Modelling of Elastic Modulus Degradation in Sheet Metal Forming Using Back Propagation Neural Network

The paper establishes the objective functional model of sheet metal forming in drawing process with ANN, a mapping between sheet forming parameters and performance evaluation indexes was built, which provides important preferences for researching and optimizing these parameters. It obtains neural network model of high precision through the training of cross experiment method. At last a model was built. According to the test results, the error of the network were less than 5%.That means the network is available, and also it establishes foundation of the process parameters optimization.

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Modelling of Friction Phenomena Existed in Drawbead in Sheet Metal Forming

Materials ◽

10.3390/ma14195887 ◽

2021 ◽

Vol 14 (19) ◽

pp. 5887

Author(s):

Tomasz Trzepieciński ◽

Andrzej Kubit ◽

Romuald Fejkiel ◽

Łukasz Chodoła ◽

Daniel Ficek ◽

...

Keyword(s):

Sheet Metal ◽

Sheet Metal Forming ◽

Metal Forming ◽

Rolling Direction ◽

Back Propagation ◽

Experimental Tests ◽

Friction Model ◽

Friction Reduction ◽

Training Algorithms ◽

Sheet Rolling

The article presents the results of friction tests of a 0.8 mm-thick DC04 deep-drawing quality steel sheet. A special friction simulator was used in the tests, reflecting friction conditions occurring while pulling a sheet strip through a drawbead in sheet metal forming. The variable parameters in the experimental tests were as follows: surface roughness of countersamples, lubrication conditions, sample orientation in relation to the sheet rolling direction as well as the sample width and height of the drawbead. Due to many factors that affect the value of the coefficient of friction coefficient, artificial neural networks (ANNs) were used to build and analyse the friction model. Four training algorithms were used to train the ANNs: back propagation, conjugate gradients, quasi-Newton and Levenberg–Marquardt. It was found that for all analysed friction conditions and sheet strip widths, increasing the drawbead height increases the COF value. The chlorine-based Heavy Draw 1150 compound provides a more effective friction reduction compared to a LAN-46 machine oil.

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Sash sheet metal development and the application of a back-propagation neural network in the calculation of the developed length

Journal of Materials Processing Technology ◽

10.1016/s0924-0136(97)00002-2 ◽

1997 ◽

Vol 69 (1-3) ◽

pp. 95-105 ◽

Cited By ~ 6

Author(s):

Zone-Ching Lin ◽

Yung-Chuan Chang

Keyword(s):

Neural Network ◽

Sheet Metal ◽

Back Propagation ◽

Back Propagation Neural Network

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Prediction for Air-Bending Springback Radius of Sheet Metal Using Back Propagation Neural Network and Micro Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.219-220.1174 ◽

2011 ◽

Vol 219-220 ◽

pp. 1174-1177

Author(s):

Ze Min Fu ◽

Guang Ming Liu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Sheet Metal ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Accurate Prediction ◽

Air Bending ◽

Micro Genetic Algorithm ◽

Bpnn Model

Springback radius is a very important factor to influence the quality of sheet metal air-bending forming. Accurate prediction of springback radius is essential for the design of air-bending tools. In this paper, a three-layer back propagation neural network (BPNN), integrated with micro genetic algorithm (MGA), is proposed to solve the problem of springback radius. A micro genetic algorithm is used for minimizing the error between the predictive value and the experimental one. Based on air-bending experiment, the prediction model of springback radius is developed by using the integrated neural network. The results show that more accurate prediction of springback radius can be obtained with the MGA-BPNN model. It can be taken as a valuable tool for air-bending forming of sheet metal.

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