Solid Model Research Articles

INTELLIGENT INFORMATION TECHNOLOGIES TO SUPPORT DECISION-MAKING WHEN APPLYING THE CAD/CAM/CAE SYSTEM OF DESIGN AND USING ADDITIVE TECHNOLOGIES

The direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. The relevance is due to the fact that the development of an intelligent decision-making support system based on neural network modeling, the development of methods for their training, tabagato-criterion optimization of design processes, will allow the creation of three-dimensional solid models of defects taking into account their spatial structure and bone substitutes for the synthesis of biomaterials with controlled composition, porosity and mechanical strength, which are optimal for a specific area of bone replacement, which will increase the effectiveness of treatment and prosthetics in orthopedics and traumatology. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization, and training is proposed. A modification of the method of learning neural networks based on gradient descent, based on the application of the theory of nonlinear dynamics, is proposed. Corresponding theoretical provisions have been developed.

Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning.

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability.

Priming with LSD1 inhibitors promotes the persistence and antitumor effect of adoptively transferred T cells

The antitumor efficacy of adoptively transferred T cells is limited by their poor persistence, in part due to exhaustion, but the underlying mechanisms and potential interventions remain underexplored. Here, we show that targeting histone demethylase LSD1 by chemical inhibitors reshapes the epigenome of in vitro activated and expanded CD8+ T cells, and potentiates their antitumor efficacy. Upon T cell receptor activation and IL-2 signaling, a timely and transient inhibition of LSD1 suffices to improve the memory phenotype of mouse CD8+ T cells, associated with a better ability to produce multiple cytokines, resist exhaustion, and persist in both antigen-dependent and -independent manners after adoptive transfer. Consequently, OT1 cells primed with LSD1 inhibitors demonstrate an enhanced antitumor effect in OVA-expressing solid tumor models implanted in female mice, both as a standalone treatment and in combination with PD-1 blockade. Moreover, priming with LSD1 inhibitors promotes polyfunctionality of human CD8+ T cells, and increases the persistence and antitumor efficacy of human CD19-CAR T cells in both leukemia and solid tumor models. Thus, pharmacological inhibition of LSD1 could be exploited to improve adoptive T cell therapy.

A Unified Thermo-Fluid–Solid Formulation for FSI and Phase Change Problems Based on the Particle Finite Element Method

The modeling of industrial processes that involve phase change (e.g., casting, welding or additive manufacturing) is challenging due to their multiscale and multiphysics nature. The coupling of the fluid and solid mechanics is difficult due to the unknown and evolving fluid–solid interface. To deal with the difficulty of the coupling, a unified approach has been developed where fluid and solid regions are represented in the same computational domain and solved by a single solver. The interaction of fluid and solid regions is therefore automatically captured and the material can locally undergo phase transition. This allows to capture the flow in the melt and the thermal stresses in the solid. The solid material model is currently limited to linear elasticity, but it opens the path to more complex material models with plasticity modeling, which already exist in the literature. The proposed method is based on the Particle Finite Element Method (PFEM), which has been shown to accurately handle both fluid and solid mechanics. In this work, we present the key aspects of this novel unified formulation and the treatment of the fluid–solid interface in the PFEM context. The methodology is presented and verified using a set of tests. A laser spot welding example test case demonstrates the potential of combining the unified formulation with the interface treatment and the phase change capabilities and is used for the validation of the present technique.

The Presence of Chaos in a Viscoelastic Harmonically Forced Von Mises Truss

Abstract This work investigates how viscoelasticity affects the dynamic behavior of a lumped-parameter model of a bistable von Mises truss. The system is controlled by a linear first-order equation and a second-order nonlinear Duffing equation with a quadratic nonlinearity that governs mechanical behavior. The second-order equation controls mechanical oscillations, while the linear first-order equation controls viscoelastic force evolution. Combined, the two equations form a third-order jerk equation that controls system dynamics. Viscoelasticity adds time scales and degrees-of-freedom to material behavior, distinguishing it from viscosity-only systems. Due to harmonic excitation, the system exhibits varied dynamic responses, from periodic to quasi-periodic to chaotic. We explore the dynamics of a harmonically forced von Mises truss with viscous damping to address this purpose. We demonstrate this system's rich dynamic behavior due to driving amplitude changes. This helps explain viscoelastic system behavior. A viscoelastic unit replaces the viscous damper, and we show that, although viscous damping merely changes how fast the trajectory decays to an attractor, viscoelasticity modifies both the energy landscape and the rate of decay. In a conventional linear solid model, three viscoelastic parameters control the system's behavior instead of one, as in pure viscous damping. This adds degrees-of-freedom that affect system dynamics. We present the parameter space for chaotic behavior and the shift from regular to irregular motion. Finally, Melnikov's criteria identify the regular-chaotic threshold. The system's viscous and elastic components affect the chaotic threshold amplitude

DFT studies on electronic and structure properties of PbSe1-xSx alloys for thermoelectric materials using VCA and RS

In this work, Density Functional Theory (DFT) was employed to investigate the crystal structure and electronic properties of lead selenide (PbSe) and lead sulfide (PbS) semiconductors and their pseudo binary alloys PbSe1-xSx. The virtual crystal approximation (VCA) and randomly substituted solid models were employed to study these materials. In addition, the thermoelectric properties as function of the alloy composition were studied. Lattice parameters obtained using the generalized gradient approximation (GGA) were compared with theoretical and experimental results. The electronic bands were calculated for different sulfur compositions (0≤x ≤ 1, Δx = 0.1) using the Generalized Gradient Approximation of TB09LDA (MGGA). It was observed that the transition from the valence band to the conduction band takes place at the L point, which agrees with previous theoretical investigations. Slight deviations from Vegard's law were observed in the lattice parameters and bandgap of the alloys, especially for the sulfur-rich alloys. Effective band diagrams obtained from the unfolding of supercell band diagrams, reported for the first time for this system, show that the impacts of alloy disorder are low in the vicinity of the L point. The influence of the alloy composition on the transport properties was studied within the framework of Boltzmann's semiclassical theory. The electrical conductivity shows a thermally activated increase which influences the total thermal conductivity above T > 600–700 K, and the figure of merit ZT. Seebeck coefficient dependence with the chemical potential suggests an ambipolar behavior of the alloys, which present both n and p character. The alloys have the ZT maxima above 900 K influenced by the PbSe1-xSx alloy composition. Remarkably, the alloy with x = 0.5 present a maximum ZT value comparable to that of PbSe, which indicates that is promising to substituting selenium by sulfur atoms to tailor the thermoelectrical properties. This work shows the suitability of the VCA approximation and the band unfolding method, to describe the composition-dependent properties of the PbSe1-xSx pseudo binary alloys.

A Physics-based Model Assisted by Machine-Learning for Sodium-ion Batteries with both Liquid and Solid Electrolytes

Abstract In the absence of experimental data of a fully developed hierarchical 3D sodium solid state batteries, we developed an improved continuum model by relying on Machine Learning-assisted parameter fitting to uncover the intrinsic material properties that can be transferred into different battery models. The electrochemical system simulated has sodium metal P2-type Na2/3[Ni1/3Fe1/12Mn7/12]O2 (NNFMO) as the cathode, paired with two types of electrolytes have been modeled viz, the organic liquid electrolyte and a solid polymer electrolyte. We implemented a 1D continuum model in COMSOL to suit both liquid and solid electrolytes, then used a Gaussian Process Regressor to fit and evaluate the electrochemical parameters in both battery systems. To enhance the generalizability of our model, the liquid cell and solid cell models share the same OCV input for the cathode materials. The resulting parameters are well aligned with their physical meaning and literature values. The continuum model is then used to understand the effect of increasing the thickness of the cathode and current density by analysing the cathode utilization, and the overpotentials arising from transport and charge transfer. This 1D model and the parameter set are ready to be used in a 3D battery architecture design.

CAD and constructive solid geometry modeling of the Molten Salt Reactor Experiment with OpenMC

In this study, we present a detailed comparison of two independently developed models of the Molten Salt Reactor Experiment (MSRE) for Monte Carlo particle transport simulations: the constructive solid geometry (CSG) model that was developed in support of the MSRE benchmark in the International Handbook of Evaluated Reactor Physics Benchmark Experiments, and a CAD model that was developed by Copenhagen Atomics. The original Serpent reference CSG model was first converted to OpenMC’s input format so that it could be systematically compared to the CAD model, which was already available as an OpenMC model, using the same Monte Carlo code. Results from simulations using the Serpent and OpenMC CSG models showed that keff agreed within 10 pcm while the flux distribution in space and energy generally agreed within 0.1%. Larger differences were observed between the OpenMC CAD and CSG models; notably, the keff computed for the CAD model was 1.00872, which is more than 1% lower than the value for the CSG model and much closer to experiment. Several areas of the reactor that were modeled differently in the CSG and CAD models were discussed and, in several cases, their impact on keff was quantified. Lastly, we compared the computational performance and memory usage between the CAD and CSG models. Simulation of the CSG model was found to be 1.4–2.3× faster than simulation of the CAD model based on the Embree ray tracer while using 4× less memory, highlighting the need for continued improvements in the CAD-based particle transport ecosystem. Finally, major performance degradation was observed for CAD-based simulations when using the MOAB ray tracer.

The Impact of Digital Economy on Domestic Trade and Investment Efficiency Measurement and Heterogeneity Analysis

Against the backdrop of a new round of booming technological revolution, the digital economy has brought about high-quality development for trade. This paper is based on the 2011-2019 China's trade and investment in China, which will have a profound impact on China's trade and investment. This paper is based on the2011-2019 China. The spatial panel data of 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan) were analysed by using OLS model, panel solid model and panel data. Construct correlation regression models using fixed effects model, panel random effects model, differential GMM and systematic GMM methods. type and "Digital Economy Development Level Indicator System" to study the impact of the digital economy on the efficiency of domestic trade and investment.so as to contribute to the promotion of high-level opening up to the outside world, the deepening of domestic demand strategy, and the construction of " trade power".

Nonlocal modelling of piezoelectric solid with cracks based on peridynamic differential operator

The paper establishes a nonlocal model for piezoelectric solids using the Peridynamic differential operator. The method transforms the displacement-electric potential equation, constitutive equation, and boundary conditions of piezoelectric solids from a local differential form to a nonlocal integral form. The solution for the displacement and electric potential distributions in piezoelectric solids is obtained using variational principles and the Lagrange multiplier method. The correctness and effectiveness of the model are validated through a comparison of the deformation analysis of piezoelectric solid square plates with analytical solutions. Analysis of piezoelectric solid square plates with edge cracks proves the applicability of this model for solving discontinuous problems. Furthermore, the paper explores the suitability of the model in analysing problems involving multiple cracks in piezoelectric solid plates, highlighting its capability to address defective piezoelectric solid problems. This model provides a new perspective for solving problems involving defects under electro-mechanical coupling conditions.

Non-linear modeling and parameter identification of a bolted joint using substructure synthesis theory

Engineering applications require accurate modeling and parameter identification to anticipate bolted joint behavior under different loading circumstances. Bolted joints are extensively used in civil and mechanical structures. In this research study, cubic stiffness non-linearity is examined and a comprehensive model, which can exhibit the non-linearity of joint behavior, is developed. This is achieved by modeling the bolted joint which is connected at one end of a cantilever beam. Mathematical model is established using concept of sub structure synthesis theory by considering non-linear translational stiffness and non-linear rotational stiffness as a boundary condition. Non-dimensional natural frequencies are estimated by assuming stiffness parameters and joint parameter estimation for translational spring having nonlinear stiffness is calculated using second order curve fitting technique. Furthermore, estimated and exact value of cubic stiffness coefficient are compared. The experimental results shows that the percentage error of estimation for all the modes are less than 8%. The experimentally determined natural frequencies are further contrasted with an analytical results to validate a precision of generated model. Present work contributes in improving the bolted joint analysis by providing a solid non-linear modeling and parameter identification methodology. This methodology enhances design practices and ensures the durability of engineering structures and mechanical systems by improving bolted joint comprehension.

Model Test and Numerical Simulation for Tunnel Leakage-Induced Seepage Erosion in Different Strata

Leakage in underground structures, especially tunnels, may cause seepage erosion in the surrounding soil, which in turn leads to ground subsidence, posing a great threat to urban safety. The current literature mainly focuses on seepage erosion in the sand but lacks a systematic study on the development process of seepage erosion induced by tunnel leakage in different strata. To investigate the different seepage erosion modes induced by tunnel leakage in different stratum types, a series of reduced-scale model tests were carried out. A coupled fluid–solid numerical model was further established to analyze the fine-scale characteristics of different seepage erosion modes. The results show that (1) the soil seepage erosion modes can be divided into three categories: no soil cave, unstable soil cave, and stable soil cave; (2) the adopted coupled fluid–solid numerical model based on DEM, which takes into account the degradation of clay during seepage erosion, can effectively simulate the erosion process of soil with different seepage erosion modes; (3) the phenomena of the three erosion modes are different in the process of erosion development; and (4) the micro-mechanisms of the three seepage erosion modes are different, which are manifested in the erosion range, soil arching effect, and displacement.

A slicing and path generation method for 3D printing of periodic surface structure

Periodic surface (PS) model holds enormous potential in the field of lightweight, heat management, and biomedical implants. To achieve tunable mechanical strength and high surface area versus volume (SA/V) ratio, it is imperative to achieve solid representation of the PS models. However, because of the complex inner structure, converting the surface model to solid model with uniform thickness remains a major challenge. In the meantime, the most pervasive practice to achieve tunable wall thickness is by offsetting the contour with a constant value or setting a constant value difference between two implicit functions. However, these existing approaches cannot guarantee uniform wall thickness. In this paper, a novel method is proposed to define an adjacent implicit surface with a uniform offset distance to the original. By repeatedly generating implicit surfaces with uniform distance from each other, the solid representation of the PS model with uniform thickness can be achieved. Meanwhile, a new slicing method allowing for the slicing without stereolithography (STL) file is developed to slice the implicit surfaces and generate the printing path so that the accumulation of slicing errors introduced by meshing steps can be avoided. By slicing each implicit surface separately, the corresponding G-code file is generated. This developed method is demonstrated by fabricating typical PS with fused filament 3D printing.

Screening Publicly Available CT Datasets of the Head for Potential Use in Studying Normal and Pathological Issues Related to Nasal Airflow

Objective: The purpose of this study was two-fold. First, we wanted to develop optimized strategies for evaluating publicly available CT datasets for use in simulated studies of airflow. Second, we sought to generate 3D models of the nasal cavity from these CT datasets and perform Computation Fluid Dynamics (CFD) modeling of airflow in the generated models. This work lays the groundwork for future investigations into studying diversity in airflow patterns and rates, analyzing microparticle and odorant deposition, how anatomical variations relate to difference in airflow and pathologies, and the role of comparative CFD analyses in surgical planning. Methods: 490 CT scans were downloaded from the CQ500 repository dataset and evaluated for effectiveness in building digital models of the nasal airway. Scans were digitally dissected and analyzed in 3D Slicer to determine eligibility for model creation and simulation. Eligible scans were further edited in 3D Slicer. All ethmoid air cells, sinuses and recesses were removed, as it was determined these areas do not contribute to the airflow we want to study. These reconstructed models made in 3D Slicer were further refined using the program MeshMixer. Once editing was completed in MeshMixer, STL files were imported back into 3D Slicer to be overlayed with the original STL file created directly from the CT scan to verify anatomical accuracy. Any inaccuracies were removed with further editing in MeshMixer. Models were then imported into Salome, where they were converted into “solid models” for CFD analysis. All CFD was performed in OpenFOAM, and results were visualized using ParaView. Results: Five models have been reconstructed and are being subjected to CFD analysis. To be a strong candidate for modeling, the CT scan had to include at least 230 slices, show all anatomy relevant to the study including turbinates, ethmoid air cells, the cribriform plate and olfactory region, nasal vestibules, and the nasopharynx, and display adequate detail. Of the five final models, four were deemed anatomically normal and all relevant anatomy was intact. One of the five models displayed a deviated septum. The four “normal variants” were used to predict normal patterns of airflow using Computational Fluid Dynamics (CFD). The results of these models compared with the deviated septum model show severe limitation of airflow in the deviated model, together with compensatory flow on the more open side of the airway. Relatively simple digital "corrections" were applied, which effectively recovered normal airflow. Conclusion: CT scans from the publicly available CQ500 repository dataset are effective in creating digital models of the nasal airway to be used for CFD. Computational modeling was shown to be a useful tool in understanding how nasal morphology contributed to the diversity in airflow patterns and rates, microparticle and odorant deposition. Thus far 5 CT scans out of 490 specimens were eligible for modeling; however, due to time constraints of the study, these were the only scans that were utilized. Four of these models were “normal variants” while the scan with a deviated septum was a “pathological variant.” One of the normal models is currently being run in the OpenFOAM CFD program and shows promising results with similar airflow patterns through both the left and right airways. All other models will also be analyzed in this CFD program and more rounds of CFD will be run to compare airflow dynamics for normal and pathological variants. This work provides insights into how a broad, comparative sample of subjects can be used in surgical planning and in understanding the physiological consequences of potential surgical interventions. Burrell College. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

White carbon dots with high quantum yield and multicolor carbon dots synthesized by the microwave method in one step

In the field of optics, carbon dots (CDs) have been extensively studied and have made rapid advancements in related fields, including bioimaging, optoelectronic devices and optical probes. However, it is difficult to efficiently synthesize high quantum yield (QY) white fluorescence CDs and multicolor CDs, which restricts their development in the field of optics. In this work, we chose formamide as the reaction solvent and used citric acid (CA) and 4-bromo-1,2-diaminobenzene to synthesize high-performance white fluorescent CDs20 with a QY of up to 87.5% and excellent stability (28 days). The reaction solvents (water, N,N-dimethylformamide and ethanol) were changed to obtain colorful CDs20 (orange, green and yellow) covering a broader visible spectrum via a microwave method. The solid model, which combines white fluorescent CDs20 and polyvinyl alcohol (PVA), exhibits strong white fluorescence as well as a solution state. To develop white-emitting diodes suitable for indoor use, a white CDs20 / PVA mixture with brilliant white fluorescence might be placed on ultraviolet (UV) chips (365 nm). The white LED had a high color purity (0.34, 0.25), excellent correlation color temperature (4250 K) and color rendering index (72.5). The results above show that white CDs have great promise for use in optical device fields.

Osr2 functions as a biomechanical checkpoint to aggravate CD8+ Tcell exhaustion in tumor.

Alterations in extracellular matrix (ECM) architecture and stiffness represent hallmarks of cancer. Whether the biomechanical property of ECM impacts the functionality of tumor-reactive CD8+ Tcells remains largely unknown. Here, we reveal that the transcription factor (TF) Osr2 integrates biomechanical signaling and facilitates the terminal exhaustion of tumor-reactive CD8+ Tcells. Osr2 expression is selectively induced in the terminally exhausted tumor-specific CD8+ Tcell subset by coupled Tcell receptor (TCR) signaling and biomechanical stress mediated by the Piezo1/calcium/CREB axis. Consistently, depletion of Osr2 alleviates the exhaustion of tumor-specific CD8+ Tcells or CAR-T cells, whereas forced Osr2 expression aggravates their exhaustion in solid tumor models. Mechanistically, Osr2 recruits HDAC3 to rewire the epigenetic program for suppressing cytotoxic gene expression and promoting CD8+ Tcell exhaustion. Thus, our results unravel Osr2 functions as a biomechanical checkpoint to exacerbate CD8+ Tcell exhaustion and could be targeted to potentiate cancer immunotherapy.

A nonlinear analytical method for pipeline strain under soil thaw subsidence load in permafrost region

In frozen terrain, the transportation of heated oil triggers thermal soil thaw and collapse, leading to pipeline distortion and breakdown. During substantial ground settlement in such terrains, current strain prediction techniques for pipelines fall short, neglecting significant pipeline bending, non-linear material behaviors, or detailed pipeline-permafrost interplay. To address this, a model focusing on pipeline-soil dynamics is devised, incorporating material non-linearity and pronounced pipeline distortion. This model derives analytical strain solutions for pipelines by aggregating incremental distortions of pipeline components via a strain iteration technique. Concurrently, a three-dimensional solid finite element model is crafted, assessing pipeline strains across varying subsidence zone dimensions, factoring in the complicated permafrost environment. The model's efficiency is affirmed by its less than 3% discrepancy from finite element method results, showcasing its enhanced accuracy and dependability in predicting pipeline strains under extensive soil settlement scenarios.

A Novel Unmanned Aerial Vehicle driven Real-time Situation Awareness for fire accidents in Chemical Tank Farms

A large number of flammable hazardous materials are stored in chemical tank farms, where fire-induced domino accidents can be easily triggered. In this study, a novel real-time fire situation awareness (FSA) approach based on UAV is proposed to capture spatio-temporal evolution characteristics and predict development trends of fire accidents. Firstly, fire images are acquired by UAV, and the key parameters of fire are extracted in real time based on YOLOv8 network. Then, the thermal radiation and impact on surrounding equipment are predicted by combining LSTM network, solid flame model and improved probit model. The proposed method is verified by small-scale tank fire experiments, which demonstrate its superiority in terms of physical consistency and prediction accuracy. The results show that the mean absolute percentage error (MAPE) of fire parameter extraction is not higher than 5.43%, the MAPE of thermal radiation prediction is not higher than 25%, and the dynamic time to failure () for the model tank at different location is predicted. This work has the potential to provide a novel solution for real-time assessment of fire size and trend prediction to support firefighting, emergency rescue and decision making in fire accident scenarios.

Nano-roughness effect on the interfacial thermal oxidation and surface catalytical recombination characteristics for silicon carbide based materials under non-equilibrium flow

With the rapid development of high-speed aircraft, the demanding for light, strong, reliable and high temperature resistive thermal protection material (TPM) is becoming urgent. High temperature material interface, such as silicon carbide (SiC) ceramic matrix composite, has received strong interest recently due to its high mechanical and thermochemical stability. For reactive interface development with nano-roughness under extremely hyperthermal non-equilibrium flow conditions for TPM applications, however, has not been reported yet. This work performs a systematic reactive molecular dynamics (RMD) study of the SiC surface morphology effect withstanding hyperthermal atomic oxygen (AO) impact via a gas–solid interface reaction model. The results show that a self-healing trend of the defect-rich silicon carbide surface with original nano-roughness is revealed towards a flatter interface accompanying with the oxidation process. The surface catalytic recombination coefficient is more sensitivity to the surface defects when wall temperature Tw > 500 K. Nano-roughness structure can also vary the main thermochemical reaction path at the thermochemical reactive interface: For the nano-groove surface model, the thermal oxidation reaction becomes dominant, while more pronounced surface catalytic recombination characteristics are revealed for the flat surface model. The mechanisms of roughness effect, surface temperature and gas impacting angle on the surface catalytic recombination, surface oxidation, and interface evolution are revealed, which advance our microscopic understanding of SiC interfacial performance with nano-roughness under non-equilibrium flow conditions.

Design of transmission mechanism of punch feeding and discharging manipulator

The press loading and unloading manipulator is a kind of intelligent equipment based on automation, which is specially developed for realizing the automation and unmanned production of the press shop. It can replace the worker’s work by implementing the manual editing program and greatly improve the efficiency of production and quality. The design optimized the traditional transmission scheme comparison and molded the structure based on the solid works modeling. The key supporting parts of the manipulator are analyzed by the finite element method. The simulation results showed that the manipulator can satisfy the designed loading requirements and guarantee the designed accuracy.