Material Parameters Research Articles

Harnessing interpretable and ensemble machine learning techniques for precision fabrication of aligned micro-fibers

Electrospinning is a robust technique for producing micro/nano-scale fibrous structures, influenced by intricate interplays of fluid dynamics, aerodynamics, and electromagnetic forces. Depending on the desired outcome, these fibers can adopt various morphologies, including solid, tubular, concentric, and gradient. Such morphologies are modulated by parameters such as collector configuration, flow rate, voltage, solution properties, and nozzle dimensions. However, the task of modeling and predicting these multifaceted morphologies remains complex. Aligned microfibers with 3D orientation hold promise in tissue engineering, regenerative medicine, and drug delivery, necessitating meticulous control over the fabrication parameters. In our research, we tapped into machine learning (ML) to address these challenges. Classification ML models were designed to predict fibrous patterns—aligned, random, or jet branching—based on determinants like voltage, flow rate, and collector configurations. Notably, the Random Forest (RF) and Support Vector Machine (SVM) models, especially with radial kernel-trick, displayed outstanding predictive capabilities on the test data. Furthermore, regression-based ML was harnessed to discern fiber alignment coherency and inter-fiber distances. Models such as Lasso and Ridge regression elucidated predictive coefficients for these characteristics, while ensemble models, like gradient-boosting (GB) decision trees (DT), showcased prowess in regression scenarios. Key findings spotlighted the significance of parameters like plate gap for alignment coherency and needle-to-collector distance for inter-fiber spacing. As we strive to gain granular control over micro/nano feature morphology in electrospinning, understanding predictor-response dynamics is imperative. Our investigation underscores the essential role of ML in enhancing both qualitative and quantitative precision in fabricating advanced fibrous structures. Moreover, fusing ML with real-time process monitoring offers groundbreaking potential, particularly in Bio-Fabrication, regenerative medicine, and tissue engineering, where high-precision manufacturing remains a top priority.

Footwear for Diabetics – Structural and Material Elements for the Prevention and Alleviation of Foot Lessions

Abstract Diabetic foot syndrome is a syndrome of specific conditions affecting the foot. It is a complication of diabetes. It occurs in 12–25% of patients with diabetes. Untreated, it leads to irreversible deformities and necrosis of the foot, often resulting in amputation. In this study the statistics and consequences of diabetic foot syndrome are described. Patients with diabetes need to take care of their lower limbs. Proper footwear can prevent foot wounds. Available solutions for the prevention and treatment of diabetic foot syndrome are presented herein: footwear, insoles and requirements for footwear materials. Appropriate equipment for a person with diabetes, especially one who has been diagnosed with diabetic foot syndrome or is at risk of such a condition, includes footwear and replaceable insoles. The parameters of footwear, insoles and footwear materials that are most optimal for patients with diabetes and diabetic foot syndrome were defined. The effect of a pulsed electromagnetic field and pulsed ultrasound on diabetic foot problems was evaluated.

Concurrent experimental testing and computational micromechanical modeling of a UD NCF GFRP composite ply

The current research work presents a detailed and thoroughly validated computational micromechanical modeling methodology to study the damage initiation and propagation in a uni-directional (UD) glass fiber-reinforced non-crimp fabric (NCF) composite ply. Under the applied transverse tension and compression loads, the effect of various microscale material and geometrical parameters on the ply level stress–strain behavior is studied. To this end, along with the distinctly modeled individual microscale constituents of the UD NCF composite ply (axial fibers, backing fibers, and matrix), the generated RVE (Representative Volume Element) model consists of manufacturing-induced defects and variabilities such as voids as well as the backing fiber’s out-of-plane waviness. The fiber/matrix interface failure behavior in the generated RVE model is simulated using cohesive zone formulations that follow bi-linear traction-separation law. Whereas the hydrostatic pressure-dependent non-linear stress–strain response followed by the fracture of the epoxy matrix is captured using the linear Drucker-Prager plasticity model coupled with the stress triaxiality-based failure criterion. The backing fibers stress–strain and failure behavior is modeled using the linear elastic and isotropic material model in conjunction with the maximum stress criterion.The proposed numerical methodology is thoroughly validated both qualitatively and quantitatively by conducting detailed experiments on a neat epoxy as well as at the composites laminate level. Upon comparing the experimental and computational results, the observed knee or slope change in the bi-linear stress–strain response of the UD NCF composite ply under transverse tension is attributed to the loss of the load-carrying ability of the axial fiber bundle due to fiber/matrix interface debonding followed by the ply splitting. Under the application of transverse compression load, the composite ply failure behavior is governed by the formation of a dominant matrix plastic shear band in the axial fiber bundles, which is in turn triggered by the fiber/matrix interface failure. Under the applied loads in the matrix-dominated direction, the presented research work provides a detailed insight into the microscale damage initiation and propagation in a UD NCF composite ply and its consequent influence on the macroscale stress–strain response.

Influence of Printing Parameters to optimize mechanical properties in additive manufacturing – A Review

Abstract Additive manufacturing (3D printing) is a revolutionary technology with widespread applications among various industries. It is a cost effective and fast prototyping technology of 20th century, the parts produced by 3D printing are not only used for pre-production samples but also for replacement of functional parts in various applications. Various methods of 3D printing technologies viz., Fused Deposition, Resin printing etc., are available, however Fused Deposition Modelling (FDM) technique is very popular since it is promising and cost effective. There are two major aspects needs to be considered in preparing a sample from 3D printing, i.e., selection of material and printing parameter. With the advancement of material technology from time to time, the efficient and quality products that are produced with this technology. The present study explores the impact of various parameters that affect mechanical properties of 3D printed Polylactic Acid (PLA) in horizontal orientation.

Constitutive modelling of deformation rate dependent response of steel timber shear connections

This paper presents experimental and numerical investigations into the deformation rate-dependent constitutive response of steel-timber shear connections with screws. After describing the test specimens and experimental arrangement, a detailed account of the complete deformation response and main mechanical parameters of the tested shear connections under three applied displacement rate levels are given. Specimens with small screw diameters had a relatively brittle response, failing shortly after yielding, whilst those with higher diameters showed ductile failure modes, with plastic hinges forming in the screws and concurrent extensive timber crushing. It was observed that the stiffness increases with the deformation rate due to the viscoelastic response of wood materials, whilst the peak load is largely constant. Nonlinear finite element simulations were carried out to validate the main numerical parameters for steel, timber, and interaction characteristics. After gaining confidence in the ability of the numerical models to predict closely the stiffness and peak load, numerical investigations were carried out to examine the influence of key material and geometric parameters on the stiffness, load resistance and deformation response. The studies showed that higher timber strength increases the initial stiffness and the peak load, while higher screw grades improved both stiffness and strength. Based on the results and observations, code-modified expressions for evaluating the stiffness and load resistance, as a function of the deformation rate, are proposed within the ranges considered, and validated against a collated database. Comparative assessments with existing literature indicate that the proposed equations provide improved estimates. Suggested closed-form relationships enable the characterisation of the full constitutive response of steel-timber shear connections, that can be adopted for discrete nonlinear modelling of connectors.

Flow of fluids with pressure-dependent viscosity down an incline: Long-wave linear stability analysis

In this paper, we investigate the linear stability of a gravity-driven fluid with pressure-dependent viscosity flowing down an inclined plane. The linear stability analysis is formulated using the long-wave approximation method. We show that the onset of instability occurs at a critical Reynolds number that depends on the material and geometrical parameters. Our results suggest that the dependence of the viscosity on pressure can influence the stability characteristics of the flow down an incline.

Effects of particle density and fluid properties on mono-dispersed granular flows in a rotating drum

Due to their simple geometric configuration and involved rich physics, rotating drums have been widely used to elaborate granular flow dynamics, which is of significant importance in many scientific and engineering applications. This study both numerically and experimentally investigates dry and wet mono-dispersed granular flows in a rotating drum, concentrating on the effects of relative densities, ρs−ρf, and rotating speeds, ω. In our numerical model, a continuum approach based on the two-phase flow and μI theory is adopted, with all material parameters calibrated from experimental measurements. It is found that, in the rolling and cascading regimes, the dynamic angle of repose and the flow region depth are linearly correlated with the modified Froude number, Fr*, introducing the relative density. At the pore scale, flow mobility can be characterized by the excess pore pressure, pf. To quantify the variance of the local pf, it is specifically nondimensionalized as a pore pressure number, K, and then manifested as a function of porosity, 1−ϕs. We find K(ϕs) approximately follow the same manner as the Kozeny–Carman equation, K∝ ϕs2/1−ϕs3. Furthermore, we present the applicability of the length-scale-based rheology model developed by Ge et al. [“Unifying length-scale-based rheology of dense suspensions,” Phys. Rev. Fluids 9, L012302 (2024)], which combines all the related time scales in one dimensionless number G, and a power law between G and 1−ϕs/ϕc is confirmed. This work sheds new lights not only on the rigidity of implementing continuum simulations for two-phase granular flows, but also on optimizing rotating drums related engineering applications and understanding their underlying mechanisms.

A Computationally Efficient Approach for Estimation of Tissue Material Parameters from Clinical Imaging Data Using a Level Set Method

A Computationally Efficient Approach for Estimation of Tissue Material Parameters from Clinical Imaging Data Using a Level Set Method

Improved thermal shock reliability of Ag paste sintered joint by adjusted coefficient of thermal expansion with Ag-Si atomized particles addition

In this study, two kinds of atomized Ag-20 wt%Si and Ag-50 wt%Si particles are fabricated and added to Ag paste to fabricate composite pastes to achieve a low coefficient of thermal expansion (CTE), and their performance stability in SiC power module is evaluated under thermal shock from −50 to 250 °C. Four kinds of composite Ag pastes using the two kinds of atomized Ag-Si addition with 10 % and 30 % were obtained, AS20-10, AS50-10, AS20-30 and AS50-30, respectively. The results show that the CTE reduction intensifies with increasing Ag-Si atomized particle content. The retention ratio of shear strength of AS20-30 sintered joint after 1000 cycles was 102.3 %, which means the shear strength did not decrease during so harsh thermal shock test. The mechanism was discussed in detail. In addition, the properties such as thermal conductivity and electrical conductivity for the composite pastes were also evaluated to gain a more comprehensive understanding for the new materials especially for the design of SiC power modules with a long-time life. Comprehensively considering the material parameters of the composite pastes and the reliability of the sintered joints under thermal shock, the optimized composite pastes as die attach material in power electronics are obtained. The findings should be inspiring in developing novel die attach materials for high reliable interconnections in SiC power modules.

New Mie Scattering Diffuse Targets Development and Characterization

Abstract Earth science remote sensing observations require the detection and measurement of light originating from bright targets, such as clouds, and darker targets, such as an open ocean. This requirement drives the need to design, develop, and characterize improved calibration targets in support of current and future NASA instruments. New diffuse targets to be used as spectral albedo calibration standards were developed and characterized. The new targets based on fused silica or/and pressed and sintered Polytetrafluoroethylene (PTFE) were developed to be Earth scene specific. The various reflectance levels are achieved by modifying the material parameters, thickness, and surface finish. The new targets were characterized in cleanroom environment. We acquired high accuracy reflectance and transmittance data using a precision optical scatterometer and spectrophotometer located in the NASA Goddard Space Flight Center (GSFC) Diffuser Calibration Lab. The Total hemispherical reflectance (THR) and Bidirectional Reflectance Distribution Function (BRDF) were measured over the range of solar incident and scattered elevation and azimuthal angles typically realized on orbit by remote sensing instruments. We intend to space certify the new calibration targets after concluding on-orbit testing on the International Space Stations (ISS) scheduled for the second half of 2023.

Analysis of mode I crack propagation in glassy polymers under cyclic loading using a molecular dynamics informed continuum model for crazing

Craze and crack propagation in glassy polymers under cyclic mode I loading are investigated by employing a recently developed continuum-micromechanical model for crazing. This model accounts for the local morphology change from microvoids to fibrils during craze initiation, viscoplastic drawing of bulk material into fibrils, and viscoelastic creep recovery of the fibrillated craze matter during unloading. To ensure consistency between the bulk and craze model parameters, the material parameters of the craze model are normalised and calibrated based on a hybrid approach integrating experimental findings from the literature and molecular dynamics results. This yields a generic, yet representative glassy polymer response.In the framework of 2D plane strain finite element simulations, we study brittle as well as ductile glassy polymers and assess the results by drawing comparisons to the experimental and numerical literature. For brittle materials, characterized by a purely elastic bulk behaviour, the model reproduces craze characteristics such as the craze opening contour, the craze length-to-width ratio, a double stress peak at the craze and crack tip, and a non-proportional stress redistribution during loading-unloading cycles. In ductile glassy polymers, the interaction of shear yielding in the bulk and crazing along the ligament is analysed. In particular, shear bands emanate from the crack tip in each loading cycle and arch forward towards the craze. This plastic zone shares resemblance to the so-called epsilon-shaped deformation zone. The current simulations capture normal fatigue crack propagation, where craze and crack growth occur near the peak load in every cycle and the craze length remains relatively constant across the loading cycles. Moreover, findings from this study suggest that plasticity-induced unloading of the craze adjacent to the crack tip impedes crack growth.

Assessment of Slope Stability Taking into Account the Uncertainty of Standard Seismic Impacts and Material Parameters of the Site

Assessment of Slope Stability Taking into Account the Uncertainty of Standard Seismic Impacts and Material Parameters of the Site

Deep learning coupled Bayesian inference method for measuring the elastoplastic properties of SS400 steel welds by nanoindentation experiment

Nanoindentation experiment has shown broad application prospects due to its ability to measure the mechanical properties of various materials at multiple scales. In this paper, a deep learning coupled Bayesian inverse approach is proposed for measuring the elastoplastic parameters of SS400 steel welds by nanoindentation experiment. The nanoindentation experiments were performed on the SS400 steel welds, including base metal (BM), weld zone (WZ), and heat affected zone (HAZ), and the experiment load–displacement (P-h) curves were obtained. The hyper-parameters tunable artificial neural network (ANN) was established to correlate elastoplastic parameters with indentation P-h curves. Based on Bayesian inference theory, the posterior density function for estimating the unknown material parameters was established. Transitional Markov chain Monte Carlo was used for sampling from the posterior density function, and the elastoplastic properties in different regions of SS400 steel welds were identified. The advantage of the established measuring method is that the hyper-parameters optimized ANN model can provide the very accurate forward relationship between material properties and indentation P-h curves. Besides, the inverse Bayesian framework can quantify the potential uncertainty of the identified elastoplastic parameters. The measured elastoplastic properties of the base metal of SS400 steel show good agreement with tensile experiment data, of which the maximum measuring error is less than 12%. The measured elastoplastic properties in WZ and HAZ are also proved to be effective. The uncertainty of the identified elastoplastic parameters of SS400 steel welds can be quantified by posterior marginal distribution, using Mean and Variance values. The results proved that the proposed inverse measuring method is reliable and effective.

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. To gain a deeper insight into the microstructure–property linkages under thermomechanical loading, it is essential to use crystal plasticity (CP) simulations. This approach can reduce the need to conduct expensive high-temperature experimental mechanical testing and account for crystallographic texture and grain morphology effects, which are known to be important in many AM processed materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning - differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading and service conditions.

Theoretical analysis for dynamic model of U-shaped electrothermal microactuator based on Chebyshev spectral method

Abstract This paper presents a largely and accurate analysis for dynamic model of U-shaped actuator that considers the material parameters dependent on temperature. The electro-thermal analysis for temperature distribution and thermo-mechanical analysis for deflection is carried out. The electro-thermal dynamic behaviour is described by piecewise one-dimensional (1D) heat transfer partial differential equations (PDE) for each beam of U-shaped actuator. A combination of Chebyshev spectral method for space domain and Euler forward difference method for time domain is used to solve these equations. The convergence test is first done to ensure the result convinced.

Low-velocity impact behavior analysis on composite double curved-shell reinforced with graphene platelet

ABSTRACT This research investigates the low-velocity impact behavior of a spherical rigid body on a composite shell reinforced with graphene platelets (GPLs), in which GPL have been uniformly dispersed and randomly arranged within each individual layer. The weight proportion of GPL has been altered across the thickness. The modified Halpin Tsai model is employed to analyze the Young's modulus and the effective material characteristics of a composite reinforced with graphene platelets (GPLRC). The mass density and Poisson's ratio of the viscoelastic multilayer composite are determined using the rule of mixture. The material parameters of each layer are defined according to the Kelvin–Voigt model. The governing equations have been derived by applying the first order shear deformation shell theory, Hamilton's principle, and modifying the Hertz contact law. The PDE problems have been transformed into ODE equations through the utilization of the Galerkin method. Subsequently, the ordinary equations have been solved utilizing the Newmark method. A parametric study is conducted to examine the effects of various factors, such as fraction weight, GPL distribution pattern, impactor mass, radius, viscoelastic modulus, and initial velocity, on the low velocity impact response of a composite shell reinforced with GPL.

Mechanical Robustness Analysis of Semiconductors with a Single Needle Probe Card Using Acoustic Emissions

The combination of indentation testing and acoustic emission (AE) is widely used to analyze the fracture toughness of test substrates. In the manufacturing of semiconductor devices this material parameter also plays an important role. During the so-called wafer testing inside a wafer prober small probe tips are pressed onto the chip surface to check its performance. To prevent damaging the chip by that, the fracture toughness and load limit of it has to be defined in a prequalification step. This paper presents a test system that imitates the wafer testing process as close as possible and uses acoustic emission to detect appearing cracks and thereby also the load limit. The frontend of the test setup consists of a modular single needle probe card which can be placed in a wafer prober. Also, the indenter properties (tip diameter, stiffness, etc.) can be adjusted to the probe used later on during productive wafer testing to ensure realistic probing conditions. A piezoelectric sensor and a full strain gauge Wheatstone bridge are implemented close to the single indenter to measure the AEs and the applied contact force respectively. The signal-to-noise-ratios (SNRs) of both sensors are improved with an own analog amplifier module before recording them with an USB oscilloscope. A developed measurement software (LabVIEW) is used to adjust measurement parameters (trigger limit, record length, conversion factors, etc.) and automatically store and separate measurement data from different acoustic events and imprints. To evaluate the result files a second software tool (MATLAB) reads the files out and clusters the recorded events to separate crack signals from electrical and mechanical disturbances. With a prototype first test results are generated and the functionality and accuracy of the measurement concept is proven.

Core Technologies of Sugarcane Chopper Harvester Extractor: A Critical Review

This review has systematically assessed the critical technologies of impurity removal fans in segmental sugarcane harvesters, highlighting their impact on minimizing harvester impurity rates. The analysis was conducted from three perspectives: Firstly, the physical properties of flexible sheet-like materials were thoroughly examined, including the methodologies for material parameter measurement, the utilization of technical approaches, and the constraints of existing methods. Secondly, the operational mechanisms of impurity removal fans were elucidated, encompassing their working principles, research subjects, and technical methodologies, and compared with the mechanisms of analogous fluid machinery. Lastly, a review of the structural design and parameters of impurity removal fans was undertaken, analyzing the interplay between operational modes, structural optimization designs, and aerodynamic characteristics. The review identified key issues in the design and performance of impurity removal fans and recognized the challenges in technological advancement. Through this triadic analytical framework, the review has identified salient design and performance deficiencies within existing impurity expulsion fan technologies and has underscored the formidable challenges confronting technological evolution in this domain. Synthesizing these insights, the review proffered prospective research vectors and avant-garde technological interventions poised to augment the operational efficacy of impurity expulsion fans, with the overarching goal of enhancing the operational proficiency of sugarcane harvesting machinery.

Spatiotemporal instability and optical filament formation in metamaterials

This study investigates the spatiotemporal instability in artificial metamaterials, with a focus on generating ultrashort pulses or light filaments. We identify the parametric regions where the metamaterial exhibits spatiotemporal instability, enabling the generation of ultrashort pulses. Our approach involves modeling nonlinear pulse propagation in the metamaterial using a (3+1)-dimensional nonlinear Schrödinger equation and performing a standard linear stability analysis to explore the spatiotemporal instability. Leveraging the engineering freedom to tailor the material parameters, we numerically demonstrate the generation of ultrashort pulses through spatiotemporal instability.

A novel optical acoustic fabric based on intensity-modulated flexible air-filled fiber adapter for sound sensing

This study proposes and demonstrates an optical acoustic fabric design method to detect sound, which is based on intensity-modulated flexible air-filled fiber adapter (FAFFA). A FAFFA was woven into the weft center of the polyamide fabric as weft, and the FAFFA was realized by connecting polymer optical fibers at each end of an flexible tube. The principle of the optical acoustic fabric is to allow the flexible tube to deform with the resonance of the polyamide fabric, subsequently changing the light intensity in the FAFFA. The macro-scale finite element method and ray tracing optical simulation investigated the operating principle of optical acoustic fabric and performance of FAFFA structures, and the influence of fabric parameters was evaluated by meso-scale finite element simulation. The yarn of the optical fiber acoustic fabric has low Young’s modulus and high flexibility. The proposed FAFFA structure is simple to prepare and has good maneuverability, which breaks away from the constraints of the traditional optical acoustic sensor membrane structure. This proposed optical acoustic fabric is easy to prepare, simple in principle and highly flexible. Along with wireless devices, the use of the optical acoustic fabric can be further extended to “hearing aids” for special individuals in wearable applications.