| 1. |
- Leberruyer, Nicolas, et al.
(författare)
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Enabling an AI-Based Defect Detection Approach to Facilitate Zero Defect Manufacturing
- 2023
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Ingår i: Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. - 9783031436659 - 9783031436666 ; , s. 643-649
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Leberruyer, Nicolas
(författare)
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Facilitating the Adoption of AI-driven Zero Defect Manufacturing in Production Systems
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 3. |
- Leberruyer, Nicolas, et al.
(författare)
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Toward Zero Defect Manufacturing with the support of Artificial Intelligence—Insights from an industrial application
- 2023
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Ingår i: Computers in industry (Print). - : Elsevier B.V.. - 0166-3615 .- 1872-6194. ; 147
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Wickberg, Philip, et al.
(författare)
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Adopting a Digital Twin Framework for Autonomous Machine Operation at Construction Sites
- 2023
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Ingår i: Proc. CAA Int. Conf. Veh. Control Intell., CVCI. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350340488
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Konferensbidrag (refereegranskat)abstract
- Autonomous machines are expected to be vastly used at construction sites as they can efficiently perform repetitive and dangerous tasks. However, ensuring the operational safety of such autonomous machines in a highly dynamic environment is challenging. Although autonomous machines usually are equipped with a perception system that permits them to navigate locally, there is a need to share a global view of the construction site to reduce the risk of accidents or errors. A digital twin of the construction site map has the potential of fusing the real-time perception from different sources at the site, such as different autonomous machines working at the construction site, analysing them and sharing the needed information to operate safely and effectively at the site. This paper proposes the adoption of the recently published standard, ISO 23247 digital twin framework for manufacturing, to implement and maintain a dynamic map of construction sites. The proposed framework will enable safe and efficient operation of autonomous machines on construction sites.
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| 5. |
- Wickberg, Philip, et al.
(författare)
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Dynamic Maps Requirements for Autonomous Navigation in Construction Sites
- 2022
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Ingår i: The 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA22).
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Konferensbidrag (refereegranskat)abstract
- Construction sites are a special kind of off-road environment that needs dedicated dynamic maps to enable autonomous navigation in such terrains. In this paper, challenges for autonomous navigation on construction sites are first identified. Later, requirements for dynamic maps for autonomous navigation on construction sites are proposed based on the identified challenges.
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