Real-time multiscale detection of defective pills during manufacturing

Craig C. Douglas; Li Deng; Yalchin Efendiev; Gunfolf Haase; Anfread Kucher; Robert A. Lodder

Real-time multiscale detection of defective pills during manufacturing

Craig C. Douglas, Li Deng, Yalchin Efendiev, Gunfolf Haase, Anfread Kucher, Robert A. Lodder

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English (US)
Pages (from-to)	169-180
Number of pages	12
Journal	International Journal of Numerical Analysis and Modeling
Volume	9
Issue number	2
State	Published - 2012

Keywords

And parallel algorithms
DDDAS and integrated sensing and processing
Dynamic data-driven application systems
High performance computing
Manufacturing defect detection

ASJC Scopus subject areas

Numerical Analysis

Cite this

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title = "Real-time multiscale detection of defective pills during manufacturing",

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 = "And parallel algorithms, DDDAS and integrated sensing and processing, Dynamic data-driven application systems, High performance computing, Manufacturing defect detection",

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AU - Deng, Li

AU - Efendiev, Yalchin

AU - Haase, Gunfolf

AU - Kucher, Anfread

AU - Lodder, Robert A.

N1 - KAUST Repository Item: Exported on 2021-09-17 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: King Abdullah University of Science & Technology This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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N2 - 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.

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.

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M3 - Article

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Real-time multiscale detection of defective pills during manufacturing

Abstract

Keywords

ASJC Scopus subject areas

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