CAREER: In-process Quality Improvement Methodologies and Implementation in Manufacturing

9624402 SHI大多数现有的质量控制技术都是离线的，即它们依赖于缺陷的发生，这些缺陷被移除或返工以提高出厂质量水平。在制造过程中提高质量包括强调缺陷预防而不是消除缺陷，以最小的方差而不是在容差内的目标生产，以及持续改进而不是可接受的质量。为了实现这些目标，本研究开发了多变量、相关过程的统计过程控制算法，然后将其与在线自动过程控制算法集成在一起。其核心思想是开发工程模型并从产品/工艺知识中预测故障模式，应用高级统计学来提取基于过程中传感的工艺特征的特征/指数，从工程模型中识别模式与从统计中获得的指数之间的内在关系，并将该知识用于工艺变化的自动诊断、预测性维护和自动补偿。将汽车车身制造作为研究的应用领域。将开发两个新的课程，这一努力的成果将纳入现有的质量控制课程。制造质量是全球市场竞争的一个重要因素，特别是在汽车行业，这是我们工业基础设施的重要组成部分。设备状态固有的多变性在很大程度上导致了质量和生产率的低下。这项工作为将在线传感器信息与操作控制相结合提供了技术基础，并有可能导致改进的设备维护策略，从而显著提高产品产量和设备可用性。

9624402 Shi Most existing quality control techniques are off-line, i.e., they depend on the occurrence of defects that are removed or reworked to improve the outgoing quality level. To improve quality during the manufacturing process involves emphasizing defect prevention rather than defect removal, on-target production with minimum variance rather than within tolerance, and continuous improvement rather than acceptable quality. To achieve these goals, this research develops statistical process control algorithms for multivariate, correlated processes that are then integrated with on-line automatic process control algorithms. The central ideas are to develop engineering models and anticipate fault patterns from product/process knowledge, to apply advanced statistics to extract features/indices of the process characteristics based on in-process sensoring, to identify the inherent relationship between the patterns from the engineering model and the indices obtained from the statistics, and to use this knowledge in automatic diagnosis, predictive maintenance and automatic compensation of process changes. Automotive body manufacturing will be used as the application area of the research. Two new courses are to be developed, and results from this effort will be incorporated into existing quality control courses. Manufacturing quality is a significant factor in global market competition, particularly in the automotive sector, a vital part of our industrial infrastructure. The variability inherent in equipment status contributes substantially to poor quality and productivity. This work provides the technical basis for integrating on-line sensor information with operational control, and it has the potential to lead to improved equipment maintenance strategies that result in significant improvements in both product yield and equipment availability.