Comparative analysis of robust design methods

Robust parameter design is an engineering methodology intended as a cost effective approach to improve the quality of products, processes and systems. Control factors are those system parameters that can be easily controlled and manipulated. Noise factors are those system parameters that are difficult and/or costly to control and are presumed uncontrollable. Robust parameter design involves choosing optimal levels of the controllable factors in order to obtain a target or optimal response with minimal variation. Noise factors bring variability into the system, thus affecting the response. The aim is to properly choose the levels of control factors so that the process is robust or insensitive to the variation caused by noise factors. Robust parameter design methods are used to make systems more reliable and robust to incoming variations in environmental effects, manufacturing processes and customer usage patterns. However, robust design can become expensive, time consuming, and/or resource intensive. Thus research that makes robust design less resource intensive and requires less number of experimental runs is of great value. Robust design methodology can be expressed as multi-response optimization problem.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Singh, Jagmeet, 1980-

Advisor dc:contributor.advisor

• Daniel D. Frey.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/35630

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/35630