Computationally efficient necking prediction using neural networks trained on virtual test data

Greve, Lars; Schneider, Bernd; Andres, Michael; Martinez, Jean Daniel; Eller, Tom; Weg, Bram van de; Hazrati, Javad; Boogaard, A.H. van den

doi:10.1088/1757-899x/651/1/012054

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Existing approaches have already demonstrated a good prediction of the forming limit curve by using feedforward backpropagation artificial neural networks (NN). These networks utilize input data obtained from various sources, including process-related data from test trials [8,9], geometric data of the test specimen [10], and calculated stress-strain values derived from material testing [11][12][13]. Other authors obtained results in good agreement with the respective reference points using adaptive network fuzzy inference systems (ANFIS).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach

Thamm,

Sawodny

et al. 2023

Materials

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An accurate description of the formability and failure behavior of sheet metal materials is essential for an optimal forming process design. In this respect, the forming limit curve (FLC) based on the Nakajima test, which is determined in accordance with DIN EN ISO 12004-2, is a wide-spread procedure for evaluating the formability of sheet metal materials. Thereby the FLC is affected by influences originating from intrinsic factors of the Nakajima test-setup, such as friction, which leads to deviations from the linear strain path, biaxial prestress and bending superposition. These disadvantages can be circumvented by an alternative test combination of uniaxial tensile test and hydraulic bulge test. In addition, the forming limit capacity of many lightweight materials is underestimated using the cross-section method according to DIN EN ISO 12004-2, due to the material-dependent occurrence of multiple strain maxima during forming or sudden cracking without prior necking. In this regard, machine learning approaches have a high potential for a more accurate determination of the forming limit curve due to the inclusion of other parameters influencing formability. This work presents a machine learning approach focused on uniaxial tensile tests to define the forming limit of lightweight materials and high-strength steels. The transferability of an existing weakly supervised convolutional neural network (CNN) approach was examined, originally designed for Nakajima tests, to uniaxial tensile tests. Additionally, a stereo camera-based method for this purpose was developed. In our evaluation, we train and test materials, including AA6016, DX54D, and DP800, through iterative data composition, using cross-validation. In the context of our stereo camera-based approach, strains for different materials and thicknesses were predicted. In this cases, our method successfully predicted the major strains with close agreement to ISO standards. For DX54D, with a thickness of 0.8 mm, the prediction was 0.659 (compared to ISO’s 0.664). Similarly, for DX54D, 2.0 mm thickness, the predicted major strain was 0.780 (compared to ISO 0.705), and for AA6016, at 1.0 mm thickness, a major strain of 0.314 (in line with ISO 0.309) was estimated. However, for DP800 with a thickness of 1.0 mm, the prediction yielded a major strain of 0.478 (as compared to ISO 0.289), indicating a divergence from the ISO standard in this particular case. These results in general, generated with the CNN stereo camera-based approach, underline the quantitative alignment of the approach with the cross-section method.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach

Thamm,

Sawodny

et al. 2023

Materials

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Comparison of Artificial Intelligence Methods for Prediction of Mechanical Properties

Lee

Hong

Lee

et al. 2020

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

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This paper compares artificial intelligence (AI) methods to predict mechanical properties of sheet metal in stamping processes. The deviation of the mechanical properties of each blank leads to unpredicted failures in stamping processes, such as fracture and spring back. The research team of this paper has been building a real time control system for stamping process in a smart factory. In order to facilitate that, it is necessary to predict the mechanical properties of each blank with non-destructive testing. The regression models based on the linear algebraic scheme have traditionally brought reliable results in terms of matching the measured non-destructive testing values to the mechanical properties. With a parallel to algebraic regression models, in recent studies on various domains, AI models have been adopted to improve the accuracy of the end-results and effectiveness of the models. This paper discusses the applicability of AI models for predicting the mechanical properties based on the eddy-current non-destructive testing method. For the study, 6 input features are collected through the eddy-current non-destructive testing to map eddy-current input data to mechanical properties of the blank. Yield stress and uniform elongation were predicted by using five AI methods, i.e., regularized linear regression, support vector regularized linear regression, support vector regression, multi-layer neural network, random forest regression, and gradient boosting regression were compared. The model performance, validated with 20% of test data that are intact during the training phase, is the main discussion point of this paper. Future works to improve the predictive accuracy of AI models is also discussed.

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Computationally efficient necking prediction using neural networks trained on virtual test data

Cited by 2 publications

References 9 publications

Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach

Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach

Comparison of Artificial Intelligence Methods for Prediction of Mechanical Properties

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