Parameter identification by neural network for intelligent deep drawing of axisymmetric workpieces

Zhao, Jun; Wang, Fengquin

doi:10.1016/j.jmatprotec.2004.08.020

Cited by 35 publications

(13 citation statements)

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%

Method of Predicting Ore Dilution Based on a Neural Network and Its Application

Zhao

Niu

2020

Sustainability

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A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r 2 of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead-zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

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Section: Model Structurementioning

confidence: 99%

Method of Predicting Ore Dilution Based on a Neural Network and Its Application

Zhao

Niu

2020

Sustainability

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“…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%

Applications of Artificial Neural Network to Sheet Metal Work - A Review

Kashid¹,

Kumar²

2013

AJIS

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Design of sheet metal components and press tools demands a high level of knowledge and industrial experience on the part of designers. Recently, various artificial intelligence (AI) techniques are being used in sheet metal work to reduce complexity; minimize the dependency on human expertise and time taken in design of parts and dies as well as to improve the design efficiency. Artificial neural network (ANN) technique is one of the most powerful tools for solving engineering design problems and minimizing errors in experimental data. This paper describes a comprehensive review of applications of ANN technique to sheet metal work. Major published research work in the domain area is summarized in tabular form. Based on the critical review of available literature, further research scope is identified. The present literature review reveals that there is stern need to apply ANN technique to press tool design and to predict tool life in sheet metal industries.

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“…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%