Modeling and Inferring of Multichannel Sensing Data in Complex Manufacturing Procsses

该奖项的目的是开发通用方法，用于从多通道非线性传感信号建模和推断，以支持复杂制造过程的质量改进。具体而言，一种新的变量选择方法，使用分层正则化将开发提取信息的传感信号和信号特征之间的关系建模的产品质量和大量的过程传感信号。将通过非参数混合效应模型分析工艺变化，以考虑异质感测信号之间的潜在互相关以及每个感测信号内不可避免的轮廓间变化。在验证后，这些模型将被纳入新的过程监测和诊断方法的开发中。这将允许在复杂的制造过程中减少变化和提高质量。如果成功，本研究的结果将提供一种有效的手段，以提高在线监测和诊断系统的自动分析和评估大量的在线传感信号，以改善制造过程操作的先进能力。在制造中实施这些方法，预计将大大降低成本，因为这将提高产品质量并减少生产停机时间。因此，该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，多学科的学术培训，推广和广泛传播，通过出版物和工业合作，将导致广泛应用的开发方法，许多其他传感器数据融合应用，是至关重要的国家的经济增长。

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.