Stamping process design using FEA in conjunction with orthogonal regression

Fırat, Mehmet; Mete, Osman H.; Kocabıçak, Ümit; Özsoy, Murat

doi:10.1016/j.finel.2010.07.005

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…Kazan et al (2009) have talked about the gauge model of spring back in the wipe-winding strategy of sheet metal was made using made neural framework approach. Firat et al (2010) have proposed an approach in perspective of the numerical re-sanctioning of stamping shapes by using explicit-incremental and implicit-iterative restricted part frameworks. Volk et al (2011) have discussed the finite element method (FEM) which was required for relentless thing change and stable creation process.…”

Section: Related Workmentioning

confidence: 99%

Prediction of springback effect by the hybridisation of ANN with PSO in wipe bending process of sheet metal

Sathish¹

2018

PIE

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In sheet metal forming springback is a phenomenon that occurs slightly due to residual stresses in the material, while bending the sheet metal. Hence it should avoid improving the metal quality by the prediction of springback angle. By predicting the springback angle, can reduce the angle by changing those parameters. Therefore, a suitable prediction method is required to predict the springback angle. One of the best prediction methods is the artificial neural network (ANN) to predict the springback angle in sheet metal. So this paper aims to improve the prediction efficiency of ANN by integrating particle swarm optimisation (PSO) algorithm. The PSO algorithm is used to train the ANN, so it can predict the springback angle efficiently. The proposed technique is compared with the experimental results and the conventional prediction techniques such as conventional ANN and Genetic algorithm based ANN.

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Section: Related Workmentioning

confidence: 99%

Prediction of springback effect by the hybridisation of ANN with PSO in wipe bending process of sheet metal

Sathish¹

2018

PIE

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“…Orthogonal experiment is a design method to study multiple factors level which is often used for the performance prediction of stamping forming of automotive panels [13][14][15][16]. During the process of automotive lower floor board stamping, the main process parameters affecting the forming performance include BHF, die corner radius, die clearance, and friction coefficient.…”

Section: Orthogonal Experiments Designmentioning

confidence: 99%

Optimization of Process Parameters of Stamping Forming of the Automotive Lower Floor Board

Huang

2014

Journal of Applied Mathematics

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There are many process parameters which have great effect on the forming quality of parts during automobile panel stamping forming process. This paper took automotive lower floor board as the research object; the forming process was analyzed by finite element simulation using Dynaform. The influences of four main process parameters including BHF (blank holder force), die corner radius, friction coefficient, and die clearance on the maximum thinning rate and the maximum thickening rate were researched based on orthogonal experiment. The results show that the influences of each value of various factors on the target are not identical. On this basis, the optimization of the four parameters was carried out, and the high quality product was obtained and the maximum thinning rate and maximum thickening rate were effectively controlled. The results also show that the simulation analysis provides the basis for the optimization of the forming process parameters, and it can greatly shorten the die manufacturing cycles, reduce the production costs, and improve the production efficiency.

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“…The aim of optimization is to get the best combination of process parameters which will lead to a desirable stamping part without any defects like fractures, wrinkles, insufficient stretching and thickness varying, etc. The optimization process of sheet metal forming can be formulated as [7][8][9]:…”

Section: Process Parameters Optimizationmentioning

confidence: 99%

A Method to Optimize Aluminum Alloy Door Impact Beam Stamping Process Using NSGA-II

Zhou

Wang

et al. 2013

MSF

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The process parameters of aluminium alloy hot stamping produce an importantly effect on production forming quality. In the case of a door impact beam inside the car doors, the fi-nite-element model of aluminium alloy hot stamping is set up. Based on the model, the forming quality is investigated under usual process condition. Using the Latin hypercube method, we sampled the data points from design space of process parameters. Data points are imported into finite-element model to calculate the forming quality indices. According to their responding values, the quadratic response surfaces between process parameter inputs and forming quality indices are initialized. By optimized the response of the process parameters exercising multi-objective genetic algorithm—NSGA-II (non-dominated sorting genetic algorithm), the Pareto combinations of blank hold force and stamping velocity are obtained. Finally, by comparison with the results of stamping trial and numerical simula-tion, it is concluded that the finite-element model can be used to predict forming defects and is consistent with actual condition. Thus the optimization method is feasible.

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Stamping process design using FEA in conjunction with orthogonal regression

Cited by 15 publications

References 18 publications

Prediction of springback effect by the hybridisation of ANN with PSO in wipe bending process of sheet metal

Prediction of springback effect by the hybridisation of ANN with PSO in wipe bending process of sheet metal

Optimization of Process Parameters of Stamping Forming of the Automotive Lower Floor Board

A Method to Optimize Aluminum Alloy Door Impact Beam Stamping Process Using NSGA-II

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