randomly selected in each experiment group, and that means the modification of this paper brings great improvement to the contact stiffness model.