Material Model Research Articles

Computational assessment of carbon fabric reinforced polymer made prosthetic knee: Mechanics, finite element simulations and experimental evaluation.

A prosthetic knee is designed to replace the functionality of an anatomical knee in transfemoral amputees. The purpose of a prosthetic knee is to restore mobility and compensate amputees for their impairment. In the present research numerical modelling and simulation of a carbon fabric reinforced polymer made polycentric prosthetic knee with four-bar mechanism was performed. Virtual prototyping with computer-aided design and computer-aided engineering software ensured geometric and structural stability of the knee design. The linkage mechanism, instantaneous centre's location and trajectory were investigated using multibody dynamics and analytical formulations. Computational simulations with a non-linear finite element model were employed with joints, contact formulations and an orthotropic material model to predict the displacement, stress formulated and life of the knee prosthesis under static and cyclic loading conditions. Finite element analysis assessed the strength and durability of knee in accordance to standards. Maximum Principal stress of 155 MPa and life expectancy of 3.1 × 106 cycles were determined for the composite knee through numerical simulations ensuring a safe design. Experimental testing was also conducted as per standards and the percentage error was estimated to be 2.52%, thereby establishing the validity of the finite element model deployed. This type of simulation-based approach can be implemented to efficiently and affordably design and prototype a prosthetic knee with desired functioning criteria.

The Mechanics of Elastomeric Sheet Reinforced With Bidirectional Fiber Mesh Subjected to Flexure on Boundaries

Abstract We investigate the concurrent three-dimensional deformations of fiber-reinforced composite sheets subjected to out-of-plane bending moments via a continuum model, where we invoke the neo-Hookean strain energy model for the matrix material of fiber-reinforced composite, and assimilate the strain energy of fiber reinforcements into the matrix material model by accounting for stretching, bending, and twisting kinematics of the fibers through the computations of the first-order and second-order gradient of deformation. Emphasis is placed on deriving the Euler equation and boundary conditions of bending moment within the framework of the variational principle and configuring composite surfaces using differential geometry. Significant attention has been given to illustrating the concurrent three-dimensional deformation of fiber composite, meshwork deformation, and fiber kinematics. The simulation results reveal that for a square fiber composite subjected to the out-of-plane bending moment, the maximum in-plane deformation of matrix material occurs along the diagonal direction of the domain while the center of the domain experiences weak in-plane deformation. Notably, the matrix material performs isotropic/anisotropic properties depending on the domain size/shape. In addition, the simulated unit fiber deformations reasonably validate the overall deformation of the network, underscoring that the deformations of the embedded fiber units govern the overall mechanical performance of the fiber meshwork. More importantly, the continuum model qualitatively provides reasonable predictions on the damage patterns of construction materials by demonstrating the kinematics of matrix material and meshwork deformation.

Mechanical and Material Analysis of 3D-Printed Temporary Materials for Implant Reconstructions—A Pilot Study

In this study, the authors analyzed modern resin materials typically used for temporary reconstructions on implants and manufactured via 3D printing. Three broadly used resins: NextDent Denture 3D, NextDent C&B MFH Bleach, and Graphy TC-80DP were selected for analysis and compared to currently used acrylic materials and ABS-like resin. In order to achieve this, mechanical tests were conducted, starting with the static tensile test PN-EN. After the mechanical tests, analysis of the chemical composition was performed and images of the SEM microstructure were taken. Moreover, numerical simulations were conducted to create numerical models of materials and compare the accuracy with the tensile test. The parameters obtained in the computational environment enabled more than 98% correspondence between numerical and experimental charts, which constitutes an important step towards the further development of numeric methods in dentistry and prosthodontics.

Different Soil-Structure Interaction Modelling Strategies for Seismic Analysis of a Masonry Church

ABSTRACT Masonry structures can be damaged different external reasons such as war, flood, earthquake etc. For protecting and transfer to the next generations, these structures can be examined detailed. Finite element analysis has generally been used for the seismic evaluation of masonry structures. The fixed base assumption has been used in traditional finite element analysis of masonry structures. Interacting with the surrounding soil and structure was generally ignored. The seismic behavior of masonry structures is significantly influenced by the soil-structure interaction (SSI). There are various soil structure interaction modelling strategies in the recent studies. In this paper, the dimensions, mechanical properties and material model of the soil are considered constant. One of them had no soil-structure interaction. The others, roller support case, fixed support case, absorbing boundary case, free field columns and Winkler supports. Variations in the seismic response of a historical masonry church were investigated using 4 different SSI models and a fixed base (SSI ignored) model. Nonlinear time history analyses were employed for the finite element analysis. The soil borehole provided the material characteristics of the surrounding soil. According to the analysis results, higher response amplitudes were obtained when SSI was considered.

Effects of Pressure Rollers with Variable Compliance in the Microfinishing Process Utilizing Abrasive Films

This article presents a comprehensive investigation into pressure rollers utilized in the microfinishing process, covering aspects such as design, experimental properties, compliance, and finite element simulation. Prototype pressure rollers with unconventional elastomer configurations were designed and analyzed to explore their effectiveness in achieving superior surface finishes. Experimental analysis and finite element simulations were conducted to gain insights into the performance and behavior of these pressure rollers under various loading conditions. This study addresses the validation of constitutive material models used in finite element simulations to ensure accuracy and reliability. The results indicate that the applied material model, validated through experimental analysis, accurately predicts pressure roller behavior. Finite element simulations reveal distinct contact zone patterns and stress distributions across the contact surfaces, highlighting the importance of considering deflection-induced variations in contact behavior. Additionally, the investigation evaluates the effectiveness of different pressure rollers in removing surface irregularities during the microfinishing process. Roller R3 demonstrates the highest efficacy in removing surface peaks, suggesting its potential for achieving superior surface finishes. Overall, this research contributes to the advancement of microfinishing techniques by providing insights into pressure roller design, performance, and behavior, thereby optimizing microfinishing processes to produce high-quality components. The urgency of this study arises from the growing need for exceptional surface finishes in various industrial sectors. With manufacturing industries increasingly pursuing high-precision components boasting flawless surface quality, the significance of microfinishing processes is highlighted.

Numerical investigation on the behaviour of concrete barriers subjected to vehicle impacts using modified K&C material model

Full-scale experimental crash tests are important to determine the occupant risk factors and understand barrier system behaviour in vehicle-barrier impacts. Due to the expensive and time-consuming nature of such crash tests, finite element simulations of full-scale crash tests to analyse and design road safety structures have gained popularity as a more practical method of assessment. In this research, a simplified simulation technique was employed by dividing the barrier system into two sections called the Impact Zone and the Rigid Zone, which was adopted for concrete crash barriers for the first time. A previously performed experimental crash test was used to successfully validate the numerical model. The numerical crash model accurately predicted the critical parameters of the barrier performance. The numerical simulation was further used to extract otherwise difficult or impossible results from the experimental crash test, such as internal energies and exit angles. The importance of the use of optimised Karagozian and Case concrete material parameters was demonstrated through a parametric study carried out with autogenerated parameters. A parametric study performed with impact speed and angle proved that increased speed is more harmful for occupants than a more oblique impact angle. The potential benefits of replacing traditional concrete with improved energy absorbing concrete to reduce occupant risks were also investigated. The comparison of results between the numerical models and experimental crash test confirms the trustworthiness of the developed models to simulate the vehicle-barrier crashes for analysing existing barrier designs and further developing new barrier designs to increase road safety.

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.

Composite Coatings Applied to Fresh and Blanched Chayote (Sechium edule) and Modeling of the Drying Kinetics and Sorption Isotherms

Sustainable methods such as convective drying have regained interest in reducing the loss and waste of food produce. Combined with techniques like blanching and edible coatings, they could serve as useful tools in food processing development. Composite coatings comprising pectin, soy protein isolate, and xanthan gum were optimized using response surface methodology with the Box–Behnken design. This optimization aimed to investigate their effects on the moisture content, water activity, total color, and rehydration ratio of fresh and blanched chayote slices. Additionally, the study explored the modeling of the drying kinetics and sorption isotherms of chayote (Sechium edule) slices. Soy protein and xanthan gum were found to primarily influence the moisture content (ranging from 5.44% to 9.93%), and pectin influenced water activity (033 to 0.53) of the fresh-coated chayote, while pectin affected the aw (2.13–8.28) and rehydration of the blanch-coated chayote. The optimized formulations for both fresh and blanched chayote were utilized to assess the drying kinetics behavior and sorption isotherms. The best fit (R2: 0.996 to 0.999) was achieved with the parabolic model for thin-layer materials. Furthermore, the sorption isotherms of chayote displayed a Type IV behavior, with the BET model being the most suitable for describing the sorption behavior of materials with low water activity. The predicted values offer valuable data for optimizing processing conditions to enhance the quality and stability of dried chayote.

A machine learning constitutive model for plasticity and strain hardening of polycrystalline metals based on data from micromechanical simulations

Machine learning (ML) methods have emerged as promising tools for generating constitutive models directly from mechanical data. Constitutive models are fundamental in describing and predicting the mechanical behavior of materials under arbitrary loading conditions. In recent approaches, the yield function, central to constitutive models, has been formulated in a data-oriented manner using ML. Many ML approaches have primarily focused on initial yielding, and the effect of strain hardening has not been widely considered. However, taking strain hardening into account is crucial for accurately describing the deformation behavior of polycrystalline metals. To address this problem, the present study introduces an ML-based yield function formulated as a support vector classification model, which encompasses strain hardening. This function was trained using a 12-dimensional feature vector that includes stress and plastic strain components resulting from crystal plasticity finite element method (CPFEM) simulations on a 3-dimensional RVE with 343 grains with a random crystallographic texture. These simulations were carried out to mimic multi-axial mechanical testing of the polycrystal under proportional loading in 300 different directions, which were selected to ensure proper coverage of the full stress space. The training data were directly taken from the stress–strain results obtained for the 300 multi-axial load cases. It is shown that the ML yield function trained on these data describes not only the initial yield behavior but also the flow stresses in the plastic regime with a very high accuracy and robustness. The workflow introduced in this work to generate synthetic mechanical data based on realistic CPFEM simulations and to train an ML yield function, including strain hardening, will open new possibilities in microstructure-sensitive materials modeling and thus pave the way for obtaining digital material twins.

Metasurface array for single-shot spectroscopic ellipsometry

Spectroscopic ellipsometry is a potent method that is widely adopted for the measurement of thin film thickness and refractive index. Most conventional ellipsometers utilize mechanically rotating polarizers and grating-based spectrometers for spectropolarimetric detection. Here, we demonstrated a compact metasurface array-based spectroscopic ellipsometry system that allows single-shot spectropolarimetric detection and accurate determination of thin film properties without any mechanical movement. The silicon-based metasurface array with a highly anisotropic and diverse spectral response is combined with iterative optimization to reconstruct the full Stokes polarization spectrum of the light reflected by the thin film with high fidelity. Subsequently, the film thickness and refractive index can be determined by fitting the measurement results to a proper material model with high accuracy. Our approach opens up a new pathway towards a compact and robust spectroscopic ellipsometry system for the high throughput measurement of thin film properties.

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

Numerical Simulation of T Joints Constructed from Hollow Steel Sections

Welded structural hollow sections are becoming increasingly used in contemporary civil engineering buildings. More specific design techniques are needed for connections in steel structures with welded structural hollow sections than for traditional connections made with gusset plates. At the connecting site, stress and local deformations show very complex, non-linear behaviors. The application of non-linear numerical analyses to determine the ultimate bearing capacity of “T” connections was demonstrated in this study. A non-linear material model was used in the analysis. In order to validate the applicable steel modeling method, a comparative study between experimentally obtained results and analytically and numerically computed ultimate bearing capacities was carried out. The comparison showed good agreement between the numerical and experimental results and pointed out the highly conservative design of such joints was obtained using the analytical solution.

On sparse regression, Lp‐regularization, and automated model discovery

AbstractSparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem‐specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias that may aggressively change the results; only L0 regularization allows us to transparently fine‐tune the trade‐off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user‐defined properties.

Development of smart materials with versatile bending capabilities using microcellular foaming process: Influence of foaming and desorption time

Abstract Herein, a smart material with versatile bending capability is developed using a microcellular foaming process (MCPs). In contrast to previous hydrogel-based approaches, the bi-layered smart material is fabricated using typical thermoplastics, polyethylene terephthalate glycol (PETG) and polymethyl methacrylate (PMMA), to achieve shape deformation in response to thermal stimuli. Further, the theoretical model for bi-layered smart materials based on the modified Timoshenko’s model is employed to predict and comprehend this thermal response phenomenon. Due to the distinct foaming characteristics of the two polymers, a reversal in the bending direction is achieved by manipulating the foaming and desorption time. The length variation after foaming differs depending on the desorption time for each polymer. PMMA decreases in length after foaming, measuring 56.25 mm at a desorption time of 40 min and 53.16 mm at 80 min. On the other hand, PETG shows an increase in length after foaming, measuring 53.33 mm at 40 min and 58.25 mm at 80 min. Consequently, when the two polymers are bonded and foamed, bending occurs depending on the desorption time, and a reversal in the bending direction is observed at the critical desorption time of around 60 min. Based on this result, the folding direction of a five-leafed flower-shaped object is successfully altered under thermal stimuli. This innovative approach extends the category of smart materials beyond the hydrogels and showcases the potential of the MCPs for the creation of smart materials for various applications that require versatile shape changes in response to temperature.

Experimental and Theoretical Insights into Interfacial Properties of 2D Materials for Selective Water Transport Membranes: A Critical Review.

Interfacial properties, such as wettability and friction, play critical roles in nanofluidics and desalination. Understanding the interfacial properties of two-dimensional (2D) materials is crucial in these applications due to the close interaction between liquids and the solid surface. The most important interfacial properties of a solid surface include the water contact angle, which quantifies the extent of interactions between the surface and water, and the water slip length, which determines how much faster water can flow on the surface beyond the predictions of continuum fluid mechanics. This Review seeks to elucidate the mechanism that governs the interfacial properties of diverse 2D materials, including transition metal dichalcogenides (e.g., MoS2), graphene, and hexagonal boron nitride (hBN). Our work consolidates existing experimental and computational insights into 2D material synthesis and modeling and explores their interfacial properties for desalination. We investigated the capabilities of density functional theory and molecular dynamics simulations in analyzing the interfacial properties of 2D materials. Specifically, we highlight how MD simulations have revolutionized our understanding of these properties, paving the way for their effective application in desalination. This Review of the synthesis and interfacial properties of 2D materials unlocks opportunities for further advancement and optimization in desalination.

CBCT-DRRs superior to CT-DRRs for target-tracking applications for pancreatic SBRT.

In current radiograph-based intra-fraction markerless target-tracking, digitally reconstructed radiographs (DRRs) from planning CTs (CT-DRRs) are often used to train deep learning models that extract information from the intra-fraction radiographs acquired during treatment. Traditional DRR algorithms were designed for patient alignment (i.e. bone matching) and may not replicate the radiographic image quality of intra-fraction radiographs at treatment. Hypothetically, generating DRRs from pre-treatment Cone-Beam CTs (CBCT-DRRs) with DRR algorithms incorporating physical modelling of on-board-imagers (OBIs) could improve the similarity between intra-fraction radiographs and DRRs by eliminating inter-fraction variation and reducing image-quality mismatches between radiographs and DRRs. In this study, we test the two hypotheses that intra-fraction radiographs are more similar to CBCT-DRRs than CT-DRRs, and that intra-fraction radiographs are more similar to DRRs from algorithms incorporating physical models of OBI components than DRRs from algorithms omitting these models.&#xD;&#xD;Main results: Intra-fraction radiographs were more similar to CBCT-DRRs than CT-DRRs for both metrics across all algorithms, with all p<0.007. Source-spectrum modelling improved radiograph-DRR similarity for both metrics, with all p<1E-6. OBI detector modelling and patient material modelling did not influence radiograph-DRR similarity for either metric.&#xD;&#xD;Significance: Generating DRRs from pre-treatment CBCT-DRRs is feasible, and incorporating CBCT-DRRs into markerless target-tracking methods may promote improved target-tracking accuracies. Incorporating source-spectrum modelling into a treatment planning system's DRR algorithms may reinforce the safe treatment of cancer patients by aiding in patient alignment.

Poisson’s Ratio of Selected Metallic Materials in the Elastic–Plastic Region

Poisson’s ratio is one of the fundamental characteristics in the material models that are used. In engineering practice, its values are assumed to be constant in the elastic and in the plastic region. In this paper, the conventionally used values of this number for steel materials and aluminum alloys are confronted with experimental results. By using non-contact strain measurements with the DIC (digital image correlation) method, the evolution of the Poisson ratio value in the regions of transition from the elastic to the plastic region as well as in the regions of large plastic deformations was documented. The obtained experimental results are graphically compared using the proposed strain scaling. The gradient of the Poisson ratio changes in the vicinity of the yield stress is significant, indicating the need for a refinement of the material models in this region. Deviations from the conventionally used value of this number were found in the large plastic deformation region. In conclusion, a possible approach for improving the accuracy of simulations in FEM softwares was formulated.

System Identification Using Self-Adaptive Filtering Applied to Second-Order Gradient Materials

For many engineering applications, it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches, length scale parameters have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modeling, as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established, there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion, system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach, it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically, it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

Integrated production-inventory model for multi-item raw materials with exponential quality degradation: a real case study

Integrated production-inventory model for multi-item raw materials with exponential quality degradation: a real case study

Investigation of the Seismic Performance of Historical Masonry Aqueducts with Eulerian-Lagrangian Coupled Analysis

ABSTRACT Throughout history, aqueducts have been built to exceed obstacles on the waterway while preserving the energy of water. Our priceless cultural heritage, aqueducts must be protected to be passed on to future generations. Filling the channel with water can increase earthquake resistance due to the damping effect of water. Investigating of fluid and structure interaction of aqueducts through numerical analysis is crucial to understand their behavior under seismic forces. The aqueducts, for which 3D finite element models were prepared, were analyzed using the combined Eulerian-Lagrangian approach, taking into account two main scenarios: with and without fluid-structure interaction. With this approach, it’s aimed to observe the damping effect of the fluid, the sloshing on its surface and possible water overflows. Using the CDP material model for masonry units, the analyzes were diversified with various ground motion records at different Peak Ground Acceleration values. The presence of water in the channel significantly reduced displacements, stresses and damage rates. In addition, it was seen that the spectral acceleration value of the earthquake record was more effective in causing damage rather than the PGA value. Graphics and contour diagrams are given to understand and compare the analysis results of aqueduct models under different conditions.