This paper is published in Volume-4, Issue-6, 2018
Area
Mechanical Engineering
Author
Ananya Bhattacharyya
Org/Univ
Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal, India
Pub. Date
01 December, 2018
Paper ID
V4I6-1293
Publisher
Keywords
Burrs, Universal radial machine, Taguchi method, ANOVA, S/N Ratio, Response surface methodology, Deburring

Citationsacebook

IEEE
Ananya Bhattacharyya. Burr formation minimization in drilling process using experimental study with statistical analysis, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Ananya Bhattacharyya (2018). Burr formation minimization in drilling process using experimental study with statistical analysis. International Journal of Advance Research, Ideas and Innovations in Technology, 4(6) www.IJARIIT.com.

MLA
Ananya Bhattacharyya. "Burr formation minimization in drilling process using experimental study with statistical analysis." International Journal of Advance Research, Ideas and Innovations in Technology 4.6 (2018). www.IJARIIT.com.

Abstract

Burrs are generally plastic deformation of the workpiece after machining process. Deburring can reduce burr formation but it is time consuming and increase the production cost. By changing some of the input parameters like Spindle Speed, Feed Rate, Depth of Cut, the formation of burr can be reduced. This research work presents an experimental study on minimizing the formation of burr in machining like drilling. In this thesis, Universal Radial machine has been used to make holes. By changing machining variables like feed, cutting velocity and speed different sizes and the type of the burrs created in aluminum are studied. Taguchi analysis has been done to analyze the predicted minimum burr height. ANOVA has also been done to analyze the maximum contribution of the parameters to form the burr. Signal to Noise(S/N) ratio plots has been shown in this research. Response surface methodology also has been conducted. In the design optimization, the application of RSM is aimed to reduce the cost of the expensive analysis methods (e.g. finite element method or CFD analysis) and their associated numerical noise.