9416666 Okogbaa This research is focused on the development of modeling tools for quality control that combines some aspects of Taguchi approach and statistics. The intended users of the tool are design engineers. The technique is based on parameter design. Parameter design involves and requires deep process knowledge and the ability to carry out detailed statistical experimental design and analysis to identify the critical process parameters. Thus, the research also involves the development of a prototype knowledge base expert system with tandem architecture that consists of a knowledge base and an algorithmic base to optimize the resulting design. With the knowledge-base in place, a user can implement parameter design without engaging in detailed statistical experimental design and the ensuing analyses. The developed knowledge-base system for parameter design can bridge the gap between some of the methodological differences between Taguchi approach and some to the alternative approaches proposed in the literature. The successful implementation of parameter design can lead to a reduction in quality losses. It can enhance the productivity of design engineers who do not have the statistical background and resources to undertake the rigors of comprehensive statistical experimental design. This benefit is particularly crucial in a concurrent engineering environment where fast response time to design is highly desirable.
