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.

9988563Del Castillo 这项研究的目标是开发新的、高效的优化技术，用于响应面方法 (RSM)。 RSM 是一套统计和优化技术，旨在通过顺序应用设计的实验和模型构建技术来提高制造过程的质量特性。这项研究的具体目标包括1）在存在大样本变异性的情况下开发新的统计搜索方法； 2) 开发新算法，用于 RSM 研究中经常出现的二次规划问题类型的全局优化，包括多个二次响应的情况。将研究用于寻找使用多项式回归技术建模的制造过程的最佳操作设置的置信区域的方法。最后，3）将开发快速响应表面方法，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用于工业实验的新优化技术，该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征来实现，即其顺序性质、高采样变异性的存在、多重响应的存在以及昂贵过程中快速优化的需要。后者将引起资本密集型制造商的兴趣，因为这些制造商应将鉴定或优化工艺所需的实验数量保持在最低限度。与工业研究人员（朗讯科技和史克比彻姆）以及宾夕法尼亚州立大学纳米制造设施的合作将为该项目开发的技术提供一个测试平台。 Leonhard 大楼是宾夕法尼亚州立大学 IE 系的新所在地，其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。