Evaluation and ANN-based prediction on functional parameters of surface roughness in precision grinding of cast iron

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.