Data Analytics and AI for Quality Assurance in Manufacturing: Challenges and Opportunities

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Catti, P (Author) Other; Freitas, A (Author) Other; Pereira, E (Author) FEUP; Gil Manuel Gonçalves (Author) FEUP; Lopes, RP (Author) Other; Nikolakis, N (Author) Other; Alexopoulos, K (Author) Other

Conference proceedings International

Title: LEARNING FACTORIES OF THE FUTURE, VOL 1, CLF 2024

Pages: 205-212

14th International Conference on Learning Factories (CLF)

Univ Twente, Enschede, NETHERLANDS, APR 17-19, 2024

Indexing

ISI Web of Knowledge - 0 Citations

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Other information

Authenticus ID: P-015-MQ4

DOI: 10.1007/978-3-031-65411-4_25

Abstract (EN): Data analytics and Artificial Intelligence (AI) have emerged as essential tools in manufacturing over recent years, providing better insight into production systems. Their importance can be highlighted by the way it can transform quality control, from prescriptive to proactive. Data analytics combined with AI can identify abnormal trends and patterns in huge amounts of data, that could uncover potential defects and allow pre-emptive action to minimize or even prevent these from happening. A direct effect of this is the contribution to waste reduction, as well as saving time and resources. While data in a digital factory is ample and the resources for developing artificial intelligence applications are accessible, the implementation of accurate, robust, standard, and economically viable quality monitoring and assessment approaches is not straightforward. This is also strengthened by the scarce skillset in today's manufacturing companies in this area. In this study, the capabilities and potential of data analytics combined with AI are reviewed with a focus on manufacturing. The implementation challenges posed for a practitioner, as well as the benefits of implementing a solution for a manufacturer using data analytics and AI for quality assessment are discussed, based on real-world experiences from existing production environments. Lastly, a learning approach utilizing a high-fidelity digital twin at its core is presented which a practitioner can utilize to create, test and continuously improve a predictive analytics model.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8