The objective of this research is to develop new and efficient optimization techniques for use in Response Surface Methodology (RSM). RSM is a set of Statistical and optimization techniques aimed at improving the quality characteristics of a manufacturing process via the sequential application of designed experiments and model building techniques. Specific goals of this research include 1) the development of new statistical search methods under the presence of large sampling variability; 2) development of new algorithms for the global optimization of the type of quadratic programming problems frequently arising in RSM studies, including the case of multiple secondary responses. Methods will be studied for finding a confidence region for the best operational settings of a manufacturing process that is modeled using polynomial regression techniques. Finally, 3) a Rapid Response Surface Methodology will be developed that will allow for fast optimization of multiple response processes.
The outcome of this research will be a new set of optimization techniques for industrial experimentation that will be well-received by Process and Quality Engineers who currently use existing RSM optimization techniques. This will be accomplished by taking into consideration the particular characteristics of experimental optimization in industrial practice, namely, its sequential nature, the existence of high sampling variability, the presence of multiple responses, and the need for rapid optimization in expensive processes. This latter will be of interest to capital-intensive manufacturers where the number of experiments required to qualify or optimize a process should be kept to a minimum. Collaboration with industrial researchers (Lucent Technologies and SmithKline Beecham) and with Penn State's Nanofabrication facility will provide a testbed for the techniques developed in this project. The manufacturing laboratories at the Leonhard building, the new home of the IE department at Penn State, will allow testing in more traditional manufacturing processes. Easy to use software will be developed that will facilitate technology transfer.
|Effective start/end date||8/1/00 → 7/31/04|
- National Science Foundation: $150,000.00