Professor Judy Jin receives research funding from NSF
Title: Modeling and Inferring of Multichannel Sensing Data in Complex Manufacturing Processes
The objective of this project 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. 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 variation reduction and cost reduction. Moreover, broad industrial collaborations 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.
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