Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming

Abbassi, Fethi; Belhadj, T.; Mistou, Sébastien; Zghal, Ali

doi:10.1016/j.matdes.2012.09.032

Cited by 119 publications

(36 citation statements)

References 26 publications

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“…7. The rupture surface itself shows large regions of dimples, the larger ones including second phase particles [16]. We observe the average void sizes are bigger, that refers to the ductile fracture [17].…”

Section: Failure Analysis (Fractography)mentioning

confidence: 75%

“…Chen and Dong [21], for instance, used GTN model coupled with Hill'48 yield criterion to predict the necking and the failure analysis of an anisotropic sheet metal forming. Furthermore, the GTN model has demonstrated that it is able to predict correctly the failure onset in sheet metal forming part [16,22,23].…”

Section: Ductile Damage Modelmentioning

confidence: 99%

“…Necking is an important limitation on material formability in sheet metal forming [26]. It appears that there are two basic geometrically nonlinear causes of the necking instability: (i) a decrease of the crosssection area of the specimen, causing a stress increase for the same load value, and (ii) the formation of micro voids leading to damage evolution and drastically decrease of the material properties in the metal [27,16]. Often, damage and fracture models have been used to study the governing factors of localization in the sheet metal forming process.…”

Section: Necking and Localization Of Deformationmentioning

confidence: 99%

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Failure analysis based on microvoid growth for sheet metal during uniaxial and biaxial tensile tests

2013

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. Failure analysis based on microvoid growth for sheet metal during uniaxial and biaxial tensile tests. Materials and Design, Elsevier, 2013, vol. 49, pp. 638-646. <10.1016/j.matdes.2013 OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. a b s t r a c tThe aim of the presented investigations is to perform an analysis of fracture and instability during simple and complex load testing by addressing the influence of ductile damage evolution in necking processes. In this context, an improved experimental methodology was developed and successfully used to evaluate localization of deformation during uniaxial and biaxial tensile tests. The biaxial tensile tests are carried out using cruciform specimen loaded using a biaxial testing machine. In this experimental investigation, Stereo-Image Correlation technique has is used to produce the heterogeneous deformations map within the specimen surface. Scanning electron microscope is used to evaluate the fracture mechanism and the micro-voids growth. A finite element model of uniaxial and biaxial tensile tests are developed, where a ductile damage model Gurson-Tvergaard-Needleman (GTN) is used to describe material deformation involving damage evolution. Comparison between the experimental and the simulation results show the accuracy of the finite element model to predict the instability phenomenon. The advanced measurement techniques contribute to understand better the ductile fracture mechanism.

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Section: Failure Analysis (Fractography)mentioning

confidence: 75%

Section: Ductile Damage Modelmentioning

confidence: 99%

Section: Necking and Localization Of Deformationmentioning

confidence: 99%

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Failure analysis based on microvoid growth for sheet metal during uniaxial and biaxial tensile tests

2013

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“…σ y , ε y and n are obtained from stress-strain curve of plain specimen obtained from quasi-static tensile test. The optimum quantities of q 1 = 1.5, S N = 0.1 and ε N = 0.3 are taken from literature [29][30][31][32]. Therefore, four constants remain to be determined for structural steel ST37.…”

Section: Polynomial Regression Methodsmentioning

confidence: 99%

Determination of the Constants of GTN Damage Model Using Experiment, Polynomial Regression and Kriging Methods

et al. 2017

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Damage models, particularly the Gurson-Tvergaard-Needleman (GTN) model, are widely used in numerical simulation of material deformations. Each damage model has some constants which must be identified for each material. The direct identification methods are costly and time consuming. In the current work, a combination of experimental, numerical simulation and optimization were used to determine the constants. Quasi-static and dynamic tests were carried out on notched specimens. The experimental profiles of the specimens were used to determine the constants. The constants of GTN damage model were identified through the proposed method and using the results of quasi-static tests. Numerical simulation of the dynamic test was performed utilizing the constants obtained from quasi-static experiments. The results showed a high precision in predicting the specimen's profile in the dynamic testing. The sensitivity analysis was performed on the constants of GTN model to validate the proposed method. Finally, the experiments were simulated using the Johnson-Cook (J-C) damage model and the results were compared to those obtained from GTN damage model.

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“…Therefore, the prediction results issued remain within the constitutive material model alone. As a result, the identified parameters only represent the FEsimulated behaviour rather than the actual behaviour of the material involved in the engineering analysis (Abbassi, Belhadj, Mistou, & Zghal, 2013;Bandara, Chan, & Thambiratnam, 2014;Manoochehri & Kolahan, 2014). In addition, the FE and ANN inverse analysis only focuses on predicting physical parameters, while the FE simulation results are significantly affected by both physical and numerical parameters.…”

Section: Introductionmentioning

confidence: 99%

Incorporating feedforward neural network within finite element analysis for L-bending springback prediction

Jamli

Ariffin

Wahab

2015

Expert Systems with Applications

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Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming

Cited by 119 publications

References 26 publications

Failure analysis based on microvoid growth for sheet metal during uniaxial and biaxial tensile tests

Failure analysis based on microvoid growth for sheet metal during uniaxial and biaxial tensile tests

Determination of the Constants of GTN Damage Model Using Experiment, Polynomial Regression and Kriging Methods

Incorporating feedforward neural network within finite element analysis for L-bending springback prediction

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