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Quantitative selection of inspection plans for variation risk management

IP.com Disclosure Number: IPCOM000128098D
Original Publication Date: 1999-Dec-31
Included in the Prior Art Database: 2005-Sep-14
Document File: 6 page(s) / 20K

Publishing Venue

Software Patent Institute

Related People

Chen, Tony J: AUTHOR [+3]

Related Documents

http://theses.mit.edu:80/Dienst/UI/2.0/Describe/0018.mit.theses/1999-142: URL

Abstract

Over the last decade, the importance of quality has increased significantly. Quality improvement efforts involve mitigating the impact of manufacturing variation through robust design, statistical process control (SPC), and inspection. This thesis focuses on the last: how to choose an inspection plan to remove the most variation at the lowest cost. The optimal inspection plan balances the cost of inspection and rework against the cost of increased quality. This thesis describes an empirical analysis and prototype software that employs Monte Carlo simulation and simulated annealing to identify the optimal inspection plan. This thesis demonstrates the functionality of the theory and the software through an example from the aircraft industry. Thesis Supervisor: Anna Thornton Title: Assistant Professor of Mechanical Engineering

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 This record is the front matter from a document that appears on a server at MIT and is used through permission from MIT. See http://theses.mit.edu:80/Dienst/UI/2.0/Describe/0018.mit.theses/1999-142 for copyright details and for the full document in image form.

Quantitative Selection of Inspection Plans for Variation Risk Management

by

Tony J. Chen
B.S. Mechanical Engineering University of California at Berkeley, 1997 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in MECHANICAL ENGINEERING

at the Massachusetts Institute of Technology

JUNE 1999
SIGNATURE OF author: [[signature omitted]]

Department of Mechanical Engineering

May 7, 1999
CERTIFIED BY: [[SIGNATURE OMITTED]]

Anna Thomton Assistant Professor of Mechanical Engineering Thesis Supervisor ACCEPTED BY: [[SIGNATURE OMITTED]]

Ain A. Sonin Professor of Mechanical Engineering Chairman, Committee for Graduate Students ARCHIVES MASSACHUSETTS INSTITUTE OF TECHNOLOGY LIBRARIES JUL 12 1999

Massachusetts Institute of Technology Page 1 Dec 31, 1999

Page 2 of 6

Quantitative selection of inspection plans for variation risk management

Quantitative Selection of Inspection Plans for Variation Risk Management

By Tony J. Chen

Submitted to the Department of Mechanical Engineering on May 7, 1999, in Partial Fulfillment of the Requirements for the Degree of Master of Science in Mechanical Engineering

ABSTRACT

Over the last decade, the importance of quality has increased significantly. Quality improvement efforts involve mitigating the impact of manufacturing variation through robust design, statistical process control (SPC), and inspection. This thesis focuses on the last: how to choose an inspection plan to remove the most variation at the lowest cost. The optimal inspection plan balances the cost of inspection and rework against the cost of increased quality. This thesis describes an empirical analysis and prototype software that employs Monte Carlo simulation and simulated annealing to identify the optimal inspection plan. This thesis demonstrates the functionality of the theory and the software through an example from the aircraft industry.

Thesis Supervisor: Anna Thornton Title: Assistant Professor of Mechanical Engineering

ACKNOWLEDGEMENT

For me, this research has been a rich and rewarding experience. I owe a debt of gratitude to all those who have given me guidance and support.

I would like to thank Professor Anna Thomton for being such an inspiring research advisor. Her zeal for excellence has been a major driving force behind this thesis.

Thanks to the Center for Innovation in Product Development for funding this research. I wc,3!d also like to recognize Chuck Hura and Sam DeLuca from industry for being the sponsors and mentors of my summer internship. Everyone else at the company had also enriched my experience in some way.

Special thanks to my family and all my friends for their support and love throughout the years. Thanks to Derrick and Michae...