Material Properties Of Model Research Articles

From Gaussian to lognormal: improving material property modeling for precise structural predictions

From Gaussian to lognormal: improving material property modeling for precise structural predictions

A Novel Approach to the Development of Natural Resin‐Based Biopolymer in the Presence of a Reusable Catalyst: Characterization and Modeling of Material Properties

ABSTRACTThe rise in environmental and health concerns has led to increasing attention to nature‐derived materials. Natural resin (NR) is secreted by pine trees, and it is a great monomer source for synthesizing biopolymers. The objective of this study is to produce terpene rosin phenolic resin (TRPR) from NR, turpentine, and phenol by applying a novel polymerization technique. An environmentally friendly and reusable catalyst (Amberlyst15) was chosen instead of traditional ones. TRPR samples were chemically characterized using Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), and gel permeation chromatography (GPC) analysis. The average molecular weight (Mw) of TRPR was detected as 560 g/mol. Artificial neural network (ANN) modeling was designed with three inputs (pressure, temperature, and terpene/NR ratio) and four outputs (reaction yield, acid value, saponification value, and softening point). The highest TRPR yield was obtained with a terpene/NR ratio of (1/2) at 80°C and under 3 atm. The lowest acid and saponification values were calculated as 90.54 and 100.11 mg KOH/g, respectively. The softening point of TRPR reached 80°C and it was suggested for use in the paper, ink, and adhesive industries.

Predicting the elastic properties of Norway spruce by its morphology

Models for the elastic material properties of wood aim to predict the mechanical response of wooden structures to external loading. Traditionally, the variability of these properties in trees is described by a taxonomy of growth defects that is typically based on visual indicators in the material. This includes curvature, knots, and spiral grain models. Existing meso- and macro-scale models fail to describe the uncertainty connected to the local heterogeneity of the material. In this paper, we propose a novel meso-scale model that describes the natural variability of Norway spruce morphology and material properties based on random field theory. Our approach removes the need for a taxonomy of growth defects and enables uncertainty quantification of the stiffness and density in a straightforward fashion using simulations. This may enhance confidence for stiffness-graded applications, where the dynamic resonant behavior of wood structures is relevant and growth anomalies are present. Further, our stochastic models can generate images that realistically mimic wood patterns, which is relevant for applications like synthetic wood panels and flooring.

Artificial intelligence (AI) within manufacturing: An investigative exploration for opportunities, challenges, future directions

<p>Artificial intelligence (AI) stands as a potent catalyst for revolutionizing manufacturing, promising unprecedented efficiency, agility, and resilience. This research embarks on an investigative journey to dissect the multifaceted landscape of AI in manufacturing, aiming to unravel its current status, intrinsic challenges, and prospective pathways. This research unveils the intricate relationship between AI technologies and manufacturing processes across diverse domains. Examining various domains, including system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material-property modeling. The research also reveals AI’s transformative potential in optimizing manufacturing operations, enhancing decision-making, and fostering innovation. By dissecting each domain, the research illuminates how AI empowers manufacturers to adapt to dynamic market demands and technological advancements, ultimately driving sustainable growth and competitiveness. Moreover, it also examines the evolving dynamics of human-robot collaboration within manufacturing settings, recognizing AI’s pivotal role in facilitating seamless communication, shared understanding, and dynamic adaptation between humans and machines. Through an exploration of AI-enabled human-robot collaboration, this research underscores the transformative power of symbiotic relationships in reshaping the future of manufacturing. While highlighting opportunities, it acknowledges the myriad challenges accompanying AI integration in manufacturing, such as data quality issues, interpretability of AI models, and knowledge transfer across domains. By addressing these challenges, the research aims to pave the way for more resilient AI-driven manufacturing systems capable of navigating complex market landscapes and technological disruptions. This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.<strong></strong></p>

Increasing control over biomineralization in conodont evolution

Vertebrates use the phosphate mineral apatite in their skeletons, which allowed them to develop tissues such as enamel, characterized by an outstanding combination of hardness and elasticity. It has been hypothesized that the evolution of the earliest vertebrate skeletal tissues, found in the teeth of the extinct group of conodonts, was driven by adaptation to dental function. We test this hypothesis quantitatively and demonstrate that the crystallographic order increased throughout the early evolution of conodont teeth in parallel with morphological adaptation to food processing. With the c-axes of apatite crystals oriented perpendicular to the functional feeding surfaces, the strongest resistance to uniaxial compressional stress is conferred along the long axes of denticles. Our results support increasing control over biomineralization in the first skeletonized vertebrates and allow us to test models of functional morphology and material properties across conodont dental diversity.

A transfer learning strategy for tensile strength prediction in austenitic stainless steel across temperatures

Austenitic stainless steel plays a pivotal role in the nuclear industry with exceptional mechanical properties and corrosion resistance, wherein high-temperature tensile strength (∼290 °C) is a critical factor in ensuring its performance and reliability. This study focuses on the predictive modeling of tensile strength in cast austenitic stainless steel at room and elevated temperatures by developing a transfer learning strategy. The application of a gradient-boosting decision tree algorithm, enhanced by key features derived from room-temperature analyses and augmented with partial high-temperature data, results in significantly increased accuracy. The effective selection of features underscores the stability of critical variables across different temperatures, affirming the efficacy of the proposed transfer learning strategy. The work sheds light on material selection and design for high-temperature applications, and lays the groundwork for future exploration into predictive modeling of material properties in extreme conditions.

Numerical ellipsometry: Artificial intelligence for rapid analysis of indium tin oxide films on silicon

Ellipsometry is a well-known material analytical method widely used to measure thickness and optical properties of thin films and surfaces across a wide range of industrial and research applications including critical dimensions in chipmaking. The method employs the fact that light undergoes a change in polarization state upon reflection from or transmission through a material. The desired properties of the surface structure are related to measurements by the electromagnetic models expressed by Maxwell’s equations as well as models of material properties. The work here demonstrates the use of artificial intelligence in the form of a multilayer perceptron artificial neural network to apply the electromagnetic model. The reflecting surface examined here is composed of indium tin oxide (ITO) films approximately 400 nm in thickness deposited on silicon substrates. Solutions are provided by 299 artificial neural networks, one per wavelength from 210 to 1700 nm across which ITO exhibits transparent as well as absorbing characteristics. Thus, it serves as a proxy for a wide range of other materials. To train the network, simulated measurements are computed at two thicknesses which differ randomly by 1–6 nm and at three different incidence angles of 55°, 65°, and 75°. Following training, results are obtained in less than one second on a conventional desktop computer.

Prediction and comparative analysis of friction material properties using a GA-SVM optimization model

PurposeWith the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.Design/methodology/approachGenetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.FindingsThe results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.Originality/valueThe relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

Stochastic modelling of symmetric positive definite material tensors

Spatial symmetries and invariances play an important role in the behaviour of materials and should be respected in the description and modelling of material properties. The focus here is the class of physically symmetric and positive definite tensors, as they appear often in the description of materials, and one wants to be able to prescribe certain classes of spatial symmetries and invariances for each member of the whole ensemble, while at the same time demanding that the mean or expected value of the ensemble be subject to a possibly ‘higher’ spatial invariance class. We formulate a modelling framework which not only respects these two requirements—positive definiteness and invariance—but also allows a fine control over orientation on one hand, and strength / size on the other. As the set of positive definite tensors is not a linear space, but rather an open convex cone in the linear space of physically symmetric tensors, we consider it advantageous to widen the notion of mean to the so-called Fréchet mean on a metric space, which is based on distance measures or metrics between positive definite tensors other than the usual Euclidean one. It is shown how the random ensemble can be modelled and generated, independently in its scaling and orientational or directional aspects, with a Lie algebra representation via a memoryless transformation. The parameters which describe the elements in this Lie algebra are then to be considered as random fields on the domain of interest. As an example, a 2D and a 3D model of steady-state heat conduction in a human proximal femur, a bone with high material anisotropy, is modelled with a random thermal conductivity tensor, and the numerical results show the distinct impact of incorporating into the constitutive model different material uncertainties—scaling, orientation, and prescribed material symmetry—on the desired quantities of interest.

Machine learning assisted prediction and analysis of in-plane elastic modulus of hybrid hierarchical square honeycombs

In this study, experimental, finite element (FE) simulation, machine learning (ML), and theoretical techniques are employed to investigate the in-plane elastic modulus (EHHSH) of hybrid hierarchical square honeycombs (HHSHs). First, HHSHs with different configurations were fabricated using a 3D printer, and in-plane quasi-static compression tests were conducted on them. Then, 234 FE models are simulated to determine the EHHSH of HHSHs with various configurations, and the results are used to train 11 ML models. Comparative analysis demonstrates that the Extreme Gradient Boosting (XGBoost) model has the best predictive capability. Moreover, a modified theory for EHHSH is established based on the XGBoost model and existing theory, and its exceptional predictive capability is verified by comparing with experimental, FE, and existing theoretical results. Finally, the upper and lower bounds of EHHSH are determined by the modified theory, and the Shapley Additive Explanation (SHAP) method is used to identify the importance of different geometric parameters on tailoring EHHSH. The combination of theoretical and ML techniques provides a promising approach for developing a robust prediction model of material properties.

Development and validation of the lead-bismuth cooled reactor system code based on a fully implicit homogeneous flow model

The liquid lead-bismuth cooled fast reactor has been in a single-phase, low-pressure, and high-temperature state for a long time during operation. Considering the requirement of calculation efficiency for long-term transient accident calculation, based on a homogeneous hydrodynamic model, one-dimensional heat conduction model, coolant flow and heat transfer model, neutron kinetics model, coolant and material properties model, this study used the fully implicit difference scheme algorithm of the convection-diffusion term to solve the basic conservation equation, to develop the transient analysis program NUSOL-LMR 2.0 for the lead-bismuth fast reactor system. The steady-state and typical design basis accidents (including reactivity introduction, loss of flow caused by main pump idling, excessive cooling, and plant power outage accidents) for the ABR have been analyzed. The results are compared with the international system analysis software ATHENA. The results indicate that the developed program can stably, accurately, and efficiently predict the transient accident response and safety characteristics of the lead-bismuth fast reactor system.

Modeling of a Magnetorheological Elastomer-Based Nanocomposite Higher-Order Beam: Tunable Dynamic and Damping Characteristics

This study involves the comprehensive modeling of material properties and a numerical investigation of the free-damped vibrations exhibited by an innovative composite beam. The beam under consideration is a laminated magnetorheological (MR) elastomer, strategically reinforced with graphene platelets (GPLs). Initially, the mechanical properties of the MR matrix are meticulously calculated using a modified generalized Kelvin–Voigt viscoelastic model. To ensure the accuracy and credibility of the proposed model, nonlinear regression analysis based on the nonlinear least squares technique is employed. This modeling approach enables the tunability of the MR matrix’s storage and loss modulus, which depend on factors such as the magnetic field, iron particles, and excitation frequency. The proposed model is verified by comparing the results with experimental data from the literature, using analyses of root mean square error (RMSE) and correlation coefficients. Subsequently, the composite media, comprising the magnetorheological elastomer (MRE) matrix and GPL reinforcements, undergoes homogenization through the application of the Halpin–Tsai micromechanical procedure. Theoretical formulations, encompassing third-order beam theory, strains, linear elasticity, and viscoelasticity, are then utilized to derive the motion equations governing the dynamic behavior of the composite beam. Hamilton’s principle guides the derivation of these equations. To obtain the complex frequencies characterizing the system, the generalized differential quadrature (GDQ) method is implemented. In this study, particular emphasis is placed on investigating the performance achieved by adding GPL reinforcements to the MRE with different iron particle volume fractions under various magnetic fields.

Process-induced distortion of triaxially braided composites considering different geometric parameters using simplified constitutive model with effective property

Herein we present a prediction method for the process-induced distortion (PID) of a 2-D triaxially braided composite (TBC) structure at the macroscopic level based on a simplified constitutive model and analytical model for the effective material properties (EMPs). This method can quickly predict the PID considering viscoelastic characteristics and different geometric parameters of the 2-D TBC. The PID trend with curing time of an L-shaped TBC flange was analyzed in detail using a finite element-based curing simulation. Furthermore, the effects of the geometric parameters on the EMPs and PID of the 2-D TBC were investigated. A primary finding is that the in-plane shear modulus of the 2-D TBC had a significant effect on the PID of the cured 2-D TBC structures. This prediction method can be utilized for designing composite structures and molds, thus contributing to the improvement of dimensional precision and capabilities of composite materials and structures.

Modeling of Stochastic Material Properties with User Subroutines in SIMULIA Abaqus

This article describes a software module component integrated with the SIMULIA Abaqus engineering analysis software package and designed to simulate random values of material parameters in a finite element model based on specified statistical characteristics, with the possibility of taking into account the physical nonlinearity of material behavior under various combinations of loads and influences. The target group of materials under study is materials of load-bearing elements of building structures, such as concrete, stone, steel. This software module can be recommended for use by specialists, engineers and scientists engaged in probabilistic analysis of the reliability of structures of buildings and structures, apparatus, machines, devices, with the combined use of complexes of computer modeling and engineering analysis. Has a certificate of state registration of the computer program «AS for modeling stochastic properties of materials» No. 2019667439 dated 12.24.2019.

Theoretical and Numerical Study of Eddy Current Pulsed Thermography to Detect Damage of Deep-Sea Manned Pressure Hull

At present, research on pressure hull safety is mainly focused on the constitutive model of material properties and the evaluation model of structural parameters aiming at fatigue life prediction. The damage identification and quantitative evaluation methods of pressure hulls have not been studied. In this study, an eddy current thermal imaging method is introduced to detect micro-cracks in a deep-sea spherical pressure hull. In the detection method, temperature is used as a parameter to identify and quantify cracks. The temperature distribution around the cracks is studied using theoretical analysis and finite element simulation. A theoretical model is established using electromagnetic theory and heat transfer theory. Moreover, the temperature difference between the cracked area and the non-cracked area can be obtained by solving the heat conduction equation. A pulsed eddy current thermal imaging testing system is established, and a defective titanium alloy specimen is tested. At the same time, the temperature around the cracks in the specimens is simulated. The specimens have the same material and welding as a deep-sea spherical pressure hull. This paper discusses the possibility of its use in a pressure hull, which will provide a reference for micro-crack damage identification and quantitative evaluation of a deep-sea spherical pressure hull.

A Review of Finite Element Modeling for Anterior Cervical Discectomy and Fusion.

The cervical spine poses many complex challenges that require complex solutions. Anterior cervical discectomy and fusion (ACDF) has been one such technique often employed to address such issues. In order to address the problems with ACDF and assess the modifications that have been made to the technique over time, finite element analyses (FEA) have proven to be an effective tool. The variations of cervical spine FEA models that have been produced over the past couple of decades, particularly more recent representations of more complex geometries, have not yet been identified and characterized in any literature. Our objective was to present material property models and cervical spine models for various simulation purposes. The outlining and refinement of the FEA process will yield more reliable outcomes and provide a stable basis for the modeling protocols of the cervical spine.

Interpretable discovery of semiconductors with machine learning

Machine learning models of material properties accelerate materials discovery, reproducing density functional theory calculated results at a fraction of the cost1–6. To bridge the gap between theory and experiments, machine learning predictions need to be distilled in the form of interpretable chemical rules that can be used by experimentalists. Here we develop a framework to address this gap by combining evolutionary algorithm-powered search with machine-learning surrogate models. We then couple the search results with supervised learning and statistical testing. This strategy enables the efficient search of a materials space while providing interpretable design rules. We demonstrate its effectiveness by developing rules for the design of direct bandgap materials, stable UV emitters, and IR perovskite emitters. Finally, we conclusively show how DARWIN-generated rules are statistically more robust and applicable to a wide range of applications including the design of UV halide perovskites.

Interpretable hardness prediction of high-entropy alloys through ensemble learning

With the development of artificial intelligence, machine learning has a wide range of applications in the field of materials. The sparsity of data on the mechanical properties of high-entropy alloys makes it difficult to balance between the generalizability and interpretability in data-driven predictive models of material properties. A machine learning model was established based on the HEA hardness data of the Al-Co-Cr-Cu-Fe-Ni system, and several modeling features were screened out through a three-step parallel approach. Model ensemble was performed for RandomForest, XGBoost, LightGBM and CatBoost using the stacking ensemble algorithm, and the coefficient of determination(R2) of the model reached 0.93 after a ten-fold cross-validation. The ensemble learning is stable and accurate for predicting HEA hardness value, and is experimentally verified. The model and selected features can also be applied to different HEA systems as well as low hardness CrFeNi MEA. In addition, we further explained the large prediction deviation of MEA in the high hardness region. Further, the effects of HEA composition and phase formation on the hardness of HEA were qualitatively analyzed based on interpretable tools like SHAP values as well as PDP/ICE plots, respectively. Finally, the model not only has the generalization of ensemble learning, but also has certain interpretability.

The influence of claw morphology on gripping efficiency.

This paper considers the effects of claw morphology on the gripping efficiency of arboreal (Varanus varius) and burrowing (Varanus gouldii and Varanus panoptes) lizards. To ensure a purely morphological comparison between the lizards, we circumvent the material effects of claws from different species, by modelling and testing claw replicates of the same material properties. We correlate climbing efficiency to critical morphological features including; claw height (hc), width (wc), length (lc), curvature () and tip angle (γ), which are expressed as ratios to normalise mechanically beneficial claw structures. We find that there is strong correlation between the static grip force Fsg and the claw aspect and the cross-sectional rigidity ratio , and milder correlation (i.e. higher scatter) with the profile rigidity ratio . These correlations are also true for the interlocking grip force Fint over different shaped and sized protuberances, though we note that certain protuberance size-shape couplings are of detriment to the repeatability of Fint. Of the three lizard species, the claws of the arboreal (V. varius) are found to be superior to those of the burrower lizards (V. gouldii and V. panoptes) as a result of the V. varius claws having a smaller aspect, a higher cross-sectional rigidity ratio and a small profile rigidity ratio, which are deemed noteworthy morphological parameters that influence a claw's ability to grip effectively.

Predictive models for material properties of cold-formed conventional steels in the corner region

Current North American Specification AISI S100-16 provides design formulae to predict the yield strength of cold-formed steels. These design formulae were proposed on the basis of Karren’s experimental works in 1967. The measured yield strength of the parent materials in Karren works only varies from 203 MPa to 315 MPa (29.5 ksi to 45.7 ksi). This paper aims to extend the validity of the design formulae to a wider range of material and geometry parameters, and a comprehensive material test program was carried out to further investigate the cold-forming effects. Nine batches of conventional steel plates were used, with the nominal thickness ranging from 2 mm to 5 mm and the nominal value of yield strength covering 235 MPa, 275 MPa and 355 MPa. A total of 81 flat coupons extracted from parent materials and 144 corner coupons sectioned from cold-formed corners were tested, with measured original yield strength up to 431 MPa and enhanced yield strength after cold-forming up to 625 MPa. These cold-formed corners were press-braked with different punch radii and included angles to achieve various geometric configurations which result in different levels of plastic deformation. The test data generated from this study were used in conjunction with data collected from the literature to establish a comprehensive database. Subsequently, predictive models were proposed to calculate the strength enhancements on the yield strength and ultimate tensile strength, and the loss in ultimate strain and elongation at fracture. It is shown that the proposed models can produce accurate predictions on the material properties of cold-formed steels.