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Degree

Doctor of Philosophy, Ohio State University, Industrial and Systems Engineering, 2002.

Abstract

Reactive in-mold coating (IMC) products have been used successfully for many years to improve the surface quality of Sheet Molding Compound (SMC) compression molded parts. IMC provides a smooth, sealed surface, used as a conductive or nonconductive primer for subsequent painting operations. The success of IMC for SMC parts has recently attracted the interest of thermoplastic injection molders. The potential environmental and economic benefits of using IMC as a primer and, in the ideal case, to replace painting completely are large. Acceptance of IMC as a competitor to the traditional painting processes will depend upon the improvement of its ability to deliver in-mold coated parts in short cycle times at the highest possible quality level. Most optimization studies in Reactive Polymer Processing involve compromising between different performance measures since, frequently, the controllable variables have conflicting effects on these measures. IMC is not the exception to the rule. The performance measures need to be balanced, each against the other, in order to obtain the best compromises. The goal of this research work is to develop an optimization strategy for the application of reactive in-mold coating to SMC and thermoplastic parts in presence of multiple and conflicting performance measures. To achieve this goal we explore the use of Artificial Neural Networks as metamodeling techniques and the use of Data Envelopment Analysis to solve multiple criteria optimization problems.

Keywords

In-Mold Coating; Multiple Criteria Optimization; Artificial Neural Networks; Data Envelopment Analysis; Reactive Polymer Processing

Advisor

Jose M Castro

Pages

210p.

Document number: osu1022105843
Permalink: http://rave.ohiolink.edu/etdc/view?acc_num=osu1022105843

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