On-line Profile-to-Profile Process Adjustment for Robust Parameter Design Scenarios

针对稳健参数设计方案的在线剖面到剖面工艺调整

• 批准号: 0825786
• 负责人: Enrique Del Castillo
• 金额: $ 23万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0825786&HistoricalAwards=false
• 关键词: line; Profile; Process; Adjustment; Robust

The goal of this research award is to solve process control and optimization problems where the response of interest of a system is a profile, that is, values of a function of interest, as opposed to a single observation measured at each of some given experimental conditions. Control of this type of systems needs to be performed by adjusting the controllable factors in the presence of uncontrollable noise factors, achieving in this way a process performance that is insensitive, or robust, to noise factor variation. This class of Robust Parameter Design (RPD) problem for profile responses abounds both in manufacturing and in non-manufacturing. For example, in manufacturing, machining settings result in geometric profiles of parts that are measured at several positions over a plane or space, and a target geometry needs to be achieved by varying the machine tool conditions in the presence of uncontrollable sources of variability. The research will propose, test and implement new statistical techniques useful when responses are profiles, based on the Statistics sub-disciplines of Functional Data Analysis and Statistical Shape Analysis. Manufacturing laboratories at both Penn State and at Politecnico di Milano, Italy, will allow testing the methods developed in this project. The main outcome of this research will be a new set of statistical optimization and control techniques aimed at solving on-line RPD problems for profile responses. To allow technology transfer, software that implements the methods developed will be written and distributed at the PI's lab web site. Research opportunities for a PhD student and for undergraduate students, incorporation of the research results in courses at Penn State, and dissemination via the technical literature and through the PI?s book on Process Optimization will take place. Collaboration with industrial researchers (Intel, GlaxoSmithKline) will provide practical expertise and valuable experiences for participating students.

该研究奖项的目标是解决过程控制和优化问题，其中系统的感兴趣响应是一个剖面，即感兴趣函数的值，而不是在每个给定的实验条件下测量的单个观察值。对这类系统的控制需要在存在不可控噪声因素的情况下通过调整可控因素来实现，以这种方式实现对噪声因素变化不敏感或鲁棒的过程性能。这类轮廓响应的鲁棒参数设计（RPD）问题在制造业和非制造业中都有广泛的应用。例如，在制造业中，加工设置导致在平面或空间上的多个位置测量零件的几何轮廓，并且需要在不可控的可变性源存在的情况下通过改变机床条件来实现目标几何形状。该研究将基于功能数据分析和统计形状分析的统计子学科，提出、测试和实施新的统计技术，当响应是概况时有用。宾夕法尼亚州立大学（Penn State）和意大利米兰理工大学（Politecnico di Milano）的制造实验室将允许测试该项目开发的方法。本研究的主要成果将是一套新的统计优化和控制技术，旨在解决剖面响应的在线RPD问题。为了允许技术转让，将编写实现所开发方法的软件，并在PI的实验室网站上发布。为博士生和本科生提供研究机会，将研究成果纳入宾夕法尼亚州立大学的课程，并通过技术文献和PI进行传播？他的关于过程优化的书将会出版。与工业研究人员（英特尔，葛兰素史克）的合作将为参与的学生提供实用的专业知识和宝贵的经验。