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

该研究奖的目标是解决过程控制和优化问题，其中系统的兴趣响应是一个轮廓，即兴趣函数的值，而不是在某些给定的实验条件下测量的单个观测值。这种类型的系统的控制需要通过在存在不可控噪声因素的情况下调整可控因素来执行，以这种方式实现对噪声因素变化不敏感或稳健的过程性能。这类用于轮廓响应的稳健参数设计(RPD)问题在制造业和非制造业中都广泛存在。例如，在制造中，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，并且需要在存在不可控的变化源的情况下通过改变机床条件来实现目标几何。这项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，这些技术在回答是概况时有用。宾夕法尼亚州立大学和意大利米兰理工大学的制造实验室将允许测试该项目中开发的方法。这项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的实验室网站上分发。将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，并通过技术文献和皮埃尔？S关于过程优化的书籍进行传播。与行业研究人员(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。

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.