Multiscale Materials Research Articles

Study on large scale direct shear test on soil-rock mixture with multiple grain grades in high fill slope engineering

Soil-rock mixtures are multiphase natural geological materials composed of mineral particles of multiple grain sizes, and have significant natural characteristics of multiscale material groups. In order to investigate the shear strength characteristics and deformation failure characteristics of soil-rock mixtures with multiple grain sizes under in-situ conditions in fill engineering, samples of soil-rock mixtures with multiple grain sizes under in-situ conditions were prepared, and large-scale in-situ direct shear tests were conducted under natural and water-soaked conditions. The test results showed that the shear strength of soil-rock mixtures is relatively high, and after saturated by water immersion, its shear strength decreases significantly, mainly manifested in the decrease of internal friction angle while the cohesion remains basically unchanged. The cohesion of soil-rock mixtures with multiple grain sizes is mainly provided by the shear-breaking cohesion of coarse particles such as gravel in the soil-rock mixture, and its degree of exertion is controlled by the grading characteristics and particle morphology of the soil-rock mixture and affected by constraints. The reason for the significant decrease in shear strength of soil-rock mixtures with multiple grain sizes after water-soaked saturation can be explained as follows: the clay film covering the surface of coarse particles becomes soft after water immersion, resulting in a significant decrease in the sliding friction resistance between coarse particles and the clay film, as well as the occlusion friction resistance generated by the rotation transfer between coarse particles through the clay film interface.

Research on the thermal properties and interface resistance of multilayer multiscale semiconductor structures

The investigation of the thermal characteristics of high-power chips is mainly based on Fourier's law, and it does not adequately explore the microscale effects of the heat transfer process. In pursuit of a more profound comprehension of the size-dependent traits manifesting in the thermal conduction across multilayer, multiscale materials, this manuscript incorporates the phonon transport's scattering mismatch attributes, introducing a MBDE methodology aimed at delineating the thermal characteristics inherent to multilayer, multiscale structures. This approach was applied to elucidate the energy transfer dynamics among bi-layer (Si/Si, Diamond/Si, and Cu/Si) configurations of differing scales. To enhance thermal transfer efficiency, this work prioritizes the minimization of interface thermal resistance and system temperature gradients as optimization objectives, employing a genetic algorithm to identify scales favorable for energy transfer. The results indicate: 1) An increase in the Knudsen number (Kn) correlates with a more pronounced energy discontinuity at the interface, thereby elevating the interface thermal resistance; 2) The disparate material combinations exhibit varied energy transfer efficiencies, where the phononic compatibility between Cu/Si is inferior to that of Diamond/Si, culminating in a notably larger energy discontinuity and, consequently, augmented energy transfer losses for Cu/Si interfaces; 3) Through the application of a genetic algorithm, it was determined that transitioning from Kn1 to Kn2 yields an optimal thermal conduction scenario, characterized by an interface thermal resistance of 1.83 and a comprehensive temperature gradient of 6.69. This work holds significant implications for guiding the thermal design of high-power integrated chips.

Standard Tessellation Language-Free Adaptive Slicing Scheme for Additive Manufacturing of Cellular Materials

Abstract In recent years, advances in additive manufacturing (AM) techniques have called for a scalable fabrication framework for high-resolution designs. Despite several process-specific handful design approaches, there is a gap to fill between computer-aided design (CAD) and the manufacturing of highly detailed multiscale materials, especially for delicate cellular material design. This gap ought to be filled with an avenue capable of efficiently slicing multiscale intricate designs. Most existing methods depend on the mesh representation, which is time consuming and memory hogging to generate. This article proposes an adaptive direct slicing (mesh-free) pipeline that exploits the function representation (FRep) for hierarchical architected cellular materials design. To explore the capabilities of the presented approach, several sample structures with delicate architectures are fabricated using a stereolithography (SLA) printer. The computational efficiency of the proposed slicing algorithm is studied. Furthermore, the geometry frustration problem brought by the connection of distinct structures between functionally graded unit cells at the microscale level is also investigated.

Multiscale Mechanical Characterization of Polyether-2-ketone (PEKK) for Biomedical Application.

Polyether-ether-2-ketone (PEKK) is a high-performance thermoplastic polymer used in various fields, from aerospace to medical applications, due to its exceptional mechanical and thermal properties. Nonetheless, the mechanical behavior of 3D-printed PEKK still deserves to be more thoroughly investigated, especially in view of its production by 3D printing, where mechanical properties measured at different scales are likely to be correlated to one another and to all play a major role in determining biomechanical properties, which include mechanical strength on one side and osteointegration ability on the other side. This work explores the mechanical behavior of 3D-printed PEKK through a multiscale approach, having performed both nanoindentation tests and standard tensile and compression tests, where a detailed view of strain distribution was achieved through Digital Image Correlation (DIC) techniques. Furthermore, for specimens tested up to failure, their fractured surfaces were analyzed through Scanning Electron Microscopy (SEM) to clearly outline fracture modes. Additionally, the internal structure of 3D-printed PEKK was explored through Computed Tomography (CT) imaging, providing a three-dimensional view of the internal structure and the presence of voids and other imperfections. Finally, surface morphology was analyzed through confocal microscopy. The multiscale approach adopted in the present work offers information about the global and local behavior of the PEKK, also assessing its material properties down to the nanoscale. Due to its novelty as a polymeric material, no previous studies have approached a multiscale analysis of 3D-printed PEKK. The findings of this study contribute to a comprehensive understanding of 3D-printed PEKK along with criteria for process optimization in order to customize its properties to meet specific application requirements. This research not only advances the knowledge of PEKK as a 3D-printing material but also provides insights into the multifaceted nature of multiscale material characterization.

New perspectives for piezoelectric material-based cochlear implants: getting to nano

Sensorineural hearing loss (SNHL) occurs when the sound transduction mechanism in the inner ear is compromised, because of impairments affecting the sensory hair cells—the actual biological transducers (90% of cases)—or the neurons. SNHL results in a broad spectrum of developmental, cognitive and psycho-social damages. To date, only cochlear implants (CIs) can offer a therapeutic solution to patients. They are multi-component electronic devices, surgically implanted, which capture, elaborate and convert the sound into electric stimuli delivered to the cochlea. Due to inherent limitations of the current electronic-based CIs, a new class of devices has been envisioned, which is based on piezoelectric materials. However, using piezoelectric membranes, the obtained sensitivity was not enough. The new frontiers for piezoelectric material-based CI aim at synergizing micro/nanofabrication aided by multiscale materials modeling with an in vivo tissue engineering approach to provide an implantable biomaterial-based system for SNHL, acting as a next-generation CI. Specifically, the envisioned device will move forward the primitive concept of bulk-structured piezoelectric CIs by designing a nanostructured material (e.g., based on nanofibers) to be precisely delivered and be intimately and efficiently integrated with the cochlear microenvironment. Piezoelectric material-based CIs are indeed hypothesized to have a much higher resolution of electrical stimulation with more than hundreds of channels, compared to maximum 22 stimulating elements present in electronic-based CIs. Moreover, the stimulation site will be closest to peripheral nerve fiber endings for maximal resolution. This would be the first sensory implant with a feedback mechanism on a micrometer scale.

MechGPT, a Language-Based Strategy for Mechanics and Materials Modeling That Connects Knowledge Across Scales, Disciplines, and Modalities

Abstract For centuries, researchers have sought out ways to connect disparate areas of knowledge. While early scholars (Galileo, da Vinci, etc.) were experts across fields, specialization took hold later. With the advent of Artificial Intelligence, we can now explore relationships across areas (e.g., mechanics-biology) or disparate domains (e.g., failure mechanics-art). To achieve this, we use a fine-tuned large language model (LLM), here for a subset of knowledge in multiscale materials failure. The approach includes the use of a general-purpose LLM to distill question-answer pairs from raw sources followed by LLM fine-tuning. The resulting MechGPT LLM foundation model is used in a series of computational experiments to explore its capacity for knowledge retrieval, various language tasks, hypothesis generation, and connecting knowledge across disparate areas. While the model has some ability to recall knowledge from training, we find that LLMs are particularly useful for extracting structural insights through Ontological Knowledge Graphs. These interpretable graph structures provide explanatory insights, frameworks for new research questions, and visual representations of knowledge that also can be used in retrieval-augmented generation. Three versions of MechGPT are discussed, featuring different sizes from 13 × 109 to 70 × 109 parameters, and reaching context lengths of more than 10,000 tokens. This provides ample capacity for sophisticated retrieval augmented strategies, as well as agent-based modeling where multiple LLMs interact collaboratively and/or adversarially, the incorporation of new data from the literature or web searches, as well as multimodality.

Intelligent X-ray waste detection and classification via X-ray characteristic enhancement and deep learning

Waste management plays an important role in environmental protection and resource recycling. In fact, traditional waste recycling requires a large number of workers to sort manually, which is not only labor-intensive and time-consuming, but also increases the health risk for workers. To further improve the efficiency of waste detection and classification, related scholars have used computer vision technology to achieve automatic waste management. However, existing methods tend to use RGB images as input, requiring workers to open waste bags and disperse waste items in advance. To avoid comprehensive preparation procedures and identify occluded waste items, we use deep learning models to detect and recognize waste items in X-ray images. Compared to RGB images, X-ray images can effectively preserve boundary information and reflect material types. To take advantage of the inherent characteristics of X-ray images, we propose a novel X-ray Characteristic Enhancement Module (XCEM) to improve the performance of X-ray waste detection. XCEM consists of three sub-modules, namely Hybrid Interaction Module (HIM), Multi-scale Material Perception Module (MMPM), and Boundary Guidance Module (BGM). Specifically, HIM not only generates soft and hard boundary maps, but also achieves information interaction and feature enhancement of different boundary maps. Meanwhile, MMPM can effectively capture different multi-scale material features. In addition, BGM uses auxiliary boundary information and attention mechanisms to effectively enhance critical material features. As a plug-and-play module, XCEM can be readily inserted into the most popular detection pipelines. We select several representative one-stage and two-stage methods and perform related experiments on the WIXRay dataset. Experimental results show that our method can achieve consistent performance gains on diverse detection paradigms. This study promotes the development of intelligent waste inspection and recycling.

A dislocation density-based meshfree computational framework for solid phase processing

This work proposes a computational modeling framework capable of predicting the behavior of materials when subject to solid phase processing (SPP). A macroscopic dislocation density based constitutive model with internal state variables correlated to microscopic dislocation densities is incorporated into a meshfree smoothed particle hydrodynamics (SPH) framework able to handle large deformations and high temperatures typical of what a material undergoes during SPP. Unlike phenomenological constitutive models that typically neglect material microstructural contributions to macroscale response, the dislocation density based constitutive model improves significantly the predictivity of our modeling framework in a wider range of conditions and allows the multiscale material responses to be simulated simultaneously. With dislocation density based model parameters calibrated to AA1100-O aluminum test data, the generality of the proposed framework is tested on two SPP techniques, namely friction extrusion (FE) and friction stir processing (FSP). Simulation results with different process parameters are systematically validated by experimental data for both cases. The validated models are then used to elucidate the inherent physics associated with SPP by revealing the distributions of temperature, strain, strain rate, and dislocation density, stick-slip contact conditions, and heat generation rates. Model-predicted high dislocation density regions in the processed AA1100-O also agree well with the dynamically recrystallized grain refinement zones observed from experiments.

From Fundamental Amyloid Protein Self-Assembly to Development of Bioplastics.

Proteins can self-assemble into a range of nanostructures as a result of molecular interactions. Amyloid nanofibrils, as one of them, were first discovered with regard to the relevance of neurodegenerative diseases but now have been exploited as building blocks to generate multiscale materials with designed functions for versatile applications. This review interconnects the mechanism of amyloid fibrillation, the current approaches to synthesizing amyloid protein-based materials, and the application in bioplastic development. We focus on the fundamental structures of self-assembled amyloid fibrils and how external factors can affect protein aggregation to optimize the process. Protein self-assembly is essentially the autonomous congregation of smaller protein units into larger, organized structures. Since the properties of the self-assembly can be manipulated by changing intrinsic factors and external conditions, protein self-assembly serves as an excellent building block for bioplastic development. Building on these principles, general processing methods and pathways from raw protein sources to mature state materials are proposed, providing a guide for the development of large-scale production. Additionally, this review discusses the diverse properties of protein-based amyloid nanofibrils and how they can be utilized as bioplastics. The economic feasibility of the protein bioplastics is also compared to conventional plastics in large-scale production scenarios, supporting their potential as sustainable bioplastics for future applications.

A direct FE[formula omitted] method for concurrent multilevel modeling of piezoelectric materials and structures

Multiscale piezoelectric materials and structures have shown their promising potential of applications in sensor, energy harvester, actuator and so on. However, the large complexity of such materials and structures poses a significant challenge to the accurate and efficient prediction of their piezoelectric behavior which is significant for guiding and optimizing their applications. In this paper, a Direct FE2 method was proposed for concurrent multilevel modeling of the coupled electromechanical behavior of multiscale piezoelectric materials and structures. The energy equilibrium and kinematic constraints between macro- and meso-scales were satisfied by prescribing periodic boundary conditions for both displacement and electric potential, which were derived from the governing equation of FEM. Another boundary condition was prescribed to the center node of each RVE, to prevent rigid motion and shifting of electric potential of meso-scale RVEs. All these boundary conditions can be easily prescribed to the Direct FE2 using multiple points constraints (MPCs), a commonly used feature in most commercial software. The accuracy, computational efficiency and ease of numerical implementation of the proposed Direct FE2 method were validated using a series of multiscale simulations, including the quasi-static response of homogeneous piezoelectric panels, an arc honeycomb structure, a regular honeycomb structure and a piezoelectric composite panel, as well as the dynamic response of the piezoelectric composite panel. It shows that the proposed Direct FE2 method will be valuable for large-scale simulations of complicated multiscale piezoelectric materials and structures.

Micromechanics-informed parametric deep material network for physics behavior prediction of heterogeneous materials with a varying morphology

Deep Material Network (DMN) has recently emerged as a data-driven surrogate model for heterogeneous materials. Given a particular microstructural morphology, the effective linear and nonlinear behaviors can be successfully approximated by such physics-based neural-network like architecture. In this work, a novel micromechanics-informed parametric DMN (MIpDMN) architecture is proposed for multiscale materials with a varying microstructure characterized by several parameters. A single-layer feedforward neural network is used to account for the dependence of DMN fitting parameters on the microstructural ones. Micromechanical constraints are prescribed both on the architecture and the outputs of this new neural network. The proposed MIpDMN is also recast in a multiple physics setting, where physical properties other than the mechanical ones can also be predicted. In the numerical simulations conducted on three parameterized microstructures, MIpDMN demonstrates satisfying generalization capabilities when morphology varies. The effective behaviors of such parametric multiscale materials can thus be predicted and encoded by MIpDMN with high accuracy and efficiency.

Tough and Moldable Sustainable Cellulose-Based Structural Materials via Multiscale Interface Engineering.

All-natural materials derived from cellulose nanofibers (CNFs) are expected to be used to replace engineering plastics and have attracted much attention. However, the lack of crack extension resistance and 3D formability of nanofiber-based structural materials hinders their practical applications. Here, a multiscale interface engineering strategy is reported to construct high-performance cellulose-based materials. The sisal microfibers are surface treated to expose abundant active CNFs with positive charges, thereby enhancing their interfacial combination with the negatively charged CNFs. The robust multiscale dual network enables easy molding of multiscale cellulose-based structural materials into complex 3D special-shaped structures, resulting in nearly twofold and fivefold improvements in toughness and impact resistance compared with those of CNFs-based materials. Moreover, this multiscale interface engineering strategy endows cellulose-based structural materials with better comprehensive performance than petrochemical-based plastics and broadens cellulose's potential for lightweight applications as structural materials with lower environmental effects.

Multiscale modeling and simulation on mechanical behavior of fiber reinforced concrete

A multiscale mechanical model is proposed to quantitatively describe the macro-mechanical behavior of fiber reinforced concrete (FRC) based on its multiscale material compositions. Firstly, according to the actual scale characteristics of compositions, the model of composite material at each scale of FRC, including hardened cement paste, cement mortar and meso-FRC, was geometrically reconstructed; Secondly, the equations of stiffness and strength of composite material at each scale were established by using the equivalent inclusion theory and Ottosen failure criterion, and a multiscale mechanical model of FRC was proposed by upscaling from stiffness and strength of microstructure to macro-mechanical behavior of FRC; Finally, after verified by the experimental results in the literature, the proposed model was used to numerically investigate the loading-induced stress response and the evolution of macro-mechanical properties of FRC in its curing process, as well as the influence of steel fiber parameters, such as volume fraction, aspect ratio and spatial orientation, on the mechanical properties of FRC.

Extended tensor decomposition model reduction methods: Training, prediction, and design under uncertainty

This paper introduces an extended tensor decomposition (XTD) method for model reduction. The proposed method is based on a sparse non-separated enrichment to the conventional tensor decomposition, which is expected to improve the approximation accuracy and the reducibility (compressibility) in highly nonlinear and singular cases. The proposed XTD method can be a powerful tool for solving nonlinear space–time parametric problems. The method has been successfully applied to parametric elastic–plastic problems and real time additive manufacturing residual stress predictions with uncertainty quantification. Furthermore, a combined XTD-SCA (self-consistent clustering analysis) strategy is presented for multi-scale material modeling, which enables real time multi-scale multi-parametric simulations. The efficiency of the method is demonstrated with comparison to finite element analysis. The proposed method enables a novel framework for fast manufacturing and material design with uncertainties.

Design and optimization of the dual-functional lattice-origami metamaterials

This study proposes a multi-scale composite lattice-origami metamaterial (MCLOM) to achieve excellent bandgap characteristics and energy absorption capacities. The MCLOMs are constructed by considering the high impedance mismatch of lattice structures, the spatial deformability of origami structures, and the tunability of the components in multi-scale composite materials. Firstly, elastic wave propagation characteristics are analyzed in the Bloch wave framework, revealing the realization of complete bandgaps and their generation mechanism by mode shape analysis and transmission spectrum. Subsequently, an optimization framework integrating the particle swarm optimization (PSO) algorithm is developed to maximize the first bandgap’s bandwidth by adjusting various component parameters. Under optimal distribution, the proposed metamaterials achieve remarkable improvements of 289% and 271% in the design objectives of two lattice-origami metamaterials with 90∘ dihedral angle compared to the initial distribution. It can be demonstrated that non-uniform distributions of multi-scale composite materials are dramatically effective for broadband wave attenuation. Additionally, while striving to widen the bandgap, the energy absorption capacities of structures are also crucial. The effect of the distribution of multi-scale composite materials with the optimal bandgap on the energy absorption performance is investigated. The results reveal that the optimal distribution of the lattice-origami metamaterials yields notable improvements of 48.26% and 34.86% under low-velocity impact, and 37.41% and 25.19% under medium-velocity impact. This work presents innovative concepts and approaches for devising and implementing novel dual-functional metamaterials, undoubtedly propelling the continual progress of material science and engineering technology in the times ahead.

Multiscale materials innovation from the bottom up, at the nexus of biology, engineering, and computation

Multiscale materials innovation from the bottom up, at the nexus of biology, engineering, and computation

A hybrid algorithm of particle swarm optimization and finite element method to identify local mesoscopic damage of concrete-like materials

Meso-scale material modeling and parameter identification are still unsettled issues in the current multi-scale methods for the field of computational mechanics. In this investigation, a hybrid algorithm of particle swarm optimization and finite element method coupled with continuum damage mechanics is proposed to forecast local mesoscopic elasticity modulus and damage of concrete-like materials based on the macroscopic stress-strain relationship data curve. The algorithm establishes a bridge between mesoscopic mechanical analysis and macroscopic mechanical analysis, which can also guarantee the consistency of analysis in different scales. A representative numerical case is conducted to support the algorithm. Additionally, the effect of different divisions of representative volume element is analyzed based on the algorithm, and the recommended size of representative volume element is 5 mm. The prediction results match well with the corresponding macroscopic experimental results and mesoscopic experimental results based on the chosen recommended size of representative volume element. The effectiveness of the algorithm is verified, which can support an effective numerical tool to optimize and identify mesoscopic mechanical parameters and damage for multi-scale material modeling and analysis.

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

On‐site inspection of invisible subsurface defects in multiscale structural materials by conventional nondestructive testing (NDT) methods, such as X‐ray and ultrasound, requires complex sample preparation and data acquisition processes. Moreover, the inspected area is very small. Herein, a simple, inexpensive, and ultrasensitive NDT method for identifying and classifying the geometries of subsurface defects using commercial cameras, digital image correlation software, and object detection (OD) algorithms is developed. Three OD algorithms—Faster region‐based convolutional neural network (Faster R‐CNN), Mask R‐CNN, and you‐only‐look‐once (YOLO)v3—are evaluated for their ability to locate defects and identify defect geometries. Specifically, bounding boxes of two sizes (large and small) are applied to the regions of defect‐induced perturbations in strain tensors, which serve as virtual representatives of invisible subsurface defects. The performance of the proposed approach is validated on test datasets of known and unknown defect types. The experimental results confirm that the proposed approach can effectively utilize the surface deformation field information to accurately and reliably locate and identify subsurface defects. The method is nondestructive and low cost, enables real‐time detection, is robust against noise‐dominated deformation fields, and can be applied to various structural deformations. The method is therefore suitable for multiscale structural health monitoring and characterization of internal defects in materials.

Moisture-driven degradation mechanisms in the viscoelastic properties of TPU-Based syntactic foams

Syntactic foams have found widespread usage in various applications including, marine, aerospace, automotive, pipe insulation, electrical cable sheathing, and shoe insoles. However, syntactic foams are often exposed to moisture when used in these applications that potentially alter their viscoelastic properties, which influences their long-term durability. Despite their significance, previous research has mainly focused on experimental studies concerning mechanical property changes resulting from filler loading and different matrix materials, overlooking the fundamental mechanisms resulting from moisture exposure. The current paper aims to bridge this gap in knowledge by elucidating the impact of long-term moisture exposure on TPU and TPU-based syntactic foam through multi-scale materials characterization approaches. Here, we choose a flexible syntactic foam manufactured using thermoplastic polyurethane elastomer (TPU) reinforced with glass microballoons (GMB) through selective laser sintering. Specifically, the research investigates the influence of moisture exposure time and the volume fraction of GMB on chemical and microphase morphological changes, along with their associated mechanisms. The study further examines how these microphase morphological changes manifest in viscoelastic properties.

Quantitative representation of directional microstructures of single-crystal superalloys in cyclic crystal plasticity based on neural networks

Nickel-based single-crystal alloys undergo microstructural degradation induced by thermal exposure. The directional rafting of microstructures significantly affects the mechanical properties and makes the material anisotropic. For structural design, establishing a quantitative description of microstructural effects in a constitutive model becomes essential and is still a tough research topic in multi-scale materials modeling. In the present work, the fabric tensor was correlated with the anisotropic cyclic crystal plasticity of nickel-based single-crystal alloys with the help of neural networks. The microstructural representative volume elements with various single-crystal morphologies were generated by the phase-field method and the deformation behaviors were studied under different crystal orientations and loading configurations. The neural network analysis confirmed that the fabric tensor can present anisotropic single-crystallographic microstructural features and describe mechanical behavior under both monotonic and cyclic multi-axial loading conditions. The history-dependent anisotropic cyclic hardening or softening behavior of the material can be captured by the introduced microstructural state variable. A principal component analysis (PCA) aided gradient-based attribution method was proposed to evaluate the importance of input variables. The characterization of different material components and their contribution to the stress–strain relationships are investigated and validated. The fabric tensor was verified to be an effective microstructural indicator for the continuum plasticity of single-crystal alloys.