Finite Element Analysis Simulation Research Articles

Robust Fiber-Shaped Flexible Temperature Sensors for Safety Monitoring with Ultrahigh Sensitivity.

Flexible temperature sensors capable of detecting and transmitting temperature data from the human body, environment, and electronic devices hold significant potential for applications in electronic skins, human-machine interactions, and disaster prevention systems. Nonetheless, fabricating flexible temperature sensors with exceptional sensing performance remains a formidable task, primarily due to the intricate process of constructing an intrinsically flexible sensing element with high sensitivity. In this study, a facile in situ two-step synthetic method is introduced for fabricating flexible fiber-shaped NiO/carbon nanotube fiber (CNTF) composites. The resulting NiO/CNTF flexible temperature sensors demonstrate outstanding deformability and temperature sensing characteristics, encompassing a broad working range (-15 to 60°C) and high sensitivity (maximum TCR of -20.2%°C-1 and B value of 3332 K). Importantly, the mechanical and thermal behaviors of the sensor in various application conditions are thoroughly examined using finite element analysis simulations. Moreover, the temperature sensors can effectively capture diverse thermal signals in wearable applications. Notably, a temperature monitoring and warning system is developed to prevent fire accidents resulting from abnormal thermal runaway in electronic devices.

Volume optimization of high-speed surface-mounted permanent magnet synchronous motor based on sequential quadratic programming technique and analytical solution

This study proposes an innovative approach that integrates the subdomain method with sequential quadratic programming (SQP) to optimize the volume of a high-speed surface-mounted permanent magnet synchronous motor in an air compressor operating at 110 000 rpm. Unlike conventional optimization tools that often rely on finite element analysis (FEA) software, by leveraging the subdomain method for electromagnetic modeling, we obtain an analytical solution that rapidly represents the behaviors of the motor. Subsequently, the SQP technique is applied to optimize the volume of the model while satisfying design constraints. The proposed model is verified by FEA simulation.

Advances in machine learning optimization for classical and quantum photonics

The development and optimization of photonic devices and various other nanostructure electromagnetic devices present a computationally intensive task. Much optimization relies on finite-difference time-domain or finite element analysis simulations, which can become very computationally demanding for finely detailed structures and dramatically reduce the available optimization space. In recent years, various inverse design machine learning (ML) techniques have been successfully applied to realize previously unexplored optimization spaces for photonic and quantum photonic devices. In this review, recent results using conventional optimization methods, such as the adjoint method and particle swarm, are examined along with ML optimization using convolutional neural networks, Bayesian optimizations with deep learning, and reinforcement learning in the context of new applications to photonics and quantum photonics.

Three-dimensional modeling of moisture transport in wood using near-infrared hyperspectral imaging and X-ray computed tomography in conjunction with finite element analysis

In past studies, finite element analysis (FEA) methods have been used to simulate the thermal and moisture coupling of wood. However, challenges remain in achieving high-quality three-dimensional (3D) simulations, mainly because of the heterogeneous and complex structure of wood and its difficult-to-detect internal structure, which makes modeling challenging, in addition to the lack of robust experimental techniques to validate simulation results. In this study, the FEA simulation model was refined by combining X-ray computed tomography (CT) and near-infrared hyperspectral imaging (NIR-HSI). CT was used to probe the 3D density of wood, and a novel FEA tetrahedral mesh was constructed based on the results. The NIR-HSI method visualizes the moisture distribution during adsorption and desorption inside the wood. This result is then used to adjust the parameters of the FEA simulation model and as a reference value to evaluate the simulation results. The visualization and simulation results fit well with the theoretical properties. The simulation results can more accurately reflect the spatial distribution and transfer trend of wood moisture at different points in time. Therefore, the CT and NIR-HSI-based 3D heat and moisture-coupled FEA model of wood proposed in this study can be used as a basis for optimizing drying parameters to provide high-quality wood.

Design Optimization of a Hybrid-Driven Soft Surgical Robot with Biomimetic Constraints.

The current study investigated the geometry optimization of a hybrid-driven (based on the combination of air pressure and tendon tension) soft robot for use in robot-assisted intra-bronchial intervention. Soft robots, made from compliant materials, have gained popularity for use in surgical interventions due to their dexterity and safety. The current study aimed to design a catheter-like soft robot with an improved performance by minimizing radial expansion during inflation and increasing the force exerted on targeted tissues through geometry optimization. To do so, a finite element analysis (FEA) was employed to optimize the soft robot's geometry, considering a multi-objective goal function that incorporated factors such as chamber pressures, tendon tensions, and the cross-sectional area. To accomplish this, a cylindrical soft robot with three air chambers, three tendons, and a central working channel was considered. Then, the dimensions of the soft robot, including the length of the air chambers, the diameter of the air chambers, and the offsets of the air chambers and tendon routes, were optimized to minimize the goal function in an in-plane bending scenario. To accurately simulate the behavior of the soft robot, Ecoflex 00-50 samples were tested based on ISO 7743, and a hyperplastic model was fitted on the compression test data. The FEA simulations were performed using the response surface optimization (RSO) module in ANSYS software, which iteratively explored the design space based on defined objectives and constraints. Using RSO, 45 points of experiments were generated based on the geometrical and loading constraints. During the simulations, tendon force was applied to the tip of the soft robot, while simultaneously, air pressure was applied inside the chamber. Following the optimization of the geometry, a prototype of the soft robot with the optimized values was fabricated and tested in a phantom model, mimicking simulated surgical conditions. The decreased actuation effort and radial expansion of the soft robot resulting from the optimization process have the potential to increase the performance of the manipulator. This advancement led to improved control over the soft robot while additionally minimizing unnecessary cross-sectional expansion. The study demonstrates the effectiveness of the optimization methodology for refining the soft robot's design and highlights its potential for enhancing surgical interventions.

Low-velocity impact response of a novel bionic turtle shell back armor sandwich structure

Turtle shell dorsal armor comprises a typical lightweight laminated composite structural material, which is constructed by cuticle and bone layer, with high specific strength and high fracture toughness. The red-eared slider turtle, a widely studied species of turtle, has a rich hierarchical structure. In this paper, according to the structure of red-eared slider turtle shell dorsal armor and the characteristics of each layer as well as by taking into full consideration of the fine structure of cuticle and ventral cortex of the shell dorsal armor, two bionic foam silicone rubber sandwich structures of different levels were prepared. Low-velocity impact test and finite element simulation analysis were carried out to study the mechanical response, specific absorbed energy, and damage mode of the two bionic sandwich structures under different impact energies, hammerhead shapes, and hammerhead radii. By comparing the mechanical response and specific energy absorption with the existing ordinary sandwich structure, we find that the two sandwich structures modeled in this paper exhibit better impact resistance than the ordinary sandwich structure. The shape and radius of the hammer head have a significant effect on the energy absorption characteristics of the sandwich structure, and the damage mode under the hemispherical hammer head is mainly dominated by tensile tearing damage, while the damage mode of extrusion fracture is more obvious under the impact of the conical hammer head. With the increase of the radius of the hammer head, the absorbed energy and specific energy absorption of the sandwich structure increases.

Exploring the Influence of Vibration Impact and Electric Spark on Aluminum Alloy Stress Fields

Vibration Impact Compound Electric Spark (VIES) is a technology that combines the advantages of vibration impact and electric spark surface enhancement. Due to its simple process, cost-effectiveness, and superior efficiency, it has attracted considerable attention in industrial applications. Through finite element simulation analysis, this study designed orthogonal experiments to explore the impact of VIES on the stress field distribution of aluminum alloy plates. The results indicate that factors such as impact frequency, impact height, and electric current have a certain influence on the distribution of longitudinal residual pressure stress in aluminum alloy plates. The experiments indicate that increasing the impact height within a certain range leads to an increase in residual stress at the impact site, but the depth of the compression stress layer decreases. Meanwhile, increasing the impact frequency increases the residual stress at the impact site, while the depth of the compression stress layer remains constant. In summary, both impact height and frequency have an impact on the stress in aluminum alloy plates. Additionally, orthogonal experimental results suggest that electric current has no significant effect on the stress field.

Development of a Large-Range XY-Compliant Micropositioning Stage with Laser-Based Sensing and Active Disturbance Rejection Control.

This paper presents a novel design and control strategies for a parallel two degrees-of-freedom (DOF) flexure-based micropositioning stage for large-range manipulation applications. The motion-guiding beam utilizes a compound hybrid compliant prismatic joint (CHCPJ) composed of corrugated and leaf flexures, ensuring increased compliance in primary directions and optimal stress distribution with minimal longitudinal length. Additionally, a four-beam parallelogram compliant prismatic joint (4BPCPJ) is used to improve the motion decoupling performance by increasing the off-axis to primary stiffness ratio. The mechanism's output compliance and dynamic characteristics are analyzed using the compliance matrix method and Lagrange approach, respectively. The accuracy of the analysis is verified through finite element analysis (FEA) simulation. In order to examine the mechanism performance, a laser interferometer-based experimental setup is established. In addition, a linear active disturbance rejection control (LADRC) is developed to enhance the motion quality. Experimental results illustrate that the mechanism has the capability to provide a range of 2.5 mm and a resolution of 0.4 μm in both the X and Y axes. Furthermore, the developed stage has improved trajectory tracking and disturbance rejection capabilities.

Masking Strategy Constructed Metal Coordination Hydrogels with Improved Mechanical Properties for Flexible Electronic Sensors.

Metal coordination hydrogels (MC-HGs) that introduce dynamically coordinate bonds together with metal ionic conduction have attracted considerable attention in flexible electronics. However, the traditional soaking method alleged to have technical scalability faces the challenge of forming MC-HGs with a "core-shell" structure, which undoubtedly reduces the whole mechanical properties and ionic stimulation responsiveness required for flexible electronics materials. Herein, a novel strategy referred to as "masking" has been proposed based on the theory of the valence bond and coordination chemistry. By regulating the masking agents and their concentrations as well as pairing mode with the metal ions, the whole mechanical properties of the resulting composites (MC-HGsMasking) show nearly double the values of their traditional soaking samples (MC-HGsSoaking). For example, the fracture stress and toughness of Fe-HGsMasking(SA,5.0g/L) are 1.55 MPa and 2.14 MJ/m3, almost twice those of Fe-HGsSoaking (0.83 MPa and 0.93 MJ/m3, respectively). Microstructure characterization combined with finite element analysis, molecular dynamics, and first-principles simulations demonstrates that the masking strategy first facilitating interfacial permeation of metal complexes and then effective coordination with functional ligands (carboxylates) of the hydrogels is the mechanism to strengthen the mechanical properties of composites MC-HGsMasking, which has the potential to break through the limitations of current MC-HGs in flexible electronic sensor applications.

Mechanical Studies of Subperiosteal Implants

When designing subperiosteal implants, mechanical testing of the implant and abutment is inevitable. Subperiosteal implants and their abutments are medical devices made to order, so each implant requires a separate design, since each patient has a different bone surface, for which the implant must be designed. For the mechanical testing of subperiosteal implants, a new test apparatus was constructed, on which mechanical simulations were carried out, the subperiosteal implants were tested together with their abutments. In addition to the finite element analysis simulation, the test apparatus can also be used to determine how much force is generated by the chewing force on the subperiosteal implant and its abutment as a result of the chewing mechanism.

Research on joint strength and energy absorption of CFRP and Al sheets bonded-FDS hybrid joining

The application of a composite-metal alloy lightweight body structure is one of the ways to achieve lightweight in an automobile. Materials such as carbon fiber reinforced polymer (CFRP) and aluminum alloys are widely used in the automotive industry due to their excellent performance. However, the significant differences in performance parameters between CFRP and aluminum alloy materials make joining heterogeneous materials challenging. Therefore, a rational joining process is crucial for the development of CFRP–aluminum alloy structures. This study proposes a hybrid joining method that combines flow drill screw (FDS) and adhesive bonding to join commonly used aluminum alloy sheets with CFRP sheets in automotive body manufacturing. Finite element analysis simulations were used to compare and characterize the failure modes of the bonded-FDS hybrid joints in tensile tests. The results were verified for accuracy. Tensile tests were conducted to compare the mechanical performance and failure modes of bonded joints, FDS joints, and hybrid joints. By analyzing the load–displacement curve, it was found that the FDS process in the hybrid joining damages the internal structure of the adhesive layer. However, the joint strength is still higher than that of the single joint strength, and the energy absorption value is about 4.7 times that of the adhesive and 1.6 times that of the FDS. This study provides a new industrial solution and serves as a reference for failure performance in composite material structures in the automotive industry, especially in cases involving one-sided access joining points.

Research on the Deformation Law of Foundation Excavation and Support Based on Fluid-Solid Coupling Theory.

To investigate the impact of underground water seepage and soil stress fields on the deformation of excavation and support structures, this study initially identified the key influencing factors on excavation deformation. Subsequently, through a finite element simulation analysis using Plaxis, this study explored the effects of critical factors, such as the excavation support form, groundwater lowering depth, permeability coefficient, excavation layer, and sequence on excavation deformation. Furthermore, a comprehensive consideration of various adverse factors was integrated to establish excavation support early warning thresholds, and optimal dewatering strategies. Finally, this study validated the simulation analysis through an on-site in situ testing with wireless sensors in the context of a physical construction site. The research results indicate that the internal support system within the excavation piles exhibited better stability compared to the external anchor support system, resulting in a 34.5% reduction in the overall deformation. Within the internal support system, the factors influencing the excavation deformation were ranked in the following order: water level (35.5%) > permeability coefficient (17.62%) > excavation layer (11.4%). High water levels, high permeability coefficients, and multi-layered soils were identified as the most unfavorable factors for excavation deformation. The maximum deformation under the coupled effect of these factors was established as the excavation support early warning threshold, and the optimal dewatering strategy involved lowering the water level at the excavation to 0.5 m below the excavation face. The on-site in situ monitoring data obtained through wireless sensors exhibited low discrepancies compared to the finite element simulation data, indicating the high precision of the finite element model for considering the fluid-structure interaction.

Parallel osteotome technique: new approach to reduce neurosensory disturbances following bilateral sagittal split osteotomy.

To determine clinically if parallel osteotome technique (POST) reduces incidence of neurosensory disturbances (NSD) following bilateral sagittal split osteotomy (BSSO), and to corroborate the findings with finite element analysis (FEA). The investigation was a quasi-experimental study involving 30 patients requiring BSSO. The sample was divided into two groups: control undergoing conventional osteotomy technique and experimental using POST. An FEA simulation of the two advocated techniques were also generated for computational analysis. Primary outcome was clinical comparison of subjective and objective measures for NSD, while secondary outcome was comparison of stress, strain and deformation. Osteotomy technique was the exposure studied. Data was analyzed for differences in proportions and means. Thirty patients (12 females and 18 males) with an age range of 18 to 28 (mean 21.6) years were recruited for the study. This provided 60 sides as sample for the study. The experimental group demonstrated significantly lower subjective scores for NSD for the 3 rd (p=0.03) and 6 th month reviews (p=0.025). Objective testing revealed lower incidence of NSD for the experimental group during the 3 rd (p=0.07), 6 th (p=0.007) and 12 th post-operative months (p=0.17). The computational analysis also revealed significantly lower stress (p=0.007), strain (p=0.009) and deformation (p=0.04) on the inferior alveolar nerve with the POST method. Incidence of NSD following BSSO is lower with POST. The technique also induces significantly lower deformational forces on the IAN when compared with the conventional osteotomy method.

Design-encoded dual shape-morphing and shape-memory in 4D printed polymer parts toward cellularized vascular grafts.

Current additive manufacturing technologies wherein as-printed simple two-dimensional (2D) structures morph into complex tissue mimetic three-dimensional (3D) shapes are limited to multi-material hydrogel systems, which necessitates multiple fabrication steps and specific materials. This work utilizes a single shape memory thermoplastic polymer (SMP), PLMC (polylactide-co-trimethylene carbonate), to achieve programmable shape deformation through anisotropic design and infill angles encoded during 3D printing. The shape changes were first computationally predicted through finite element analysis (FEA) simulations and then experimentally validated through quantitative correlation. Rectangular 2D sheets could self-roll into complete hollow tubes of specific diameters (ranging from ≈6 mm to ≈10 mm) and lengths (as long as 40 mm), as quantitatively predicted from FEA simulations within one minute at relatively lower temperatures (≈80 °C). Furthermore, shape memory properties were demonstrated post-shape change to exhibit dual shape morphing at temperatures close to physiological levels. The tubes (retained as the permanent shape) were deformed into flat sheets (temporary shape), seeded with endothelial cells (at T < Tg), and thereafter triggered at ≈37 °C back into tubes (permanent shape), utilizing the shape memory properties to yield bioresorbable tubes with cellularized lumens for potential use as vascular grafts with improved long-term patency. Additionally, out-of-plane bending and twisting deformation were demonstrated in complex structures by careful control of infill angles that can unprecedently expand the scope of cellularized biomimetic 3D shapes. This work demonstrates the potential of the combination of shape morphing and SMP behaviors at physiological temperatures to yield next-generation smart implants with precise control over dimensions for tissue repair and regeneration.

Effects of cutting conditions on the cutting forces in machining additively manufactured Ti6Al4V alloy

Machining of additive manufacturing (AM) process results in excessive consumption of the cutting power due to the high strength and hardness of the material produced that led to high cutting forces and short tool life during machining process. This paper aims to investigate the effect of cutting conditions and coefficient of friction on the cutting forces, during finite element analysis (FEA) of an orthogonal cutting of AM Ti6Al4V alloy bar. The FEA simulation considers three different experiments at low, medium, and high cutting conditions. The result shows that as the depth of cuts gets deeper and the cutting speed increases, the cutting forces were found to increase, leading to serrated or segmented chips formation. A cutting force of 1300.36N was observed at a cutting speed of 150 m/min and a coefficient of friction of 0.9. The cutting force was found to be weaker at the higher shear plane angle of 42.2o and higher at a smaller angle of 37.57o. The coefficient of friction was observed to be insignificant at lower value of 0.09 and was seemed to affect the cutting force at value around 1.

Measurements and Simulation of Compressive Behavior of Biodegradable Seedling Containers

Highlights Compression tests were conducted on biodegradable containers with various conditions. The lateral loading reduced the compressive strength compared with the vertical loading. A model was developed using FEA to simulate the compressive behavior of the biocontainer. Model results showed similar ductile behaviors of the biocontainers with test results. The compressive strengths of wet biocontainers were below 20% of dry ones. Abstract. Biodegradable seedling containers (biocontainers) are becoming increasingly popular for use in transplanting and greenhouse applications. However, biocontainers have reduced mechanical strength when compared to plastic containers, often being damaged during the handling process. In this study, Canadian peat moss biocontainers were tested to study their compressive properties under different scenarios of various container sizes (small and large), moisture conditions (dry and wet), and orientation (vertical and lateral) relative to the loading direction. Also, simulations were performed using the Finite Element Analysis (FEA) for the selected scenario, focusing on the small container with two moisture conditions. The testing results showed that the force-displacement behavior of biocontainers varied dramatically with the orientation. In the lateral orientation, the biocontainers were able to carry loads while being largely deformed. However, the lateral orientation resulted in a reduced compressive strength of 21% compared with the vertical orientation. The moisture condition was found to be the second most dominant factor affecting the result. The compressive strength of the wet biocontainer was only 15.4%-19.6% of the dry ones. FEA simulation results showed similar ductile behaviors of the biocontainers under compression. Compared with the testing results, the relative errors of the simulated compressive strengths were 24.0% for the small, dry container and 9.89% for the small, wet one. FEA simulations were conducted with various Young’s Modulus values, revealing a logarithmic relationship with the container strength and deformation. The obtained knowledge of how the biocontainers react when subjected to compression provides insight to the failure mechanism of the container. The information is valuable for guiding the proper handling of biocontainers. Keywords: Biocontainer, Compression, FEA, Moisture condition, Property.

Topology optimization design of microstructures with zero Poisson's ratio

Mechanical metamaterials are materials that possess unconventional mechanical properties that are not found in homogeneous materials, achieved through specific artificial microstructures. Topology optimization is an effective design method for such materials. In this paper, a topology optimization-based method for designing zero Poisson's ratio mechanical metamaterials is proposed. Firstly, a zero Poisson's ratio topology optimization objective function is constructed based on the energy homogenization method, and the optimal microstructure topology configuration and elastic coefficient matrix under different initial topologies and volume fractions are obtained through the boundary density evolution topology optimization method. To improve the saw tooth effect of the boundaries, the node density level set method is used to smooth the boundaries of the microstructure. Then, in finite element simulation analysis, it is demonstrated that the proposed method can effectively design microstructures with zero Poisson's ratio properties. It is also shown that microstructures with expected stiffness and zero Poisson's ratio properties can be obtained by changing the volume fraction and selecting materials with different stiffnesses. Finally, the unit cell is periodically arranged to form a multi-lattice metamaterial, and its zero Poisson's ratio mechanical performance in both X and Y directions is verified.

Analysis of structural response characteristics of a bidirectional separated electromagnetic coil drive device

In order to alleviate the common problems of skeleton fracture and failure in traditional propulsion systems, the insulation degradation and structural instability existing in integrated drive structures during operation are investigated in this work. By using stress-strain calculations of a typical integrated drive structure and voltage-withstanding discharge tests after acceleration experiments, key factors are identified, and it is believed that the tensile stress inside the driving structure is one of the reasons for structural instability. Owing to the electromagnetic force acting on the coil, the integrated driving structure exhibits high tensile stress and strain on the inner wall and interphase partition, accompanied by significant deformation, which is not conducive to the overall structural stability. Based on the above calculation results, a novel modular drive structure with bidirectional separation is proposed, which can realize the radial separation between the phase partition and the skeleton inner cylinder, as well as axial separation between different driving coils. Finite element simulation analysis is conducted to evaluate its acceleration performance and structural response during operation. The results indicate that under the same excitation conditions, the new driving structure greatly reduces the interaction between the coil and the inner wall during operation, so the stress-strain on the inner wall of the new driving structure is much smaller than that of the integrated structure. The maximum deformation decreases from approximately 10&lt;sup&gt;–2&lt;/sup&gt; m in the integrated structure to about 10&lt;sup&gt;–5&lt;/sup&gt; m to 10&lt;sup&gt;–6&lt;/sup&gt; m in the new design. These findings emphasize the potential of new structure to improve reliability while ensuring propulsion performance, providing valuable insights for optimizing electromagnetic coil drive structures. For this new structure, there will be plans to conduct high-pressure propulsion experiments in the future to verify its reliability.

Deep Learning for Strain Field Customization in Bioreactor with Dielectric Elastomer Actuator Array.

In the field of biomechanics, customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology, primarily due to the intricate coupling and nonlinear actuation of actuator arrays, which complicates the precise control of strain fields. This paper introduces a bioreactor designed with a 9×9 array of independently controllable dielectric elastomer actuators (DEAs), addressing this challenge. We employ image regression-based machine learning for both replicating target strain fields through inverse control and rapidly predicting feasible strain fields generated by the bioreactor in response to control inputs via forward control. To generate training data, a finite element analysis (FEA) simulation model was developed. In the FEA, the device was prestretched, followed by the random assignment of voltages to each pixel, yielding 10,000 distinct output strain field images for the training set. For inverse control, a multilayer perceptron (MLP) is utilized to predict control inputs from images, whereas, for forward control, MLP maps control inputs to low-resolution images, which are then upscaled to high-resolution outputs through a super-resolution generative adversarial network (SRGAN). Demonstrations include inputting biomechanically significant strain fields, where the method successfully replicated the intended fields. Additionally, by using various tumor-stroma interfaces as inputs, the bioreactor demonstrated its ability to customize strain fields accordingly, showcasing its potential as an advanced testbed for tumor biomechanics research.

Improving dynamic process stability in finishing of thin-walled workpieces by optimal selection of stock shape

In 5-axis milling of thin-wall parts, flexibility of the in-process-workpiece (IPW) governs static and dynamic deflections, especially at the semi-finishing and finishing stages. Thus, the near net shape preform, i.e. the stock shape left for semi/finishing is crucial for chatter stability. In this study, a methodology is presented to design the stock shape for improved stability. Coupled process simulation, which includes material removal, finite element analysis (FEA) and process stability simulations, is used to show the effect of stock shape on chatter stability. Spindle speed is optimized along the toolpath based on the varying process dynamic behavior for selected cutter locations (CL). The proposed method is experimentally demonstrated on case studies.