Minimum additional material volume prediction for preform product by using abductive network and Taguchi method

• Autorzy: Lin F. C. , Lin S. Y. , Hsu Y. C. , Lee B. C. , Kwan C. T.
• Języki publikacji: EN

Abstrakty

EN

Purpose: To predict the minimum value of additional material volume for an acceptable preform product. To predict an acceptable preform product without shape defect such as unfilling in a closed-die forging operation. Design/methodology/approach: In order to reduce the number of experiments, an orthogonal array from the Taguchi's experimental method will be utilized to design the process parameter combinations for database sets to promote the prediction precision. Also, in order to reduce the number of experiments to get the minimum additional material volume of preform, the abductive network is applied to synthesize the data sets obtained from the numerical simulation. Findings: The minimum additional material volume can be determined as 7.6% for an acceptable preform product in conjunction with the billet settle position, E, of 11.8 mm and the aspect ratio of width to height, B/H, of 1.4. Research limitations/implications: The Taguchi method can be used to narrow the ranges of process parameters for database sets which can promote the precision of abductive network to search for the the minimum additional material volume for an acceptable preform product. The abductive network is applied to synthesize the data sets obtained from the numerical simulation of the reduced ranges of the process parameters. Practical implications: The combination of the abductive network and Taguchi method can be used as a reference and guidance for the development of searching the minimum or maximum value of one of the process parameters, accompanying by the other suitable parameters. Originality/value: An assessment model of the closed-die forging process is developed using a neural network system and Taguchi method. Based on the developed neural network, the additional material volume of preform product, one of the forging process parameters can be minimum accompanying by the other suitable process parameters to get an acceptable product.

Słowa kluczowe

EN

abductive network; Taguchi method; finite element method; preform design

PL

sieć innowacyjna; metoda Taguchi; metoda elementów skończonych; wstępne kształtowanie; kucie matrycowe

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2008
• Tom: Vol. 33, nr 1
• Strony: 57--60
• Opis fizyczny: Bibliogr. 16 poz.

Twórcy

autor

Lin F. C.

autor

Lin S. Y.

autor

Hsu Y. C.

autor

Lee B. C.

autor

Kwan C. T.

• Department of Mechanical and Computer-Aided Engineering, National Formosa University, 64, Wunhua Rd., Huwei, Yunlin 632, Taiwan,

Bibliografia

• [1] R.Y. Lapovok, P.F. Thomson, An approach to preform design, International Journal of Machine Tools and Manufacture 35 (1995) 1537-1544.
• [2] G. Zhao, E. Wright, R.V. Grandhi, Computer aided preform design in forging using the inverse die contact tracking method, International Journal of Machine Tools and Manufacture 36 (1996) 755-769.
• [3] G. Guoqun, G. Wang, R.V. Grandhi, Preform design of a generic turbine disk forging process, Journal of Materials Processing Technology 84 (1998) 193-201.
• [4] B.C. Lee, Y.S. Kang, D.Y. Yang, J.H. Moon, A study on the formability estimation of deep drawing process by using Taguchi method, Proceedings of the Korean society of Precision Engineering Autumn Annual Meeting, Korea, 1996, 938-942.
• [5] S. Poy, S. Ghosh, R. Shivipuri, A new approach to optimal design of multi-stage metal forming processes with micro genetic algorithms, International Journal of Machine Tools and Manufacture 37 (1997) 29-33.
• [6] R.D. Lorenzo, F. Micari, An inverse approach for the design of the optimal preform shape in cold forging, Annals of the CIRP 47 (1998) 189-192.
• [7] B.Y. Lee, H.S. Liu, Y.S. Tarng, Modelling and optimization of drilling process, Journal of Materials Processing Technology 74 (1998) 149-157.
• [8] W.H. Yang, Y.S. Tarng, Design optimization of cutting parameters for turning operations based on the Taguchi method, Journal of Materials Processing Technology 84 (1998) 122-129.
• [9] D.C. Ko, D.H. Kim, B.M. Kim, J.C. Choi, Methodology of preform design considering workability in metal forming by the artificial neural network and Taguchi method, Journal of Materials Processing Technology 80-81 (1998) 487-492.
• [10] D.C. Ko, D.H. Kim, B.M. Kim, Application of artificial neural network and Taguchi method to preform design in metal forming considering workability, International Journal of Machine Tools and Manufacture 39 (1999) 771-785.
• [11] D.J. Kim, B.M. Kim, Application of neural network and FEM for metal forming processes, International Journal of Machine Tools and Manufacture 40 (2000) 911-925.
• [12] G. Zhao; G. Wang; R.V. Grandhi, Die cavity design of near flashless forging process using FEM-based backward simulation, Journal of Materials Processing Technology 121 (2002) 173-181.
• [13] R.K. Ohdar, S. Pasha, Prediction of the process parameters of metal power preform gorging using artificial neural network, Journal of Materials Processing Technology 132 (2003) 227-234.
• [14] F.C. Lin, C.T. Kwan, Application of abductive network and FEM to predict an acceptable product on T-shape tube hydroforming process, Computers and Structures 82 (2004) 1189-1200.
• [15] SFTC, Deform-2D user’s Manual, Version 7.2, Scientific Forming Technologies Corporation, Columbus, Ohio, 2003.
• [16] A.G. Ivakhnenko, Polynomial theory of complex system, IEEE Transitions on Systems 1 (1971) 364-378.