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

本学院早期职业发展（CAREER）研究建议提供资金，以开发，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。对具有改进的功能性和上市时间的产品的日益增长的需求给生产系统带来了巨大的压力，导致不断增长的多级（即，过程级和站级）的复杂性。针对这些复杂性，研究包括几个关键步骤。首先，一个有效的迭代建模技术将被开发，以确定复杂的过程级的变化流。利用过程级模型，可以将传播变异和站级局部变异分离。然后，由于局部变化源而导致的质量数据的空间和时间模式将从数据中逐渐学习并积累以形成自我改进的特征库。最后，基于该模型实现了对工艺设计的偏差源诊断和工艺设计评价。除了研究，该项目还包括一个重要的教育组成部分，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。如果成功的话，这项研究的结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来填补复杂过程控制的研究空白，从而为美国工业的整体竞争力提供实质性的提升。综合教育活动将有助于制造业劳动力培训。除了制造，该项目的成功还将为具有复杂信息流的系统提供通用建模和分析工具。广泛传播所制定的方法可导致传播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.