Statistical Tolerancing in Design for Six Sigma (Digital Short Cut)

Nonfiction, Science & Nature, Technology, Quality Control, Engineering, Mechanical
Cover of the book Statistical Tolerancing in Design for Six Sigma (Digital Short Cut) by Randy C. Perry, David W. Bacon, Pearson Education
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Randy C. Perry, David W. Bacon ISBN: 9780132712903
Publisher: Pearson Education Publication: August 28, 2006
Imprint: Prentice Hall Language: English
Author: Randy C. Perry, David W. Bacon
ISBN: 9780132712903
Publisher: Pearson Education
Publication: August 28, 2006
Imprint: Prentice Hall
Language: English

This is the eBook version of the printed book.

Development of a new product requires the product development team to address many complex customer requirements during the commercialization process. Consider a situation in which a new product being developed must meet specified upper and lower specification limits based on Voice of the Customer interviews. The design team must model and understand the sources of potential variation in the new product that need to be monitored and controlled if the product is to meet the identified customer needs. The process of analyzing component variation and designing a final product that meets customer tolerance requirements is known as statistical tolerancing.

 

In this Short Cut, various Design for Six Sigma techniques for determining the impact of multiple sources of variation on a final product are examined in detail. A procedure is described for using representative models for individual product components to estimate the expected overall level of variation in the performance of a final product.

 

Three methods of tolerance analysis are presented and the merits of each are discussed: Worst Case Analysis, Root Sum of Squares Analysis, and Six Sigma Tolerance Analysis. A detailed case study example, involving multiple sources of variation, is employed to illustrate the application of these methods. Minitab® is used to identify the best-fitting distributions from data sets for individual components. Monte Carlo Simulation with Crystal Ball® is then employed to determine the most important individual sources of variation and the overall variation of the final product. Finally, Crystal Ball's OptQuest® optimization feature is utilized to determine the required design value for each key parameter to meet final customer requirements.

 

Contents

What This Short Cut Covers

Introduction

Worst Case Analysis

Root Sum of Squares Analysis

Six Sigma Tolerance Analysis

What's in the Book Commercializing Great Products with Design for Six Sigma

About the Authors

Related Publications

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This is the eBook version of the printed book.

Development of a new product requires the product development team to address many complex customer requirements during the commercialization process. Consider a situation in which a new product being developed must meet specified upper and lower specification limits based on Voice of the Customer interviews. The design team must model and understand the sources of potential variation in the new product that need to be monitored and controlled if the product is to meet the identified customer needs. The process of analyzing component variation and designing a final product that meets customer tolerance requirements is known as statistical tolerancing.

 

In this Short Cut, various Design for Six Sigma techniques for determining the impact of multiple sources of variation on a final product are examined in detail. A procedure is described for using representative models for individual product components to estimate the expected overall level of variation in the performance of a final product.

 

Three methods of tolerance analysis are presented and the merits of each are discussed: Worst Case Analysis, Root Sum of Squares Analysis, and Six Sigma Tolerance Analysis. A detailed case study example, involving multiple sources of variation, is employed to illustrate the application of these methods. Minitab® is used to identify the best-fitting distributions from data sets for individual components. Monte Carlo Simulation with Crystal Ball® is then employed to determine the most important individual sources of variation and the overall variation of the final product. Finally, Crystal Ball's OptQuest® optimization feature is utilized to determine the required design value for each key parameter to meet final customer requirements.

 

Contents

What This Short Cut Covers

Introduction

Worst Case Analysis

Root Sum of Squares Analysis

Six Sigma Tolerance Analysis

What's in the Book Commercializing Great Products with Design for Six Sigma

About the Authors

Related Publications

More books from Pearson Education

Cover of the book The Mentoring Manual by Randy C. Perry, David W. Bacon
Cover of the book Easy Computer Basics, Windows Vista Edition by Randy C. Perry, David W. Bacon
Cover of the book CCENT ICND1 100-105 Exam Cram by Randy C. Perry, David W. Bacon
Cover of the book Mastering Project, Program, and Portfolio Management by Randy C. Perry, David W. Bacon
Cover of the book Changeability by Randy C. Perry, David W. Bacon
Cover of the book CompTIA Network+ N10-007 Exam Cram by Randy C. Perry, David W. Bacon
Cover of the book Content Marketing by Randy C. Perry, David W. Bacon
Cover of the book Easy OS X Mavericks by Randy C. Perry, David W. Bacon
Cover of the book Photoshop Elements 8 for Mac OS X by Randy C. Perry, David W. Bacon
Cover of the book Microsoft Office Home and Student 2013 Step by Step by Randy C. Perry, David W. Bacon
Cover of the book Modern VLSI Design by Randy C. Perry, David W. Bacon
Cover of the book Learning Android Game Programming by Randy C. Perry, David W. Bacon
Cover of the book Code Complete by Randy C. Perry, David W. Bacon
Cover of the book CompTIA Security+ SY0-401 Cert Guide, Deluxe Edition by Randy C. Perry, David W. Bacon
Cover of the book Epigenetics in Health and Disease by Randy C. Perry, David W. Bacon
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy