Optimization Techniques in Response Surface Methodology for Quality Improvement

9988563Del Castillo本研究的目的是开发新的和有效的优化技术，用于响应面法（RSM）。响应面法是一套统计和优化技术，旨在通过连续应用设计的实验和模型构建技术来改善制造过程的质量特性。本研究的具体目标包括：1）在存在大的抽样变异性的情况下开发新的统计搜索方法; 2）开发新的算法，用于在响应面研究中经常出现的二次规划问题的全局优化，包括多个次级响应的情况。方法将被研究用于找到一个置信区域的最佳操作设置的制造过程，使用多项式回归技术建模。最后，3）将开发一种快速响应面方法，该方法将允许快速优化多个响应过程。这项研究的成果将是一套新的工业实验优化技术，将受到目前使用现有RSM优化技术的工艺和质量工程师的欢迎。这将通过考虑工业实践中实验优化的特殊特性来实现，即其顺序性，高采样可变性的存在，多个响应的存在，以及在昂贵的过程中快速优化的需要。后者将是资本密集型制造商感兴趣的，在这些制造商中，鉴定或优化工艺所需的实验数量应保持在最低限度。与工业研究人员（朗讯科技和史克必成）和宾夕法尼亚州立大学的纳米纤维设施的合作将为该项目中开发的技术提供一个测试平台。宾夕法尼亚州立大学工业工程系的新家莱昂哈德大楼的制造实验室将允许在更传统的制造工艺中进行测试。将开发便于使用的软件，以促进技术转让。

9988563Del CastilloThe 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.