Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Online multi-response assessment using Taguchi and artificial neural network

Online multi-response assessment using Taguchi and artificial neural network Engineering problems often embody many characteristics of a multi-response optimisation problem, and these responses are often conflicting in nature. To address this issue, this work uses grey-based Taguchi method to express surface roughness of drilled holes and drill flank wear into an equivalent single response. Experiments have been conducted in a radial drilling machine with five input parameters using L27 orthogonal array. It has been observed that combined response of flank wear and surface roughness is affected by almost all input parameters; however, drill diameter is statistically most significant. The prediction results obtained via. Taguchi method is compared with Back Propagation Neural Network (BPNN). (Received 20 November 2008; Revised 4 September 2009; Accepted 1 March 2010) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Manufacturing Research Inderscience Publishers

Online multi-response assessment using Taguchi and artificial neural network

Download PDF
22 pages

Loading next page...
 
/lp/inderscience-publishers/online-multi-response-assessment-using-taguchi-and-artificial-neural-r4ufwYys7f

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1750-0591
eISSN
1750-0605
DOI
10.1504/IJMR.2010.033469
Publisher site
See Article on Publisher Site

Abstract

Engineering problems often embody many characteristics of a multi-response optimisation problem, and these responses are often conflicting in nature. To address this issue, this work uses grey-based Taguchi method to express surface roughness of drilled holes and drill flank wear into an equivalent single response. Experiments have been conducted in a radial drilling machine with five input parameters using L27 orthogonal array. It has been observed that combined response of flank wear and surface roughness is affected by almost all input parameters; however, drill diameter is statistically most significant. The prediction results obtained via. Taguchi method is compared with Back Propagation Neural Network (BPNN). (Received 20 November 2008; Revised 4 September 2009; Accepted 1 March 2010)

Journal

International Journal of Manufacturing Research – Inderscience Publishers

Published: Jan 1, 2010

There are no references for this article.