Material Parameters Research Articles

Parameter identification of Yoshida–Uemori combined hardening model by using a variable step size firefly algorithm

Abstract The material behavior under cyclic loading is more complex than under monotonic loading and the usage of the sophisticated constitutive models is required to accurately define the elastoplastic behaviors of the advanced high-strength steels and aluminum alloys. These models involve the numerous material parameters that are determined from cyclic tests and accurate calibration of the variables has a great influence on the description of the material response. Therefore, the development of a precise and robust identification method is needed to obtain reliable results. In this study, a systematic methodology depending upon the firefly algorithm (FA) with variable step size has been developed and Yoshida–Uemori combined hardening model parameters of a dual-phase steel (DP980) and an aluminum alloy (AA6XXX-T4) are determined. The identified parameters are verified based on comparisons between the finite element simulations of the cyclic uniaxial tension-compression tests and experimental data and also the search performance of the variable FA is evaluated by comparing it with the standard FA. It is seen from these comparisons that variable FA can easily find and rapidly converge to the global optimum solutions.

Electrical Heating of Carbon Textile Reinforced Concrete—Possible Effects on Tensile Load-Bearing Behavior

Carbon-textile-reinforced concrete (CTRC) is currently used as a high-performance composite material in the construction industry, comprising concrete and a non-metallic reinforcement. In addition to remarkable material properties such as tensile-load-bearing behavior, durability and density, this innovative material features high electrical conductivity, offering the potential for electrical heat generation within building components. This paper contributes to the field by exploring the unique combination of properties exhibited by carbon-textile reinforcements (CTR) electrically heated up to 80 °C. The impact of the electrical heating of CTR was evaluated by conducting stationary tests on load-bearing behavior. The tests were conducted on two different CTRs: one impregnated with polystyrene, and the other with epoxy resin additionally surface-modified with quartz sand. In order to quantify the influence of individual material parameters, tensile tests were conducted on the components comprising CTR and mortar, as well as the composite CTRC. The analysis focused on electrically heated carbon-textile reinforcements, comparing them through experiments conducted at varying ambient temperatures. This study presents pioneering findings on heated CTRC, determining that electrical heating decreases tensile strength with increasing temperature for the investigated reinforcement materials. The softening of the impregnation materials proved to be a decisive factor. This interdisciplinary approach bridges materials science with thermal management in construction, offering insights into the practical applications of CTR in innovative building designs.

A Data-Driven Approach for Cutting Force Prediction in FEM Machining Simulations Using Gradient Boosted Machines

Cutting simulations via the Finite Element Method (FEM) have recently gained more significance due to ever increasing computational performance and thus better resulting accuracy. However, these simulations are still time consuming and therefore cannot be deployed for an in situ evaluation of the machining processes in an industrial environment. This is due to the high non-linear nature of FEM simulations of machining processes, which require considerable computational resources. On the other hand, machine learning methods are known to capture complex non-linear behaviors. One of the most widely applied material models in cutting simulations is the Johnson–Cook material model, which has a great influence on the output of the cutting simulations and contributes to the non-linear behavior of the models, but its influence on cutting forces is sometimes difficult to assess beforehand. Therefore, this research aims to capture the highly non-linear behavior of the material model by using a dataset of multiple short-duration cutting simulations from Abaqus to learn the relationship of the Johnson–Cook material model parameters and the resulting cutting forces for a constant set of cutting conditions. The goal is to shorten the time to simulate cutting forces by encapsulating complex cutting conditions in dependence of material parameters in a single model. A total of five different models are trained and the performance is evaluated. The results show that Gradient Boosted Machines capture the influence of varying material model parameters the best and enable good predictions of cutting forces as well as deliver insights into the relevance of the material parameters for the cutting and thrust forces in orthogonal cutting.

Design method for lens desensitization adapted to refractive index error

The design of optical systems not only considers the imaging performance but also the manufacturing difficulty and feasibility of the system. In practice, errors in the manufacturing process of glass materials and deviations in glass material parameters introduced in complex environments can both lead to degradation in the imaging quality of optical systems. Optical systems that are sensitive to glass material errors face increased manufacturing difficulty and reduced stability. This paper, based on geometrical optics theory, establishes an evaluation function for refractive index error sensitivity and analyzes its relationship with optical parameters and glass materials. It proposes a design method to reduce the refractive index sensitivity of optical systems. Through simulation verification and analysis using examples, the validity of the desensitization design method is confirmed.

Hydrostatic approximation and optimal convergence rate for the Second-Grade fluid system

This paper is devoted to the study of the Second-Grade fluid system from three aspects. Firstly, we establish the well-posedness of the scaled anisotropic Second-Grade fluid system and the hydrostatic Second-Grade fluid system in a thin strip R×T with convex Gevrey-2 initial data. Secondly, we justify the limit from the anisotropic Second-Grade fluid system to the hydrostatic Second-Grade fluid system. Finally, we prove that the hydrostatic Second-Grade fluid system will converge to the hydrostatic Navier–Stokes system as material parameter goes to zero.

Hydrothermal hydrolysis of triglycerides: Tunable and intensified production of diglycerides, monoglycerides, and fatty acids

This study focuses on utilizing subcritical and supercritical water technology to produce intensive and selective diglycerides, monoglycerides, and fatty acids. The water properties can be easily modified by adjusting pressure and temperature, which promotes an improving solubility of the triglycerides in water and a selective production of glycerides. Triacetin and sunflower oil were used as models for short and long-chain components, exploring a range of temperatures (250 to 400 °C), pressures (22.0 to 25.0 MPa), and reaction times (0.7 to 29 s). A conversion of 98.5 % of sunflower oil is reached at 375 °C and 25.0 MPa in 29 s. The resulting products are 16.3 % diglycerides, 27.9 % monoglycerides, 48.9 % fatty acids, and 5.3 % glycerol. On the other hand, only 13 % of the oil is hydrolyzed at 300 °C, 22.0 MPa, with a reaction time of 23.6 s. The main product at this condition was fatty acids with a yield of 3.7 %. Furthermore, a kinetic model was developed to obtain comprehensive kinetic parameters for both raw materials to evaluate their reactivity during hydrolysis. This study provides valuable insights into the hydrolysis kinetics and the most promising conditions to make valuable products like fatty acids, mono and diglycerides, and glycerol.

An improved hidden Markov model with magnetic Barkhausen noise and optimized Gaussian mixture feature for fatigue prediction

Evaluating fatigue states of metallic materials is essential for predicting their failures and ensuring structural safety. Magnetic Barkhausen noise (MBN) analysis, a non-destructive testing method, provides efficient and reliable methods for identifying and categorising material parameters such as hardness and residual stresses. To establish a quantitative relationship between MBN signals and fatigue states, an improved hidden Markov model (HMM) is proposed based on optimised Gaussian mixture features (GMFs) and the Kullback–Leibler (KL) divergence measure for fatigue prediction. The MBN-GMFs replicate the probability characteristics of MBN signals and track the fatigue degradation trend throughout the fatigue life; thus, they are superior to some widely used statistical features. A Gaussian component optimisation algorithm is proposed to automatically adjust the appropriate number of components in the Gaussian mixture model and enhance the representation of MBN-GMFs. Then, the KL divergence is introduced to quantify the similarity and accurately classify the degree of MBN-GMF migration. The HMM is constructed to obtain the probability transfer relationship between the observations and states and obtain accurate fatigue predictions. Experiments on two 20R metallic materials at three excitation frequencies are conducted to collect the MBN signals. The experimental results and comparisons indicate that the proposed HMM can accurately predict fatigue states and provide a practical and robust analysis tool for MBN-based fatigue predictions.

Rheology of plant protein–polysaccharide gel inks for 3D food printing: Modeling and structure–property relations

Plant protein–polysaccharide composite hydrogels and emulsion gels are attractive materials for production of edible inks for 3D food printing. Their ability to satisfy the printability requirements is conventionally evaluated in small- and large-amplitude oscillatory shear tests. A model is derived that describes experimental data in linear and nonlinear rheological tests on protein gels in a unified manner. The model is based on the population-balance approach and involves a reasonably small number of adjustable parameters. Fitting observations on soy protein isolate–xanthan gum, peanut protein–gellan gum, peanut protein–carrageenan, pea protein–carrageenan and pea protein isolate–flaxseed gum gels demonstrates that material parameters evolve consistently with composition of the gels. The governing equations describe adequately the effects of pH and ionic strength of aqueous solutions on the mechanical properties of the gels and predict the stress–strain Lissajous curves and the crossover amplitudes for transition from the solid-like to fluid-like response. Based on the analysis of observations in large-amplitude oscillatory tests with various frequencies, a mechanism is proposed for transition from the type-I (strain thinning) to type-III (weak strain overshoot) response of protein–polysaccharide gels.

Jiezi: An open-source Python software for simulating quantum transport based on non-equilibrium Green's function formalism

We present a Python-based open-source library named Jiezi, which provides the means of simulating the electronic transport properties of nanoscaled devices on the atomistic level. The key feature of Jiezi lies in its core algorithm, i.e., self-consistent orchestration between the non-equilibrium Green's function (NEGF) method and a Poisson's equation solver. Beyond the construction of the tight-binding (TB) Hamiltonian with empirical parameters for conventional materials, the package offers a comprehensive framework for constructing the Wannier-based Hamiltonian matrix, enabling the investigation of novel materials and their heterostructures. To expedite the solution of NEGF systems, a methodology based on renormalization theory is proposed for reducing the dimension of the Hamiltonian matrix. Additionally, we adopt a non-linear Poisson equation solver with no analytical approximation in this software. The software facilitates seamless integration with external tools for geometry and mesh generation and post-processing. In this paper, we present the main capabilities and workflow by demonstrating with a simulation for the carbon nanotube field-effect transistor (CNTFET). Program summaryProgram Title: JieziCPC Library link to program files:https://doi.org/10.17632/nk79kbtww4.1Developer's repository link:https://github.com/Jiezi-negf/JieziLicensing provisions: GPLv3Programming language: PythonNature of problem: Simulates the quantum transport property of nano-scaled transistors based on the predefined device structure and the material composition.Solution method: Solves the coupled Schrödinger equation and Poisson equation by NEGF and finite element method.

A Method for Evaluating Systematic Risk in Dams with Random Field Theory

The parameters of gravity dams and foundation materials objectively exhibit spatial variability due to environmental and load influences, which significantly affect the safety status of dam structures. Therefore, a safety risk analysis method for a gravity dam–foundation system based on random field theory is proposed in this paper. Spatial variabilities in materials are particularly considered by using the finite element method. Then, composite response surface equations for the performance function (PF) of strength and stability failure are established, and then, the system failure risk is obtained using the Monte Carlo method. The proposed method solves the problem wherein the effect of spatial variability on failure risk cannot be reflected accurately by the performance function of multi-element sliding paths, and the difficulties in solving the failure risk of the series–parallel system due to multiple failure paths and their complex correlations. The application of a gravity dam shows that the developed method overcomes the disadvantages of the traditional method, such as the homogenization of the spatially random characteristics of parameters and the overestimation of failure risk in the system due to large variance estimation.

ВОЛНОВОДНО ДИЭЛЕКТРИЧЕСКИЙ МЕТОД И УСТРОЙСТВА ДЛЯ ИЗМЕРЕНИЯ КОНЦЕНТРАЦИИ ВОДНЫХ РАСТВОРОВ И ОБЛУЧЕНИЯ БИОЛОГИЧЕСКИХ ОБЪЕКТОВ

A technique for studying the absorption of microwave radiation of biological objects in a thin dielectric capillary is proposed. The results of experimental studies are presented and the mechanism of the detected effects of the biological action of microwave radiation is proposed. It was found that at a certain ratio between the dimensions of the waveguide, the diameter and thickness of the walls of the capillary, the dielectric parameters of the biological material that fills the capillary and the wavelength of microwave radiation in the waveguide, a significant increase in the absorbed power in a rather narrow frequency band (waveguide-dielectric resonance) is possible. This phenomenon must be taken into account in biotechnological experiments on the irradiation of suspensions. As a result of the work done on the study of the absorption of microwave radiation by hydrocarbon mixtures and models of biological objects in aquatic environments, new designs of measuring devices were created and a technique for hydrocarbon aquametry was developed. The created products are protected by the authors of this work by a patent for an invention.

The Effect of Friction Stir Weld (FSW) Process Parameters on Tensile Strength, Macro Structure, and Hardness in Results of AA7075 Butt Joints

Friction Stir Welding (FSW) is an innovative technique that enhances the conventional method of joining metals. Notably ecofriendly due to its energy efficiency, FSW involves minimal energy input, reduces pollution, and saves time and costs. It finds applications in diverse sectors such as automotive, aerospace, and industry. Each material requires specific process parameters, which leads to this study focusing on identifying suitable parameters for AA7075 aluminum with a 6mm thickness. Using a tool featuring a tapered cylindrical thread pin and a flat shoulder, the study aims to investigate the influence of FSW process parameters, rotation speed, and traverse speed on the mechanical strength of butt joint connections. The study's experimental design varies these parameters and evaluates the joints through tensile strength testing, hardness testing, and macrostructural analysis. Utilizing Response Surface Methodology (RSM), the data highlights the impact of rotation and traverse speed on tensile strength. Hardness test results present variations within heat zones, analyzing the effects of the mentioned variables. The findings demonstrate minimal flash and successful surface outcomes but also identify wormholes within the stir zone (SZ). Tensile strength testing reveals a definite correlation between RPM and traverse speed with joint strength. In contrast, hardness testing indicates that these parameters do not significantly affect joint hardness. Macrostructure examination suggests RPM and traverse speed have negligible effects on the heat-affected zone. In conclusion, FSW presents a sustainable and effective welding approach with implications for multiple industries, and this research provides insights into optimizing its parameters for specific aluminum materials.

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

Design of non-linear, viscoelastic architected foams

Flexible viscoelastic foams with relative density in the range of 5-50% are widely used to control interaction forces, notably in cushions for impact protection, shoes, mechanical grippers, etc. The design of new foams to improve performance is challenging, due to the lack of efficient models to connect cellular architecture, viscoelasticity of the base resin, and non-linear responses that stem from large deformation of open pores. This paper presents highly efficient solutions for the response of linearly viscoelastic, kinematically non-linear and shear-deformable struts that are loaded at an oblique angle to their axis. The models can be used to predict structure-property relationships for strut-based metamaterials, including non-linear stress-strain response and accompanying hysteresis. To illustrate this, the entire design space of a simple unit cell is mapped in terms of a broad range of strut angles and thicknesses; the unit cell is inspired by features in stochastic foams that dominate their response. An enormous range of stress-strain responses can be obtained from this single foam-like cell, including purely softening, purely stiffening, and non-monotonic stiffness with adjustable tangent moduli. The model is also used to illustrate connections between viscoelastic material parameters, loading rate, and hysteresis for two distinct classifications of non-linear stress-strain response.

Anisotropic Hyperelastic Strain Energy Function for Carbon Fiber Woven Fabrics.

The present paper introduces an innovative strain energy function (SEF) for incompressible anisotropic fiber-reinforced materials. This SEF is specifically designed to understand the mechanical behavior of carbon fiber-woven fabric. The considered model combines polyconvex invariants forming an integrity basisin polynomial form, which is inspired by the application of Noether's theorem. A single solution can be obtained during the identification because of the relationship between the SEF we have constructed and the material parameters, which are linearly dependent. The six material parameters were precisely determined through a comparison between the closed-form solutions from our model and the corresponding tensile experimental data with different stretching ratios, with determination coefficients consistently reaching a remarkable value of 0.99. When considering only uniaxial tensile tests, our model can be simplified from a quadratic polynomial to a linear polynomial, thereby reducing the number of material parameters required from six to four, while the fidelity of the model's predictive accuracy remains unaltered. The comparison between the results of numerical calculations and experiments proves the efficiency and accuracy of the method.

Advanced Image Analysis and Machine Learning Models for Accurate Cover Factor and Porosity Prediction in Knitted Fabrics: Tailored Applications in Sportswear, Swimwear, and Casual Wear

This paper presents a study focused on developing robust algorithms for cover factor and porosity calculation through digital image analysis. Computational models based on machine learning for efficient cover factor prediction based on fabric parameters have also been developed. Five algorithms were devised and implemented in MATLAB: the single threshold algorithm (ST); multiple linear threshold algorithms, ML-1 and ML-2; and algorithms with multiple thresholds obtained by the Otzu method, MT-1 and MT-2. These algorithms were applied to knitted fabrics used for football, swimming, and leisure. Algorithms ML-1 and MT-1, employing multiple thresholds, outperformed the single threshold algorithm. The ML-1 variant yielded the highest average porosity value at 95.24%, indicating the importance of adaptable thresholding in image analysis. Comparative analysis revealed that algorithm variants ML-2 and MT-2 obtain lower cover factors compared to ML-1 and MT-1 but can detect potential void areas in fabrics with higher reliability. Algorithm MT-1 proved to be the most sensitive when it came to distinguishing between different fabric samples. Computational models that were developed based on random tree, random forest, and SMOreg machine learning algorithms predicted cover factor based on fabric parameters with up to 95% accuracy.

A Kriging Surrogate Model for Ball Grid Array Electronic Packaging With Stochastic Material and Geometrical Parameters

Abstract Ball grid arrays (BGAs) offer significant advantages in the automotive industry, such as their small size and high integration density, making them a promising electronic packaging approach. However, the operating environment of automobiles is more complex compared to other applications, primarily due to vibrations generated by power engines and oscillations caused by pavement roughness. The resonant frequencies of electronic packaging structures play a crucial role in system reliability and safety. However, accurately describing the implicit relationship between system resonant frequencies and material and geometrical parameters can be challenging. A Kriging surrogate model (KSM) is proposed by the combination of the Latin Hypercube stochastic sampling with finite element computation. Four different BGA configurations are established with either the initial values in the deterministic model or the specified sampling interval ranges in the stochastic model. The results of the finite element model (FEM) for BGA electronic packaging are validated and demonstrate qualitative agreement with published literature. The impacts of material and geometrical parameters on the resonant frequencies are investigated and compared. The mean, maximum, minimum, and variance are recorded based on a large database of stochastic samples. The feasibility of KSM for the resonant frequency prediction of BGA is confirmed by its satisfactory accuracy and computational efficiency.

Review of Underwater Anechoic Coating Technology Under Hydrostatic Pressure

AbstractThe underwater anechoic coating technology, which considers pressure resistance and low-frequency broadband sound absorption, has become a research hotspot in underwater acoustics and has received wide attention to address the increasingly advanced low-frequency sonar detection technology and adapt to the working environment of underwater vehicles in deep submergence. One the one hand, controlling low-frequency sound waves in water is more challenging than in air. On the other hand, in addition to initiating structural deformation, hydrostatic pressure also changes material parameters, both of which have a major effect on the sound absorption performance of the anechoic coating. Therefore, resolving the pressure resistance and acoustic performance of underwater acoustic coatings is difficult. Particularly, a bottleneck problem that must be addressed in this field is the acoustic structure design with low-frequency broadband sound absorption under high hydrostatic pressure. Based on the influence of hydrostatic pressure on underwater anechoic coatings, the research status of underwater acoustic structures under hydrostatic pressure from the aspects of sound absorption mechanisms, analysis methods, and structural designs is reviewed in this paper. Finally, the challenges and research trends encountered by underwater anechoic coating technology under hydrostatic pressure are summarized, providing a reference for the design and research of low-frequency broadband anechoic coating.

Calibration of the DEM sliding friction and rolling friction parameters of a cohesionless bulk material

The discrete element method (DEM) has become a valuable tool for understanding the mechanical behaviour of granular assemblies, however, the accuracy of the DEM simulations depends on several interaction parameters such as the sliding friction and rolling friction coefficients. Generally, these parameters are estimated using bulk calibration approach (BCA) where the draw down test has been suggested as an effective way to approach these coefficients. This test provides up to four bulk criteria, the angle of repose, shear angle, mass flow rate and the mass loss which are used to narrow down the possible coefficients. However, there is still more research needed around it to fully understand how this methodology works. An experimental and numerical study was carried out using the draw down test to assess the influence of different mass flow rates and particle shapes on DEM parameters for a cohesionless bulk material. It is concluded that use of multi-sphere particles and three aperture sizes in the draw down test to calibrate the sliding and rolling friction parameters of a cohesionless bulk material can converge to a small feasible region in which a single combination of the friction coefficients can be selected. The calibrated sliding and rolling friction coefficients were validated using multi-sphere particles, where the maximum deviation was 5.9% from the experimental values.

CCZM‐based fatigue analysis and reliability assessment for wind turbine blade adhesive joints considering parameter uncertainties

AbstractWind turbine blades are complex structures composed of multiple bonded components. The fatigue performance of these adhesive joints is crucial for ensuring operational safety over the blade's lifespan. Traditional structural fatigue analysis methods are inadequate for evaluating the fatigue properties of these joints due to the unique characteristics of adhesive materials. Variations in material and dimensional parameters, as well as fluctuating operational loads, further complicate the fatigue analysis of adhesive joints in wind turbine blades. To tackle this issue, this study introduces a fatigue analysis and reliability assessment method for the adhesive joints of wind turbine blades, employing the Cyclic Cohesive Zone Model (CCZM) and accounting for parameter uncertainties. Specifically, a novel methodology for fatigue analysis based on the CCZM is presented. The methodology is programmatically implemented to obtain a fatigue life dataset through multiple simulations, considering uncertainties in material parameters, adhesive dimensions, and loads. Subsequently, a fatigue reliability model is formulated to evaluate the fatigue reliability of adhesive joints in wind turbine blades under different parameter conditions, and the sensitivity of fatigue reliability to each parameter is investigated. The findings offer valuable insights for improving the safety and reliability of adhesive structures in wind turbine blades.