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Toward Zero Defect Manufacturing with the support of Artificial Intelligence—Insights from an industrial application
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Volvo Construction Equipment, Eskilstuna, Sweden.ORCID iD: 0000-0002-4251-366X
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-5963-2470
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-9933-8532
Volvo Construction Equipment, Eskilstuna, Sweden.ORCID iD: 0000-0002-0474-2904
2023 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 147, article id 103877Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Elsevier B.V. , 2023. Vol. 147, article id 103877
Keywords [en]
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
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:mdh:diva-62091DOI: 10.1016/j.compind.2023.103877ISI: 000950335500001Scopus ID: 2-s2.0-85149173024OAI: oai:DiVA.org:mdh-62091DiVA, id: diva2:1743431
Available from: 2023-03-15 Created: 2023-03-15 Last updated: 2024-02-16Bibliographically approved
In thesis
1. Facilitating the Adoption of AI-driven Zero Defect Manufacturing in Production Systems
Open this publication in new window or tab >>Facilitating the Adoption of AI-driven Zero Defect Manufacturing in Production Systems
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The increasing focus on sustainability is pushing companies to update their production systems. These systems need to facilitate the production of products with the latest sustainable technologies and innovations, while also producing these new products with lower environmental impact. To maintain high customer satisfaction, these systems must consistently deliver high-quality products. However, current quality management approaches, focused on minimal variations, might hinder this shift.

Zero Defect Manufacturing (ZDM), an emerging quality approach, leverages Artificial Intelligence (AI) to monitor products and processes in real-time, allowing for early defect detection and prevention. Many production systems generate vast amounts of data which is often not used to its full potential. Research shows that AI has the potential to unlock the hidden insights within this data, leading to transformative improvements in quality and overall efficiency. However, successfully adopting AI-driven ZDM requires expertise in AI and production while also overcoming technological and organizational challenges.

The purpose of this licentiate thesis is to investigate the adoption of AI-driven ZDM in production systems, examining its impacts, challenges, and facilitators during the development process. The research involved collaboration with a company producing transmission components for the heavy-duty automotive industry. A two-year case study was conducted, enabling the in-depth exploration of data throughout the development of four real-world AI-driven ZDM applications in a production system. This approach provided valuable insights into the practicalities of adopting AI to ensure ZDM.

The findings show that successful implementation requires specific prerequisites: lean manufacturing practices lay the groundwork for AI integration, a high-impact quality issue motivates investment and data collection, collaboration among diverse experts is crucial, and robust IT capabilities ensure smooth data storage and analysis. Furthermore, anomaly-detection AI models and the generation of "plausible defects" are key enablers for overcoming data limitations in complex defect detection. The study emphasizes the importance of early engagement to identify data needs, define extraction methods, and address potential implementation limitations. In addition, it recommends an iterative approach to continuously improving the solution and incorporating feedback throughout the process. This comprehensive approach can pave the way for a future of sustainable manufacturing, leading to significant cost savings and increased customer satisfaction.

Place, publisher, year, edition, pages
Eskilstuna: Mälardalen University, 2024
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 354
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Systems
Identifiers
urn:nbn:se:mdh:diva-66064 (URN)978-91-7485-636-1 (ISBN)
Presentation
2024-05-03, A2-004, Mälardalens universitet, Eskilstuna, 09:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2024-03-12 Created: 2024-02-16 Last updated: 2024-04-12Bibliographically approved

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Leberruyer, NicolasBruch, JessicaAhlskog, MatsAfshar, Sara Zargari

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