CAREER: Multilevel Self-Improving Variation Modeling and Diagnosis for Complex Manufacturing Processes

该学院早期职业发展（Career）研究计划提供资金，用于开发、实施和教授复杂制造过程的多层次、自我改进的变异建模和诊断方法。对具有改进功能和上市时间的产品不断增长的需求给生产系统带来了巨大的压力，导致制造过程中不断增长的多层次（即过程级和工位级）复杂性。针对这些复杂性，研究包括几个关键步骤。首先，将开发一种有效的迭代模型构建技术来识别复杂的过程级变化流。利用过程级模型，可以将传播变化和站级局部变化分离开来。然后，从数据中逐渐学习到局部变异源导致的质量数据的时空格局，并进行积累，形成一个自完善的特征库。最后，利用该模型进行了变型源诊断和工艺设计评价。除了研究之外，该项目还包括大量的教育组成部分，包括课程和实验室开发，学生咨询，来自代表性不足群体的学生的参与，以及各种外展活动，包括行业参与，高中参与和国际合作。如果取得成功，本研究成果将通过提供过程和站级复杂性的整体建模，有效的诊断能力和对各种过程的通用适用性，填补复杂过程控制方面的研究空白，从而大大提高美国工业的整体竞争力。综合教育活动将有助于制造业劳动力的培训。除了制造之外，项目的成功还将为具有复杂信息流的系统提供通用的建模和分析工具。广泛传播已开发的方法可能导致扩散到对国家经济增长和安全至关重要的其他领域。

This Faculty Early Career Development (CAREER) research proposes to provide funding to develop, implement, and teach a multilevel, self-improving variation modeling and diagnosis methodology for complex manufacturing processes. The growing demand for products with improved functionality and time to market puts an enormous strain on production systems, resulting in ever-growing multilevel (i.e., both process level and station level) complexity in manufacturing processes. Targeting on these complexities, the research consists of several key steps. First, an efficient iterative model-building technique will be developed to identify the complex process-level variation flow. With the process-level model, the propagated variation and station-level local variation can be separated. Then, the spatial and temporal patterns of the quality data due to local variation sources will be gradually learned from the data and accumulated to form a self-improving signature library. Finally, the variation source diagnosis and process design evaluation are achieved based on this model. In addition to research, this project includes a substantial education component that includes curriculum and lab development, student advising, involvement of students from underrepresented groups, and various outreach activities including industry participation, high school involvement, and international collaboration. If successful, the results of this research will fill the research gap in the control of complex processes by providing holistic modeling of process- and station- level complexities, effective diagnostic capability, and generic applicability to various processes, and thus provide a substantial boost to the overall competitiveness of US industries. The integrated education activities will contribute to manufacturing workforce training. Beyond manufacturing, the success of the project will also provide generic modeling and analysis tools for systems with complex flows of information. Broad dissemination of the developed methodologies could lead to diffusion to other fields vital to the nation's economic growth and security.