Influence of specimen preparation, microstructure anisotropy, and residual stresses on stress–strain curves of rolled Al2024 T351 as derived from spherical indentation tests

Heerens, J.; Mubarok, Fahmi; Huber, N.

doi:10.1557/jmr.2009.0116

Cited by 19 publications

(10 citation statements)

References 23 publications

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“…Because of the large strains that occurred during this test, it was more sensitive to the yield stress behaviour around a 10% plastic strain. It showed larger uncertainties below, above and, in particular, near the yield point [13][14][15]. Thus, this approach is favourable for studies that investigate the deformation behaviour of specimens under significant plastic deformation.…”

Section: Indentation Testmentioning

confidence: 95%

Asymmetric mechanical properties and tensile behaviour prediction of aluminium alloy 5083 friction stir welding joints

Rao

Huber

Heerens

et al. 2013

Materials Science and Engineering: A

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The asymmetric material flow, severe plastic deformation and thermal cycle imposed on the base material during friction stir welding (FSW) result in unique microstructural development, which causes a gradient in local mechanical properties in the weld region. Micro-tensile and indentation testing were applied to determine the local mechanical properties in a friction stir welded joint. The local stress-strain curves exhibited a drastic change at the advancing side (AS) due to a steep gradient of mechanical properties. Finite Element Model (FEM) predictions of the tensile performance of the welded joints, based on the local mechanical properties measured by micro-tensile testing, were in very good agreement with the macro-tensile test data.

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Section: Indentation Testmentioning

confidence: 95%

Asymmetric mechanical properties and tensile behaviour prediction of aluminium alloy 5083 friction stir welding joints

Rao

Huber

Heerens

et al. 2013

Materials Science and Engineering: A

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“…The prediction of rising or falling material hardness, based on residual stresses and contact pressure of spherical indentation tests was investigated by several groups via experimental and FEM simulations. Heerens et al (2009) presented a model that allows to compute the change in hardness for arbitrary in-plane biaxial residual stress states including the special cases of uniaxial, equibiaxial, and pure shear residual stress. Relevant for this review is the way this model was found.…”

Section: Predictivementioning

confidence: 99%

“…Therefore, as major outcome of the applied machine learning approach, it can be concluded that both the normalized von-Mises stress and hydrostatic pressure are equally important to solve the problem. The model published by Heerens et al (2009) is based on this insight and would not exist without the intermediate step of using the ANN. While the ANN was descriptive and limited to the range of training data, the derived model is general and predictive.…”

Section: Predictivementioning

confidence: 99%

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

et al. 2019

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Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment-and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant Bock et al. Machine Learning in Materials Mechanics identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

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“…A three-dimensional finite element model was used, which has been validated regarding its accuracy and pre-stress application elsewhere [19]. The finite element model is presented in Fig.…”

Section: Theoretical Model 21 Finite Element Simulationsmentioning

confidence: 99%

On the effect of a general residual stress state on indentation and hardness testing

Huber¹,

Heerens²

2008

Acta Materialia

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147

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Residual stresses superimpose the stress field from an indentation experiment and do therefore influence the measurement of the volume of interest. The residual stress state can be very different in magnitude and biaxiality and error that may be caused in the measured hardness is difficult to estimate. A prediction of the pressure ratio for stressed and unstressed material is carried out by new model that accounts for nonlinearities caused by the von Mises flow rule. The model can also be used for the correction of the effect of a general residual stress state before further analysis of the measured indentation data towards the underlying mechanical properties, such as the stress-strain behaviour. The model does further allow estimating the measurement uncertainty when the stress ratio or the strength of the material is unknown, which is a typical scenario for hardness testing in welded steels and aluminium alloys.

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Influence of specimen preparation, microstructure anisotropy, and residual stresses on stress–strain curves of rolled Al2024 T351 as derived from spherical indentation tests

Cited by 19 publications

References 23 publications

Asymmetric mechanical properties and tensile behaviour prediction of aluminium alloy 5083 friction stir welding joints

Asymmetric mechanical properties and tensile behaviour prediction of aluminium alloy 5083 friction stir welding joints

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

On the effect of a general residual stress state on indentation and hardness testing

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