3-HSS并联机床整机静刚度预估

In this paper, the springback is controlled via a stepped variable binder force and an efficient procedure is suggested to inversely determine the variable binder force parameters in U-shaped part forming.
Han X. College of Mechanical & Vehicle Engineering, Hunan University, Changsha 410082, China
e-mail:hanxu@
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Abstract: In this paper, a neural network procedure is suggested to inversely determine the variable binder force in U-shaped forming from the deforming shape of the part.
Acknowledgements This work is supported by the national 973 program under the grant number 2004CB719402 and the program for New Century Excellent Talents in University, 2004 (NCET).
3.3.1 NN model
Mathematically, the NN model represents a non-linear
mapping between inputs X xi ,i 1, , Nand outputs
References 1. Cao J., Kinsey B., Solla S.A., Consistent and minimal
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Proceedings of the 9th International Conference on Frontiers of Design and Manufacturing July 17~19, 2010, Changsha, China
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An Inversion Procedure for Determination of Variable Binder Force in U-Shaped Forming
springback using a stepped binder force trajectory and neural network control. J. of Engineering Materials and Technology, 122, 113-118,2000 2. Schmocket D., Beth M., Springback reduction in draw bending process of sheet metals. Annals of CIRP, 42 (1):339-342,1993 3. Cho S., Cho Y., Yoon S., Reliable roll force prediction in cold mill using multiple neural networks. IEEE Trans. on Neural Networks, 8(4), 874-882,1997 4. Liu G.R., Han X., Computational inverse techniques in nondestructive evaluation, CRC Press, Florida,2003 5. Liu G.R., Han X., Xu Y.G., Lam K.Y., Material characterization of functionally graded material by means of elastic waves and a progressive-learning neural network, Composites Science and Technology, 61(10), 1401-1411, 2001
Y yi ,i 1, , Mvia the following equation:
Y g(W, X )
(1)
where W wikj ,i 1, , Ni , j 1, , N j , k 1, 2,3 is a
matrix of weights corresponding to the connections
Figure 1 The pattern of the stepped variable binder force
3 The Inverse Procedure 3.1 Teacher The FEM is employed as a teacher for the NN model. Two-dimensional degenerated shell element and elasto-plastic material model are used.
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between the layers, and Ni and N j are the numbers of
neurons for the ith and jth layers respectively [5].
4 Conclusions In this paper, an NN procedure is given to inversely determine the variable binder force based on the desired deformation shape in the U-shaped part forming. A method is suggested to obtain the desired deformation shape based on the CBF simulation for the designers short of the engineering experiences. The computation example indicates that the present procedure can seek the appropriate variable binder force curve according to the designer’s expectation.
2 Statement of the Problem As shown in Fig. 1, the stepped variable binder force curve is different from the constant binder force (CBF) as its binder force is not a constant in the forming process.
3.2 Progressive neural network (NN) An NN model is referred to as a type of computational model. It is made up of three parts: input layer, output layer and hidden layers.
Keywords: Springback, U-Shaped Part, Inverse Problem
1 Introduction The accuracy of the products has become the key issue of the manufacturing process. Springback is a very complex mechanical phenomenon that involves the material property and the processing parameters such as friction coefficient, thickness of the metal plate and temperature, etc. In recent years, many works have been reported for the investigation of the springback [1~4].