I E 583
Response Surface Methodology and Process Optimization (3) Response Surface Methodologies used for sequential experimentation and optimization of production processes. Statistical design and analysis of such experiments.
I E 583 Response Surface Methodology & Process Optimization (3)
This course considers Response Surface Methodology (RSM), a collection of statistical and optimization techniques aimed at improving the quality characteristics of a manufacturing process through the sequential application of statistically-designed experiments and model-building techniques. Optimization techniques for response surfaces, functions that can exhibit large sample variability, are highlighted. Multiple response optimization problems, which occur frequently in practice, are considered, and their relation to Taguchi's Robust Parameter Design problem is emphasized. The course also includes an introduction to the design, analysis, and optimization of mixture problems, which occur frequently in food manufacturing, metallurgy, and semiconductor manufacturing. The practical aspects of RSM are considered through a final project in which the students optimize a (simulated) manufacturing process. For this purpose, a Web-based process simulator has been designed. The Software packages Design Expert, SAS, and Minitab will be used by the students in the class. MATLAB and MAPLE programs will support some of the topics in the class. Recent papers from the technical literature will be covered. The prerequisites of this course are either I E 511, which is an introductory course in Design of Experiments, or STAT 501, an introductory course to linear regression.
General Education: None
Bachelor of Arts: None
Effective: Spring 2002
Prerequisite: I E 511 orSTAT 501
Note : Class size, frequency of offering, and evaluation methods will vary by location and instructor. For these details check the specific course syllabus.