Knowledge Acquisition Process for a Generative Model Development

• Autorzy: Jałowiecki A. , Skarka W.
• Języki publikacji: EN

Abstrakty

EN

Knowledge engineering becomes more frequently seen in engineering practice. The possibility of developing an application which is able to increase speed and quality of for example designers’ work is invaluable useful. The main task of Knowledge Based Engineering (KBE) is providing new and intelligent applications to support routine engineering work tasks. To prepare such kind of application it is necessary to go through the procedure of knowledge acquisition and knowledge postprocessing. This process requires a lot of a knowledge engineer work and experience. In this paper authors shown the main things related to knowledge acquisition process in the context of a Generative Model development. Additionally, some new approach to knowledge storage with Microsoft Excel is presented.

Słowa kluczowe

EN

Knowledge Based Engineering; Generative Model; knowledge acquisition; Knowledge Based System

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Machine Dynamics Research
• Rocznik: 2017
• Tom: Vol. 41, No. 3
• Strony: 127--136
• Opis fizyczny: Bibliogr. 11 poz., schem., tab.

Twórcy

autor

Jałowiecki A.

• Silesian University of Technology, Institute of Fundamentals of Machinery Design

autor

Skarka W.

• Silesian University of Technology, Institute of Fundamentals of Machinery Design

Bibliografia

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• 2. Jałowiecki, A., Kłusek, P., and Skarka, W. (2017). The methods of knowledge acquisition in the product lifecycle for a generative model’s creation process. Procedia Manufacturing, 11:2219–2226.
• 3. Jalowiecki, A. and Skarka, W. (2016). Generative modeling in ultra-efficient vehicle design. In ISPE TE, pages 999–1008.
• 4. Kendal, S. and Creen, M. (2007). An introduction to knowledge engineering. Springer Science & Business Media.
• 5. La Rocca, G. (2012). Knowledge based engineering: Between ai and cad. review of a language based technology to support engineering design. Advanced engineering informatics, 26(2):159–179.
• 6. Rumpe, B. (2016). Modeling with UML: Language, Concepts, Methods. Springer.
• 7. Skarka, W. (2003). Collecting and sharing designers’ knowledge in collaborative environment. In Proceedings of 10th ISPE International Conference on Concurrent Engineering: Research and Applications. Advanced Design, Production and Management Systems, pages 26–30.
• 8. Skarka, W. (2007). Application of moka methodology in generative model creation using catia. Engineering Applications of Artificial Intelligence, 20(5):677–690.
• 9. Skarka, W. and Neumann, T. (2017). New methods of designing stamping dies assemblies by using generative models. In Transdisciplinary Engineering: A Paradigm Shift: Proceedings of the 24th ISPE Inc. International Conference on Transdisciplinary Engineering, July 10-14, 2017, volume 5, page 456. IOS Press.
• 10. Stjepandić, J., Verhagen,W. J., Liese, H., and Bermell-Garcia, P. (2015). Knowledge-based engineering. In Concurrent Engineering in the 21st Century, pages 255–286. Springer.
• 11. Verhagen, W. J., Bermell-Garcia, P., van Dijk, R. E., and Curran, R. (2012). A critical review of knowledge-based engineering: An identification of research challenges. Advanced Engineering Informatics, 26(1):5–15.