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Residual stress is rarely considered as an initial condition for Finite Element Analysis (FEA), or used in the component design. A comprehensive numerical and experimental investigation into prestressed steel sheets is conducted. Residual stresses at specific locations were introduced into the steel sheets by dimple formation. FEA is used to simulate dynamic dimple forming of a mild steel disk due to a tool impact and detect the existence of prestress after dimple formation. A physical drop-hammer was used to form a range of dimple sizes in disks to experimentally validate the FEA predictions associated with dimple depth. Dimples were then created using a screw press and the prestress was measured by strain gauges beyond the zone of permanent out-of-plane deformation associated with a dimple. The experimental strain gauge measurements during dimple formation were found to be consistent with FEA predictions. The results demonstrated that the predicted and measured radial and tangentially projected stress components remain in steel sheets after forming a dimple. Furthermore, the predicted prestress was greater near to the dimple formation and occurred at higher magnitudes in the tangential direction than the radial direction. The highest prestress remaining in the disk was at the magnitude of the material's ultimate tensile stress, predicted in the tangential direction at the edge of the dimple. The numerical and experimental results showed the three stages of prestress formation: the development of stresses during dimple formation, the elastic recovery after the dimple forming, and the final prestressed equilibrium state of the rimmed disk. [ABSTRACT FROM AUTHOR]

To understand the connection among the brain and facial muscles, we decoded the correlation of facial muscles and brain activities. We applied information theory (Shannon entropy) to EEG and EMG signals recorded from 12 subjects (7 M, 5 F) during rest and stimulation using three odors (pineapple, banana, vanilla). The results indicated that a larger variation in the molecular complexity of odors causes a larger variation in the information of EMG and EEG signals. Therefore, facial muscles' activity is strongly coupled (r = −0.8399) to the brain's activity. Our analysis method could be further employed to examine the relationship between the brain and other organs by comparing other physiological signals with the brain signals. [ABSTRACT FROM AUTHOR]

Our skin reacts to various stimuli that we receive. Since all parts of the human body are controlled by the brain, a relationship should exist among brain and skin activities. This study evaluates the relation among skin and brain activities. As such, we benefited from the information-based analysis. We collected GSR and EEG signals of eight participants in various olfactory stimulations. Accordingly, we ran Shannon entropy-based analysis to evaluate the correlation between the information contents of these two signals. The results showed that the alterations in the complexity of stimulus and the information of GSR and EEG signals are strongly correlated. We also verified the results of the analysis of Shannon entropy of signals by calculating their Hurst exponent to quantify their memory. According to the results, the alterations of the memory of EEG and GSR signals are similar to the alterations of the information of these signals. [ABSTRACT FROM AUTHOR]

This paper carries out experimental and numerical investigations on the axial performance of circular Reinforced Concrete (RC) precast slender columns with an intermediate connection. A series of tests are carried out to study the effects of longitudinal reinforcement ratio (ρ), and embedded length of steel bars (Lb) of precast columns on their axial performance under concentric loading. Test results show that increasing the reinforcement ratio and the embedded length of the precast columns significantly improves the axial performance of the precast slender columns. Nonlinear finite element models (FEM) are developed to simulate the performance of the columns experimentally observed. The accuracy of the FEM is validated against the test results and found to reasonably predict the axial performance of tested columns. [ABSTRACT FROM AUTHOR]

The rising energy prices and soaring environmental concerns have put an immense pressure on the wide usage of machining processes. The total power consumption during machining includes the power consumed by the machine itself and the power used to remove the material from the workpiece. An accurate prediction of energy consumption during the machining process is the basis for energy reduction. In this study, the specific cutting energy and surface finish for low and moderate-speed orthogonal machining of the aluminum alloy 2014 are evaluated. The measured values for the specific cutting energy and surface roughness are presented as maps on a grid, which is based on the machining parameters including the following: (1) cutting speed and (2) undeformed chip thickness. The specific cutting energy map depicts low energy consumption values of 0.52 J/mm3 for the aluminum alloy 2014 at medium speed machining. The roughness maps depict high roughness values at high cutting speeds. Both maps help in optimizing the machining process to achieve a required surface roughness with minimal energy consumption. A review of a specific cutting energy map demonstrates that energy consumption decreases by increasing the cutting speeds. The decrease in energy consumption at moderate speeds corresponds to the low cutting forces. This potentially happens as a result of thermal softening of the material caused by adiabatic heating. This subsequently leads to an increase in the machinability of the aluminum alloy 2014 at moderate cutting speeds. Furthermore, the decreasing chip thickness and increasing shear angle as a result of increasing the cutting speed confirms the increased machinability of the workpiece at moderate speeds. [ABSTRACT FROM AUTHOR]

This paper investigates the accuracy of the existing empirical design models and different machine learning (ML) models, known as Decision Tree (DT), Random Forest (RF), K‐Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Gradient Boosting Regression Tree (GBRT), and Extreme Gradient Boosting (XGBoost) in predicting the ultimate axial load of circular concrete‐filled stainless steel tubular (CFSST) columns under axial loading. A test database encompassing the test results of 142 CFSST columns is used to validate the accuracy of the existing empirical design and different ML models. It was demonstrated that all the ML models can provide a better estimation of the ultimate axial load than the existing empirical design models do, in which XGBoost can provide the best estimation of the ultimate axial load of CFSST columns. Finally, a simple equation is proposed based on the XGBoost model for the practical design of CFSST columns. [ABSTRACT FROM AUTHOR]

A Levi-type analytical solution procedure is developed to characterize static and dynamic deformation response of smart laminated simply-supported composite rectangular plates induced by inclined piezoelectric actuators under (1) constant electrical voltage and (2) time-dependent electrical voltage with excitation frequency. The key to development of this analytical solution is to employ higher order finite integral transform and discretized higher order partial differential unit step function equations. Unlike earlier studies, this research aims to investigate the effect of inclination angle of piezoelectric actuators on static and dynamic deformation response of laminated composite plates under both static and dynamic conditions. The developed analytical solution procedure is implemented computationally through Matlab-based computer code. Its accuracy is initially investigated through convergence study and results comparison with the published literature for a particular case when inclination angle is θ = 0°, which is only limited to bending deformation response. Since there is no published benchmark data for twisting deformation response analysis caused by inclination angle of piezoelectric actuators (θ ≠ 0°), a set of robust and realistic numerical analysis using Abaqus finite element analysis (FEA) is conducted. Good agreement between the analytical and numerical results is observed. Unlike applied electrical voltage, inclination angle of a piezoelectric actuator does not have a significant impact on twisting deformation response during static mode; whereas, both the excitation frequency and inclination angle can significantly influence maximum amplitude of vibration. [ABSTRACT FROM AUTHOR]

This paper reports on an investigation into the relationship between stiffness and applied force of an advanced biological composite structure using four techniques: experimental observation; finite element analysis (FEA); regression learner analysis; and, artificial neural networks (ANNs). The entire hydrated third metacarpal bones (MC3) from 16 thoroughbred horses were loaded in compression in an MTS machine. The stiffness was then determined from the applied force, MC3 displacement, and load exposure time. A variety of mathematical functions were fitted to the sample data points using MATLAB to demonstrate force-dependent stiffness. Two functions were found that exhibited a strong correlation between force and stiffness (R2 = 0.75). Additionally, a power function was found that demonstrated a stronger correlation between the stiffness and force (R2 = 0.81) if the exposure time was also incorporated. FEA considered the calculated force-dependent stiffness when assigning material properties. FEA results were compared with experimental data (for verification and validation), and a good agreement was found for the displacement (RMSE = 0.032 mm) and strain (RMSE = 61.85 με). Machine learning regression models were also employed to predict the stiffness of this complex structure. Applied force, exposure time, MC3 geometry (length and area of cross sections), and age were defined as the independent variables. The regression learner offered excellent reliability (R2 = 0.98) for the prediction of stiffness. Also, feedforward back-propagation artificial neural networks were employed to improve and generalize the stiffness prediction ability to a wider population. ANN regression analysis showed R = 0.992 for training, R = 0.99 for testing, and R = 0.991 for the all datasets. To confirm its accuracy, the ANN was used to predict stiffness of specific samples that were not used in its training. This offered excellent reliability in predicting real-world data that ANNs have not seen before (R = 0.976). [ABSTRACT FROM AUTHOR]

An analytical solution has been developed developed in this research for electro-mechanical flexural response of smart laminated piezoelectric composite rectangular plates encompassing flexible-spring boundary conditions at two opposite edges. Flexible-spring boundary structure is introduced to the system by inclusion of rotational springs of adjustable stiffness which can vary depending on changes in the rotational fixity factor of the springs. To add to the case study complexity, the two other edges are kept free. Three advantages of employing the proposed analytical method include: (1) the electro-mechanical flexural coupling between the piezoelectric actuators and the plate’s rotational springs of adjustable stiffness is addressed; (2) there is no need for trial deformation and characteristic function—therefore, it has higher accuracy than conventional semi-inverse methods; (3) there is no restriction imposed to the position, type, and number of applied loads. The Linear Theory of Piezoelectricity and Classical Plate Theory are adopted to derive the exact elasticity equation. The higher-order Fourier integral and higher-order unit step function differential equations are combined to derive the analytical equations. The analytical results are validated against those obtained from Abaqus Finite Element (FE) package. The results comparison showed good agreement. The proposed smart plates can potentially be applied to real-life structural systems such as smart floors and bridges and the proposed analytical solution can be used to analyze the flexural deformation response. [ABSTRACT FROM AUTHOR]

Shape memory alloys are increasingly used in numerous smart engineering structures. This study experimentally investigates static flexural and free vibration characteristics of composite beams reinforced with shape memory alloy wires. The key to this study is using shape memory alloy fibers as a means for influencing and tuning the static and dynamic responses of structures. A series of static three-point bending and modal experiments is performed to capture the capability of shape memory alloy wires in controlling the static and dynamic responses of a reinforced beam. Static and dynamic behaviors of the fiber-reinforced beam with different volumetric fiber fractions are examined. Before heat excitation, increasing the number of shape memory alloy wires leads to higher beam stiffness and lower beam deflection. However, with both heat activation and the higher number of shape memory alloy wires, beam deflection is significantly reduced. The modal vibration tests demonstrated that when shape memory alloy wires are not activated, the magnitude of natural frequencies slightly decreases by increasing the number of shape memory alloy wires. However, with heat excitation, the higher number of shape memory alloy wires, in contrast, increases the magnitude of natural frequencies. Furthermore, the higher number of activated shape memory alloy wires shows to predominantly increase the magnitude of higher modes of vibration rather than lower modes. [ABSTRACT FROM AUTHOR]

The knee plays a significant role in locomotion and stability of the entire body through supporting the body weight and assisting the lower body kinematics during walking. However, the knee is at constant risk of becoming weakened due to disease, age, and accidents. One approach to treating weakened knee is wearing an assistive knee brace. To design a clinical knee brace, many factors such as weight and compliant mechanism should be considered. In this study, a novel smart assistive knee brace mechanism incorporated with wire actuators made of shape memory alloys is proposed to ameliorate the issues associated with weight and flexibility of existing brace designs. Unlike earlier studies, the proposed orthosis includes pressure sensor, shape memory actuator, and smart linkage. Furthermore, two distinct shape memory alloy actuator design concepts with improved stiffness are developed, and the best option is chosen systematically and prototyped. The novel mechanism proposed in this research overcomes the weight of the lower limb during swing phase using the combined shape memory alloy actuation and feed-forward controller design. As such, it can be used as a potential replacement to its conventional counterparts when the higher weight reduction as well as a flexible and controllable mechanism are simultaneously sought. [ABSTRACT FROM AUTHOR]

This paper presented a systematic approach toward localized failure inspection of internally pressurized laminated ellipsoidal woven composite domes. The domes were made of thin glass fiber reinforced polymer (GFRP) woven composite layups [0,0,0], [0,30,0], [0,45,0], and [0,75,0]. The analytical results demonstrated that the circumferential regions near meridian φ = 45° in prolate ellipsoidal domes and near meridian φ = 90° in oblate ellipsoidal domes sustain the highest deformation under internal pressure. This observation was then confirmed by the numerical and experimental results. In addition, the numerical and experimental results showed localized rather than uniform failure in those regions, irrespective of changes in laminate stacking sequence. It was observed that localized failure occurs since the woven fibers configuration in some areas of woven remains in initial geometry (square shape), while the rests are deformed into the rhombic shape. In other words, by moving along the circumferential direction from the area close to θ = 0° to θ = 45° and θ = 45° to θ = 90°, the shape of woven fibers gradually changes from square (strong area) to rhombic (weak area), and rhombic to square, respectively. Thus, to minimize failure pressure, the meridian region vulnerable to failure must initially be identified. Afterwards, the rhombic regions in the circumference corresponding to that meridian must be strengthened. [ABSTRACT FROM AUTHOR]

Abstract Dome curvatures of pressure vessels often sustain highest level of stresses when subjected to various loading conditions. This research is aimed at investigating the effect of dome geometrical shape (hemispherical, torispherical, and ellipsoidal domes) on mechanical deformation and crack length of laminated woven reinforced polymer (GRP) composite pressure vessels under low-velocity impact (LVI) (case one) or combination of LVI and internal pressure (case two). The study is based on finite element (FE) simulations with laboratory-based experimental validation studies. It was observed that the maximum vertical displacements (U 1 *) and crack length along the diameter of deformation (a) are both of lower magnitude in case one. Damage intensity and fracture differ for different combinations of loading. Only matrix breakage and debonding occurs in case one and fiber breakage occurs in case two. The dome geometric shapes used in this study were found to be invariant to both damage intensity and failure modes. Irrespective of the type of load applied, the magnitude of U 1 * and crack length correlate with dome geometric shape as the maximum and the minimum U 1 * occur in torispherical and hemispherical domes, respectively. The maximum and the minimum crack lengths also take place in torispherical and hemispherical domes, respectively. [ABSTRACT FROM AUTHOR]

Commercial poly methyl methacrylate (PMMA)-based cement is currently used in the field of orthopedics. However, it suffers from lack of bioactivity, mechanical weakness, and monomer toxicity. In this study, a PMMA-based cement nanocomposite reinforced with hydroxyapatite (HA) nanofibers and two-dimensional (2D) magnesium phosphate MgP nanosheets was synthesized and optimized in terms of mechanical property and cytocompatibility. The HA nanofibers and the MgP nanosheets were synthesized using a hydrothermal homogeneous precipitation method and tuning the crystallization of the sodium-magnesium-phosphate ternary system, respectively. Compressive strength and MTT assay tests were conducted to evaluate the mechanical property and the cytocompatibility of the PMMA-HA-MgP nanocomposites prepared at different ratios of HA and MgP. To optimize the developed nanocomposites, the standard response surface methodology (RSM) design known as the central composite design (CCD) was employed. Two regression models generated by CCD were analyzed and compared with the experimental results, and good agreement was observed. Statistical analysis revealed the significance of both factors, namely, the HA nanofibers and the MgP nanosheets, in improving the compressive strength and cell viability of the PMMA-MgP-HA nanocomposite. Finally, it was demonstrated that the HA nanofibers of 7.5% wt and the MgP nanosheets of 6.12% wt result in the PMMA-HA-MgP nanocomposite with the optimum compressive strength and cell viability. [ABSTRACT FROM AUTHOR]

This paper introduces a new spark plasma sintering technique that is able to order crystalline anisotropy by in-series/in situ DC electric coupled magnetic field. The process control parameters have been investigated on the production of anisotropic BaFe12O19 magnets based on resulted remanence (Mr). Sintering holding time (H.T.), cooling rate (C.R.), pressure (P), and sintering temperature (S.T.) are optimized by Taguchi with L9 orthogonal array (OA). The remanent magnetization of nanocrystalline BaFe12O19 in parallel (Mrǁ) and perpendicular (MrꞱ) to the applied magnetic field was regarded as a measure of performance. The Taguchi study calculated optimum process parameters, which significantly improved the sintering process based on the confirmation tests of BaFe12O19 anisotropy. The magnetic properties in terms of Mrǁ and MrꞱ were greatly affected by sintering temperature and pressure according to ANOVA results. In addition, regression models were developed for predicting the Mrǁ as well as MrꞱ respectively. [ABSTRACT FROM AUTHOR]