An Approach of Weighted Principal Component Analysis Based Taguchi Method for Parametric Optimization of Submerged Arc Welding

Abstract:

Quality is the collection of features and characteristics of a product that contribute to its ability to meet given requirements. The quality of a weld joint is directly influenced by the welding parameters during the welding process. Often, a common problem that has been faced by the manufacturer is the control of the process parameters to obtain a good welded joint. Quality level is denoted as the probability of conformance and is a function of system variables and system specification. The present work is aimed to improve the quality of the weldment in SAW using Weighted Principal Component based Taguchi method. Three process parameters such as voltage, carriage speed and stand of distance are identified to carry out the study. Mechanical properties such as tensile strength and hardness are considered to be the response parameters in the present study.

• Periodical: Advanced Materials Research (Volumes 488-489)
• Pages: 866-870
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.488-489.866
• Online since: March 2012
• Authors: J. Deb Barma, Mitul B. Shil, P.K. Dan, A. Bandyopadhyay
• Keywords: Principal Component Analysis (PCA), S/N Ratio, SAW, Taguchi Method

References

[1] J.E. R Dhas, and S. Kumanan,: Weld quality prediction of Submerged Arc Welding Process using a function replacing Hybrid System, Advance in Production Engineering & Management 5 (2010), 1, 5-12.

[2] G. Nandi, S. Datta, P.K. Pal, A. Bandyopadhyay: Analyses of Hybrid Taguchi Methods For Optimization of Submerged Arc Weld, Seminar on Joining Processes : Challenges for Quality , Design & Development, NITA, India, 5-6 March (2010).

[3] O.E. Canyurt: Estimation of Welded Joint Strength using Genetic Algorithm Approach, International Journal of Mechanical Sciences 47 (2005) 1249–1261.

DOI: 10.1016/j.ijmecsci.2005.04.001

[4] J. Antony and F. J. Antony: Teaching the Taguchi method to Industrial Engineers, Work Study Volume 50. Number 4. 2001. p.141 – 149.

DOI: 10.1108/00438020110391873

[5] L.J. Yang, R.S. Chandel, M.J. Bibby: An Analysis of Curvilinear Regression equations for modeling the Submerged-Arc Welding process, Journal of Materials Processing Technology, 37 (1993) 601-611.

DOI: 10.1016/0924-0136(93)90121-l

[6] K-L Hsieh and Lee-Ing T Tamkang: Parameter Optimization for Quality Response with Linguistic Ordered Category by Employing Artificial Neural Networks: A Case Study, Journal of Science and Engineering, Vol. 2, No. 4 (2000).

[7] T-S Chang, Allen C. Ward and J. Lee: Conceptual Robustness in Simultaneous Engineering: An Extension of Taguchi's Parameter Design, Research in Engineering Design (1994) 6: 211-222.

DOI: 10.1007/bf01608400

[8] Y.S. Tarng, S.C. Juang, and C. H. Chang: The Use of Grey-Based Taguchi Methods to Determine Submerged Arc Welding Process Parameters in Hardfacing, Journal of Materials Processing Technology (2002), Volume 128, pp.1-6.

DOI: 10.1016/s0924-0136(01)01261-4

[9] P. Kanjilal, T. K. Pal, and S. K. Majumdar: Prediction of Element Transfer in Submerged Arc Welding, Indian Welding Journal (2007), VOL. 86.