Abstract
We developed a knowledge-based system GENMAI (Artificial Intelligence Mesh GENerator) to auto-generate two-dimensional structured meshes. GENMAI is easily applicable to various kinds of application domains. Mesh generation is one of the major tasks confronted in computational simulation. The quality of generated meshes affects computational accuracy and computing time. Since various kinds of domain knowledge are needed to generate high quality structured meshes, the knowledge-based approach has been found effective and successful. Before designing GENMAI, we analyzed mesh generation jobs in plastic deformation analysis and computational fluid dynamics. Then, we formulate GENMAI so that it searches feasible plural divided patterns combinatorially and selects the best pattern. The characteristics of GENMAI are as follows: the meta-inference mechanism and its knowledge representation are widely applicable to various kinds of application domains; and plural patterns can be efficiently obtained at the same time by a search technique based on global dependency and local dependency. We applied GENMAI to forging simulation and developed AI-FESTE, which integrated a rigid-plastic deformation analysis program and GENMAI. Forging designers can easily decide shapes of a forging product and dies and also plan the forming sequence using AI-FESTE. AI-FESTE automates a series of forging analysis operations and shortens the execution time from 1 or 2 day(s) to a few hours. As a result, not only can AI-FESTE shorten the turn-around time, but it can improve the quality of product and die design.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
J.C. Canvendish, “Automatic triangulation of arbitrary planar domains for the finite element method,” Int'l J. Numer. Methods Eng., vol. 8, pp. 679-697, 1974.
E.J. Probert, O. Hassan, K. Morgan, and J. Peraire, “An adaptive finite element method for transient compressible flows with moving boundaries,” J. Comp. Phys., vol. 32, pp. 751-765, 1991.
J.F. Dannenhoffer, “Computer-aided block structuring through the use of optimization and expert system techniques,” in AIAA-91-1585-CP, 1991, pp. 654-661.
A.E. Andrews, “Knowledge-based zonal grid generation for computational fluid dynamics,” in NASA Conf. Publ., vol. NASA-CP-3019, 1988, pp. 73-80.
P.D. Dabke, I. Haque, M. Srikrishna, and J.E. Jackson, “A knowledge-based system for generation and control of finite-element meshes in forging simulation,” Materials Engineering and Performance, vol. 1,no. 3, pp. 415-428, 1992.
T.D. Blacker, J.L. Mitchiner, L.R. Phillips, and Y.T. Lin, “Knowledge system approach to automated two-dimensional quadrilateral mesh generation,” in ASME Int'l Comput. Eng. Conf., 1988, pp. 153-162.
IntelliCorp, Software Development System KEEworlds Reference Manual, 1987.
J. de Kleer, “An assumption-base tms,” Artificial Intelligence, vol. 28, pp. 127-162, 1986.
J. de Kleer, “Extending the atms,” Artificial Intelligence, vol. 28, pp. 163-196, 1986.
A. Barr and E.A. Feigenbaum, The Handbook of Artificial Intelligence, William Kaufmann, 1981.
J. Doyle, “A truth maintenance system,” Artificial Intelligence, vol. 12, pp. 231-272, 1979.
J. de Kleer, “Back to backtracking: Controlling the atms,” in Proc. of AAAI-86, 1986, pp. 910-917.
E. Rich, Artificial Intelligence, McGRAW-Hill, 1983.
K. Nakanishi, A. Danno, O. Takata, T. Yamazaki, and T. Imai, “Expert system for process design of multistage cold forging,” in Proc. 3rd. Int. Conf. on Tech. of Plasticity Workshop, vol. 2, 1993.
N. Kurosu, M. Fujii, and K. Oguchi, “Production instruction expert system for bumper coating line,” in Proc. of IMACS/SICE International Symposium on Robotics, Mechatronics and Manufacturing System, vol. 2, 1992, pp. 1191-1196.
H. Yano et al., “Application of analytical simulation to forged part design,” Toyota Technical Review, vol. 43,no. 2, pp. 22-27, 1993.
L.G. Valiant, “A theory of the learnable,” CACM, vol. 27, pp. 1134-1142, 1984.
K. Satoh and S. Okamoto, “Toward pac-learning of weights from qualitative distance information,” in Proc. of AAAI-94 Workshop on CBR, 1994, pp. 128-132.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Takata, O., Nakanishi, K., Horinouchi, N. et al. A Knowledge-Based Mesh Generation System for Forging Simulation. Applied Intelligence 11, 149–168 (1999). https://doi.org/10.1023/A:1008324413477
Issue Date:
DOI: https://doi.org/10.1023/A:1008324413477