Evaluating material failure of AHSS using acoustic emission analysis

Stockburger, Eugen

doi:10.21741/9781644902417-47

Cited by 4 publications

(6 citation statements)

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“…The module for the new adaptive test methodology is based on a prior developed test module shown by Stockburger et al in [5]. In this test module, according to the test principle of Arcan et al [6], a butterfly specimen can be tested under different orientations or loading angles 𝛼𝛼 to the normal loading direction of the tensile testing machine, whereby different stress states can be achieved in the test area of the specimen.…”

Section: New Experimental-numerical Failure Characterisation Methodologymentioning

confidence: 99%

Improved failure characterisation of high-strength steel using a butterfly test rig with rotation control

Stockburger

2023

Materials Research Proceedings

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Abstract. A forming limit diagram is the standard method to describe the forming capacity of sheet materials. It predicts failure due to necking by limiting major and minor strains. For failure due to fracture, the fracture forming limit diagram is used, but fracture caused by plastic deformation at a shear-dominated stress state cannot be predicted with a conventional fracture forming limit diagram. Therefore, stress-based failure models are used as an alternative. These models are describing the fracture of sheet materials based on the failure strain and the stress state. Material-specific parameters must be determined, but a standardised procedure for the calibration of stress-based failure models is currently not established. Most test procedures show non-constant stress paths and varying stress states in the crack initiation area, which leads to uncertainties and inaccuracies for modelling. Therefore, a new test methodology was invented at the IFUM: a prior presented butterfly test rig was extended to enable an online rotation to adapt the loading angle while testing. First, butterfly tests with CP800 were performed for three fixed loading conditions. The tests were modelled numerically with boundary conditions corresponding to the tests. Based on the numerical results, the stress state as well as failure strain were identified and the stress state deviations were calculated. Afterwards, the necessary angular displacements to compensate the stress state deviations for the adaptive test rig were iteratively determined with numerical simulations using an automatised Python script. Finally, the butterfly tests were performed experimentally with the determined adaptive loading angles to identify the specimen failure and compared to the simulations for validation.

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Section: New Experimental-numerical Failure Characterisation Methodologymentioning

confidence: 99%

Improved failure characterisation of high-strength steel using a butterfly test rig with rotation control

Stockburger

2023

Materials Research Proceedings

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“…Further, the acoustic emissions of the butterfly specimens during the tests were recorded using the acoustical measuring system AMSY-6 from Vallen Systeme GmbH (Wolfratshausen, Germany). A more detailed description of the test setup is presented by Stockburger et al in [24].…”

Section: Failure Behaviourmentioning

confidence: 99%

Failure Modelling of CP800 Using Acoustic Emission Analysis

2023

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Advanced high-strength steels (AHHS) are widely used in many production lines of car components. For efficient design of the forming processes, numerical methods are frequently applied in the automotive industry. To model the forming processes realistically, exact material data and analytical models are required. With respect to failure modelling, the accurate determination of failure onset continues to be a challenge. In this article, the complex phase (CP) steel CP800 is characterised for its failure characteristics using tensile tests with butterfly specimens. The material failure was determined by three evaluation methods: mechanically by a sudden drop in the forming force, optically by a crack appearing on the specimen surface, and acoustically by burst signals. As to be expected, the mechanical evaluation method determined material failure the latest, while the optical and acoustical methods showed similar values. Numerical models of the butterfly tests were created using boundary conditions determined by each evaluation method. A comparison of the experiments, regarding the forming force and the distribution of the equivalent plastic strain, showed sufficient agreement. Based on the numerical models, the characteristic stress states of each test were evaluated, which showed similar values for the mechanical and optical evaluation method. The characteristic stress states derived from the acoustical evaluation method were shifted to higher triaxialities, compared to the other methods. Matching the point in time of material failure, the equivalent plastic strain at failure was highest for the mechanical evaluation method, with lower values for the other two methods. Furter, three Johnson–Cook (JC) failure models were parametrised and subsequently compared. The major difference was in the slope of the failure models, of which the optical evaluation method showed the lowest slope. The reasons for the differences are the different stress states and the different equivalent plastic strains due to different evaluation areas.

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“…To measure the exact displacement of the butterfly specimen in x-and y-direction, the tests were also monitored with DIC. A more detailed description of the test setup is presented by Stockburger et al in [6].…”

Section: Experimental Fracture Characterisation and Fracture Modellingmentioning

confidence: 99%

“…As for this example, the material fracture is determined earlier by the AEA evaluation using burst signals than by the DIC evaluation using the surface crack as an indicator. The evaluation methods are described in further detail for the steel HCT780C in [8]. Based on the two evaluation methods, the experiments for the seven loading angles were evaluated similarly for their displacements in x-and y-direction at the point of material fracture.…”

Section: Experimental Fracture Characterisation and Fracture Modellingmentioning

confidence: 99%

Optimised parametrisation of the MMC fracture model using AE and FE analysis

Stockburger

Wester

Behrens

2023

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

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To save resources in component production, finite element (FE) simulations are state of the art during process design to prevent expensive tool modifications. For a high accuracy of a FE simulation, realistic material data is required. Unlike for flow behaviour, the determination of the fracture behaviour is not standardised. Conventionally, tensile tests with different geometries are performed, monitored with digital image correlation (DIC) and fracture is determined on the surface. However, for many specimens the initiation of fracture is assumed to occur inside the material prior to a macroscopic fracture on the surface. Therefore, various tensile tests with butterfly specimens were performed for a HCT980X steel and monitored with a DIC as well as an acoustic emission analysis (AEA) system. The displacement at fracture was evaluated conventionally for fracture on the specimen’s surface based on the DIC and unconventionally for fracture inside the specimen by AEA. FE models of the butterfly tests were created with fracture displacements of both evaluation methods as boundary conditions. Using the numerically determined stress state and fracture strain from the butterfly models, the MMC fracture model was parametrised for both evaluation methods and compared by applying both models to a FE simulation of a forming process.

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Evaluating material failure of AHSS using acoustic emission analysis

Cited by 4 publications

References 0 publications

Improved failure characterisation of high-strength steel using a butterfly test rig with rotation control

Improved failure characterisation of high-strength steel using a butterfly test rig with rotation control

Failure Modelling of CP800 Using Acoustic Emission Analysis

Optimised parametrisation of the MMC fracture model using AE and FE analysis

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