GOALI: Going Beyond Design of Experiments (DOE) - Process Improvement by Joint Adjustment and Monitoring

在许多情况下，过程控制面临的挑战是，如果不理会某个过程，它往往会偏离目标，从而导致质量受到影响。在过程输入和输出不断变化的工业环境中尤其如此。传统的过程控制集中于检测数据中的重要模式所暗示的特殊原因，这些模式表明存在意外的变化或信号。然而，在许多情况下，某些过程信号是预期的，因为它们是系统或操作的特征。该项目中使用的方法是使用实​​验设计 (DOE) 导航到良好的操作区域来评估一组变量，然后将控制工作集中在关键变量上。 过程传递函数将用于优化这些变量的控制调整，并使用 Cuscore 统计数据通过利用预期信号的结构来有效地监控过程。 对于过程调整和监控，将研究以下问题： (1) 对于常见过程特征，适当的控制器和 Cuscore 统计量是什么？ (2) 新的监测算法在检测信号方面效果如何？ (3) 控制器和监视器对其关键参数的近似值（或偏差）的鲁棒性如何？ 这项研究的贡献将包括经验和理论的发展，以通过更好的过程理解、监测和控制来提高产出。特别是，该研究有望提供一种连接实验设计和工艺改进知识的方法论，提供全套调整和Cuscore监控算法及其性能的统计评估，生成改进日常过程操作和控制的工具和图表算法，并展示联合调整和监控在实际工业应用中的可行性。这项工作包括与 Springs Window Fashions Division, Inc.（威斯康星州米德尔顿）的合作。 Springs 是一家领先的家用窗饰制造商，其加工业务将作为实施结果的试验台。它还包括研究生研究助理在该项目和公司工作的机会。

The challenge for process control in many settings is that if a process is left alone it will tend to drift and wander away from target and as a result, quality will suffer. This is especially true in an industrial setting where both process inputs and outputs continuously change. Traditional process control has centered on detecting special causes that are suggested by significant patterns in the data which point to the existence of unexpected changes or signals. Many situations occur, however, where certain process signals are anticipated because they are characteristic of a system or operation. The approach to be used in this prjoect is to navigate to a good operating region using design of experiments (DOE) to evaluate a set of variables, then focus the control effort on the critical variables. Process transfer functions will be used to optimize the control adjustment of these variables and use Cuscore statistics to efficiently monitor the process by capitalizing on the structure of the anticipated signal. For process adjustment and monitoring, the following questions will be investigated: (1) What are the appropriate controllers and Cuscore statistics for common process characterizations? (2) How well will the new monitoring algorithms work in detecting signals? (3) How robust are the controllers and monitors to approximations of (or deviations from) their key parameters? The contributions of this research will include both empirical and theoretical developments to deliver improved output through better process understanding, monitoring, and control. In particular, the research is expected to provide a methodology for connecting the knowledge on experimental design and process improvement, provide a full set of adjustment and Cuscore monitoring algorithms and statistical evaluation of their properties, generate tools and charting algorithms to improve daily process operation and control, and demonstrate the feasibility of joint adjustment and monitoring for real industry applications. This work includes a partnership with Springs Window Fashions Division, Inc. (Middleton, WI). Springs is a leading manufacturer of home window treatments and their processing operations will serve as a test bed for implementation of the results. It also includes an opportunity for a graduate research assistant to work on the project and with the company.