Springback Analysis in Air Bending Process through Experiment based Artificial Neural Networks

Şenol, Özgü; Esat, Volkan; Darendelıler, Haluk

doi:10.1016/j.proeng.2014.10.131

Cited by 16 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was observed that the BP neural network prediction model of the proposed PSO algorithm improved not only the accuracy of prediction, but also the learning speed of the network. Şenol et al [ 28 ] used ANN to predict the amounts of springback of stainless steel sheets through experiment-based networks. It was concluded that ANN can be effectively applied to determine springback in the air bending process.…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Trzepieciński

Lemu

2020

Materials

View full text Add to dashboard Cite

This paper presents the results of predictions of springback of cold-rolled anisotropic steel sheets using an approach based on a multilayer perceptron-based artificial neural network (ANN) coupled with a genetic algorithm (GA). A GA was used to optimise the number of input parameters of the multilayer perceptron that was trained using different algorithms. In the investigations, the mechanical parameters of sheet material determined in uniaxial tensile tests were used as input parameters to train the ANN. The springback coefficient, determined experimentally in the V-die air bending test, was used as an output variable. It was found that specimens cut along the rolling direction exhibit higher values of springback coefficient than specimens cut transverse to the rolling direction. An increase in the bending angle leads to an increase in the springback coefficient. A GA-based analysis has shown that Young’s modulus and ultimate tensile stress are variables having no significant effect on the coefficient of springback. Multilayer perceptrons trained by back propagation, conjugate gradients and Lavenberg–Marquardt algorithms definitely favour punch bend depth under load as the most important variables affecting the springback coefficient.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Trzepieciński

Lemu

2020

Materials

View full text Add to dashboard Cite

show abstract

“…The results show ANN outperforming the regression models approach for the evaluated cases. The authors of [32] also investigated the effect of bending and springback angles in bending processes. In this case, experimental data obtained from FEA models was used to design and train the developed ANN models.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Bending Processes and Material Identification

Cruz

Barbosa

Santos

et al. 2021

Metals

View full text Add to dashboard Cite

The increasing availability of data, which becomes a continually increasing trend in multiple fields of application, has given machine learning approaches a renewed interest in recent years. Accordingly, manufacturing processes and sheet metal forming follow such directions, having in mind the efficiency and control of the many parameters involved, in processing and material characterization. In this article, two applications are considered to explore the capability of machine learning modeling through shallow artificial neural networks (ANN). One consists of developing an ANN to identify the constitutive model parameters of a material using the force–displacement curves obtained with a standard bending test. The second one concentrates on the springback problem in sheet metal press-brake air bending, with the objective of predicting the punch displacement required to attain a desired bending angle, including additional information of the springback angle. The required data for designing the ANN solutions are collected from numerical simulation using finite element methodology (FEM), which in turn was validated by experiments.

show abstract

“…For this reason, many studies have been aimed at understanding and reducing springback. Springback can occur in all bending methods: tube bending [5,6], L-bending [7][8][9], Ubending [10,11], press bending [12,13], V-bending [14,15], air bending [16,17].…”

Section: Introductionmentioning

confidence: 99%

Experimental, Statistical, and Analytical Evaluation of The Springback Behavior of Martensitic 1400 Sheet in V-Bending

N¹,

Seçgin²

2020

Preprint

View full text Add to dashboard Cite

Ultra-high-strength Martensitic 1400 steel is preferred in the automotive industry because of its high strength as well as its light weight. In this study, Martensitic 1400 steel was subjected to the V-bending process. As a result of 48 different tests, the amount of springback resulting from the V-bending process was determined. A finite element (FE) model was then created based on these experimental data. After it was determined that the experimental results concurred with the FE model, without performing new experiments, further analyses were carried out at different temperatures. Using the results of a total of 96 FE analyses, variance analysis was performed and the effects of the operation parameters on springback were determined. As a result of the study, it was concluded that the most effective parameter on springback in the V-bending process was the die angle and that holding time had no significant effect. It was observed that the springback increased in parallel with the punch radius and die angle and that increases in temperature reduced the springback.

show abstract

Springback Analysis in Air Bending Process through Experiment based Artificial Neural Networks

Cited by 16 publications

References 5 publications

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Application of Machine Learning to Bending Processes and Material Identification

Experimental, Statistical, and Analytical Evaluation of The Springback Behavior of Martensitic 1400 Sheet in V-Bending

Contact Info

Product

Resources

About