scholarly journals Review of Surface Roughness Prediction in Cylindrical Grinding process by using RSM and ANN

2018 ◽  
Vol 4 (12) ◽  
pp. 59-65 ◽  
Keyword(s):  
Surface Roughness ◽  
Grinding Process ◽  
Surface Roughness Prediction ◽  
Cylindrical Grinding ◽  
Roughness Prediction

An Intelligent Prediction of Surface Roughness on Precision Grinding

2017 ◽  
Vol 261 ◽  
pp. 221-225
Author(s):  
Ning Ding ◽  
Chang Long Zhao ◽  
Xi Chun Luo ◽  
Qing Hua Li ◽  
Yao Chen Shi
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Precision Grinding ◽  
Wheel Wear ◽  
Surface Roughness Prediction ◽  
Fuzzy Neural ◽  
Roughness Prediction ◽  
Wear Condition

Precision grinding is generally used as the final finishing process, and it determines the surface quality of the machined component. It’s very difficult to achieve on-line measurement of the surface roughness. The purpose of this research was to study the surface roughness prediction and avoid the defect happening in the grinding process. A surface roughness prediction model was proposed in this paper, which presented the relationship between surface roughness and the wear condition of grinding wheel and grinding parameters. An AE sensor was used to collect the grinding signals during the grinding process to obtain the grinding wheel wear condition. Besides, a fuzzy neural network was used to obtain the prediction surface roughness. Grinding trials were performed on a high precision CNC cylindrical grinder (MGK1420) to evaluate the surface roughness prediction model. Experiment verified that the developed prediction system was feasible and had high prediction accuracy.

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