Quality Improvement by Using Inverse Gaussian Model in Robust Engineering

Quality Improvement by Using Inverse Gaussian Model in Robust Engineering The concept of robust engineering (RE) which is based on the philosophy of Genichi Taguchi aims at providing industries with a cost effective methodology for enhancing their comptetive position in the global market. Since in most cases it is not possible to model the mathematical relationship between quality characteristic (QC), parameter designs and noise factors of situation under study, a proper statistical model in design of experiments (DOE) is proposed. However, the used statistical procedures in DOE are based on normality assumption of real data of QC or its transformed distribution. In many engineering cases, the data is highly skewed and therefore cannot be always removed by usual transformations; and even if it will be removed to a great extend, it may lead to inaccurate inferences in model parameters. Alternatively, the Inverse Gaussian family of distributions is flexible enough to provide a suitable model for these types of data. In this study, in dealing with such type of data, the concept of RE method is combined with Inverse-Gaussian (IG) model to reduce total deviations from target values of 17 quality characteristics in oil pump housings produced by Iranian diesel engine manufacturing (IDEM) company. As the distridution of data obtained from RE methodology follows the IG, the analysis without any data transformation (uncontrary in traditional RE procedure) is done straight forward through an IG model, and then its analysis is compared with customary analysis of RE method. This paper consists of four sections. The first section provides a brief description of problem. Section two gives a brief introduction to RE methodology. Section three devoted to introducing the proposed DOE model which is base upon inverse-Gaussian distribution. In section four, application of the two approaches to improve quality of produced oil pump housings in IDEM are considered and their relative results are obtained. And finally, in section five, the analysis results of application of the two models are compared. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Quality Improvement by Using Inverse Gaussian Model in Robust Engineering

Loading next page...
Kluwer Academic Publishers
Copyright © 2006 by Springer
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
Publisher site
See Article on Publisher Site


You’re reading a free preview. Subscribe to read the entire article.

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches


Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.



billed annually
Start Free Trial

14-day Free Trial