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)|
|Number of pages||12|
|Journal||International Journal of Numerical Analysis and Modeling|
|State||Published - Jun 29 2012|
Bibliographical noteKAUST 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.
- 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