Drilling is one of the most common and fundamental machining processes. Since approximately 40% of all the cutting operations are drilling in industry. It is most frequently performed, material removing process and is used as a preliminary step for many operations, such as reaming, tapping and boring. Because of their importance in nearly all production operations twist drills have been the subject of numerous investigations. Surface finish quality of a machined work piece is an issue of main concern to the manufacturing industry. The aim of the present work is to identify suitable parameters for the prediction of surface roughness. Back propagation neural networks were used for detection of surface roughness. Drill diameter, cutting speed, feed, and machining time were given as inputs to the neural network structure and surface roughness was estimated. Drilling experiments with 10 mm drill size were performed at three cutting speeds and feeds. The number of neurons were selected from 1,2,3,…, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The best structure of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. For statistical analysis, it was assumed that surface roughness depends on cutting speed, feed and machining time. For the mathematical analysis inverse coefficient matrix method was used for calculating the estimated values of surface roughness. Comparative analysis has been done between the actual values and the estimated values obtained by statistical analysis, mathematical analysis and neural network structure.
A study of surface roughness in drilling using mathematical analysis and neural networks
