Abstract
The large amount and different types of data and knowledge generated within the Additive Manufacturing (AM) value chain are highly challenging in terms of management and organization. Understanding the interconnections between all these immaterial corpuses is important for decision making and process optimization issues. Moreover, AM has more parameters than conventional manufacturing processes, and many of these parameters are difficult to assess and monitor. Therefore, it becomes important to develop computer-based solutions that are able to aid the decision maker and to support the management of all information along the AM value chain. In this paper, a knowledge-based decision support framework using ontological models and mechanisms is proposed for the above objective. Cost estimation is conducted as an application of the proposed framework.
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Acknowledgement
The presented results were conducted within the French national project “SOFIA” (SOlution pour la Fabrication Industrielle Additive métallique). This project has received the support from the French Public Investment Bank (Bpifrance) and the French National Center for Scientific Research (CNRS). The authors would like to thank all industrial and academic partners for their involvement in this research.
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Jarrar, Q., Belkadi, F., Bernard, A. (2021). A Knowledge-Based Approach for Decision Support System in Additive Manufacturing. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-030-85914-5_34
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DOI: https://doi.org/10.1007/978-3-030-85914-5_34
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