Image-guided Quality Control of Biomanufacturing Process

Chen Kan, Ruimin Chen, Hui Yang

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

Technical advances in 3D metrology bring the increasing availability of imaging data, which are critical to quality inspection and process improvement. Dealing with 3D imaging data has become a general problem facing both traditional and next-generation innovation practices in biotechnology. Traditional methodologies in statistical quality control focus on key characteristics of the product, and are limited in the ability to model spatiotemporal patterns in imaging streams. This paper presents a dynamic network methodology for monitoring and control of high-dimensional imaging streams. The developed methodology is implemented and evaluated for process monitoring of living cells during the synthesis of bio-products.

Original languageEnglish (US)
Pages (from-to)168-174
Number of pages7
JournalProcedia CIRP
Volume65
DOIs
StatePublished - Jan 1 2017
Event3rd CIRP Conference on BioManufacturing 2017 - Chicago, United States
Duration: Jul 11 2017Jul 14 2017

Fingerprint

Quality control
Imaging techniques
Process monitoring
Biotechnology
Innovation
Inspection
Cells
Availability
Monitoring

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Kan, Chen ; Chen, Ruimin ; Yang, Hui. / Image-guided Quality Control of Biomanufacturing Process. In: Procedia CIRP. 2017 ; Vol. 65. pp. 168-174.
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Image-guided Quality Control of Biomanufacturing Process. / Kan, Chen; Chen, Ruimin; Yang, Hui.

In: Procedia CIRP, Vol. 65, 01.01.2017, p. 168-174.

Research output: Contribution to journalConference article

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