Experimental Mechanics最新文献

Background

Multiaxial dynamic loading situations occur in many industrial cases and multiaxial dynamic test development is thus a crucial issue.

Objective

To meet this challenge, a biaxial compression Hopkinson set-up with four symmetric input bars is designed.

Methods

The set-up consists of a vertical single striker, a sliding surface mechanism that transfers the impact energy to four horizontal tension bars, and four horizontal Hopkinson bars whose extremities are dynamically compressed by the previous tension bars. Strain gauges on two positions of each Hopkinson bar enable for force and displacement measurements at the bar-sample interfaces.

Results

Simple and biaxial compression tests are carried out on cuboid and cross samples, and the sample material dynamic behavior is deduced from simple compression tests.

Conclusions

The displacements are also estimated using digital image correlation, which confirms the previous measurements. The consistency of the global sample behavior identified from a biaxial compression test is checked by processing numerical simulations based on the behavior determined in simple compression. The results show that the experimental device can be used to identify any behavior law in dynamic biaxial compression.

Background

Layered composites consisting of dissimilar materials have shown tremendous improvements in balancing strength with ductility. The details of strain partitioning across the layers, resulting in high ductility even in the brittle layer, are not well understood.

Objective

This study aims to quantify strain partitioning and understand the failure of rolled sheets of alternating austenite and martensite layers through in situ tensile experiments.

Methods

A novel high density speckle pattern with the sample surface as background is generated to resolve strain within and across the interface at the microscale. Simultaneous imaging of both the layered and top surfaces was performed to correlate strain and understand the localization leading to failure. Microstructural analysis and numerical simulations were performed to further understand the role of phase transformation and predict the stress–strain response, respectively.

Results

Both axial and transverse strain field heterogeneity was observed across the layers, with pronounced strain partitioning in the transverse direction and steep gradients near the interfaces. The restriction to the growth of micro-deformation sites in the thin austenitic layers led to a long neck region with local strain as high as 40% compared to the global fracture strain of 20%. During plastic deformation, the austenitic layers underwent phase transformation in the region of high Schmid factor, and the martensitic layers experienced texture evolution.

Conclusions

Small deformation bands within each layer grew and formed macroscopic shear bands leading to fracture. Finally, experimental results were compared with finite element simulations and the rule of mixtures, demonstrating a satisfactory agreement between the different approaches.

Background

Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed.

Objective

The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy.

Methods

A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data.

Results

The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem.

Conclusion

Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

Background

Geometric parameter optimization, novel design, and mechanism modeling of auxetic materials have been widely studied. However, manipulating the topology of the 3d printed auxetic unit cells and its influence on the damage have yet to be explored.

Objective

This study aims to characterize the energy absorption properties and damage mechanisms of the modified auxetic unit cells.

Methods

In the current study, bending-dominated re-entrant auxetic unit cells (Cell0), torsion-dominated auxetic unit cells with cross elements (CellX), buckling-dominated auxetic unit cells with vertical elements (CellB), and bending-dominated auxetic unit cells with panels (CellW) have been fabricated by FDM (Fused deposition modeling). Uniaxial compression testing of the PLA (Polylactic acid) unit cells has been carried out, and a camera has observed their deformation behavior. SR- µCT (Synchrotron radiation microtomography) and an SEM (Secondary electron microscope) accomplished further damage analysis of the struts.

Results

Adding additional struts hinders the lateral shrinking of the re-entrant auxetics, and re-entrant auxetic unit cells with cross elements have shown higher energy absorption capacity and efficiency than others. The struts’ damage has been governed by building direction, printed material, and strut dimensions. Intra-layer and interlayer fracture of the layers and rupture in the circumferential direction of the PLA struts have been observed in the SR- µCT slices.

Conclusions

By additional struts, it is possible to fabricate complex auxetic structures with enhanced energy absorption properties, but their inherent characteristics dominate the damage of the struts in the auxetic unit cells.

Graphical Abstract

Background

To minimize measurement uncertainties and create seamless procedures between tests and simulations for the characterization and prediction of damage in large scale structures, a system capable of monitoring the quantities of interest at different scales throughout the test is required.

Objective

The aim of this work is to develop a multiview DIC framework at varying scales in which kinematic fields are expressed on a unique mesh.

Methods

A three-point flexural test was performed on a concrete beam and the images acquired by three different cameras were used to perform DIC calculations.

Results

Displacement and strain fields were measured using mono and multiview implementations; their associated uncertainties were assessed. Damage initiation and growth within the sample was quantified based on the standard displacement uncertainty.

Conclusion

The reported results show that the proposed method reduced the associated displacement uncertainties. The onset and propagation of damage was successfully quantified.

Background

In polycrystal mechanics, determination of stress is associated with diffraction methods that measure (the inherently-related) elastic strain. Microscopic digital image correlation (DIC), while commanding much higher intragranular resolution, measures total strain, and its local accuracy is typically insufficient to evaluate elastic strain magnitudes.

Objective

In situ DIC measurements over a partial unload of the polycrystal, where strains are virtually elastic, are explored for grain-averaged elastic strains and then, through a posed formalism, the stresses at the point of unload. Grain averaging is functionally employed to improve the DIC accuracy. The large objective is to emulate in situ complementary diffraction.

Methods

Nickel with high elastic anisotropy is chosen. The utilized highly-automated instrument offers maximal resolution for DIC with optical microscopy over a gross grain field. Orientations are predetermined for the same grain layer via electron backscatter diffraction. High-accuracy grain masks are produced to isolate the strain fields of individual grains.

Results

Very promising results are shown over a number of grains with sensible apparent compliance and stress values as well as linear unload behavior. Grains with sane results are largely predicted by a posed objectivity test that relies on DIC repeated with multiple reference loads.

Conclusion

Though it will require extremely careful implementations of microscopic DIC with high intragranular resolution, the premise of measuring intergranular stress fields via partial unloads seems to be viable and worthy of further exploration and verification. This capability that is superposed over strain measurement offers a more stringent validation of high-fidelity crystal plasticity models.

Background

The potential effect of image noise artefacts on Digital Volume Correlation (DVC) analysis has not been thoroughly studied and, more particularly quantified, even though DVC is an emerging technique widely used in life and material science over the last decade.

Objective

This paper presents the results of a sensitivity study to shed light on the effect of various noise artefacts on the full-field kinematic fields generated by DVC, both in zero and rigid body motion.

Methods

Various noise artefacts were studied, including the Gaussian, Salt & Pepper, Speckle noise and embedded Ring Artefacts. A noise-free synthetic microstructure was generated using Discrete Element Modelling (DEM), representing an idealistic case, and acting as the reference dataset for the DVC analysis. Noise artefacts of various intensities (including selected extreme cases) were added to the reference image datasets using MATLAB (R2022) to form the outline of the parametric study. DVC analyses were subsequently conducted employing AVIZO (Thermo Fisher). A subset-based local approach was adopted. A three-dimensional version of the Structural Similarity Index Measure (SSIM) was used to define the similarity between the compared image datasets on each occasion. Sub-pixel rigid body motion was applied on the DEM-generated microstructure and subsequently “poisoned” with noise artefacts to evaluate mean bias and random error of the DVC analysis.

Results

When the local approach is implemented, the sensitivity study on zero motion data revealed the insignificant effect of the Gaussian, Salt & Pepper, and Speckle noise on the DVC-computed kinematic field. Therefore, the presence of such noise artefacts can be neglected when DVC is executed. On the contrary, Ring Artefacts can pose a considerable challenge and therefore, DVC results need to be evaluated cautiously. A linear relationship between SSIM and the correlation index is observed for the same noise artefacts. Gaussian noise has a pronounced effect on the mean bias error for sub-pixel rigid body motion.

Conclusions

Generating synthetic image datasets using DEM enabled the investigation of a variety of noise artefacts that potentially affect a DVC analysis. Given that, any microstructure – resembling the material studied – can be simulated and used for a DVC sensitivity analysis, supporting the user in appropriately evaluating the computed kinematic field. Even though the study is conducted for a two-phase material, the method elaborated in this paper also applies to heterogeneous multi-phase materials also. The conclusions drawn are valid within the environment of the AVIZO DVC extension module. Alternative DVC algorithms, utilising different approaches for the cross-correlation and the sub-pixel interpolation methods, need to be investigated.

Background

Intracranial aneurysm is a pathology related to the biomechanical deterioration of the arterial wall. As yet, there is no method capable of predicting rupture risk based on quantitative, in vivo mechanical data. This study is part of a large-scale project aimed at providing clinicians with a non-invasive, patient-specific decision support tool, based on the in vivo mechanical characterisation of the aneurysm wall. To this end, an original arterial wall deformation device was developed and tested on polymeric phantom arteries. Concurrently, a computational model coupled with the experimental study was developed to improve understanding of the interaction between the arterial wall deformation device and the aneurysm wall.

Objective

An original procedure was implemented to validate the numerical model against experimental results.

Methods

The deformation induced by the device on the polymeric phantom arteries is quantified by Digital Image Correlation. The Fluid-Structure Interaction between the device and the arterial wall was modelled numerically with the Finite Element method. The validation procedure encompasses the extraction and the interpolation of the numerical results. The computed strains were compared with the data measured experimentally.

Results

The numerical results interpolated on the experimental reference image were associated with several deformation device locations. These configurations induced strains and displacements ranges that included the experimental results, which validates the proposed model.

Conclusions

The reliability of the procedure was validated with various study cases and artery materials. The procedure could be extended to experimental studies involving more complex phantom arteries in terms of shape and wall heterogeneity.

Background

Excessive levels of residual stresses (RS) in metallic components of flexible pipes can lead to failure by fatigue or stress-corrosion cracking (SCC) mechanisms. Accurate measurements of residual stresses are essential in order to support comprehensive integrity assessments of these structures.

Objective

This paper aims at proposing a methodology for in-field evaluation of the residual stresses in the tensile armour of flexible pipes. This methodology is evaluated by comparing results from the contour method (CM), a portable (PXRD) and a benchtop (BXRD) X-ray diffractometer.

Methods

A set of samples with a controlled RS state was used to evaluate PXRD depth-profiles by comparison with BXRD measurements. CM was used to investigate stress values elsewhere in the cross-section of the samples. PXRD was then used to measure the total stress on a full-scale flexible riser.

Results

Strong texture was seen in XRD measurements due to the preferential orientation in the samples, but a method to overcome this is proposed. RS measurements with PXRD were in close agreement with BXRD values in all samples. Total stress profiles obtained by PXRD on a tensile armour of flexible pipe are given. CM did not provide reliable results in the near-surface region of shot-peened samples, but reasonable agreement was found with BXRD after manual adjustment of a smoothing parameter when depths from 0.3 mm to 1.5 mm were analyzed.

Conclusion

A methodology has been established for non-destructive in-loco profiling of the total stress state in tensile armours of flexible pipes with PXRD; in a real application scenario it could provide valuable information that could help to understand damage initiation mechanisms.

Background

Deep learning-based digital image correlation (DL-based DIC) has gained increasing attention in the last two years. However, existing DL-based DIC algorithms are impractical because their application scenarios are mostly limited to small deformations.

Objective

To enable the use of DL-based DIC in real-world general experimental mechanics scenarios that would involve large deformations and rotations, we propose to improve DL-based DIC with the domain decomposition method (DDM).

Methods

In the improved method, the region of interest is divided into subimages, and subimages are pre-aligned using the preregistered control points to effectively eliminate the large deformation components. The residual deformations in each subimage are small and limited, which can be well extracted using existing DL-based DIC methods.

Results

Through synthesized and real-world experiments, the improved DL-based DIC method can achieve high-accuracy pixelwise matching in practical applications with strong robustness and high computational efficiency.

Conclusions

The improved DL-based DIC combines the advantages of traditional and DL-based DIC methods but overcomes the limitations, greatly improving the robustness and applicability of existing DL-based methods.