2005
DOI: 10.1016/j.jmatprotec.2004.08.020
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Parameter identification by neural network for intelligent deep drawing of axisymmetric workpieces

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Cited by 35 publications
(13 citation statements)
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References 7 publications
(6 reference statements)
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“…The BP neural network adopted in this paper is a multilayer feedforward neural network based on the error BP algorithm [20][21][22]. The three-layer BP neural network has excellent nonlinear mapping capability and is thus adopted for modeling in this work [23]; that is, there is one input layer, one hidden layer, and one output layer.…”
Section: Model Structurementioning
confidence: 99%
“…The BP neural network adopted in this paper is a multilayer feedforward neural network based on the error BP algorithm [20][21][22]. The three-layer BP neural network has excellent nonlinear mapping capability and is thus adopted for modeling in this work [23]; that is, there is one input layer, one hidden layer, and one output layer.…”
Section: Model Structurementioning
confidence: 99%
“…They used back propagation algorithm for trained input and output data of ANN. Zhao and Wang [37] presented a feed-forward NN model based on the LM algorithm (put forward by Levenberg and Marquardt) to realize real-time identification of material properties and friction coefficient for deep drawing of an axisymmetric work piece. Singh and Kumar [38] used ANN to predict the thickness along a cup wall in hydro-mechanical deep drawing.…”
Section: Summingmentioning
confidence: 99%
“…Schiller (2007) shows NN emulation of the forward model allows us to calculate the Jacobian of the forward model efficiently; thus the Levenberg-Marquardt optimization scheme can be used to determine the parameters of interest best fitting the measurements. Zhao and Wang (2005) presented a feed-forward neural network model based on the LM algorithm (put forward by Levenberg and Marquardt), which is established to realize real-time identification of material properties and friction coefficient for deep drawing of an axisymmetric workpiece. Compared with the previous BP model (neural network based on back propagation algorithm) and GA-ENN (evolutionary neural network based on genetic algorithm) model, the error goal of parameter identification by the LM model is stepped downward to a new level.…”
Section: Neural Networkmentioning
confidence: 99%
“…Based upon the above literatures, this research operates back-propagation neural networks by one hidden layer; the structure of neural networks is drawn in Fig. 6 (Zhao & Wang, 2005).…”
Section: Construction Of Neural Networkmentioning
confidence: 99%