Analysis and prediction of surface roughness for robotic belt grinding of complex blade considering coexistence of elastic deformation and varying curvature

Abstract

Precision prediction of machined surface roughness is challenging facing the robotic belt grinding of complex blade, since this process is accompanied by significant elastic deformation. The resulting poor prediction accuracy, to a great extent, is attributed to the existing prediction model which less considers the dynamics. In this paper, an improved scallop height model is developed to predict and assess the machined surface roughness by taking into account the elastic deformation and the varying curvature of blade, then robotic belt grinding experiments are carried out to evaluate the proposed model from the perspective of surface roughness. Finally factors that influence the scallop height are analyzed, and the suitable empirical equation of surface roughness is proposed to assess and predict the surface quality from the aspect of blade concave and convex surface by adopting the constant scallop height machining.

Surface Roughness Prediction of Titanium Alloy during Abrasive Belt Grinding Based on an Improved Radial Basis Function (RBF) Neural Network

“…According to Equation ( 20), the range of the number of nodes in the middle layer can be roughly determined as [3,12]. Therefore, the influence of the number of nodes in the interval 3-16 on training error (MSE) was explored.…”

“…In recent years, with the advancement of intelligence, numerous sophisticated algorithms have been employed to forecast the surface roughness of workpieces and enhance their predictive power. The causes of high and low surface roughness, as well as roughness prediction and modeling, have currently been developed as a near-complete theoretical system [10][11][12]. Tian et al [13] developed a prediction model for the association between different process factors and workpiece surface roughness using a BP neural network based on the experimental results.…”

Experimental and numerical study on surface generated mechanism of robotic belt grinding process considering the dynamic deformation of elastic contact wheel

“…Yang et al instituted an energy efficiency model based on the friction coefficient model of a single spherical grain from the perspective of abrasive geometry [11], which showed that the ploughing energy took more proportion than the cutting energy and the scratching energy, while the grinding depth of a single particle was much smaller than the radius of grains. Based on the mechanism evaluation of robotic belt grinding, a noval grinding force model is accomplished by Xu et al to predict the grinding removal depth and profit a quantificational machining process of robotic belt grinding [12,13]. Agustina and Segreto appraised the surface roughness obtained by robot-assisted polishing experiments with the analysis of the acoustic emission signal frequency domain features [14,15].…”