CBE-Conveyor: A Case-Based Reasoning System to Assist Engineers in Designing Conveyor Systems
In this paper, we address the use of CBR in collaboration with numerical engineering models. This collaborative combination has a particular application in engineering domains where numerical models are used. We term this domain “Case Based Engineering” (CBE), and present the general architecture of a CBE system. We define and discuss the general characteristics of CBE and the special problems which arise. These are: the handling of engineering constraints of both continuous and nominal kind; interpolation over both continuous and nominal variables, and conformability for interpolation. In order to illustrate the utility of the method proposed, and to provide practical examples of the general theory, the paper describes a practical application of the CBE architecture, known as CBE-CONVEYOR, which has been implemented by the authors.Pneumatic conveying is an important transportation technology in the solid bulks conveying industry. One of the major industry concerns is the attrition of powders and granules during pneumatic conveying. To minimize the fraction of particles during pneumatic conveying, engineers want to know what design parameters they should use in building a conveyor system. To do this, engineers often run simulations in a repetitive manner to find appropriate input parameters. CBE-Conveyor is shown to speed up conventional methods for searching for solutions, and to solve problems directly that would otherwise require considerable intervention from the engineer.
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- 1.Bergmann, R., Richter, M.M., Schmitt, S., Stahl, A., Vollrath, I.: Utility-oriented Matching: A New Research Direction for Case-Based Reasoning. In: Proceedings of the 9th German Workshop on Case-Based Reasoning, GWCBR 2001, Baden-Baden, März, pp. 14–16 (2001)Google Scholar
- 2.Chatterjee, N., Campbell, J.A.: Adaptation through Interpolation for Time Critical Case-Based Reasoning. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS (LNAI), vol. 837, pp. 221–233. Springer, Heidelberg (1994)Google Scholar
- 8.Knight, B., Woon, F.L.: Case Base Adaptation Using Solution-Space Metrics. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI 2003, Acapulco, Mexico, pp. 1347–1348 (2003)Google Scholar
- 9.Knight, B., Woon, F.L.: Case Base Adaptation Using Interpolation over Nominal Values. In: Proceedings of the 24th Specialist Group on Artificial Intelligence (SGAI) International Conference on Innovative Techniques and Applications of Artificial Intelligence, Research and Development in Intelligent Systems XXI, Cambridge, UK, pp. 73–86 (2004)Google Scholar
- 10.Kolodner, J.: Case Based Reasoning. Morgan Kaufmann Publishers, San Francisco (1993) ISBN: 1558602372Google Scholar
- 11.Chapelle, P., Christakis, N., Abou-Chakra, H., Tuzun, U., Bridle, I., Bradley, M.S.A., Patel, M.K., Cross, M.: Computational model for prediction of particle degradation during dilte phase pneumatic conveying. Modelling of dilute phase pneumatic conveying, Advanced Powder Technology 15(1), 31–49 (2004)Google Scholar