Abstract
This work presents a real-life application developed to assist adhesive experts in the selection of suitable adhesives. As the popularity of adhesive joints in industry increases, so does the need for tools to support the selection process. While such tools already exist, they are either too limited in scope, or offer too little flexibility in use. In this work, we first extract experts’ knowledge about this domain and formalize it in a Knowledge Base (KB). The IDP-Z3 reasoning system can then be used to derive the necessary functionality from this KB. Together with a user-friendly interactive interface, this creates an easy-to-use tool capable of assisting the adhesive experts. The experts are positive about the tool, stating that it will help save time and find more suitable adhesives.
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Notes
- 1.
such as www.adhesivestoolkit.com and www.adhesives.org.
- 2.
also written as FO-dot.
- 3.
- 4.
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Acknowledgements
This research received funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme and Flanders Make vzw.
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Vandevelde, S., Jordens, J., Van Doninck, B., Witters, M., Vennekens, J. (2022). Knowledge-Based Support for Adhesive Selection. In: Gottlob, G., Inclezan, D., Maratea, M. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2022. Lecture Notes in Computer Science(), vol 13416. Springer, Cham. https://doi.org/10.1007/978-3-031-15707-3_34
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