Multisensor data fusion can enable comprehensive representation of manufacturing processes, thereby contributing to improved part quality control. The effectiveness of data fusion depends on the nature of the input data. This paper investigates orthogonality as a measure for the effectiveness of data fusion, with the goal to maximize data correlation with part quality toward manufacturing process control. By decomposing sensor data into a lifted-dimensional space, contribution from each of the sensors for quantifying part quality is revealed by the corresponding projection vector. Performance evaluation using data measured from polymer injection molding confirmed the effectiveness of the developed technique.
Orthogonal Analysis of Multisensor Data Fusion for Improved Quality Control
Manuscript received January 31, 2017; final manuscript received May 19, 2017; published online August 24, 2017. Assoc. Editor: Ivan Selesnick.
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Wang, P., Fan, Z., Kazmer, D. O., and Gao, R. X. (August 24, 2017). "Orthogonal Analysis of Multisensor Data Fusion for Improved Quality Control." ASME. J. Manuf. Sci. Eng. October 2017; 139(10): 101008. https://doi.org/10.1115/1.4036907
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