Modeling, Analysis, and Control of Variation Propagation in Manufacturing Processes

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

This grant provides funding for the development of a methodology for modeling, analysis, and control of variation propagation in complicated manufacturing processes. Process variation and inconsistency are the major quality concerns in a manufacturing process. For a complicated manufacturing process involving multiple operation steps, the process variation at different steps will be accumulated on the product and propagates along the process. This project aims at developing a systematic methodology to describe and reduce the process variation and hence improve the process quality. A quantitative variation propagation model will be developed. Both analytical and empirical methods based on product/process design and engineering knowledge are used to link the key process variation sources and key product quality characteristics in this model. This quantitative model allows system theory and advanced statistical techniques (e.g., variance component analysis of linear mixed models) to be adopted in order to conduct forward and backward analysis of variation propagation. The forward analysis can identify important process stages and provide guidelines for design improvement, while the backward analysis can quickly identify the root causes of quality variations. The research results will be finally validated in industrial settings. If successful, this research project will contribute to the science base of process control and quality improvement for manufacturing processes. Using the developed methodology, vast amounts of information from product design, process design, in-process sensing, and product quality inspection will be integrated under a quantitative model. This integrated model lays a foundation to develop effective techniques of variation propagation analysis and quick variation root cause identification. Effective implementation of the developed methodology in industry will provide a set of powerful tools for computer-aided product/process design and process monitoring and diagnosis for variation reduction, and thus provide a competitive boost to U.S. industry. The research accomplishments will be transferred into undergraduate and graduate curricula as well, which will generate long-term impact on the education of quality engineering and manufacturing.