Authors: Elizabeth A. Cudney, David Drain, Naresh K. Sharma
Addresses: Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA. ' Brewer Science Inc., 2401 Brewer Drive, Rolla, MO 65401, USA. ' Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
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.
Keywords: expected loss; univariate; multivariate; inverted beta loss function; IBLF; quality characteristics; optimum manufacturing targets; product engineering; shaft bearings; microcontrollers; process variables; specification limits.
International Journal of Quality Engineering and Technology, 2011 Vol.2 No.2, pp.173 - 184
Published online: 18 Mar 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article