Forecasting manufacturing variation using historical process capability data : applications for random assembly, selective assembly, and serial processing

In today's competitive marketplace, companies are under increased pressure to produce products that have a low cost and high quality. Product cost and quality are influenced by many factors. One factor that strongly influences both is manufacturing variation. Manufacturing variation is the range of values that a product's dimensions assume. Variation exists because no production process is perfect. Often times, controlling this variation is attempted during production when substantial effort and resources, e.g., time, money, and manpower, are required. The effort and resources could be reduced if the manufacturing variation could be forecast and managed during the design of the product. Traditionally, several barriers have been present that make forecasting and managing variation during the design process very challenging. The first barrier is the effort required of a design engineer to know the company's process capability, which makes it difficult to specify tolerances that can be manufactured reliably. The second barrier is the difficulty associated with understanding how a single manufacturing process or series of processes affects the variation of a product. This barrier impedes the analysis of tradeoffs among processes, the quantifying of the impact incoming stock variation has on final product variation, and the identification of sources of variation within the production system. The third barrier is understanding how selective assembly influences the final variation of a product, which results in selective assembly not being utilized efficiently. In this thesis, tools and methods to overcome the aforementioned barriers are presented. A process capability database is developed to connect engineers to manufacturing data to assist with

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Kern, Daniel C. (Daniel Clifton), 1974-

Advisor dc:contributor.advisor

• Anna Thornton.

• 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/1721.1/29960

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/29960