Toward Zero Defect Manufacturing with the support of Artificial Intelligence—Insights from an industrial application

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Leberruyer, NicolasMälardalens universitet,Innovation och produktrealisering,Volvo Construction Equipment, Eskilstuna, Sweden (author)

Toward Zero Defect Manufacturing with the support of Artificial Intelligence—Insights from an industrial application

Article/chapterEnglish2023

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Elsevier B.V.2023
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LIBRIS-ID:oai:DiVA.org:mdh-62091
https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-62091URI
https://doi.org/10.1016/j.compind.2023.103877DOI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

The Zero Defect Manufacturing (ZDM) concept combined with Artificial Intelligence (AI), a key enabling technology, opens up new opportunities for improved quality management and advanced problem-solving. However, there is a lack of applied research in industrial plants that would allow for the widespread deployment of this framework. Thus, the purpose of this article was to apply AI in an industrial application in order to develop application insights and identify the necessary prerequisites for achieving ZDM. A case study was done at a Swedish manufacturing plant to evaluate the implementation of a defect-detection strategy on products prone to misclassification and on an imbalanced data set with very few defects. A semi-supervised learning approach was used to learn which vibration properties differentiate confirmed defects from approved products. This method enabled the calculation of a defect similarity ratio that was used to predict how similar newly manufactured products are to defective products. This study identified four prerequisites and four insights critical for the development of an AI solution supporting ZDM. The key finding demonstrates how well traditional and innovative quality methods complement one another. The results highlight the importance of starting data science projects quickly to ensure data quality and allow a ZDM detection strategy to build knowledge to allow for the development of more proactive strategies, such as the prediction and prevention of defects.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Maskinteknik Produktionsteknik, arbetsvetenskap och ergonomi hsv//swe
ENGINEERING AND TECHNOLOGY Mechanical Engineering Production Engineering, Human Work Science and Ergonomics hsv//eng
Data-driven decision
Defect detection
Machine learning
Quality 4.0
Quality control
Smart production
Defects
Industrial plants
Industrial research
Quality management
Applied research
Data driven decision
Enabling technologies
Machine-learning
Manufacturing concepts
Problem-solving
Zero defects

Added entries (persons, corporate bodies, meetings, titles ...)

Bruch, JessicaMälardalens universitet,Innovation och produktrealisering(Swepub:mdh)jbh01 (author)
Ahlskog, Mats,1970-Mälardalens universitet,Innovation och produktrealisering(Swepub:mdh)mag03 (author)
Afshar, Sara ZargariVolvo Construction Equipment, Eskilstuna, Sweden(Swepub:mdh)sar01 (author)
Mälardalens universitetInnovation och produktrealisering (creator_code:org_t)

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In:Computers in industry (Print): Elsevier B.V.1470166-36151872-6194

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