Modeling and Evaluating of Surface Roughness Prediction in Micro-grinding on Soda-lime Glass Considering Tool Characterization

The current research of micro-grinding mainly focuses on the optimal processing technology for different materials. However, the material removal mechanism in micro-grinding is the base of achieving high quality processing surface. Therefore, a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography is proposed in this paper. The differences of material removal mechanism between convention grinding process and micro-grinding process are analyzed. Topography characterization has been done on micro-grinding tools which are fabricated by electroplating. Models of grain density generation and grain interval are built, and new predicting model of micro-grinding surface roughness is developed. In order to verify the precision and application effect of the surface roughness prediction model proposed, a micro-grinding orthogonally experiment on soda-lime glass is designed and conducted. A series of micro-machining surfaces which are 78 nm to 0.98 ~tm roughness of brittle material is achieved. It is found that experimental roughness results and the predicting roughness data have an evident coincidence, and the component variable of describing the size effects in predicting model is calculated to be 1.5x 107 by reverse method based on the experimental results. The proposed model builds a set of distribution to consider grains distribution densities in different protrusion heights. Finally, the characterization of micro-grinding tools which are used in the experiment has been done based on the distribution set. It is concluded that there is a significant coincidence between surface prediction data from the proposed model and measurements from experiment results. Therefore, the effectiveness of the model is demonstrated. This paper proposes a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography, which would provide significant research theory and experimental reference of material removal mechanism in micro-grinding of soda-lime glass.

Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece

Machined surface roughness will affect parts?service performance.Thus,predicting it in the machining is important to avoid rejects.Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing.Aiming at surface roughness prediction in the machining process,this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis.To reduce the stochastic error of measuring the machined surface profile height,the surface area is repeatedly measured three times,and Pauta criterion is adopted to eliminate abnormal points.The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model.Seven acceleration features are extracted,and valley,which has the highest/^-square proving the effectiveness of the filtering features,is selected as the input of the prediction model by mutual information coefficients.Finally,by comparing the measured and predicted surface roughness curves,they have the same trends,with the average error of 16.28%and the minimum error of 0.16%.Moreover,the prediction curve matches and agrees well with the actual surface state,which verifies the accuracy and reliability of the model.

LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor

Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector machine（LS-SVM）-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing（CSA） and standard simplex（SS） methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is-4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.

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

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.