Mechanical Behavior Of Materials Research Articles

A micromechanics-based artificial neural networks model for rapid prediction of mechanical response in short fiber reinforced rubber composites

The complex microstructural characteristics inherent in short fiber reinforced rubber composites (SFRRCs) impose considerable computational burdens in predicting the mechanical behavior of such composite materials. To address this challenge, this research extends the applicability of the homogeneous model predicated on the orientation averaging method to encompass composite materials featuring hyperelastic matrices. Combined with finite element method, a comprehensive mechanical response database encompassing various volume fractions and fiber orientation distributions is established. Leveraging this database, a micromechanics-based artificial neural network (ANN) model is meticulously designed to rapidly predict the mechanical response of SFRRCs across varying volume fractions and fiber orientation distributions, utilizing a fixed strain step strategy. To ascertain the efficacy and precision of the developed ANN model, representative volume elements portraying both planar and three-dimensional random distributions of composites are constructed and subjected to finite element analysis. Results indicate that the predicted outcomes from the ANN model align closely with finite element calculations within a certain strain range, while significantly reducing computational costs.

Fast parameter optimization for high-fidelity crystal plasticity simulation using active learning

Crystal plasticity (CP) simulation is a powerful tool for studying and understanding the mechanical behavior of materials. A critical aspect of this method is the accurate determination of CP parameters, which ensures that the constitutive model accurately represents the real deformation behavior of a material, especially in high-fidelity simulations. However, identifying these parameters poses a significant challenge due to the high computational cost and the difficulty of finding optimal solutions within a vast and complex parameter space. To address these challenges, we propose a fast search strategy that leverages active learning (AL) and experimental data to accelerate the optimization of CP parameters. Using the Al-Cu eutectic materials as a case study, we introduced a quantitative index, C ecp, to measure the consistency between simulated and experimental stress-strain curves. We demonstrated that Gaussian process regression (GPR) serves as the most appropriate surrogate model for relating CP parameters and C ecp based on our dataset. After only six iterations guided by AL, the optimal CP parameters were successfully identified, resulting in a high-fidelity CP model for analyzing the mechanical behavior of Al-Cu eutectic materials. The machine learning-enhanced strategy is far superior to traditional methods in terms of both efficiency and accuracy. It advances our understanding of the macro-micro relationships of materials and accelerates the material design process.

Predicting resilient modulus for pavement design: a comprehensive review of artificial neural network applications

ABSTRACT The Resilient Modulus (M R) is an essential parameter describing the mechanical behaviour of pavement materials, but the tests to obtain it are time-consuming and costly. Therefore, machine learning, especially Artificial Neural Networks (ANN), has recently been an effective alternative for predicting M R. Although there has been an increase in articles on ANN for predicting M R, no systematic review has yet categorised the publications, algorithm structures, predictive parameters, and model accuracy. This review provides a comprehensive overview of studies using ANN to predict M R for pavement materials. It examines various ANN subtypes and model structures, addressing a gap in understanding these models and identifying prevalent predictive parameters. Twenty-five peer-reviewed articles published in English-language journals were identified using keywords related to neural networks and M R. For fine soils, all models reviewed use moisture content as a predictive parameter. In contrast, granular soils models typically include stress state and physical characteristics of the materials. For recycled or stabilised materials, there is significant variability in model inputs. This review underscores the potential of ANN in enhancing predictive models for M R in pavement engineering, pointing towards future research and application refinement in this area of study.

Review of Modelling the Compaction of High Aspect Ratio AgriculturalFeedstock Materials

Agriculture feedstock materials have low bulk density and are difficult to handle and transport. Compaction can enhance the agricultural feedstock density and could be an effective solution for handling problems. To develop a better compaction process, mathematical modelling can help understand the compaction mechanism and can help predict the mechanical behaviour of feedstock materials. This paper presents a review on the energy consumption, applied pressure, as well as rheological models to predict their compaction behaviour and thus help improve compacts’ mechanical strength and reduce densification costs. In summary, energy requirement for densification of biomass depends primarily upon the pressure applied, holding on time and properties of the material. From the published literature, it could be found that the Cooper-Eaton and the Kawakita-Ludde models that considers particle rearrangement and deformation mechanism can fit pressure and density of feedstock materials during compaction. The parameters in the rheological models were correlated to the properties of the feedstock materials and can be used for optimization of machine and operational parameters.

Split tensile strength of fiber-reinforced coral aggregate concrete: Deep learning model and experimental validation

Machine learning methods are increasingly becoming an important research field for solving the complex mechanical behavior of concrete materials. A reliable deep learning model was developed based on a dataset consisting of 319 experimental data points to predict the split tensile strength of fiber-reinforced coral aggregate concrete (FRCAC). The feature selection was carried out using correlation analysis, and the neural network structure was optimized by adjusting hyperparameters based on the 10-fold cross-validation (CV). Subsequently, six statistical indicators were employed to evaluate the accuracy and generalization ability of the model. The deep neural network (DNN) model demonstrates excellent performance, with R2, MAE, and MAPE of 0.98, 0.181 MPa, and 0.037 for the training dataset and 0.95, 0.286 MPa, and 0.074 for the test dataset, respectively. Additionally, 24 sets of concrete proportions are designed using the trained DNN model, and the results indicate that the absolute error and relative error between predicted and experimental values are less than 0.4 MPa and 6.8 %, respectively. The feature analysis based on SHapley Additive exPlanations (SHAP) reveals that volume fraction, curing age, water-binder ratio, and the tensile strength of fiber have the highest contribution in estimating the split tensile strength of FRCAC. This research can provide a reference for optimizing the design of FRCAC.

Theoretical model for analysis of elastic constants in orthotropic materials considering shear stress

Nowadays, a general theoretical model to describe the mechanical behavior of anisotropic or orthotropic materials is still an open challenge. In this study, we propose a new theoretical model to determine the elastic constants of these materials considering the shear components of the stress tensor. To analyze the consistency of new approach in biaxial stress state on thin films, we used data reported in the literature, based in the $\sin^2 \psi$ technique. For the first time, the shear modulus value equal to $G_{xz} = 0.3 GPa$, for a polycrystalline Au thin film, was calculated, in addition to other elastic constants. Finally, we demonstrate that the new proposal theoretical model considering shear stress can be useful to determine elastic constants in orthotropic materials from experimentally measured data.

Crashworthiness and stiffness improvement of a variable cross-section hollow BCC lattice reinforced with metal strips

The ultra-lightweight structures with high mechanical properties and energy absorption behaviors are focused on the innovation structural design. The design strategy of implementing novel unit cells within architecture materials such as lattice materials enables the attainment of unparalleled combinations of mechanical properties and functionalities while minimizing weight. The most common method to design light-weight lattice materials is optimizing the geometric configurations of the unit cells. However, changing the geometric boundary of lattice structures can also be a good solution to improve the mechanical characteristics and energy absorption behaviors of the lattice materials. In this work, a novel variable cross-section hollow (VCH) lattice was established to improve the energy absorption capacity. By adding metal strips on the edge of the VCH lattices, a new reinforced variable cross-section hollow (RVCH) lattice with metal strips was developed to further enhance the stiffness and energy absorption capacity. The stainless VCH and RVCH lattices were additively manufactured by Selective Laser Melting (SLM) using an EP-M450H metal 3D printer. The quasi-static compressive characteristics and energy absorption behaviors of RVCH lattices were studied experimentally. Finite element modeling was implemented to study the energy absorption mechanism of RVCH lattices. A parametric analysis based on the finite element models was conducted to study the influence of different metal strips on the energy absorption capacity of RVCH lattices. The results show that the RVCH lattices with metal strips attaching to the vertical edges can signally enhance energy absorption of lattice structures with good load uniformity. Furthermore, the natural frequencies analysis indicate that the higher bending stiffness and the tensile stiffness can be achieved for RVCH lattices. This novel lattice is more stable as a load-bearing structure and more efficient as an energy absorber, which can provide guidance in designing innovative lattice structures with excellent mechanical properties and energy absorption capacity.

Role of severe plastic deformation on mechanical behavior of irradiated materials: A case study with Nb-1Zr alloy

In this investigation, the effect of 5.6 MeV proton irradiation on the microstructure and mechanical properties of coarse grained (CG) and nanocrystalline (NC) Nb-1wt.%Zr (NZ) has been analysed. Bulk nanocrystalline microstructure was obtained by subjecting the alloy to room temperature high pressure torsion under 6 GPa hydrostatic pressure and 5 rotations. The CG and NC samples were irradiated at doses of 1.9 × 1017 p/cm2 and 1.8 × 1017 p/cm2, respectively. Microstructural parameters like crystallite size, dislocation density, and dislocation arrangements were studied in detail using X-ray line profile analysis (XLPA) by Convolutional Multiple Whole Profile (CMWP) fitting. Microscopic observations were made with electron microscopy techniques in the scanning and transmission modes. Differential Scanning Calorimetry (DSC) was performed to estimate the concentration of vacancies after HPT processing and irradiation. Tensile tests of irradiated CG and NC irradiated samples were performed and compared to those in unirradiated conditions. In the NC condition, not only did the irradiated sample show higher ultimate tensile strength but also twice the amount of uniform elongation as compared to the irradiated CG sample. The fracture surface clearly exhibited this higher plasticity post-irradiation in the NC samples. The change in deformation mechanisms due to nano-structuring of the microstructure has been anticipated to be a reason for the increase in ductility in a single-phase alloy has been explained thereafter.

Cutting behaviors of cortical bone ultrasonic vibration-assisted cutting immersed in physiological saline

In orthopedic surgery, cortical bone cutting usually involves washing and cooling with physiological saline. However, how the saline changes the cutting behaviors of bone ultrasonic vibration cutting remains challenging. Hence, this paper simulates the clinical ultrasonic cutting condition in orthopedics to reveal the cutting behaviors of bone ultrasonic vibration orthogonal cutting immersed in physiological saline. The dynamic equation and motion process curves of ultrasonic cavitation bubbles were established. The results showed that the bone cutting immersed in physiological saline significantly improved the surface quality, reduced surface roughness and mechanical damage, and avoided large brittle cracks propagation. In saline immersed cutting, the physiological saline changes the mechanical behaviors of bone materials, resulting in plastic behaviors for the material removal and crack deflection. This study establishes the influence of physiological saline on the ultrasonic vibration cutting performance, providing guidance for orthopedic bone cutting surgery methods.

A review on the mechanical and biocorrosion behaviour of iron and zinc-based biodegradable materials fabricated using powder metallurgy routes

Abstract Over the past two decades, research on alloys and composites based on Mg, Fe, and Zn has focused on biodegradable orthopaedic implants. Mg-based materials face issues like excessive corrosion rates and hydrogen gas evolution, while Fe and Zn-based materials show lower corrosion rates. However, these rates are slower than the optimal rate, which can be modified using powder metallurgy (PM) manufacturing. The PM process offers precise control over porosity distribution which in turn affects the mechanical and corrosion properties of the fabricated specimen. The highest rate of corrosion i.e. 0.944 mmpy was observed with the alloying of 2 wt% Pd in Fe and by using conventional sintering technique. Similarly, Zn-based samples fabricated by conventional sintering was found to exhibit higher corrosion rate as compared to microwave and spark plasma sintered specimen. PM-fabricated Fe and Zn-based bone scaffolds have been investigated for in-vitro corrosion and osseointegration. A higher porosity in the Fe and Zn scaffolds (>60 %) resulted in high corrosion rate which adversely impacted the cell proliferation. This timely review critically assessed PM-fabricated Fe and Zn-based materials that have the potential to transform regenerative medicine and patient care by redefining the field of biodegradable implants.

Recent studies on multiscale modeling of carbon fiber-reinforced composites

Multiscale modeling has garnered attention as an analytical method for investigating the mechanical behavior of composite materials. This paper introduces a comprehensive multiscale modeling approach developed by the authors’ group, which enables comprehensive analysis of the material properties from the atomic-scale reaction characteristics to laminate-scale fracture analysis; an overview of relevant literature is included as well. First, the methods of analysis at the atomic/molecular, microscopic, and mesoscopic scales are separately explained. Finally, a multiscale modeling approach that integrates these models and bridges the gaps between the scales is presented. The multiscale model is used to predict the fracture behavior of carbon fiber-reinforced composites based on the mechanical properties of epoxy resin.

Influence of carbon nanotubes on the absorption performance and mechanical behavior of basalt fiber composite materials

AbstractBasalt fibers (BF) are widely used in shipbuilding due to their excellent corrosion resistance. However, basalt fibers do not possess good electromagnetic wave absorption properties, failing to meet the stealth performance requirements of ships. Therefore, this study focuses on basalt fiber composite materials, modifying the matrix by adding different amounts of carbon nanotubes (CNTs). Carbon nanotube‐basalt fiber‐epoxy resin absorptive composite materials are prepared using Vacuum Assisted Resin Transfer Molding (VARTM) technique to investigate the influence of CNT content on the materials. Samples with added CNTs can effectively absorb electromagnetic waves in the low‐frequency range, reducing surface reflectivity. In the high‐frequency range, the impedance matching value of the samples initially increases slowly. When the CNT content reaches 1.5 wt%, at a thickness of 2.5 mm and a frequency of 9.6 GHz, the minimum reflection loss of the sample is −14.40 dB, with an effective absorption bandwidth of 0.7 GHz. Also, CNTs can enhance the mechanical properties of composite materials. With increasing CNT content, both the bending strength and tensile strength of the composite materials are improved. When the CNT content reaches 1.5 wt%, the average tensile fracture strength of the material increases by 21%, and the bending strength increases by 9%.Highlights Enhanced low‐frequency electromagnetic wave absorption from CNTs and reduced polarization. Improved impedance matching and attenuation at high frequencies with CNTs. Significant enhancement in flexural and tensile strength due to CNTs and basalt fibers.

Novel Prediction Model for Validating the Mechanical Behaviour of Composite Materials Using Deep Learning

Novel Prediction Model for Validating the Mechanical Behaviour of Composite Materials Using Deep Learning

Studying of Mechanical Behavior of Waste Materials Modified Cement Mortar

Large-scale environmental problems have been triggered by ceramic tiles from demolished buildings and animal bone waste. Therefore, optimizing their potential for reuse and recycling is an important goal of sustainable solid waste management. The primary objective of this study is to evaluate the feasibility of partial replacement of the fine aggregates in cement mortar with a combination of natural animal bone powder and industrial waste materials which is wall tiles ceramic powder. This study measures thermal and mechanical characteristics. Recycling construction waste is one of many ways to deny waste material from entering landfills and create new eco-friendly material that reduces energy consumption in buildings. Modified mortar cement specimens with 5% up to 25% mixtures were combined using a 1:3 ratio and a 0.5 W/C ratio to see how the ratios would affect the material’s properties. The addition of a superplasticizer boosted the cement mortar’s workability and compressive strength. The compressive strength, flexural strength, density, and thermal conductivity were evaluated for each mortar mixture ratio. Results showed that compared to regular cement mortar, cement mortar combined with 20% bone and ceramic powder wastes reached the optimal replacement percentage. Compressive strength increased by 54% and flexural strength by about 67%. At the 28-day mark, thermal insulation was lowered by 25%.It is concluded from this study that it is possible to use bone waste as a natural material with ceramic waste as an industrial material to modify the insulation and mechanical properties of cement mortar.

Ageing effects on the mechanical properties stability of 3D printed material under compression

The paper deals with the examination of the ageing effects on the mechanical properties stability of 3D printed material via stereolithography under compression when subjected to various conditions, including UV radiation, X-rays, and the effects of time, from the opening of the bottle with the material to the 3D-printing process. The sets of samples under investigation were subjected to quasi-static and dynamic compression loading using an Split Hopkinson Pressure Bar. The aim of this paper is to investigate the long-term stability of the samples in terms of their mechanical properties and material behaviour and their degradation pattern. Despite the manufacturer’s information, it was found that the mechanical behaviour of the printed samples was significantly affected by the ageing process.

Capturing the random mechanical behaviour of granular materials: a comprehensive stochastic discrete-element method study

This research pioneers a stochastic discrete-element method (DEM) by integrating the probability density evolution method (PDEM), offering a novel approach to connect particle-scale property uncertainties, specifically inter-particle friction coefficient (μ) and particle shear modulus (Gp), with macro-scale soil behaviour. Through 1100 DEM simulations, this study reveals that, for uniform particle size distribution, the uncertainty in μ substantially affects large-strain soil behaviour, with its effect being associated with packing density and soil state. The uncertainty effect of μ remains pronounced at the critical state, while the packing density effect diminishes. Stress distribution appears insensitive to uncertainty of μ, rather suggesting a predominant influence of particle size distributions. In contrast, the uncertainty effect of μ becomes negligible on small-strain behaviour, demonstrating a limited effect on small-strain stiffness. Uncertainty in Gp presents limited effects on large-strain behaviour, including stress ratios and dilatancy. At small strains, Gp shows a significant impact on stiffness, diverging from minimal influence identified for μ. This study presents a framework that integrates experimental techniques to study particle-scale uncertainty propagation, enhancing predictions of macro-scale soil behaviour. This approach could be beneficial for precise multi-scale simulations, incorporating particle-level uncertainties in engineering-scale models, thereby improving geotechnical practice predictability.

Effect of prefatigue-induced persistent slip bands on the strength-ductility match in -oriented Cu single crystals

To explore the effect of persistent slip bands (PSBs) formed during cyclic loading on the static mechanical behavior of materials, [1‾12]-oriented Cu single crystals are selected as target materials, and the tensile behavior of Cu single crystals prefatigued to the stress saturation stage at different plastic shear strain amplitudes (γpl) and the corresponding microstructures, surface deformation features and fracture morphologies are systematically studied. The results show that the formation of few soft PSB ladder-like structures in hard dislocation veins during prefatigue deformation at γpl = 3.7 × 10−4 below the plateau region of cyclic stress-strain (CSS) curve can promote a synchronous improvement of strength and ductility (SISD) compared with the unfatigued crystal. As γpl increases to a lower value of 9.0 × 10−4 within the plateau region, the strength and ductility reduce sharply compared with the case at γpl = 3.7 × 10−4 due to more concentrated plastic deformation caused by a high volume fraction of prefatigue-induced PSBs. With continuously increasing γpl to a higher value of 2.3 × 10−3 within the plateau region, non-uniform dislocation distribution in labyrinth structures and the short spacing between labyrinth walls as well as the fine dislocation cells formed during tension jointly give rise to a slight increase in strength and ductility compared with the crystal prefatigued at γpl = 9.0 × 10−4. As γpl is increased to the level beyond the plateau region, the ductility and ultimate tensile strength continuously increase, resulting from the co-existence of wider deformation bands and dense dislocation walls.

Mechanical Behavior of Polymeric Materials: Recent Studies

This Special Issue is devoted to one of the most exciting fields in polymer science and technology: the many factors that influence the properties of polymer-based materials [...]

Study on influencing factors of controllable mechanical behavior of rock-like porous materials.

Due to the discrete and non-homogeneous of similar materials and the inability to realize large-size and original scale modeling, it is difficult to restore the structure and stress state of underground coal and rock mass in similar simulation tests. To solve this problem, a lightweight and suitable for large-scale modeling similar material, rock-like porous material has been developed. The quasi-static uniaxial compression experiment was carried out by using the large tonnage multi-module electronic control test system. And the influencing factors of controllable mechanical behavior of rock-like porous materials were studied. The results showed that, under uniaxial compression conditions, the material stress-strain curve exhibits three phases: elastic stage, failure stage, and platform stage. The uniaxial compressive strength, elasticity modulus, stress drop, and softening modulus of rock-like porous materials basically increase with the increase of density. The stress after peak strength changes from a slow decrease to a "stepped" or even "cliff like" downward trend. Polypropylene fibers have the effect of enhancing the uniaxial compressive strength, elasticity modulus, stress drop, softening modulus, shear deformation, and residual strength stability of rock-like porous materials. The rock-like porous material has a critical loading velocity, and it increases with density. At the critical loading velocity, the material shows obvious shear failure, and the shear inclination angle is the largest, and so is the uniaxial compressive strength. Through the experimental research, the influence laws of density, polypropylene fiber, and loading velocity on the failure mode, mechanical parameters, and mechanical behavior of the material are clarified, and the quantitative relationship between density and each mechanical parameter is obtained. The research is helpful to realize the accurate control of mechanical behavior of rock-like porous materials and further inverts the deformation and failure mechanism of underground coal and rock structures through indoor similar simulation tests.

Probabilistic framework for strain-based fatigue life prediction and uncertainty quantification using interpretable machine learning

Establishing a unified fatigue life prediction model and quantifying the uncertainty in the mechanical behavior of materials are critical to ensure the structural integrity and equipment performance. For the commonly-used strain-based fatigue methods, existing estimation methods exhibit inevitable deviations, while data-driven methods have shown poor extrapolation ability and interpretability. Therefore, this paper aims to develop a probabilistic framework for strain-based fatigue life prediction and uncertainty quantification (UQ) to provide an indication for fatigue design/assessment using interpretable machine learning (ML) techniques. Based on Shapley additive explanations (SHAP) and symbolic regression (SR), interpretable prediction models with concise expressions and outstanding prediction performance are established and optimized according to the priori physical knowledge. Moreover, accounting for the material variability, the probabilistic assessment with UQ excellently validates the prediction model, and quantifies the variability of ε-N curves. The proposed framework provides valuable reference and shows promising prospects in fatigue design for engineering components.