Springback prediction of high-strength sheet metal under air bending forming and tool design based on GA–BPNN

Fu, Zemin; Mo, Jinhan

doi:10.1007/s00170-010-2846-5

Cited by 42 publications

(13 citation statements)

References 11 publications

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“…It is difficult for traditional BPNNs to find out the global optimum solution of the prediction application [26,27]. A genetic algorithm (GA) is a method to obtain global optimum solution of the proposed problems based on a natural selection process which mimics the biological evolution process [28][29][30][31].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Zhou

et al. 2019

Energies

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The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 × 10−5, with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.

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Section: Genetic Algorithmmentioning

confidence: 99%

Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Zhou

et al. 2019

Energies

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“…F. Zemin and M. Jianhua studied springback prediction of air bending of high strength steel sheets with genetic algorithm method and back propagation neural network approach method. They have conducted series of experiments, from which neural network was "trained" to find optimal bending process parameters [8].…”

Section: Figurementioning

confidence: 99%

“…In this paper, the HC260Y tensile tests were performed in order to obtain true stress-true strain material models (7) and (8). The mathematical material models were furthermore used for the analytical calculation of bending moments and sheet metal unloading angle after springack.…”

Section: Conlusionmentioning

confidence: 99%

Bending accuracy of the HC260Y steel in different V-tool configurations

2016

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Original scientific paperThe automobile chassis is produced from various metal formed parts that are welded together. Trends in the automobile industry show that most of the car manufacturers tend to use high strength, lightweight materials. The main reasons for this are minimization of the used material and better fuel economy of the produced cars. Such lightweight materials often have high strength which causes large amounts of elastic unloading (springback) after sheet metal forming operations. Mostly used sheet metal forming process is the bending process, where the elastic springback can be severe thus diminishing the desired accuracy of the part. In this paper, the comparison between mathematical models, FEM method and experiments is shown, and the effects of Vtool bending process parameters can be seen on the resulting sheet angle of the HC260Y steel. Keywords: bending; FEM; V-tool Točnost savijanja čeličnog lima HC260Y u različitim konfiguracijama V-alataIzvorni znanstveni članak Karoserija automobila je izrađena zavarivanjem različitih dijelova koji su prethodno izrađeni tehnologijama oblikovanja lima deformiranjem. Trendovi u automobilskoj industriji pokazuju kako sve vise proizvođača koristi lake, visokočvrste materijale. Glavni razlozi ovom trendu su minimizacija korištenog materijala i bolja ekonomičnost automobila u vidu potrošnje goriva i manje emisije ispušnih plinova. Takvi visokočvrsti materijali imaju, kako naziv govori visoku vlačnu čvrstoću i granicu razvlačenja, što uzrokuje velike iznose elastičnog povrata nakon operacija oblikovanja deformiranjem. Najviše je u proizvodnji raširen postupak savijanja, gdje za visokočvrste materijale veliki elastični povrat može narušiti željeni oblik i preciznost izrađenog proizvoda. U ovom radu je prikazana usporedba između rezultata dobivenih matematičkim modelima, metode konačnih elemenata i rezultata eksperimenata i vidljiv je utjecaj procesnih parametara u postupku savijanja V-alatom na konačni kut savijenog lima od HC260Y čeličnog lima.

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“…Change of sheet thickness and bending angle before/after spring back was examined [9]. Fu and Mo used integrated neural network back propagation and genetic algorithm to develop the prediction model of spring back in the air bending process of high-strength sheet metal [10]. Vorkov et al, investigated the spring back behaviour of high strength steels experimentally with different parameters (different material, large radius punches, sheet thickness, die openings) using the air bending technique [11].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Spring Back in Air V Bending Processing using Taguchi and RSM Method

Özdemir

2020

mech

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In this study, springback behaviour of Dp600 (Dual Phase Steel) sheet material was investigated using air V bending technique. As bending parameters, four different punch tip radii and four different sheet thicknesses were used. The springback values obtained from the experiments were compared with each other. At the same time, the experimental data were analysed with Response Surface Method (RSM) and Taguchi (L16) orthogonal array. The experimental data and the simulation results were found to be consistent. The signal-to-noise (S/N) ratio is used to determine the most appropriate parameter of minimum springback behaviour. Additionally, it is possible to estimate the optimal combination of the bending parameters. The signal-to-noise (S/N) ratio and the design of experiment (DOE) were utilised for the analysis of the experimental results. It was determined that the effect of these parameters on the air V bending process was based on springback behaviours.

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Springback prediction of high-strength sheet metal under air bending forming and tool design based on GA–BPNN

Cited by 42 publications

References 11 publications

Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Bending accuracy of the HC260Y steel in different V-tool configurations

Optimization of Spring Back in Air V Bending Processing using Taguchi and RSM Method

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