Year: 2012
Author: C. C. Douglas, L. Deng, Y. Efendiev, G. Haase, A. Kucher, R. A. Lodder
International Journal of Numerical Analysis and Modeling, Vol. 9 (2012), Iss. 2 : pp. 169–180
Abstract
We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactures. The goal is to detect 100% of the defects, not just statistically sample a small percentage of the products and draw conclusions that may not be 100% accurate. Removing all of the defective products, or halting production in extreme cases, will reduce costs and eliminate embarrassing and expensive recalls. We use the knowledge that experts have accumulated over many years, dynamic data derived from networks of smart sensors using both audio and chemical spectral signatures, multiple scales to look at individual products and larger quantities of products, and finally adaptive models and algorithms.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2012-IJNAM-618
International Journal of Numerical Analysis and Modeling, Vol. 9 (2012), Iss. 2 : pp. 169–180
Published online: 2012-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Manufacturing defect detection dynamic data-driven application systems DDDAS and integrated sensing and processing high performance computing and parallel algorithms.