Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315264 
Year of Publication: 
2024
Citation: 
[Journal:] Flexible Services and Manufacturing Journal [ISSN:] 1936-6590 [Volume:] 36 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 1422-1449
Publisher: 
Springer US, New York, NY
Abstract: 
Reducing waste through automated quality control (AQC) has both positive economical and ecological effects. In order to incorporate AQC in packaging, multiple quality factor types (visual, informational, etc.) of a packaged artifact need to be evaluated. Thus, this work proposes an end-to-end quality control framework evaluating multiple quality control factors of packaged artifacts (visual, informational, etc.) to enable future industrial and scientific use cases. The framework includes an AQC architecture blueprint as well as a computer vision-based model training pipeline. The framework is designed generically, and then implemented based on a real use case from the packaging industry. As an innovate approach to quality control solution development, the data-centric artificial-intelligence (DCAI) paradigm is incorporated in the framework. The implemented use case solution is finally tested on actual data. As a result, it is shown that the framework’s implementation through a real industry use case works seamlessly and achieves superior results. The majority of packaged artifacts are correctly classified with rapid prediction speed. Deep-learning-based and traditional computer vision approaches are both integrated and benchmarked against each other. Through the measurement of a variety of performance metrics, valuable insights and key learnings for future adoptions of the framework are derived.
Subjects: 
Computer vision
Quality control
DCAI
Deep learning
Packaging
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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