Causal Models and Intelligent Data Management

Abstract

Surface roughness is an index which determines the quality of machined products and is influenced by the cutting parameters. In this study the average surface roughness R a (value) for Aluminum after ball end milling operation has been measured. 84 experiments have been conducted varying cutter axis inclination angle (φ degree), spindle speed (S rpm), feed rate (f y mm/min), radial depth of cut (feed f x mm), axial depth of cut (t mm) in order to find R a. This data has been divided into two sets on a random basis; 68 training data set and 16 testing data set. The training data set has been used to train different ANN and ANFIS models for R a prediction. And testing data set has been used to validate the models. Better ANFIS model has been selected based on the minimum value of Root Mean Square Error (RMSE) which is constructed with three Gaussian membership functions (gaussmf) for each input variables and linear membership function for output. Similarly better ANN model has been selected based on the minimum value of Root Mean Square Error (RMSE) and Mean Absolute Percentage of Error (MAPE). The Selected ANFIS model has been compared with theoretical equation output, ANN and Response Surface Methodology (RSM). This comparison is done based on RMSE and MAPE. The comparison shows that selected ANFIS model gives better result for training and testing data. So, this ANFIS model can be used further for predicting surface roughness of Aluminum for three dimensional end milling operation.