Prediction and elimination of springback in straight flanging using computer-aided design methods

Livatyalı, Haydar; Wu, Hsien-Chih; Altan, Taylan

doi:10.1016/s0924-0136(01)01161-x

Cited by 53 publications

(33 citation statements)

References 2 publications

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“…Moreover, the results are in good agreement with the inverse proportionality between springback and thickness (K=R/t) [18]. The reason of springback decrease with increase in thickness can be stated as decrease in the remained tensile occurred in bending region [23].…”

Section: Resultssupporting

confidence: 72%

Investigation of Springback Behavior of DP Series Sheet Metal in Bending Process

Uslu¹,

Tosun²,

Tosun³

2016

Fourth International Conference on Advances in Mechanical and Automation Engineering - MAE 2016

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Abstract-In the study, the springback behavior took place during V bending of DP series Sheet metals having different quality was experimentally investigated. For this purpose, sheet metal having quality of DP800 and DP600 with size of 45x90 mm thickness of 1, 1.5 and 2 mm and V bending die having 60, 90, and 120 o angles were used. The data obtained from results of experimental studies was graphically represented and the evaluated.

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Section: Resultssupporting

confidence: 72%

Investigation of Springback Behavior of DP Series Sheet Metal in Bending Process

Uslu¹,

Tosun²,

Tosun³

2016

Fourth International Conference on Advances in Mechanical and Automation Engineering - MAE 2016

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“…Lubrication of the parts and the tools before bending operation is done with a full-strength oil [37,38]. The following relations were investigated:…”

Section: Experimental Bending Processmentioning

confidence: 99%

Comparison between three optimization methods for the minimization of maximum bending load and springback in wiping die bending obtained by an experimental approach

Bahloul

Ayed²,

Potiron

et al. 2009

Int J Adv Manuf Technol

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The sheet metal bending process is widely used in the automotive industries, and it is actually one of the most important manufacturing processes. The robustness and the reliability of the bending operation, like many other forming operations, depend of several parameters (geometry, material, and process). In this paper, the die radius and the clearance between the punch and the sheet are optimised in order to reduce the maximum bending load and the springback. Two optimization problems are formulated, and three optimization procedures based on the response surface method are proposed and used to find the optimum solutions. Global and local approximations are used to replace the initial optimization problem, which is implicit by an explicit problem, and the optimum is localised using two algorithms: a sequential quadratic programming and an evolution strategies. The objective functions are evaluated experimentally into a limited points number, which are defined using a design of experiments technique. Good results are obtained from the three optimization procedures. The ability of each technique to find the optimal solution is evaluated, and the results show a good agreement between those three methods.

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“…By making a more extensive use of the finite element method (FEM) for studying and researching sheet metal forming, Nagtegaal and Taylor [14] designed a very effective way of controlling and monitoring of the calculation of material springback by an implicit method [10]. Livatyali and Altan [15] designed a computer-aided method for straight flanging of sheet metal with the help of the finite element method. Ling, Lee and Cheok [10] found out that the geometry, that is the form of the tool for separate products, experiences many iterations and simulations in the stage of searching for and eliminating errors before attaining a geometry which is satisfactory and adequate.…”

Section: Introductionmentioning

confidence: 99%

Prediction of technological parameters of sheet metal bending in two stages using feed forward neural network

et al. 2016

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Original scientific paper This paper describes sheet metal bending in two stages as well as predicting and testing of the final bend angle by means of a feed-forward neural network. The primary objective was to research the technological parameters of bending sheet metal in two stages and to develop an intelligent method that would enable the predicting of those technological parameters. The process of bending sheet metal in two stages is presented by demonstrating the various technological parameters and the test tool used to carry out tests and measurements. The results of the tests and measurements were of decisive guidance in the evaluation of individual technological parameters. Developed method for prediction of the final bend angle is based on a feed-forward neural network that receives signals at the input level. These signals then travel through the hidden level to the output level, where the responses to input signals are received. The input to the neural network is composed of data that affect the selection of the final bend angle. Only five different inputs are used for the total neural network. By choosing the desired final bend angle by means of the trained neural network, bending sheet metal in two stages is optimised and made more efficient. Keywords: bending in two stages; intelligent system; neural network; prediction of the final bend angle Predviđanje tehnoloških parametara savijanja lima u dvije faze pomoću usmjerene neuronske mrežeIzvorni znanstveni rad Članak prikazuje savijanje lima u dvije faze i predviđanje konačnog kuta savijanja pomoću usmjerene neuronske mreže. Glavni cilj je bio istražiti tehnološke parametre savijanja lima u dvije faze i razviti inteligentan način, koji će omogućiti predviđanje tih tehnoloških parametara. Prikazan je proces savijanja lima u dvije faze, gdje se prikazuju i razni tehnološki parametri i ispitni alati sa kojima su provedena ispitivanja i mjerenja. Rezultati ispitivanja i mjerenja su bili ključ u donošenju procjene pojedinih tehnoloških parametara. Opisano je predviđanje konačnog kuta savijanja lima korištenjem usmjerene neuronske mreže, koja prima signale na ulazu. Ti signali tada prolaze kroz skrivenu razinu do izlaza, gdje dobiju odgovor na ulazne signale. Za ulaz u neuronsku mrežu upotrebljavaju se podaci koji utječu na odabir kuta konačnog savijanja. Za neuronsku mrežu se koristi pet različitih inputa. Odabirom željenog kuta savijanja pomoću neuronske mreže, može se doprinijeti optimizaciji savijanja lima u dvije faze.Ključne riječi: inteligentni sustav; neuronske mreže; predviđanje konačnoga kuta savijanja; savijanje u dvije faze

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Prediction and elimination of springback in straight flanging using computer-aided design methods

Cited by 53 publications

References 2 publications

Investigation of Springback Behavior of DP Series Sheet Metal in Bending Process

Investigation of Springback Behavior of DP Series Sheet Metal in Bending Process

Comparison between three optimization methods for the minimization of maximum bending load and springback in wiping die bending obtained by an experimental approach

Prediction of technological parameters of sheet metal bending in two stages using feed forward neural network

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