Determining the Optimum Manufacturing Target using the Inverted Normal Loss Function

Abstract

Spiring and Yeung (1998) introduced the concept of inverting a normal probability density function to provide a more realistic loss function. Numerous loss functions have been proposed that use various distributions to depict loss. in this research, the concept of the inverted normal loss function is furthered to accurately model losses in a product engineering context. Expected loss can be computed by numerical integration, the integral of the product of the loss function and the probability density function. If the actual process parameter distribution and a realistic loss function are given, expected loss can be determined numerically. a case study involving a shaft bearing for a microcontroller product is given to illustrate the inverted loss function. Two experiments were performed to determine the process variables having the strongest effect on the product's yield and the ideal process target and the specification limits. © 2011 Inderscience Enterprises Ltd.

Department(s)

Engineering Management and Systems Engineering

Second Department

Mathematics and Statistics

Keywords and Phrases

expected loss; IBLF; inverted beta loss function; loss function; multivariate; quality characteristic; univariate

International Standard Serial Number (ISSN)

1757-2185; 1757-2177

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Inderscience, All rights reserved.

Publication Date

01 Jan 2011

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