Prediction and comparison of surface roughness in CNC-turning process by machine vision system using ANN-BP and ANFIS and ANN-DEA models

Machine vision methods of roughness measurement are being developed worldwide due to their inherent advantages including non-contact and rapid surface measurement capability. In this work, a back propagation (BP) and a differential evolution algorithm (DEA) based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) model have been used for the prediction of surface roughness in turning operations. Cutting speed, feed rate, depth of cut and average grey level of the surface image of work-piece, acquired by computer vision were taken as the input parameters and surface roughness as the output parameter. The results obtained from the ANN-BP, ANFIS and ANN-DEA models were compared with observed values. It is found that the predicted values are in good agreement with the experimental values. It is also found that the error percentage is minimal and it is also observed that the convergence speed for the ANN-DEA model is higher than the ANN-BP and ANFIS.

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