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Forecasting Manufacturing Quality and Optimizing Product Robustness Using Process Capability Data

[+] Author Affiliations
Daniel Kern

Massachusetts Institute of Technology, Cambridge, MA

Xiaoping Du

University of Missouri at Rolla, Rolla, MO

Agus Sudjianto

Ford Motor Company, Dearborn, MI

Paper No. IMECE2003-42159, pp. 351-359; 9 pages
doi:10.1115/IMECE2003-42159
From:
  • ASME 2003 International Mechanical Engineering Congress and Exposition
  • Manufacturing
  • Washington, DC, USA, November 15–21, 2003
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 0-7918-3720-3 | eISBN: 0-7918-4663-6, 0-7918-4664-4, 0-7918-4665-2
  • Copyright © 2003 by ASME

abstract

A company’s success is highly dependent on its ability to manufacture quality products. Designing products that can be manufactured to meet customer needs with an acceptable level of variation is challenging because design engineers are often unfamiliar with the company’s manufacturing capability or are unable to effectively use the capability data to improve a design. The authors present an approach to forecast the manufacturing quality of a product and optimize its robustness while it is being designed. The system comprises a database that stores process capability data and simulation models to simulate process capability data when actual, appropriate data are nonexistent. These data and tools are used with a new probabilistic approach through the inverse reliability strategy to optimize the robustness of a design by locating values of design parameters that enhance the performance of the design and are insensitive to manufacturing variation. Design engineers can use this approach to set design parameter values that will improve the functionality of the product while ensuring it can be produced with high capability. This approach is demonstrated with a design example of an engine valvetrain.

Copyright © 2003 by ASME

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