PREDICTIVE MODELLING OF DRILL WEAR: COMPARATIVE ANALYSIS OF ANN AND FUZZY LOGIC TECHNIQUES

Today's fast growing technology has raised the bar when it comes to the accuracy of machined components. The primary objective of this research is to estimate drill wear. In this study, drill wear estimation is carried out by considering Acoustic Emission (AE), Vibration Velocity and Drill Tool Chatter measured using image features obtained by Machine Vision system. In order to identify the tool wear conditions based on the signal measured, an Artificial Neural Network, using a Feed Forward - Back-Propagation algorithm, and Fuzzy Logic approach, have been adopted. The neural network is trained to estimate the average drill wear and after each drilling operation the drill wear is measured with Tool Maker's Microscope. The input parameters that are being used for estimation in this project were found to be non-linearly varying with the desired output. Due to this, the interpretation and prediction of data becomes very difficult. Hence, the two expert systems, i.e., Artificial Neural Network and Fuzzy Logic toolboxes will be used to analyse the best fit model in predicting the output of tool wear for this specific drill job. The prediction accuracy is then compared to analyse which model could give better results so that it can be recommended for machine learning and future work. When ANN and MAMDANI FIS methods were used and the actual tool wear and predicted tool wear were compared, it was observed that ANN produced better correlations and hence it is selected for predictions of tool wear for the present work conditions.