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    Advantages of Multiscale Detection of Defective Pills during Manufacturing

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    Type
    Book Chapter
    Authors
    Douglas, Craig C.
    Deng, Li
    Efendiev, Yalchin R. cc
    Haase, Gundolf
    Kucher, Andreas
    Lodder, Robert
    Qin, Guan
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2010
    Permanent link to this record
    http://hdl.handle.net/10754/597475
    
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    Abstract
    We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactured. 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. © 2010 Springer-Verlag.
    Citation
    Douglas CC, Deng L, Efendiev Y, Haase G, Kucher A, et al. (2010) Advantages of Multiscale Detection of Defective Pills during Manufacturing. High Performance Computing and Applications: 8–16. Available: http://dx.doi.org/10.1007/978-3-642-11842-5_2.
    Sponsors
    This research was supported in part by NSF grants OISE-0405349, ACI-0305466, CNS-0719626, and ACI-0324876, DOE grant DE-FC26-08NT4, and Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Springer Nature
    Journal
    High Performance Computing and Applications
    DOI
    10.1007/978-3-642-11842-5_2
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-11842-5_2
    Scopus Count
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    Publications Acknowledging KAUST Support

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