Prediction of cutting performance using artificial neural network during buffered impact damper-assisted boring process

Abstract

Abstract In manufacturing industry tool vibration, tool wear and surface finish which considerably affects production and its costs. During boring process, overhanging length of the tool holder generates tool vibration which affects the quality of surface finish, hastened tool life, and further reduction in machine tool life. In order to enhance the cutting performance, buffered impact dampers were designed, developed and tested in this research work. A set of 27-run cutting experiment were conducted by varying particle size, material and filling. From the results it was observed that the performance of buffered impact damper shows a significant improvement in cutting efficiency when compared with standard tool holder. Artificial Neural Network (ANN) was used to predict the impact of buffered damper on surface roughness, tool wear, tool vibration and cutting force. Results obtained in ANN model was compared with the experimental results for MSE, AAD, MAPE & R and it was observed that the experimental results concurred more with predicted results by using ANN.