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

Catti, Paolo; Freitas, Artur; Pereira, Eliseu; Gonçalves, Gil; Lopes, Rui Pedro; Nikolakis, Nikolaos; Alexopoulos, Kosmas

http://hdl.handle.net/10198/31099

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Abstract(s)

Data analytics and Artificial Intelligence (AI) have emerged as essen- tial tools in manufacturing over recent years, providing better insight into pro- duction 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 pre- vent 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 ac- cessible, the implementation of accurate, robust, standard, and economically vi- able 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 chal- lenges 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 dis- cussed, based on real-world experiences from existing production environments. Lastly, a learning approach utilizing a high-fidelity digital twin at its core is pre- sented which a practitioner can utilize to create, test and continuously improve a predictive analytics model.

Keywords

Analytics Artificial intelligence Manufacturing Quality assurance

URI

http://hdl.handle.net/10198/31099

Citation

Catti, Paolo; Freitas, Artur; Pereira, Eliseu; Gonçalves, Gil; Lopes, Rui Pedro; Nikolakis, Nikolaos; Alexopoulos, Kosmas (2024). Data Analytics and AI for Quality Assurance in Manufacturing: Challenges and Opportunities. In 14th International Conference on Learning Factories, CLF 2024. Cham: Springer Nature, p. 205-212. ISBN 978-3-031-65410-7