Modeling and Inferring of Multichannel Sensing Data in Complex Manufacturing Procsses

该奖项的目标是开发用于建模和从多通道非线性传感信号推断的通用方法，以支持复杂制造过程中的质量改进。具体而言，将开发一种新的基于层次正则化的变量选择方法，用于提取产品质量与海量过程感知信号之间关系的信息感知信号和信号特征。过程变化将通过非参数混合效应模型进行分析，以考虑异构传感信号之间潜在的相互关联以及每个传感信号中不可避免的剖面之间的变化。经过验证后，这些模型将被纳入新的过程监测和诊断方法的开发。这将允许在复杂的制造过程中减少变异和提高质量。如果成功，本研究结果将为提高在线监测和诊断系统的先进能力提供有效手段，该系统具有自动分析和评估大量在线传感信号的能力，以改善制造过程操作。在制造业中实施这些方法有望显著降低成本，从而提高产品质量并减少生产停机时间。因此，这项研究有可能通过提高美国制造业的竞争力来产生广泛的经济影响。此外，通过出版物和工业合作进行多学科学术培训、推广和广泛传播，将导致开发的方法广泛应用于许多其他传感器数据融合应用，这对国家的经济增长至关重要。

The objective of this award is to develop general methodologies for modeling and making inferences from multichannel nonlinear sensing signals, to support quality improvement in complex manufacturing processes. Specifically, a new variable selection method using hierarchical regularization will be developed to extract informative sensing signals and signal features in modeling of the relationship between product quality and massive process sensing signals. Process variations will be analyzed by nonparametric mixed-effect models to consider the underlying cross-correlation among heterogeneous sensing signals and the inevitable between-profile variations within each sensing signal. When validated, the models will be incorporated in the development of new process monitoring and diagnosis methods. This will permit variation reduction and quality improvement in complex manufacturing processes. If successful, the results of this research will provide an effective means to enhance an online monitoring and diagnosis system with the advanced capability of automatically analyzing and assessing massive online sensing signals to improve manufacturing process operations. Implementation of the methodologies in manufacturing is expected to provide significant cost reduction as an outcome of improving product quality and reducing production downtime. Consequently the research has the potential to generate broad economic impacts by improving the competitiveness of the U.S. manufacturing industry. Moreover, multidisciplinary academic training, outreach, and broad dissemination through publications and industrial collaboration will lead to wide application of the developed methodologies to many other sensor data fusion applications that are of vital importance to the nation's economic growth.