Finite element simulation on the deep drawing of titanium thin-walled surface part

Gao, En-zhi; Li, Hongwei; Kou, Hongchao; Chang, Hui; Li, Jinshan; Zhou, Lian

doi:10.1007/s12598-010-0019-1

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Lubricant films between the punch and blank and a piece of plastic between the matrix, blank-holder and blank are taken into account used. It should be noted that the friction coefficient is selected based on the previous studies (Colgan and Monaghan, 2003; Watiti and Labeas, 2010; Gao et al, 2010). In the second experiment, shown in Fig.…”

Section: Validation Of Fea Resultsmentioning

confidence: 99%

Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network

Najafabadi

Talebi-Anaraki

Moradi³

2017

jtam

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In this paper, the deep drawing process of an automobile panel in order to select the appropriate amount of parameters has been investigated. The parameters include friction between the blank and die, blank width and length, blank thickness and gap between the blank and blank-holder. A multi-layer artificial neural network (ANN) trained by finite element analyses (FEA) is applied in order to improve forming parameters and achieve a better quality. As the FEA results are used to train the ANN, the FEA results have been verified by three experiments. Finally, an appropriate amount of each parameter is predicted by the trained ANN and a FEA has been done based on the ANN prediction to evaluate the accuracy of the trained ANN. Moreover, it is shown that the ANN could predict results within a 10 percent error. In addition, the proposed method for prediction of the appropriate parameters (ANN) is confirmed by comparing with the Taguchi design of experiment prediction. It is also shown that the model obtained by the former method has lower errors than the latter one. In this study, the Taguchi model is used to evaluate the effect of parameters on tearing and wrinkling. Based on the Taguchi design of experiment, while the blank length is the most effective parameter on tearing, the maximum height of wrinkles on flanged parts mainly depends on the blank thickness.

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Section: Validation Of Fea Resultsmentioning

confidence: 99%

Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network

Najafabadi

Talebi-Anaraki

Moradi³

2017

jtam

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“…Numerous researchers developed the finite element model and used orthogonal tests to assess the effects of process variables such as die corner radius, BHF, blank holder spacing, friction coefficient, mold gap, and stamping speed. [9][10][11][12] Using the response surface approach as the basis, the researchers created a multiple regression model with the damage value of molded parts as the target and BHF, stamping speed, mold gap, and die corner radius as the influencing factors. 13,14 Numerous studies have discovered that the mold structure and shape of molded parts limit the influence of process parameters on molding quality and that the influence varies.…”

Section: Introductionmentioning

confidence: 99%

Influence and improvement of stamping process parameters on V-shaped clamp forming quality

Hu,

Qiu,

Mei

et al. 2024

Advances in Mechanical Engineering

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Aiming at the problems that a V-shaped clamp is easy to crack, wrinkle, and springback in the stamping process, a method to improve forming quality by setting the best stamping process parameters is put forward in this paper. The stamping process of the V-shaped clamp was simulated by using the software of ABAQUS, which showed that the stamping process parameters, such as friction coefficient, stamping speed, and mold gap, can affect the forming quality. The optimal combination of stamping process parameters was obtained through an orthogonal experiment. Simulation, profile dimensional measurement, and Fuji paper test were conducted to evaluate and examine the quality of V-shaped clamps before and after optimization. According to the findings of the study, the stamping speed is the main factor affecting the simulation results. The comprehensive effect of the maximum thinning rate, maximum thickening rate, and maximum springback is the best with the stamping speed of 10.00 mm·s−1. The ideal specifications for the stamping process are a 0.16 friction coefficient, a 10.00mm·s−1 stamping speed, and a 2.20 mm mold gap. Process parameter modification can greatly increase the dimensional accuracy and mechanical properties of molded parts.

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Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process

Manoochehri

Kolahan

2014

Int J Adv Manuf Technol

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Finite element simulation on the deep drawing of titanium thin-walled surface part

Cited by 6 publications

References 10 publications

Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network

Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network

Influence and improvement of stamping process parameters on V-shaped clamp forming quality

Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process

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