Complex Shapes Research Articles

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

Finding the distribution of the velocities and pressures of a fluid by solving the Navier–Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and in design of pipeline systems. With existing solvers, such as OpenFOAM and Ansys, simulations of fluid dynamics in intricate geometries are computationally expensive and require re-simulation whenever the geometric parameters or the initial and boundary conditions are altered. Physics-informed neural networks (PINNs) are a promising tool for simulating fluid flows in complex geometries, as they can adapt to changes in the geometry and mesh definitions, allowing for generalization across fluid parameters and transfer learning across different shapes. We present a hybrid quantum PINN (HQPINN) that simulates laminar fluid flow in 3D Y-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a PINN, resulting in a 21% higher accuracy compared to a purely classical neural network. Our findings highlight the potential of machine learning approaches, and in particular HQPINN, for complex shape optimization tasks in computational fluid dynamics. By improving the accuracy of fluid simulations in complex geometries, our research using hybrid quantum models contributes to the development of more efficient and reliable fluid dynamics solvers.

Nylons with Applications in Energy Generators, 3D Printing and Biomedicine.

Linear polyamides, known as nylons, are a class of synthetic polymers with a wide range of applications due to their outstanding properties, such as chemical and thermal resistance or mechanical strength. These polymers have been used in various fields: from common and domestic applications, such as socks and fishing nets, to industrial gears or water purification membranes. By their durability, flexibility and wear resistance, nylons are now being used in addictive manufacturing technology as a good material choice to produce sophisticated devices with precise and complex geometric shapes. Furthermore, the emergence of triboelectric nanogenerators and the development of biomaterials have highlighted the versatility and utility of these materials. Due to their ability to enhance triboelectric performance and the range of applications, nylons show a potential use as tribo-positive materials. Because of the easy control of their shape, they can be subsequently integrated into nanogenerators. The use of nylons has also extended into the field of biomaterials, where their biocompatibility, mechanical strength and versatility have paved the way for groundbreaking advances in medical devices as dental implants, catheters and non-absorbable surgical sutures. By means of 3D bioprinting, nylons have been used to develop scaffolds, joint implants and drug carriers with tailored properties for various biomedical applications. The present paper aims to collect evidence of these recently specific applications of nylons by reviewing the literature produced in recent decades, with a special focus on the newer technologies in the field of energy harvesting and biomedicine.

Enhanced forming height and accuracy of thin-walled aluminum alloy by impact pneumatic forming

To enhance the plastic deformation capacity of hard-to-deform metal materials, the utilization of impact pneumatic forming technology is proposed for shaping complex thin-walled structures within milliseconds at room temperature. In this study, an efficient numerical simulation model is established and experiments were performed to investigate the forming height, limits, and accuracy of aluminum alloy components. The experimental results show a 21.4 % increase in the ultimate forming height when employing impact pneumatic forming as compared to quasi-static pneumatic forming. Moreover, as the initial gas pressure increases, part forming height experiences an increase but the gas compression rate experiences a decline. The simulation results reveal a maximum deformation velocity of 43.19 m/s and a limited strain rate exceeding 134 s−1, affirming the connection between increased forming height and high strain rate. Furthermore, the actual contours of parts under different gas pressures align with the simulation results, validating the reliability of the simulation. It is shown that a high gas pressure of more than 20 MPa applied for a long time can improve the forming accuracy of flat bottom and wave parts. Microstructural analysis shows that dislocations in the specimens subjected to IPF conditions are better distributed, and the dimples at the fracture sites are more pronounced. This further substantiates that the IPF process improves the plastic deformation capacity of hard-to-deform materials.

Enhancing metasurface fabricability through minimum feature size enforcement

Abstract The metasurfaces have shown great potential for miniaturizing conventional optics while offering extended flexibility. Recently, there has been considerable interest in using algorithms to generate meta-atom shapes for these metasurfaces, as they offer vast design freedom and not biased by the human intuition. However, these complex designs significantly increase the difficulty of fabrication. To address this, we introduce a design process that rigorously enforces the fabricability of both the material-filled (fill) and empty (void) regions in a metasurface design. This process takes into account specific constraints regarding the minimum feature size for each region. Additionally, it corrects any violations of these constraints across the entire device, ensuring only minimal impact on performance. Our method provides a practical way to create metasurface designs that are easy to fabricate, even with complex shapes, hence improving the overall production yield of these advanced meta-optical components.

Improving Polyp Segmentation with Boundary-Assisted Guidance and Cross-Scale Interaction Fusion Transformer Network

Efficient and precise colorectal polyp segmentation has significant implications for screening colorectal polyps. Although network variants derived from the Transformer network have high accuracy in segmenting colorectal polyps with complex shapes, they have two main shortcomings: (1) multi-level semantic information at the output of the encoder may result in information loss during the fusion process and (2) failure to adequately suppress background noise during segmentation. To address these challenges, we propose a cross-scale interaction fusion transformer for polyp segmentation (CIFFormer). Firstly, a novel feature supplement module (FSM) supplements the missing details and explores potential features to enhance the feature representations. Additionally, to mitigate the interference of background noise, we designed a cross-scale interactive fusion module (CIFM) that combines feature information between different layers to obtain more multi-scale and discriminative representative features. Furthermore, a boundary-assisted guidance module (BGM) is proposed to help the segmentation network obtain boundary-enhanced details. Extensive experiments on five typical datasets have demonstrated that CIFFormer has an obvious advantage in segmenting polyps. Specifically, CIFFormer achieved an mDice of 0.925 and an mIoU of 0.875 on the Kvasir-SEG dataset, achieving superior segmentation accuracy to competing methods.

Influence of modelling approach for reinforced concrete underground structures, with application to the CMS cavern at CERN

Representative modelling of reinforced concrete (RC) components in underground structures is essential for accurate assessment of structural performance (deformations and internal forces) within numerical simulations. This paper examines the implications of selecting different structural modelling approaches within the seismic (dynamic) finite element analysis of a buried structure of complex shape, using the CMS (Compact Muon Solenoid) Detector Cavern of the Large Hadron Collider in Geneva, Switzerland, as a case study. Two alternate modelling approaches were employed to model the cavern lining: (i) a composite continuum approach, with the concrete and embedded reinforcement being explicitly modelled; and (ii) the use of a nonlinear elasto-plastic plate element. The pre-earthquake ground initial conditions were determined through simulation of the construction and detector installation operations consistent with field measurements from extensometers and internal survey of floor deformations. The results demonstrate the importance of adopting a non-linear continuum modelling approach in representing the RC lining under strong shaking events to avoid under-prediction of seismic actions at locations of potential seismically induced damage. Such an approach will be essential in 3D problems where multi-axial dynamically varying stresses are applied on the RC section. Finally, it offers a realistic approach in representing structures of complex shape and that contains volume and thick elements.

Effect of the Axial Profile of a Ceramic Grinding Wheel on Selected Roughness Parameters of Shaped Surfaces Obtained in the Grinding Process with a Dual-Tool Grinding Head.

The use of CNC equipment that integrates several machining operations eliminates downtime due to changes in setup and clamping of workpieces in more than one machining device. A review of CNC equipment and tools known from the literature and from manufacturers' offerings indicates that new technical solutions are being developed to integrate two or more technological operations. However, these examples have numerous limitations and are mostly not suitable for machining surfaces with complex shapes. An example of such solutions is the use of a dual-tool grinding head, which integrates the process of rough grinding with a ceramic grinding wheel and finish grinding with a flexible grinding wheel. Unfortunately, it has the disadvantage of being limited by the angular shape of the ceramic grinding wheel, making it unable to adapt to the complex geometries of the shaped surfaces being ground. The need to overcome this limitation became the motivation for the research work described in this article. By means of experimental research, it was verified what effect the radial outline on the periphery of a ceramic grinding wheel realized by rough grinding would have on the surface roughness parameters obtained in the process of grinding shaped surfaces. For this purpose, grinding processes using a ceramic wheel with a conical and radial outline were compared. The result of the study was a summary of the surface roughness parameters Sa, St, Sq, Spk, Str, and Sds obtained after two-stage machining (rough and finish grinding). The obtained analysis results showed that changing the axial outline of the ceramic grinding wheel makes it possible to significantly expand the range of applications of the dual-tool head without negatively affecting the quality of the machined surface. Thus, such an improvement will make it possible to increase the applicability of the head by grinding shaped surfaces with a radial profile of curvature.

Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.

This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape analysis (SSA) by comparing patellofemoral instability (PFI) and normal femur models, and developed a machine learning (ML)-based prediction model. Twenty (19 patients) and 31 MRI scans (30 patients) of femurs with PFI and normal femurs, respectively, were used. Bone and cartilage segmentation of the distal femurs was performed and subsequently converted into 3D reconstructed models. The pointwise distance map showed anterior elevation of the trochlea, particularly at the central floor of the proximal trochlea, in the PFI models compared with the normal models. Principal component analysis examined shape variations in the PFI group, and several principal components exhibited shape variations in the trochlear floor and intercondylar width. Multivariate analysis showed that these shape components were significantly correlated with the PFI/non-PFI distinction after adjusting for age and sex. Our ML-based prediction model for PFI achieved a strong predictive performance with an accuracy of 0.909 ± 0.015, and an area under the curve of 0.939 ± 0.009 when using a support vector machine with a linear kernel. This study demonstrated that 3D MRI-based SSA can realistically visualize statistical results on surface models and may facilitate the understanding of complex shape features.

R-matrix calculations for opacities: III. Plasma broadening of autoionizing resonances

A general formulation is employed to study and quantitatively ascertain the effect of plasma broadening of intrinsic autoionizing (AI) resonances in photoionization cross sections. In particular, R-matrix data for iron ions described in the previous paper in the RMOP series (RMOP-II, hereafter RMOP2) are used to demonstrate underlying physical mechanisms due to electron collisions, ion microfields (Stark), thermal Doppler effects, core excitations, and free–free transitions. Breit–Pauli R-matrix cross sections for a large number of bound levels of Fe ions are considered, 454 levels of Fe XVII, 1184 levels of Fe XVIII and 508 levels of Fe XIX. Following a description of theoretical and computational methods, a sample of results is presented to show significant broadening and shifting of AI resonances due to extrinsic plasma broadening as a function of temperature and density. The redistribution of AI resonance strengths broadly preserves their integrated strengths as well as the naturally intrinsic asymmetric shapes of resonance complexes which are broadened, smeared and flattened, eventually dissolving into the bound-free continua.

Preparation and Analysis of New Hybrid PAEK Nano-Composites Containing MWCNT, Inorganic and Organic Nano-Particles

In modern era light weight material with complex shape and high material properties is required for advanced applications at room temperature. In this concern, the experimental study analysis to observe the effect of different nanofiller material like GO, MWCNT, NH2 and COOH modified MWCNT as organic nanofiller and WS2 and MoS2 as inorganic nanofiller reinforced in pure PAEK. This experimental study focuses on incorporation of organic and inorganic nanofillers separately and combine as hybrid nanofiller nanocomposite. On preparation of PAEK nano-composite and hybrid nanocomposite the comparative study of mechanical properties like tensile, flexural and impact is carried out. The nano-composites were prepared by melt compounding method. Scanning Electron Microscopy confirm the homogenous dispersion and alignment of nanofilers. The hybrid nano-composites show highest tensile strength as compared to the organic and in-organic nanofiller nanocomposite. As the percentage of MWCNT increases from 0.2 to 1 wt% , the properties of nano-composites decreases due agglomeration tendency of MWCNT that results in low load transfer capacity of nanocomposite, therefore agglomeration tendency of MWCNT required to be controlled for preparation of nano-composite and this is achieved by reinforcing Hybrid nanofillers to pure PAEK matrix. However, organic filler shows 75 MPa to 83 MPa tensile properties and which is lower compared to in-organic and hybrid filler due agglomeration tendency of organic nanofiller confirmed by SEM analysis.

Multi-objective optimization of machining variables for wire-EDM of LM6/fly ash composite materials using grey relational analysis

Abstract With the enhancement in science and technology, necessity of complex shapes in manufacturing industries have become essential for more versatile applications. This leads to the demand for lightweight and durable materials for applications in aerospace, defense, automotive, as well as sports and thermal management. Wire electric discharge machining (WEDM) is an extensively utilized process that is used for the exact and indented shaped components of all materials that are electrically conductive. This technique is suitable in practically all industrial sectors owing to its widespread application. The present investigation explores WEDM for LM6/fly ash composites to optimize different process variables for attaining performance measures in terms of maximum material removal rate (MRR) and minimum surface roughness (SR). Taguchi’s L27 OA design of experiments, grey relational analysis, and analysis of variance (ANOVA) were employed to optimize SR and MRR. It has been noted from ANOVA that reinforcement (R) percentage and pulse on time are the most influential aspects for Grey Relational Grade (GRG) with their contributions of 28.22 and 18.18%, respectively. It is found that the best process variables for achieving the highest MRR and lowest SR simultaneously during the machining of the composite are gap voltage of 30 V, pulse on time of 10 µs, pulse off time of 2 µs, wire feed of 8 m/min, and R of 9%. The predicted GRG is 0.84, and the experimental GRG value is 0.86. The validation experiments at the optimized setting show close agreement between predicted and experimental values. The morphological study by optical microscopy revealed a homogenous distribution of reinforcement in the matrix which enhances the composite’s hardness and decreases the density.

The status of the Romanian literary avant-garde after 2000: from marginalization to recovery

The present paper proposes a theoretical approach related to the critical reception of the Romanian literary avant-garde after 2000, a literary phenomenon in‑between marginalization and recovery tendencies. Starting from interwar critical studies from Romania (E. Lovinescu, G. Călinescu, etc.), with predominantly negative perspectives regarding the critical reception of the avant-garde, we will observe how certain clichés of the reception of the phenomenon, seen as “extremist”, as marginal, were perpetuated, with the “barrier” of the apparently impossible literary canonization. After 2000, however, literary studies discuss the historicization of the avant-garde phenomenon, which therefore became canonical in Romanian literary history. However, several elements of the “niche” avant-garde remained in the subsidiary, in the “shadow”. This is what we call the marginal(ized), the secondary avant-garde, which includes a series of less known and researched avant-garde writers, but who contributed to the complex shaping of the avant-garde imaginary. We will analyse several types of works published after 2000, in order to highlight the complexity of the avant-garde, under constant recovery: literary anthologies (Ion Pop, Nicolae Bârna), avant-garde dictionaries (Lucian Pricop, Dan Grigorescu), several critical studies after 2000 (Ion Bogdan Lefter, Ovidiu Morar, Emilia Drogoreanu, Paul Cernat, Dan Gulea, Emanuel Modoc, Delia Ungureanu, Daniel Clinci, Petre Răileanu, Gabriela Glăvan, Ion Pop, etc.). Many of them focus on the recovery trends of some forgotten writers, with the possibility of their inclusion in the central, canonical avant-garde, while other studies pursue new research methodologies, such as the avant-garde seen in a transnational context, in world literature context, etc. An issue that we develop in the context of this extended future research, which is also highlighted upon in the present work, is that of post-Urmuz epigonism within Romanian literary avant-garde, a fact that explains the placement of many writers in the sphere of critical marginalization. Thus, many texts by less researched writers are forgotten, being always associated with the central avant-garde models, especially Urmuz, but also Tristan Tzara or other influential writers within the central avant‑garde groups. It is precisely this problem that made us analyse the way in which the writers who are part of the marginal dimension of the avant-garde are recovered through contemporary literary studies from Romania.

Real-Time Shape Estimation of Hyper-Redundant Flexible Manipulator Using Coiled Fiber Sensors.

The shape of flexible endoscopic surgical robot should be obtained to increase control accuracy and prevent unwanted tissue damage. To estimate the shape of flexible manipulator, space efficiency, cost-effectiveness, system complexity, and ease of calibration should be considered to integrate sensors into the manipulator. In this article, we propose a real-time method to estimate the shape of a hyper-redundant manipulator having embedded coiled fiber sensors. The main advantage of this method is guaranteeing shape recognition even when the manipulator is subjected to an external load. The fiber sensors are highly flexible, compact, and inexpensive, as well as they can functionally measure both compressive and tensile strain of hyper-redundant manipulator. The sensor design was optimized to achieve durability and sensitivity. The numbers of sensor and the placements were determined by the analysis of the kinematics and moment distribution of the manipulator. The accuracy of shape estimation was validated experimentally under both free-loading and loading conditions. The proposed method achieved real-time estimating capability with a mean maximum error of each joint position smaller than 3.54% in free-loading condition and 5.47% in loading condition.

Understanding photoacoustic signal formation in the presence of transparent thin films

Strain-induced variation of the refractive index is the main mechanism of strain detection in photoacoustic experiments. However, weak strain-optic coupling in many materials limits the application of photoacoustics as an imaging tool. A straightforward deposition of a transparent thin film as a top layer has previously been shown to provide signal enhancement due to elastic boundary effects. In this paper, we study photoacoustic signal formation in metal covered by thin transparent films of different thicknesses and demonstrate that in addition to boundary effects, the photoacoustic response is affected by optical effects caused by the presence of the top layer. The interplay of optical effects leads to a complex temporal signal shape that strongly depends on the thickness of the thin film.

Experimental investigations of temperature-sensitive shape memory polymer composites for 4D printing

Shape memory polymers (SMPs) and their composites (SMPCs) have emerged as popular materials in a variety of industries due to their unique properties of shape-changing behavior in response to external stimuli. The inclusion of reinforcement may modify the SMPs to enhance their thermal and shape memory properties. Different types of bio ceramics have already been used to alter the thermal and shape memory behavior of SMPs. However, using bioactive glass (BG) as filler to modify these properties has not yet been explored. Despite the significant advantages that shape-memory polymers (SMPs) offer when combined with 3D/4D printing technology, their potential in 3D printing has been explored only to a limited extent. This work created a solvent-based 4D-printed temperature-sensitive shape memory polymer composites (SMPCs) system using polylactic acid (PLA) and bioactive glass (BG). The influence of BG on the thermal as well as shape-memory capabilities of composites was further examined. An increase in the degree of crystallinity and viscoelastic characteristics of PLA/BG composites led to improved shape memory properties, like shape fixity and shape recovery. These findings suggest the potential for using the developed SMPC printed through 4D printing technology, to develop complex shapes for self-foldable structures and smart biomedical devices in the future.

An improvement method for pancreas CT segmentation using superpixel-based active contour

Objective. Pancreas is one of the most challenging organs for Computed Tomograph (CT) image automatic segmentation due to its complex shapes and fuzzy edges. It is simple and universal to use the traditional segmentation method as a post-processor of deep learning method for segmentation accuracy improvement. As the most suitable traditional segmentation method for pancreatic segmentation, the active contour model (ACM), still suffers from the problems of weak boundary leakage and slow contour evolution speed. Therefore, a convenient post-processor for any deep learning methods using superpixel-based active contour model (SbACM) is proposed to improve the segmentation accuracy. Approach. Firstly, the superpixels with strong adhesion to edges are used to guide the design of narrowband and energy function. A multi-scale evolution strategy is also proposed to reduce the weak boundary leakage and comprehensively improve the evolution speed. Secondly, using the original image and the coarse segmentation results obtained from deep learning methods as inputs, the proposed SbACM method is used as a post-processor for fine segmentation. Finally, the pancreatic segmentation public dataset TCIA from the National Institutes of Health(NIH, USA) is used for evaluation, and the Wilcoxon Test confirmed that the improvement of proposed method is statistically significant. Main results. (1) the superpixel-based narrowband shape and dynamic edge energy of the proposed SbACM work for boundary leakage reduction, as well as the multi-scale evolution strategy and dynamic narrowband width for the evolution speed improvement; (2) as a post-processor, SbACM can increase the Dice similarity coefficients (DSC) of five typical UNet-based models, including UNet, SS-UNet, PBR UNet, ResDSN, and nnUNet, 2.35% in average and 9.04% in maximum. (3) Based on the best backbone nnUNet, the proposed post-processor performs better than either adding edge awareness or adding edge loss in segmentation enhancement without increasing the complexity and training time of deep learning models. Significance. The proposed SbACM can improve segmentation accuracy with the lowest cost, especially in cases of squeezed fuzzy edges with similar neighborhood , and complex edges.

New approach to low-cycle fatigue lifetime prediction for deep-rectangular notched components with finite residual thickness: Experiment and simulation

The introduction of notches in engineering components often has detrimental effects on fatigue behavior. Current research on finite residual thickness is extremely limited, especially when dealing with more complex geometric shapes of notches. In this study, the low-cycle fatigue (LCF) life of cylindrical components with deep-rectangular notches were predicted based on the theory of critical distance (TCD) and the total stress field (TSF) method. The results indicated that the presence of stress concentration near the notch accelerated the initiation and propagation of cracks. Furthermore, finite element method (FEM) was conducted, revealing the need to comprehensively consider the stress distribution across the residual thickness zone (RTZ) for notched specimens. Through numerical method improvement and geometric modeling optimizations, the accuracy of fatigue lifetime prediction has been refined to within a margin of ± 1.5. Comparative simulations on notched specimens with various ratio of notch depth to residual thickness were conducted, characterizing the fatigue process zone (FPZ) near the notch root under LCF loading for Q345R steel, indicating that characteristic material parameter d* exhibits minimal fluctuations with changes in the notch size, while the corresponding local stress levels display a logarithmic upward trend, substantiating that fatigue lifetime decreases with larger notch sizes.

A Method for Straightening Distorted Giga-Cast Large Thin-Walled Components.

Giga-casting, a revolutionary approach for manufacturing large, single-piece car body components from aluminium, has emerged as a potential game-changer in the automotive industry. However, these large, thin-walled castings are prone to distortions during solidification and heat treatment processes. Straightening these distortions is crucial to ensure structural integrity, facilitate downstream assembly, and maintain aesthetic qualities. This paper proposes a novel method for straightening giga-cast components using a multi-pin straightening machine. The machine's versatility stems from its ability to adapt to various geometries through multiple strategically controlled straightening pins. This paper introduces the concept of a "straightening stroke decision algorithm" to achieve precise straightening and overcome the challenges of complex shapes. This algorithm determines the stroke length for each pin, combining a polynomial model representing the global stiffness of the component with a machine learning model that captures the stiffness changes arising from the current geometry. The effectiveness of the proposed approach is evaluated through comprehensive numerical experiments using finite element analyses. The straightening performance is assessed for the straightening algorithm with different machine learning models (deep neural network and XGBoost) and compared to a traditional optimisation method. The proposed surrogate models decided the straightening strokes so that the maximum remaining distortion became 0.02% of the largest dimension of each target geometry. The results of the numerical experiment showed that the proposed straightening method is suitable for straightening distortion in large thin-walled components.

Simulating time-harmonic acoustic wave effects induced by periodic holes/inclusions on surfaces

This paper introduces a localized meshless method to analyze time-harmonic acoustic wave propagation on curved surfaces with periodic holes/inclusions. In particular, the generalized finite difference method is used as a localized meshless technique to discretize the surface gradient and Laplace-Beltrami operators defined extrinsically in the governing equations. An absorbing boundary condition is introduced to reduce reflections from boundaries and accurately simulate wave propagation on unclosed surfaces with periodic inclusions. Several benchmark examples demonstrate the efficiency and accuracy of the proposed method in simulating acoustic wave propagation on surfaces with diverse geometries, including complex shapes and periodic holes or inclusions.

Influence of Various Processing Routes in Additive Manufacturing on Microstructure and Monotonic Properties of Pure Iron—A Review-like Study

Additive manufacturing processes have attracted broad attention in the last decades since the related freedom of design allows the manufacturing of parts with unique microstructures and unprecedented complexity in shape. Focusing on the properties of additively manufactured parts, major efforts are made to elaborate process-microstructure relationships. For instance, the inevitable thermal cycling within the process plays a significant role in microstructural evolution. Various driving forces contribute to the final grain size, boundary character, residual stress state, etc. In the present study, the properties of commercially pure iron processed on three different routes, i.e., hot rolling as a reference, electron powder bed fusion, and laser powder bed fusion, using different raw materials as well as process conditions, are compared. The manufacturing of the specimens led to five distinct microstructures, which differ significantly in terms of microstructural features and mechanical responses. Using optical and electron microscopy as well as transmission electron microscopy, the built specimens were explored in various states of a tensile test in order to reveal the microstructural evolution in the course of quasistatic loading. The grain size is found to be most influential in enhancing the material’s strength. Furthermore, substructures, i.e., low-angle grain boundaries, within the grains play an important role in terms of the homogeneity of strain distribution. On the contrary, high-angle grain boundaries are found to be regions of strain localization. In summary, a holistic macro-meso-micro-nano investigation is performed to evaluate the behavior of these specific microstructures.