OCTOBER 19-20, 2012 - YMCA University of Science & Technology

Proceedings of the National Conference on
Trends and Advances in Mechanical Engineering,
YMCA University of Science & Technology, Faridabad, Haryana, Oct 19-20, 2012
APPLICATION OF TAGUCHI METHOD IN PROCESS
OPTIMIZATION
Shyam Kumar Karna 1 , Ran Vijay Singh 2 , Rajeshwar Sahai 3
1 Ph.D. Scholar, M. R. I. U., Faridabad
2 Professor & Head, Mechanical Engineering Department, MRIU, Faridabad
3
Professor, Mechanical Engineering Department, BSAITM, Faridabad
e-mail: Skkarna2005@gmail.com
Abstract
The objective of the study is to optimize the process by applying the Taguchi method with orthogonal array
robust design. Taguchi Parameter Design is a powerful and efficient method for optimizing the process, quality
and performance output of manufacturing processes, thus a powerful tool for meeting this challenge. Off-line
quality control is considered to be an effective approach to improve product quality at a relatively low cost. The
Taguchi method is one of the conventional approaches for this purpose. This procedure eliminates the need for
repeated experiments, time and conserves the material by the conventional procedure. Optimization of process
parameters is done to have great control over quality, productivity and cost aspects of the process. Off-line
quality control is considered to be an effective approach to improve product quality at a relatively low cost. The
Taguchi method is a powerful tool for designing high quality systems. The approach is based on Taguchi
method, the signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) are employed to study the
performance characteristics.
1. Introduction
After World War II, the Japanese manufacturers were struggling to survive with very limited resources. If it were
not for the advancements of Taguchi the country might not have stayed afloat let alone flourish as it has. Taguchi
revolutionized the manufacturing process in Japan through cost savings. He understood, like many other
engineers, that all manufacturing processes are affected by outside influences, noise. However, Taguchi realized
methods of identifying those noise sources, which have the greatest effects on product variability. His ideas have
been adopted by successful manufacturers around the globe because of their results in creating superior
production processes at much lower costs.
Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured
goods and more recently also applied to engineering (Rosa et al. 2009), biotechnology (Rao et al. 2008, Rao et
al. 2004), marketing and advertising (Selden 1997). Professional statisticians have welcomed the goals and
improvements brought about by Taguchi methods, particularly by Taguchi's development of designs for studying
variation.
2. State-of-art
There is a broad consensus in academia and industry that reducing variation is an important area in quality
improvement (Shoemaker et al. 1991, Thornton et al. 1999, Gremyr et al. 2003 and Taguchi et al. 2005). "The
enemy of mass production is variability. Success in reducing it will invariably simplify processes, reduce scrap,
and lower costs” (Box and Bisgaard 1988). Definition of quality loss as “the amount of functional variation of
products plus all possible negative effects, such as environmental damages and operational costs” supports this
view (Taguchi’s 1993). In the 1980s Genichi Taguchi (1985; 1986; 1993) received international attention for his
ideas on variation reduction, starting with the translation of his work published in Taguchi and Wu (1979).
The main objective in the Taguchi method is to design robust systems that are reliable under uncontrollable
conditions (Taguchi1978, Byrne1987 and Phadke1989). The method aims to adjust the design parameters
(known as the control factors) to their optimal levels, such that the system response is robust – that is, insensitive
to noise factors, which are hard or impossible to control (Phadke1989).
Some very informative studies were found that were conducted using the Taguchi Parameter Design method for
the purpose of optimizing turning parameters (Vernon et al.2003, Davim2001, 2003, Lin 2004, Manna et al.
2004, Yih-Fong2006). These studies made use of various work piece materials and controlled parameters to
optimize surface roughness, dimensional accuracy, or tool wear. Each utilized different combinations and levels
of cutting speed, feed rate, depth of cut, cutting time, work piece length, cutting tool material, cutting tool
geometry, coolant, and other machining parameters. These studies all discovered clear and useful correlations
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