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The assembly simulation in tolerance analysis is one of the most important steps for the tolerance design of mechanical products. However, most assembly simulation methods are based on the rigid body assumption, and those assembly simulation methods considering deformation have a poor efficiency. This paper aims to propose a novel efficient and precise tolerance analysis method based on stable contact to improve the efficiency and reliability of assembly deformation simulation.

The proposed method comprehensively considers the initial rigid assembly state, the assembly deformation and the stability examination of assembly simulation to improve the reliability of tolerance analysis results. The assembly deformation of mating surfaces was first calculated based on the boundary element method with optimal initial assembly state, then the stability of assembly simulation results was assessed by the density-based spatial clustering of applications with noise algorithm to improve the reliability of tolerance analysis. Finally, combining the small displacement torsor theory, the tolerance scheme was statistically analyzed based on sufficient samples.

A case study of a guide rail model demonstrated the efficiency and effectiveness of the proposed method.

The present study only considered the form error when generating the skin model shape, and the waviness and the roughness of the matching surface were not considered.

To the best of the authors’ knowledge, the proposed method is original in the assembly simulation considering stable contact, which can effectively ensure the reliability of the assembly simulation while taking into account the computational efficiency.

Fuselage structures are subjected to combinations of axial, bending, shear and differential pressure loads. The validation of advanced metallic and composite fuselage designs against such loads is based on the full-scale testing of the fuselage barrel, which, however, is highly demanding from a time and cost viewpoint. This paper aims to assist in scaling-down the experimentation to the stiffened panel level which presents the opportunity to validate state-of-the-art designs at higher rates than previously attainable.

Development of a methodology to successfully design tests at the stiffened panel level and realize them using advanced, complex and adaptable test-rigs that are capable of introducing independently a set of distinct load types (e.g. internal overpressure, tension, shear) while applying appropriate boundary conditions at the edges of the stiffened panel.

A baseline test-rig configuration was developed after extensive parametric modelling studies at the stiffened panel level. The realization of the loading and boundary conditions on the test-rig was facilitated through innovative supporting and loading system set-ups.

The proposed test bench is novel and compared to the conventional counterparts more viable from an economic and manufacturing point of view. It leads to panel responses, which are as close as possible to those of the fuselage barrel in-flight and can be used for the execution of static or fatigue tests on metallic and thermoplastic curved integrally stiffened full-scale panels, representative of a business jet fuselage.

The mortality rate due to skin cancers has been increasing over the past decades. Early detection and treatment of skin cancers can save lives. However, due to visual resemblance of normal skin and lesion and blurred lesion borders, skin cancer diagnosis has become a challenging task even for skilled dermatologists. Hence, the purpose of this study is to present an image-based automatic approach for multiclass skin lesion classification and compare the performance of various models.

In this paper, the authors have presented a multiclass skin lesion classification approach based on transfer learning of deep convolutional neural network. The following pre-trained models have been used: VGG16, VGG19, ResNet50, ResNet101, ResNet152, Xception, MobileNet and compared their performances on skin cancer classification.

The experiments have been performed on HAM10000 dataset, which contains 10,015 dermoscopic images of seven skin lesion classes. The categorical accuracy of 83.69%, Top2 accuracy of 91.48% and Top3 accuracy of 96.19% has been obtained.

Early detection and treatment of skin cancer can save millions of lives. This work demonstrates that the transfer learning can be an effective way to classify skin cancer images, providing adequate performance with less computational complexity.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

This paper aims to study numerically the flow, heat transfer, and entropy generation of aqueous copper oxide-silver hybrid nanofluid over a down-pointing rotating vertical cone, with linear surface temperature (LST) and linear surface heat flux (LSHF), in the presence of a cross-magnetic field. In industrial applications, such as oil and gas plants, food industries, steel factories and nuclear packages, the real bodies may contain nonorthogonal walls and variable cross-section three-dimensional forms which this issue can clarify the importance of selective geometry in the present research.

The mass-based scheme is accomplished for the simulation, and the entropy generation and Bejan number will be analyzed in conjunction with the aforementioned model. It has been hypothesized that two types of boundary conditions (LST and LSHF) as well as five nanoparticle shapes (sphere, brick, cylinder, platelet and disk) present a collection of crucial results. The overseeing PDEs are changed over completely to the dimensionless ODEs, and these are solved by Runge–Kutta–Fehlberg approach combined with a shooting methodology for certain values of physical parameters.

Subsequent to the fantastic compromise of the computational outcomes with past reports, the outcomes are introduced to conduct the investigation of the hydrodynamics/thermal boundary layers, the skin friction and the Nusselt number, as well as entropy generation and Bejan number. A state of hybrid nanofluid, which exhibits a remarkable increase in heat transfer in comparison to the states of mono-nanofluid and regular fluid, has been found to have the highest Nusselt number; however, the skin friction values should always be taken into account and managed. The entropy generation improves with the mass of the second nanoparticle (silver), while the opposite pattern is exhibited for the Bejan number. Furthermore, the lowest value of entropy generation number belongs to the cylindrical shape of nanoparticles in the LST case. In final, a significant accomplishment of the current study is the accurate output of the mass-based scheme for an entropy analysis problem.

To the best of the authors’ knowledge, for the first time, in this study, a new development of natural convective flow of a hybrid nanofluid about the warmed (LST and LSHF) and down-pointing rotating vertical cone by the mass-based algorithm has been presented. The applied methodology considers the masses of base fluid (water) and nanoparticles (Ag and CuO) as an alternative to the first and second nanoparticles volume fraction. Indeed, the combination use of the Tiwari–Das nanofluid model and the mass-based hybridity algorithm for the entropy generation analysis can be the main novelty of this work.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this paper is to investigate the free vibration response of a laminated honeycomb sandwich panels (LHSP) for aerospace applications. Higher order shear deformation theory (HSDT) was simplified for the dynamic analysis of LHSP. Furthermore, the effects of honeycomb parameters on the value of natural frequency (NF) of vibration were explored.

This paper applies HSDT to the analysis of composite LHSP to derive four vibration differential equations of motion and solve it to find the NF of vibration. Two analytical models (Nayak and Meunier models) were selected from literature for comparison of the NF of vibration. In addition, a numerical model was built by using ABAQUS and the results were compared. Furthermore, parametric studies were conducted to explore the effect of honeycomb parameters on the value of the NF of vibration.

The present model is successful in simplifying HSDT for the analysis of LHSP. The first five natural frequencies of vibration were calculated analytically and numerically. In the parametric study, increasing core height or young’s modulus or changing laminate layup will increase the value of NF of vibration. Furthermore, increasing plate constraint (using clamped edge boundary condition) will increase the value of NF of vibrations.

The current analysis is suitable for all-composite symmetric LHSP. However, for isotropic or non-symmetric materials, minor modifications might be adopted.

The application of simplified HSDT to the analysis of LHSP is one of the important values of this research. The other is the successful and complete dynamic analysis of all-composite LHSP.

This paper aims to investigate the responses of single piles and pile groups due to tunneling-induced ground movements in a two-layered soil system. The analyses mainly focus on the additional single pile responses in terms of bending moment, lateral deflection, axial force, shaft resistance and pile settlement. Subsequently, a series of parametric studies were carried out to better understand the responses of single piles induced by tunneling. To give further understanding regarding the pile groups, a 2 × 2 pile group with two different pile head conditions, namely, free and capped, was considered.

Using the PLAXIS three-dimensional (3D) software, a full 3D numerical modeling is performed to investigate the effects of ground movements caused by tunneling on adjacent pile foundations. The numerical model was validated using centrifuge test data found in the literature. The relevance of the 3D model is also judged by comparison with the 2D plane strain model using the PLAXIS 2D code.

The numerical test results reveal that tunneling induces significant displacements and internal forces in nearby piles. The magnitude and distribution of internal forces depend mainly on the position of the pile toe relative to the tunnel depth and the distance between the pile and the vertical axis of the tunnel. As the volume loss increases from 1% to 3%, the apparent loss of pile capacity increases from 11% to 20%. By increasing the pile length from 0.5 to 1.5 times, the tunnel depth, the maximum pile settlement and lateral deflection decrease by about 63% and 18%, respectively. On the other hand, the maximum bending moment and axial load increase by about 7 and 13 times, respectively. When the pile is located at a distance of 2.5 times the tunnel diameter (Dt), the additional pile responses become insignificant. It was found that an increase in tunnel depth from 1.5Dt to 2.5Dt (with a pile length of 3Dt) increases the maximum lateral deflection by about 420%. Regarding the interaction between tunneling and group of piles, a positive group effect was observed with a significant reduction of the internal forces in rear piles. The maximum bending moment of the front piles was found to be higher than that of the rear piles by about 47%.

Soil is a complex material that shows differently in primary loading, unloading and reloading with stress-dependent stiffness. This general behavior was not possibly being accounted for in simple elastic perfectly plastic Mohr–Coulomb model which is often used to predict the behavior of soils. Thus, in the present study, the more advanced hardening soil model with small-strain stiffness (HSsmall) is used to model the non-linear stress–strain soil behavior. Moreover, unlike previous studies THAT are usually based on the assumption that the soil is homogeneous and using numerical methods by decoupled loadings under plane strain conditions; in this study, the pile responses have been exhaustively investigated in a two-layered soil system using a fully coupled 3D numerical analysis that takes into account the real interactions between tunneling and pile foundations. The paper presents a distinctive set of findings and insights that provide valuable guidance for the design and construction of shield tunnels passing through pile foundations.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

Several graphs, streamlines, isotherms and 3D plots are illustrated to enlighten the noteworthy fallouts of the investigation. Embedding flow factors for velocity, induced magnetic field and temperature have been determined using parametric analysis.

Ternary hybrid nanofluids has outstanding hydrothermal performance compared to classical mono nanofluids and hybrid nanofluids owing to the presence of triple tiny metallic particles. Ternary hybrid nanofluids are considered as most promising candidates in solar energy, heat exchangers, electronics cooling, automotive cooling, nuclear reactors, automobile, aerospace, biomedical devices, food processing etc. In this work, a ternary hybrid nanofluid flow that contains metallic nanoparticles over a wedge under the prevalence of solar radiating heat, induced magnetic field and the shape factor of nanoparticles is considered. A ternary hybrid nanofluid is synthesized by dispersing iron oxide (Fe3O4), silver (Ag) and magnesium oxide (MgO) nanoparticles in a water (H₂O) base fluid. By employing similarity transformations, we can convert the governing equations into ordinary differential equations and then solve numerically by using the Runge–Kutta–Fehlberg approach.

There is no fund for the research work.

This kind of study may be used to improve the performance of solar collectors, solar energy and solar cells.

This investigation unfolds the hydrothermal changes of radiative water-based Fe₃O₄-Ag-MgO-H₂O ternary hybrid nanofluidic transport past a static and moving wedge in the presence of solar radiating heating and induced magnetic fields. The shape factor of nanoparticles has been considered in this study.