Experimental issues in the instrumented 3 point bending VDA238-100 test

Wagner, Leopold; Larour, P.; Dolzer, D.; Leomann, F.; Suppan, Clemens

doi:10.1088/1757-899x/967/1/012079

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…There are no automatic quantitative criteria available to assess HET in AHSS. Previous studies have shown that failure tests of AHSS with load drop criterion, such as three-point bending, are not feasible because of the load drop delay after cracking [ 43 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Hole Expansion Ratio for Advanced High-Strength Steel with Image Feature Analysis of Sheared Edge

Jeong

Lee

et al. 2023

Materials

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The punching process of AHSS induces edge cracks in successive process, limiting the application of AHSS for vehicle bodies. Controlling and predicting edge quality is substantially difficult due to the large variation in edge quality, die wear induced by high strength, and the complex effect of phase distribution. To overcome this challenge, a quality prediction model that considers the variation of the entire edge should be developed. In this study, the image of the entire edge was analyzed to provide a comprehensive evaluation of its quality. Statistical features were extracted from the edge images to represent the edge quality of DP780, DP980, and MART1500 steels. Combined with punching monitoring signals, a prediction model for hole expansion ratio was developed under punch conditions of varying clearance, punch angle, and punch edge radius. It was found that the features of grayscale variation are affected by the punching conditions and are related to the double burnish and uneven burr, which degrades the edge quality. Prediction of HER was possible based on only edge image and monitoring signals, with the same performance as the prediction based solely on punching parameters and material properties. The prediction performance improved when using all the features.

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Section: Discussionmentioning

confidence: 99%

Prediction of Hole Expansion Ratio for Advanced High-Strength Steel with Image Feature Analysis of Sheared Edge

Jeong

Lee

et al. 2023

Materials

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“…In fact, this test does not require any special sample preparation, such as machining and can be easily conducted on a universal tensile testing machine. However, despite its simplicity, challenges arise due to the involvement of axial and transverse forces in the bending deformation [61]. Additionally, factors such as friction, local deformation beneath the contact points [62], and the determination of the stiffness of the testing system [63] can affect the results.…”

Section: Three-point Bending Testmentioning

confidence: 99%

Recurrent Neural Networks and Three-Point Bending Test on the Identification of Material Hardening Parameters

Cruz,

Barbosa,

Santos

et al. 2024

Metals

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The continuous evolution of metallic alloys in the automotive industry has led to the development of more advanced and flexible constitutive models that attempt to accurately describe the various fundamental properties and behavior of these materials. These models have become increasingly complex, incorporating a larger number of parameters that require an accurate calibration procedure to fit the constitutive parameters with experimental data. In this context, machine learning (ML) methodologies have the potential to advance material constitutive modeling, enhancing the efficiency of the material parameter calibration procedure. Recurrent neural networks (RNNs) stand out among various learning algorithms due to their ability to process sequential data and overcome limitations imposed by nonlinearities and multiple parameters involved in phenomenological models. This study explores the modeling capabilities of long short-term memory (LSTM) structures, a type of RNN, in predicting the hardening behavior of a sheet metal material using the results of a standardized experimental three-point bending test, with the aim of extending this methodology to other experimental tests and constitutive models. Additionally, a variable analysis is performed to select the most important variables for this experimental test and assess the influence of friction, material thickness, and elastic and plastic properties on the accuracy of predictions made by neural networks. The required data for designing and training the network solutions are collected from numerical simulations using finite element methodology (FEM), which are subsequently validated by experiments. The results demonstrate that the proposed LSTM-based approach outperforms traditional identification techniques in predicting the material hardening parameters. This suggests that the developed procedure can be effectively applied to efficiently characterize different materials, especially those extensively used in industrial applications, ranging from mild steels to advanced high-strength steels.

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“…The assumption that crack initiate at the max force measured during bending isn't too many cases valid for all steel grades and cannot be universally trusted [9], [13], [14], [15].…”

Section: Test Proceduresmentioning

confidence: 99%

Bendability assessment of advanced high strength steels thanks to artificial neural networks

Teaca,

Dietsch,

Durrenberger

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Thanks to their very important strength in addition to their considerable formability and ductility, the newly developed Advanced High Strength Steel (AHSS) allow carmakers to save mass on the vehicles they produce. However, before industrial implementation by OEMs, any grade has to be approved. The bending test defined in VDA238-100 [1] test specification is a key indicator of the material ductility and is increasingly requested in the scope of their homologation. The test consists in bending a square sample of sheet material with a knife. The sample is supported by two rolls. The aim of this test is to determine the maximum angle reachable by the sample before it breaks. We can also derive the maximum strain reachable by the material before fracture initiation. Mainly 2 different angles are defined: the angle after springback, that can be measured manually and the angle at maximum load, which is to be considered in the VDA238-100. The test specification gives instructions on the determination of the bending angle. Nevertheless, the approach is purely geometric and only relies on the parameters of the test. No influence of the tested material is taken into account. This approach can be improved because for AHSS that are very ductile and have a bending angle over 100°, the proposed formula can provide unphysical results. For this reason, the innovative proposed approach is to calculate the bending angle at maximal force and to determine the fracture strain thanks to Artificial Neural Networks, one of the most promising tools for resolution of engineering problems. This new approach considers a large panel of materials tested experimentally or numerically and significantly improves the prediction.

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Experimental issues in the instrumented 3 point bending VDA238-100 test

Cited by 7 publications

References 12 publications

Prediction of Hole Expansion Ratio for Advanced High-Strength Steel with Image Feature Analysis of Sheared Edge

Prediction of Hole Expansion Ratio for Advanced High-Strength Steel with Image Feature Analysis of Sheared Edge

Recurrent Neural Networks and Three-Point Bending Test on the Identification of Material Hardening Parameters

Bendability assessment of advanced high strength steels thanks to artificial neural networks

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