Date of Award

12-2006

Degree Name

Doctor of Philosophy

Department

Industrial and Manufacturing Engineering

First Advisor

Dr. Paul V. Engelmann

Second Advisor

Dr. Steven Butt

Third Advisor

Dr. Abdolazim Houshyar

Fourth Advisor

Dr. Leonard Lamberson

Abstract

Attribute defects cause a majority of the scrap for many manufacturers injection-molding aesthetic parts, yet these defects receive little attention duringproduct development. Methods for preventing attribute defects have been hampered by the inability to quantify complex cause-effect relationships. However, the qualities of a burgeoning branch of knowledge-based systems, called Bayesian networks, have benefited various fields faced with similar circumstances.Bayesian networks have the capability of presenting causal relationships between variables in a graphical structure and quantitatively explaining their effects throughout that structure. These effects can be characterized by probabilities derived from sources, such as equations, historical data, rules-of-thumb or expert knowledge.

Thus, a Bayesian network was selected to model the decisions and uncertainty involved in preventing shear splay in polypropylene products. Shear splay is an attribute defect that occurs when shear generated during processing causes the polymer to degrade and release volatiles, which appear as silvery streaks on apart's surface. The shear splay network consisted of 17 conditional probability tables, 859 probabilities and 37 variables from areas of materials, part and mold design and processing.

A computer-based system was created to elicit 296 subjective probabilities from 25 experts in the field of plastics. A user interface based on past research was designed to interact with experts to efficiently provide relatively unbiased and consistent probabilities. A bootstrap method using trimmed means of their responses was employed to develop a statistic for the probabilities, make inferences about the sample and assess the variability. Interpolation and modified shear rate equations were used to calculate the remaining probabilities.

Both the qualitative and quantitative aspects of the network were evaluated by introducing evidence to validate relationships, comparing data of existingproducts against actual results and allowing several experts to review the model first-hand. Overall, the network performed as expected. It displayed the ability to be used as a decision-support system in preventing shear splay and explained cause-effect relationships that until now have never been quantified. Inaddition, the elicitation software and method for analyzing response data showed potential for becoming a standard in building Bayesian networks.

Comments

5th Advisor: Dr. Daniel Mihalko

Access Setting

Dissertation-Open Access

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