Optimization Techniques in Response Surface Methodology for Quality Improvement

用于质量改进的响应面方法中的优化技术

• 批准号: 9988563
• 负责人: Enrique Del Castillo
• 金额: $ 15万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9988563&HistoricalAwards=false
• 关键词: Optimization; Techniques; Response; Surface; Methodology

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.

本研究的目的是为响应面法（RSM）开发新的高效优化技术。RSM是一套统计和优化技术，旨在通过设计实验和模型构建技术的顺序应用来提高制造过程的质量特征。本研究的具体目标包括：1)在存在大样本变异的情况下，开发新的统计搜索方法；2)针对RSM研究中经常出现的二次规划问题（包括多个二次响应的情况）的全局优化问题，开发了新的算法。方法将研究为使用多项式回归技术建模的制造过程的最佳操作设置找到置信区域。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。这项研究的结果将是一套用于工业实验的新的优化技术，这将受到目前使用现有RSM优化技术的过程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征来实现，即，它的顺序性质，高采样可变性的存在，多重响应的存在，以及在昂贵过程中快速优化的需要。后者将对资本密集型制造商感兴趣，在这些制造商中，合格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SmithKline Beecham）以及宾夕法尼亚州立大学的纳米制造设施合作，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。将开发易于使用的软件，以促进技术转让。