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Volume 9, Issue 2
Real-Time Multiscale Detection of Defective Pills During Manufacturing

C. C. Douglas, L. Deng, Y. Efendiev, G. Haase, A. Kucher & R. A. Lodder

Int. J. Numer. Anal. Mod., 9 (2012), pp. 169-180.

Published online: 2012-09

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  • 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.

  • Keywords

Manufacturing defect detection, dynamic data-driven application systems, DDDAS and integrated sensing and processing, high performance computing, and parallel algorithms.

  • AMS Subject Headings

62H35, 68U10

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-9-169, author = {Douglas , C. C.Deng , L.Efendiev , Y.Haase , G.Kucher , A. and Lodder , R. A.}, title = {Real-Time Multiscale Detection of Defective Pills During Manufacturing}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2012}, volume = {9}, number = {2}, pages = {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.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/618.html} }
TY - JOUR T1 - Real-Time Multiscale Detection of Defective Pills During Manufacturing AU - Douglas , C. C. AU - Deng , L. AU - Efendiev , Y. AU - Haase , G. AU - Kucher , A. AU - Lodder , R. A. JO - International Journal of Numerical Analysis and Modeling VL - 2 SP - 169 EP - 180 PY - 2012 DA - 2012/09 SN - 9 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/618.html KW - Manufacturing defect detection, dynamic data-driven application systems, DDDAS and integrated sensing and processing, high performance computing, and parallel algorithms. AB -

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.

Douglas , C. C.Deng , L.Efendiev , Y.Haase , G.Kucher , A. and Lodder , R. A.. (2012). Real-Time Multiscale Detection of Defective Pills During Manufacturing. International Journal of Numerical Analysis and Modeling. 9 (2). 169-180. doi:
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