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

小行星9624402 大多数现有的质量控制技术是离线的，即，它们取决于缺陷的发生，这些缺陷被去除或返工以提高出厂质量水平。 在制造过程中提高质量涉及强调缺陷预防而不是缺陷消除，以最小差异而不是公差范围内的目标生产，以及持续改进而不是可接受的质量。 为了实现这些目标，本研究开发的统计过程控制算法的多变量，相关的过程，然后与在线自动过程控制算法集成。 其核心思想是开发工程模型并从产品/过程知识中预测故障模式，应用先进的统计学基于过程中的传感来提取过程特性的特征/指标，识别来自工程模型的模式和从统计学获得的指标之间的内在关系，并将这些知识用于自动诊断，预测性维护和自动补偿过程变化。 汽车车身制造将被用作研究的应用领域。 将编制两个新的课程，这项工作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。 设备状态固有的可变性在很大程度上导致质量和生产率低下。 这项工作提供了集成在线传感器信息与操作控制的技术基础，它有可能导致改进的设备维护策略，从而显着提高产品产量和设备的可用性。

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.