Material Model Research Articles

Pricing Decision-Making Considering Ambiguity Tolerance in Consumers: Evidence from Recycled Building Material Enterprises

Globally, recycled building materials have attracted much attention, but the ambiguity of the use of recycled building materials makes it difficult for building material remanufacturers (BMRs) to compete with building material manufacturers (BMMs). Brand building is an important strategic tool for enterprises to increase product competitiveness. From the new perspective of the supply chain, this paper aims to examine the decision-making behavior of enterprises under two scenarios of consumer ambiguity neutrality and ambiguity tolerance and to analyze the impact of ambiguity tolerance on the pricing decisions of building materials supply chains in a brand-building scenario. This paper constructs a building material model consisting of BMMs and BMRs, according to the cognitive–affective personality system (CAPS) theory and through the Stackelberg game. The main findings are as follows. (1) Strengthening brand building can mitigate the negative impact of ambiguity tolerance on new product pricing. The selling price of recycled building materials is positively related to ambiguity tolerance. (2) When the BMM has higher brand value, there is a U-shaped trend between profit and ambiguity tolerance at a cost coefficient above the threshold value of 0.61. (3) When the BMR has higher brand value, profit is negatively related to ambiguity tolerance at operational inefficiencies and cost coefficients below the threshold value of 0.45. Otherwise, profits and ambiguity tolerance follow a U-shaped trend. This paper not only expands the research on brand building and ambiguity tolerance but also provides theoretical guidance for enterprises to make effective decisions in response to consumers’ ambiguity psychology.

FEM modelling of hydrogen embrittlement in API 5L X65 steel for safe hydrogen transportation

Abstract Hydrogen is crucial for decarbonization efforts due to its abundance, environmental friendliness, and versatility. To maximize its potential, an efficient transportation infrastructure is essential. While utilizing the natural gas pipeline network for transporting hydrogen is cost-effective, hydrogen embrittlement (HE) poses a significant challenge. When hydrogen enters the metal, it significantly compromises its fracture toughness. This study investigates the impact of high-pressure hydrogen on the mechanical properties of API 5L X65 carbon steel through a combined experimental and computational approach. To quantify the extent of HE, tensile tests were performed on identical specimens, one set pre-exposed to high-pressure hydrogen and another set kept in an inert environment for comparison. Finite element modelling, employing the Bai-Wierzbicki material model (BWMM), was used to simulate the material behaviour under large plastic deformations and correlate with experimental results. This synergistic approach integrates experimental data with simulations, creating a framework for predicting and preventing catastrophic failures.

Research on Grouting Pressure of Earth–Rock Dams Reinforced by Polymer Based on Discrete Elements

As a novel seismic reinforcement measure for earth–rock dams, the PFA-reinforced rockfill materials technology lacks comprehensive investigation into both its reinforcement efficacy and the underlying mechanisms. In this study, we establish a fluid–solid coupling model of PFA-reinforced rockfill materials utilizing the matrix discrete element software MatDEM3.24, developed independently by Nanjing University. The model simulates the dynamic process of polymer grouting within the rockfill body and analyzes the impact of slurry diffusion patterns and various grouting pressures on polymer grouting. Our findings reveal that the diffusion of polymer foam in rockfill occurs in three distinct stages, which are characteristic of penetration grouting. Moreover, we observed that grouting pressure had a significant effect on the diffusion range of the slurry, with greater sensitivity noted in the Z direction. Additionally, we observed a decrease in porosity with increasing grouting pressure, and stress augmentation exhibited an approximately linear relationship with grouting pressure, but the stress augmentation in different directions was different. These research outcomes offer valuable insights into the practical implementation and optimization of PFA-reinforced rockfill materials technology, bearing considerable engineering implications.

Confidence evaluation for feature selection in expanded feature space based on density of states

In materials informatics, feature selection and model selection are utilized in the knowledge extraction process, and the confidence evaluation of the selection result is crucial for ensuring the reliability of the extracted knowledge. In this study, we propose a novel method to quantitatively evaluate the significance of low-dimensional models obtained from an expanded feature space using the density of states (DoS) for the evaluation metrics. This method allows us to compare the performance of the selected model to those of other models and evaluate its significance. We further propose an evaluation method for feature importance based on marginal posterior probabilities using the Bayesian model averaging (BMA) framework, which considers all models. We demonstrate the effectiveness of our proposed methods through their application to a crystal structure dataset. Our results show that the DoS analysis reveals the presence of a large number of models with a comparable performance with the best model reported in a previous research work. This suggests that knowledge extracted from a single selected model can be unreliable and that considering other models is crucial. We also show that the BMA-based feature importance evaluation provides valuable insights into the importance of features, highlighting both primary features and functional forms. In addition, we demonstrate that the LASSO + L0 method commonly used in existing research deteriorates the search space for model selection. Our findings suggest that our proposed methods provide valuable tools for assessing the significance of low-dimensional models and extracting knowledge from large-scale data in materials informatics.

Development of interatomic potential suitable for molecular dynamics simulation of Ni oxidation and Ni-NiO interface.

Large-scale molecular dynamics (MD) simulations enabled by computationally efficient semiempirical potentials are an invaluable tool for materials modeling. In the case of metallic alloys, embedded atom method (EAM) and Finnis-Sinclair (FS) potentials are a reasonable choice based on their good balance of quality and computational cost. However, these semiempirical potentials are not suitable for simulating ionic systems, which prevents their use in studying many technologically relevant metal-oxide systems. The charge transfer ionic potential (CTIP), which can utilize EAM/FS potentials available in the literature together with a variable charge representation of electrostatic interactions, should be a reasonable choice for performing reliable and computationally efficient MD simulations of such systems. However, only a few such potentials are available in the literature, and their computational cost is much higher compared to EAM/FS potentials. In the present work, we have attempted to remedy these deficiencies by combining several modifications to the CTIP model proposed in the literature and efficiently implementing them into the widely used Large-scale Atomic/Molecular Massively Parallel Simulator MD code. Using these modifications, we have developed a new Ni-O CTIP parameterization, which has been tested in several different scenarios of interest. First, the early stages of Ni surface oxidation were simulated, demonstrating the nucleation and growth of a crystalline NiO film across the surface. Second, solidification and vitrification in the Ni-O system were investigated, demonstrating that the new CTIP parameterization provides reasonable agreement with the experimentally determined equilibrium phase diagram. Finally, we studied the interaction of dislocations in a Ni matrix with a NiO inclusion using a simulation cell with an unprecedented number of atoms for a variable charge MD simulation. Thus, the approach utilized in the present study is an efficient method to simulate large scale atomic mechanisms in metal-oxide systems.

Efficient thermo-mechanically coupled and geometrically nonlinear two-scale FE-FFT-based modeling of elasto-viscoplastic polycrystalline materials

Efficient thermo-mechanically coupled and geometrically nonlinear two-scale FE-FFT-based modeling of elasto-viscoplastic polycrystalline materials

A Thermal-Induced Prestress Method for Enhancing Magnetostrictive Material Properties

A technique for inducing prestress in magnetostrictive materials through thermal-induced prestressing is presented. A magneto-thermo-mechanical coupling model has been developed based on the nonlinear constitutive model of the magnetostrictive material and classical laminated beam theory, and the influences of curing temperature and substrate thermal expansion coefficient on the properties of magnetostrictive material have been analyzed. The magnetostrictive composite beams have been manufactured, and the experimental observations confirm the congruence with the proposed model. The composite beam, which includes a sapphire crystal substrate and experiences a temperature shift of ∆T=60℃, demonstrates an increase in magnetostriction by approximately 1.43 times and an enhancement in the maximum piezoelectric coefficient by around 1.45 times. This straightforward and efficient technique for creating pre-stress is especially appropriate for tiny applications, including sensors and actuators.

Inverse Physics-Informed Neural Networks for transport models in porous materials

Inverse Physics-Informed Neural Networks for transport models in porous materials

Effective Thermoelastic Moduli of Discrete Composite Layers Within the Framework of the Orthotropic Material Model

Effective Thermoelastic Moduli of Discrete Composite Layers Within the Framework of the Orthotropic Material Model

Investigation of the Effect of Alloying Elements on the Density of Titanium-Based Biomedical Materials Using Explainable Artificial Intelligence

Titanium alloys are widely preferred in the healthcare sector as biocompatible materials due to their superior properties such as low density and exceptional mechanical strength. Their low density provides lightweight solutions, and their density is closer to that of human bone compared to other metallic alloys with similar strength. This similarity facilitates a balanced load distribution between the bone and the implant, enhancing biomechanical compatibility. This study investigates the effects of alloying elements on the density of titanium-based biomedical materials using a computational materials science approach. A total of 72 different compositions of Ti-Al-V alloys were modeled using JMatPro software, and their densities were simulated at room temperature (25°C). The simulation produced a comprehensive dataset, which was utilized to train an explainable artificial intelligence (XAI) model. Advanced interpretability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP), were employed to elucidate the influence of each alloying element on the density. The dataset was analyzed using an XAI-based regression model implemented with the Artificial Neural Network (ANN) algorithm. The interpretability graphs provided insights into the individual contributions of the alloying elements, revealing their positive or negative effects on the density. The findings offer a deeper understanding of the role of alloying elements in optimizing the performance of titanium-based biomedical materials, particularly in achieving lightweight designs. This study highlights the potential of integrating computational material modeling with explainable AI to advance the design and development of high-performance lightweight materials for biomedical applications.

Multi-Scale Modeling of Transport Properties in Cementitious Materials with GO Admixture

In coastal areas, the presence of concrete cracks provides pathways for hazardous ions to ingress from the exterior into the interior of concrete, while the migration of the ions further accelerates concrete deterioration and causes durability problems. The incorporation of graphene oxide (GO) into concrete can inhibit crack initiation and development starting at the nanoscale, improving the concrete microstructure, thereby affecting concrete’s resistance to hazardous ion transport and the resulting deterioration. In this study, a multi-scale transport model for cementitious materials with a GO admixture was established to predict the resistance to hazardous ions. Based on the determination of hydration types and hydration kinetics, microstructure modeling was conducted at three scales, the sub-microscale, microscale, and mesoscale, upon which transport property simulations were performed. At the microscale, the effects of both the cement paste matrix and the interfacial transition zone (ITZ) were considered. Through the simulation, it was found that the addition of GO reduced the duration of the induction period and increased the rate of hydration development after the induction period. Moreover, the incorporation of GO could reduce the porosity of cementitious materials at all simulation scales at both early and later ages. At the microscale, it improved the pore structure of the cement matrix and ITZ by reducing large pores and increasing small pores. At all three simulation scales, GO could increase the diffusion tortuosity in hydration products, suppress ion transport, and improve the resistance to hazardous ions of cementitious materials.

Plasmonic electro-optic modulators based on epsilon-near-zero materials: comparing the classical drift-diffusion and Schrödinger-Poisson coupling models

We present the design, modeling, and optimization of high-performance plasmonic electro-optic modulators based on indium tin oxide (ITO), leveraging voltage-gated carrier density modulation. The carrier density is modeled using the classical drift-diffusion (CDD) and nonlinear Schrödinger-Poisson coupling (SPC) methods, with the latter providing precise carrier distribution profiles, particularly in epsilon-near-zero (ENZ) media like ITO. By combining the nanoscale field confinement of surface plasmon polaritons with the ENZ effect, our modulators, integrated with silicon waveguides and optimized for operation at λ = 1550 nm, achieve a 3-dB bandwidth of 210 GHz, an insertion loss of 3 dB, and an extinction ratio of 5 dB for a device length of under 4 µm. These results highlight the critical trade-offs between high-speed modulator operation and low insertion loss vs. extinction ratio, underscoring the necessity of precise carrier distribution modeling for ENZ materials in optoelectronic devices.

Spectroscopic Ellipsometry and Wave Optics: A Dual Approach to Characterizing TiN/AlN Composite Dielectrics

In this paper, we present a method for retrieving the optical properties of a nano-designed TiN/AlN composite dielectric, using spectroscopic ellipsometry for experimental measurements and wave optics simulations for numerical analysis. Composite cermets have gained attention for solar–thermal energy conversion, but their fundamental optical properties are not well understood. While characterizing uniformly deposited layers is generally straightforward, the process becomes more complex for nanoparticulate composites. The refractive index is essential for investigating and tuning the optical characteristics of the composite. Our method employs COMSOL Multiphysics software, validated by experimental spectroscopic ellipsometry studies. The strong agreement between experimental and numerical results supports this approach as a rational way to design material models for optical property studies across a broad spectrum.

Development of Physics Learning Media Integrated with Rebuttal Text (PhyLeM-ReT) on Work and Energy Material

This study aimed to develop Physics Learning Media assisted by Rebuttal Texts (PhyLeM-ReT), integrated with the Predict, Observe, Explain, Apply, and Write (POEAW) model for work and energy material. The development research design employed in this study was ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The participants were 30 tenth-grade students from a private high school in Bandung. A student response questionnaire to the media was implemented, which comprised 19 statements (10 positive and nine negative) and was evaluated using a Likert scale. This study implemented Rasch analysis. The result showed that 25 students agreed with the positive statements and disagreed with the negative statements related to interest in learning physics, the use of media, enthusiasm for learning, and usefulness in understanding concepts. Thus, PhyLeM-ReT integrated with the POEAW model facilitates students' comprehension of energy and work material. The developed media can also serve as an alternative for teachers in teaching, specifically enhancing and transforming students' conceptions. This research is crucial in an era where misconceptions regarding physics persist, as it aims to offer effective alternative learning media that can address these obstacles. Additionally, this research presents opportunities for further development in integrating comparable methods into other subject areas of physics that necessitate a profound conceptual approach.

Advanced Thermoelastic Modeling of Porous Materials with Dual-Phase Lag and Memory-Dependent Effects

Advanced Thermoelastic Modeling of Porous Materials with Dual-Phase Lag and Memory-Dependent Effects

Viscoelastic Memory Effects in Cyclic Thermomechanical Loading of Epoxy Polymer and Glass-Reinforced Composite: An Experimental Study and Modeling Under Variable Initial Stress and Cycle Durations

This article presents a study of the viscoelastic behavior of an epoxy polymer and a glass-reinforced composite based on it under cyclic thermomechanical loading. The goal is to model and explain the experimentally observed stress state formation, including the accumulation of residual stresses under various initial mechanical stress levels and heating/cooling cycle durations. An improved material model, implemented as a Python script, is used, allowing for the consideration of memory effects on thermomechanical loading depending on the level and nature (mechanical or thermal) of the initial stresses. A Python script was developed to determine the viscoelastic parameters (elastic modulus E1, elastic parameter E2, and viscosity) for the three-element Kelvin–Voigt model. These parameters were determined at different temperatures for both the polymer and the glass-reinforced composite used in the modeling. The accumulation of stresses under different ratios of mechanical and thermal stresses was also investigated. Experiments showed that high levels of residual stress could form in the pure epoxy polymer. The initial stress state significantly influences residual stress accumulation in the pure epoxy polymer. Low initial tensile stresses (0–1.5 MPa) resulted in substantial residual stress accumulation, exceeding the initial stresses by up to 2.7 times and reaching values of up to 2.1 MPa. Conversely, high initial stresses (around 4 MPa) suppressed residual stress accumulation due to the dominance of relaxation processes. This highlights the critical role of the initial loading conditions in predicting long-term material behavior. In the glass-reinforced plastic, the effect of residual stress accumulation was significantly weaker, possibly due to the reinforcement and high residual stiffness, even at elevated temperatures (the studies were conducted from 30 to 180 °C for the composite and from 30 to 90 °C for the polymer). The modeling results show satisfactory qualitative and quantitative agreement with the experimental data, offering a plausible explanation for the observed effects. The proposed approach and tools can be used to predict the stress–strain state of polymer composite structures operating under cyclic thermomechanical loads.

Development of 4D-Printed Arterial Stents Utilizing Bioinspired Architected Auxetic Materials

The convergence of 3D printing and auxetic materials is paving the way for a new era of adaptive structures. Auxetic materials, known for their unique mechanical properties, such as a negative Poisson’s ratio, can be integrated into 3D-printed objects to enable them to morph or deform in a controlled manner, leading to the creation of 4D-printed structures. Since the first introduction of 4D printing, scientific interest has spiked in exploring its potential implementation in a wide range of applications, from deployable structures for space exploration to shape-adaptive biomechanical implants. In this context, the current paper aimed to develop 4D-printed arterial stents utilizing bioinspired architected auxetic materials made from biocompatible and biodegradable polymeric material. Specifically, three different auxetic materials were experimentally examined at different relative densities, under tensile and compression testing, to determine their mechanical behavior. Based on the extracted experimental data, non-linear hyperelastic finite element material models were developed in order to simulate the insertion of the stent into a catheter and its deployment in the aorta. The results demonstrated that among the three examined structures, the ‘square mode 3’ structure revealed the best performance in terms of strength, at the same time offering the necessary compressibility (diameter reduction) to allow insertion into a typical catheter for stent procedures.

Proposing a New Crack Driving Force Parameter for Fatigue Crack Growth Rate of C–Mn Steel

ABSTRACTThe present study is aimed at analyzing the fatigue crack growth rate data on compact tension (CT) and three‐point‐bend (TPB) specimens of C–Mn steel under different positive load ratios. Detailed elastic–plastic finite element analyses have been performed using nonlinear kinematic hardening rule of Chaboche material model. The numerically calculated plastic zone ahead of crack tip has been compared with measured plastic zone using digital image correlation technique. The fatigue crack growth rate curves have been analyzed with respect to different crack driving forces such as (i) single‐parameter stress intensity factor (SIF) range, (ii) two‐parameter‐based SIF (), and (iii) cyclic plasticity zone–based models. A new crack driving force has been proposed considering cyclic plastic zone and representative plastic strain accumulation within this zone. The parameter and proposed model resulted in improved assessments for different load ratios and constraint geometries (CT and TPB).

Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities

Additive manufacturing (AM) or 3D printing technology creates a tangible object by adding successive layers of materials. Nowadays, 3D printing is used for developing both metal and non-metal products. In the advancement of 3D printing technology, material specimen design, modification, and testing become very simple, especially for non-metal materials, such as hyperelastic, thermoplastic, or rubber-like materials. However, proper material modeling and validation are required for the analysis of these types of materials. In this study, 3D printed poly lactic acid (PLA+) material behavior is analyzed numerically for validation in the counterpart of experimental analysis to evaluate their behavior in both cases. The specimen was designed in SolidWorks by following ASTM D638 dimension standards with proper infill densities and raster angle or infill orientation angle. These infill layer densities and angles of orientation play an important role in the mechanical behavior of the specimen. This paper aims to present a numerical validation of five infill densities (20%, 40%, 60%, 80%, and 100%) for a ±45-degree infill angle orientation by incorporating a nonlinear hyperelastic model. Results indicate that infill densities affect the mechanical behavior of PLA+ material. The result also suggested that neo-Hookean and Mooney–Rivlin are the best-fitted hyperelastic material models for these five separate linear infill densities. However, neo-Hookean is easier to analyze, as it has only one parameter and a new equation is developed in this study for determining the parameter for different infill densities.

Material Lateral Position Guiding During Calendering Process Via Material Width Change Modeling and Model Predictive Control

Abstract One of important control problems in calendering system is the web lateral position control: during calendering, the web deforms with a reduction of thickness and an expansion in width, this leads to potential lateral position deviation, especially when the web is not flat. This problem sometimes causes quality issues, hence, precise modeling and control need to be developed. This paper focuses on the web lateral position guiding by modeling and controlling a calendering system: a modeling method and a corresponding model predictive control are proposed. The study is based on electrode web calendering for Li-ion battery production and is supported by data collected from experiments on an industrial-scale winding machine.