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Enabling an AI-Based Defect Detection Approach to Facilitate Zero Defect Manufacturing
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. (Production Development Research Group)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
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-0474-2904
2023 (English)In: Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures / [ed] Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D, 2023, p. 643-649Conference paper, Published paper (Refereed)
Abstract [en]

Artificial Intelligence (AI) has proven effective in assisting manufacturing companies to achieve Zero Defect Manufacturing. However, certain products may have quality characteristics that are challenging to verify in a manufacturing facility. This could be due to several factors, including the product’s complexity, a lack of available data or information, or the need for specialized testing or analysis. Prior research on using AI for challenging quality detection is limited. Therefore, the purpose of this article is to identify the enablers that contributed to the development of an AI-based defect detection approach in an industrial setting. A case study was conducted at a transmission axle assembly factory where an end-of-line defect detection test was being developed with the help of vibration sensors. This study demonstrates that it was possible to rapidly acquire domain expertise by experimenting, which contributed to the identification of important features to characterize defects. A regression model simulating the normal vibration behavior of transmission axles was created and could be used to detect anomalies by evaluating the deviation of new products compared to the model. The approach could be validated by creating an axle with a built-in defect. Five enablers were considered key to this development.

Place, publisher, year, edition, pages
2023. p. 643-649
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:mdh:diva-64470DOI: 10.1007/978-3-031-43666-6_43Scopus ID: 2-s2.0-85174445324ISBN: 978-3-031-43665-9 (print)ISBN: 978-3-031-43666-6 (electronic)OAI: oai:DiVA.org:mdh-64470DiVA, id: diva2:1803016
Conference
IFIP International Conference on Advances in Production Management Systems, APMS 2023
Available from: 2023-10-06 Created: 2023-10-06 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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