Date of Award

4-2005

Degree Name

Master of Science in Engineering

Department

Industrial and Entrepreneurial Engineering and Engineering Management

Department

Industrial and Manufacturing Engineering

First Advisor

Dr. Steven Butt

Second Advisor

Dr. Paul Engelmann

Third Advisor

Dr. Daniel Mihalko

Access Setting

Masters Thesis-Open Access

Abstract

There have been many advancements that share similar tools and techniques that help reduce the manufacture of nonconformities. These include computer-aided analysis, design reviews, total quality management, multivariate analysis, process monitoring and control, and root cause analysis to mention a few.

This work details the methodology developed for manufacturing companies to predict attribute defects. Injection molding was used to demonstrate the proposed methodology. Data were collected on a variety of tool design and construction attributes thought to affect the performance of a tool. The independent variables consisted of categorical and numerical data types. The dependent variable was a nominal four-tuple describing the types of defects that can coexist on one part.

A series of steps taken to prepare the data set for classification tree analysis can be categorized by the following: 1) variable screening and selection due to missing data and high dimensionality and 2) causal analysis and similarity computations for combining defects, thus reducing the number of classes in the four-tuple. A method was designed for classification tree analysis. The models provided a way for designers and engineers to assess the potential for success prior to production.

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