Evaluation and ANN-Based Prediction on Functional Parameters of Surface Roughness in Precision Grinding of Cast Iron

Bin Zhao; Song Zhang; Jian Feng Li

doi:10.4028/www.scientific.net/AMR.1017.166

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1017Evaluation and ANN-Based Prediction on Functional...

Evaluation and ANN-Based Prediction on Functional Parameters of Surface Roughness in Precision Grinding of Cast Iron

Abstract:

Three-dimensional surface roughness parameters are widely applied to characterize frictional and lubricating properties, corrosion resistance, fatigue strength of surfaces. Among them, the functional parameters of surface roughness, such as S_bi, S_ci, and S_vi, are used to evaluate bearing and fluid retention properties of surfaces. In this study, the effects of grinding parameters, including wheel linear speed (V_s), workpiece linear speed (V_w), grinding depth (a_p), longitudinal feed rate (f_a), and dressing rate (F), on functional parameters were studied in grinding of cast iron. An artificial neural network (ANN) model was developed for predicting the functional parameters of three-dimensional surface roughness. The inputs of the ANN models were grinding parameters (V_s, V_w, a_p, f_a, F), and the output parameters of the models were functional parameters of surface roughness (S_bi, S_ci, S_vi). With small errors (e.g MSE = 0.09%, 0.61%, and 0.0014%. ), the ANN-based models are considered sufficiently accurate to predict functional parameters of surface roughness in grinding of cast iron.

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Periodical:

Advanced Materials Research (Volume 1017)

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166-171

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https://doi.org/10.4028/www.scientific.net/AMR.1017.166

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Online since:

September 2014

Authors:

Bin Zhao*, Song Zhang, Jian Feng Li

Keywords:

Artificial Neural Network (ANN), Cast Iron, Functional Parameters, Grinding, Surface Roughness (SR)

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