Collaborative Research: Process Monitoring and Control in Autocorrelated Multistage Manufacturing Processes

随着现代制造业变得越来越复杂，通常会发现涉及多个生产阶段的生产过程，例如制药，化学工业以及半导体和汽车制造中的生产过程。在多阶段制造过程中，数据流中的三种类型的相关性（阶段之间，质量特性之间，以及随着时间的推移）在变化建模，分析和控制中引入了重大挑战。 该项目旨在开发有效的方法，用于自相关多阶段过程的监测、控制和优化，以提高过程输出的质量。这是一个具有挑战性的问题，由于多阶段过程的复杂性和观测的自相关性，使得输出和输入变量之间的关系非常复杂。这将通过新的模型的传播的变量的手段和方差到后续阶段进行调查，使用动态状态空间模型，使识别的变化源传播和监测/诊断的过程。 该方法将与行业合作伙伴合作开发和验证。该研究有助于建立方法和算法的科学基础，以最大限度地减少自相关多级制造过程中的变化传播并快速检测变化点。该项目的成功完成将为检测多阶段过程的异常行为提供在线监测和诊断方法。研究结果将广泛适用于各种行业，以提高生产系统的整体质量和生产率。 更广泛的影响也将产生通过新的课程模块，在线软件工具包的实施，并涉及代表性不足的本科生和研究生的研究经验计划，以提高美国的人力资源人才产业。

As modern manufacturing industries become more sophisticated, it is common to find a production process involving multiple stages of production such as those found in pharmaceutical manufacturing, the chemical industry and in semiconductor and auto manufacturing. Three types of correlations in the data streams (among stages, among quality characteristics, and over time) in a multistage manufacturing process introduce significant challenges in variation modeling, analysis, and control. This project aims at developing efficient methodologies for the monitoring, control and optimization of autocorrelated multistage processes in order to improve the quality of the process output. This is a challenging problem due to the complexity of multistage processes and autocorrelations of observations that make the relationship between the output and input variables extremely complicated. This will be investigated through novel models of the propagation of variable means and variances to subsequent stages, using dynamic state space models that enable the identification of the variation source propagation and monitoring/diagnosis of the processes. The methodologies will be developed and validated in collaboration with industry partners.The research contributes to the science base of methods and algorithms to minimize the propagation of variations and quickly detect change points in autocorrelated multistage manufacturing processes. Successful completion of this project will provide online monitoring and diagnosis methods for detecting abnormal behaviors of multistage processes. The results will be broadly applicable in a variety of industries to improve the overall quality and productivity of production systems. Broader impacts will be also generated through new curriculum modules, online software toolkits for implementation, and involving underrepresented undergraduate and graduate students in research experience programs to enhance the human resource talent for U.S. industry.