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...
 
/lp/springer_journal/quality-improvement-by-using-inverse-gaussian-model-in-robust-qLv2O6jFLK
Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2006 by Springer
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-005-8082-7
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Quality & QuantitySpringer Journals

Published: May 30, 2005

References

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

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off