3D Computer Aided Design Model Research Articles

An effective parametric sensitivity analysis to improve electrical and thermal energy/exergy efficiency of PVT system using nanofluid

A 3D steady-state model of a photovoltaic/Thermal system with nanofluid is studied to investigate the electrical and thermal performance of PVT by utilizing a combination of statistical modeling and numerical simulation. The 3D CAD (Computer Aided Design) modeling of the photovoltaic thermal (PV/T) is performed using Ansys Space Claim. The current study is based on using a 2-way coupling FSI approach for modeling nanofluid-based PVT systems. Five output responses electrical power, thermal power, electrical exergy, thermal exergy, and entropy generation are chosen against four input parameters, solar radiation (G_sun), ambient temperature (Tamb), mass flow rate (ṁ), and nanoparticles concentration % (φ) to analyze system performance. A statistical prediction model is developed using response surface methodology (RSM) that starts from the design of experiment (DOE). The predicted outcomes obtained from the statistical model and the numerical simulation of the heat transfer model exhibit the existence of good fitness. Furthermore, optimization is performed using RSM for various optimization objectives to suggest the ideal design parameter values for optimal system performance. Sensitivity analysis illustrates that a rise in ambient temperature causes a drop in electrical power production and leads to an increase in thermal power. However, the increase in solar radiation boosts electrical power and causes a drop in thermal exergy, entropy generation increases with an increase in solar irradiation. Similarly, an increase in mass flow rate leads to an increase in thermal power. Multi-objective optimization with the highest composite desirability values depicts the influence of input design parameters and has a favorable effect on the objective of concurrently maximizing electrical and thermal power.

Automated Assessment Tool for 3D Computer-Aided Design Models

Computer-aided design (CAD) has become an integral part of engineering education, particularly for those studying mechanical engineering. By providing practical skills that are highly valued in the engineering industry, proficiency in CAD systems enhances students’ employability. Generally, CAD systems provide students with the tools and knowledge necessary to excel in their engineering education and future careers. In order to help teachers to give the best training to their students and to make the right evaluations, an automatized tool is needed to support the evaluation of CAD models during training sessions. After an extensive bibliographical search, this paper proposes a CAD Model Automatized Assessment (MAA) Tool for mechanical courses called the CAD MAA Tool. This tool is mainly based on a developed model that takes into account different aspects of modeling, such as geometric, feature-based, and parametric modeling. To correctly evaluate a given part compared to a reference one, the proposed model uses different coefficients fixed by the teacher according to their teaching strategies or course objectives.

Method for estimating management points for dimensional quality management in shipbuilding and offshore plant construction using terrestrial laser scanning and a computer-aided design model

This study proposes a method for estimating management points for dimensional quality management in shipbuilding and offshore plant construction using terrestrial laser scanning. The dimensional quality is managed by calculating errors between quality management points in a 3-dimensional (3D) computer-aided design (CAD) model and their corresponding measurement points in a fabricated block. The proposed method utilizes a point cloud of the fabricated block together with a 3D CAD model of the block to estimate the measurement points. To achieve this, global registration, extraction of the region of interest, point segmentation in the region of interest, plane estimation of the segmented points, and estimation of measurement points are performed in an automated manner. To verify the proposed method, it is implemented and experimented with actual ship blocks. The experimental results reveal that the estimated measurement points are within the allowable tolerance of 7 mm.

VQ-CAD: Computer-Aided Design model generation with vector quantized diffusion

Computer-Aided Design (CAD) software remains a pivotal tool in modern engineering and manufacturing, driving the design of a diverse range of products. In this work, we introduce VQ-CAD, the first CAD generation model based on Denoising Diffusion Probabilistic Models. This model utilizes a vector quantized diffusion model, employing multiple hierarchical codebooks generated through VQ-VAE. This integration not only offers a novel perspective on CAD model generation but also achieves state-of-the-art performance in 3D CAD model creation in a fully automatic fashion. Our model is able to recognize and incorporate implicit design constraints by simply forgoing traditional data augmentation. Furthermore, by melding our approach with CLIP, we significantly simplify the existing design process, directly generate CAD command sequences from initial design concepts represented by text or sketches, capture design intentions, and ensure designs adhere to implicit constraints.

Fabrication of 3D-printed GNP/TiO2/epoxy composites: an investigation on mechanical and photocatalytic properties

PurposeThree-dimensional (3D) printing is popular for many applications including the production of photocatalysts. This paper aims to focus on developing of 3D-printed photocatalyst-nano composite lattice structure. Digital light processing (DLP) 3D printing of photocatalyst composites was performed using photosensitive resin mixed with 0.5% Wt. of TiO2 powder and varying amounts (0.025% Wt. to 0.2% Wt.) of graphene nanoplatelet powder. The photocatalytic efficiency of DLP 3D-printed photocatalyst TiO2 composite was investigated, and the effects of nano graphite powder incorporation on the photocatalytic activity, thermal and mechanical properties were investigated.Design/methodology/approachMethods involve 3D computer-aided design modeling, printing parameters and comprehensive characterization techniques such as structural equation modeling, X-ray diffraction, thermogravimetric analysis, Fourier-transform infrared (FTIR) and mechanical testing.FindingsResults highlight successful dispersion and characteristics of TiO2 and graphene nanoplatelet (GNP) powders, intricate designs of 3D-printed lattice structures, and the influence of GNPs on thermal behavior and mechanical properties.Originality/valueThe study suggests applicability in wastewater treatment and environmental remediation, showcasing the adaptability of 3 D printing in designing effective photocatalysts. Future research should focus on practical applications and the long-term durability of these 3D-printed composites.Graphical abstract

Multi-modal fusion network guided by prior knowledge for 3D CAD model recognition

Currently, the 3D CAD model has found extensive use in various applications. The more accurate recognition and more reliable reuse of 3D computer-aided design (CAD) models could significantly save labor costs and time in the production and design of 3D products. However, existing 3D model recognition methods are constrained by a single representation form that lacks supplementary information from multiple sources, and there is a lack of corresponding guidance for relationships between different data. In this paper, we focus on multi-modal information for 3D model representation and propose a Multi-modal Fusion Network (MMFN) guided by prior knowledge for 3D CAD model recognition. In particular, we consider prior knowledge about the class distribution of 3D models: The designed labeling information is utilized as the prior knowledge to guide the multi-modal information fusion via the cross-attention structure. Then, the contrastive learning method is utilized in the optimization step, further increasing the aggregation of similar samples and contributing to enhancing the discriminative capability of features. Finally, we conduct extensive experiments on the public datasets, ModelNet, ShapeNet, and a challenging industrial 3D CAD dataset built by ourselves. Compared to state-of-the-art approaches, our MMFN provides competitive results. The source code has been published on Github: https://github.com/WhiteTJU/MMFN.

Weight reduction analysis of a four-door passenger car using computer-aided modelling and experimental investigation

The door of a passenger car is one of the key components used for the safety of passengers from side collisions. Mostly car doors are made of Steel that increase the overall weight of the vehicle since steel is the primary metal used for the construction of door. The main objective of this research is to replace Steel material of a car door with low-cost automotive materials such as aluminium alloy, magnesium alloy, and carbon fibre material to perform weight reduction analysis without compromising the strength and safety. A comprehensive theoretical study of the existing car door has been performed with 3D Computer-Aided Design (CAD) models through commercially available software SolidWorks. Results of this research work revealed that by selecting the appropriate engineering materials of the doors for a commercially available passenger car, the overall weight reduced to 45% when used aluminium material and 64% when used magnesium and carbon-fibre materials compared to the total weight of four doors made of typical steel material. Furthermore, reduction in fuel consumption and CO2 emission was also obtained by reducing the overall weight of the passenger car doors. Lastly, the performance was measured through power-to-weight ratio (PWR) analysis for steel and aluminium which revealed that the latter material was more economical and lighter in weight than the former material.

Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers.

The introduction of artificial intelligence (AI) to ultra-high-frequency (UHF) partial discharge (PD) monitoring systems in power transformers for the localization of PD sources can help create a robust and reliable system with high usability and precision. However, training the AI with experimental data or data from electromagnetic simulation is costly and time-consuming. Furthermore, electromagnetic simulations often calculate more data than needed, whereas, for localization, the signal time-of-flight information is the most important. A tailored pathfinding algorithm can bypass the time-consuming and computationally expensive process of simulating or collecting data from experiments and be used to create the necessary training data for an AI-based monitoring system of partial discharges in power transformers. In this contribution, Dijkstra's algorithm is used with additional line-of-sight propagation algorithms to determine the paths of the electromagnetic waves generated by PD sources in a three-dimensional (3D) computer-aided design (CAD) model of a 300 MVA power transformer. The time-of-flight information is compared with results from experiments and electromagnetic simulations, and it is found that the algorithm maintains accuracy similar to that of the electromagnetic simulation software, with some under/overestimations in specific scenarios, while being much faster at calculations.

Optimizing the printing parameters for dimensional accuracy of distal femur bone by using Taguchi’s method

BackgroundFused deposition modelling (FDM) is a popular additive manufacturing technique with capability of producing complex and integrate shapes. One of the critical aspects of FDM is the dimensional accuracy of 3D (three-dimension) printed model, especially in medical science applications, as proper fit and function with human body can prevent patient’s discomfort, complication or even harm.ObjectiveIn this research work, the optimisation of print parameters: layer height, nozzle temperature, printing speed, infill pattern and infill density for improving the dimensional accuracy of distal femur bone, an irregular and complex shaped geometry is carried out using Taguchi’s method and to study its influence using ANOVA (analysis of variance).Methodology3D CAD (computer-aided design) model of the distal femur bone is generated from a CT (computerized tomography) scan using 3D slicer and its associated errors are corrected using Ansys SpaceClaim. The model is prepared for printing using Ultimaker Cura as per L16 orthogonal array experimental layout where TEA (trans epicondylar axis), which is the distance between the most prominent point of the lateral and medial epicondyle, is set at 45° from X-axis in XY plane, i.e. diagonally on the plane of printing bed. It is then printed with PLA (polylactic acid) filament. Length along TEA is compared accordingly with 3D CAD model. Taguchi’s method of ‘smaller the better’ is applied for reducing deviation. Further, ANOVA analysis is done on the data set and a linear regression model is also developed.ResultThrough Taguchi’s method, the optimum parameters were found to be triangle for infill pattern, 200 °C for nozzle temperature, 30 mm/s for nozzle speed, 0.1 mm for layer height and 40% for infill density. ANOVA analysis shows that all parameters contribute significantly with layer height being the most influential parameter, followed by infill pattern, nozzle speed, nozzle temperature and infill density. Mathematical model through multiple linear regression method was developed with determination of coefficient value of 96.91% and standard residual value is within the acceptable range of ± 2 indicating that there is no outliner in the data.

Recursive autoencoder network for prediction of CAD model parameters from STEP files

Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many potential applications in improving efficiency within design processes. This paper presents a novel asymmetric autoencoder model, consisting of a recursive encoder network and fully-connected decoder network, for the reproduction of CAD models through prediction of the parameters necessary to generate a 3D part design. Inputs to the autoencoder are STEP (standard for the exchange of product data) files, an ISO standard CAD model format, compatible with all major CAD software. A complete 3D model can be accurately reproduced using a STEP file, meaning that all geometric information can be used to contribute to the final encoded vector, with no loss of small detail.In a CAD model of overall size 10 × 10 × 10 units, for 90% of models, the class of an added feature is estimated with maximum error of 0.6 units, feature size with maximum error of 0.4 units and coordinate values representing position with maximum error of 0.3 units. These results demonstrate the successful encoding of complex geometric information, beyond merely the shape of the 3D object, with potential application in the design of search engine functionality.

NFTs For 3D Models: Sustaining Ownership In Industry 4.0

Digital manufacturing (DM) is actively adopted to the production lifecycles of a variety of critical industries, and this rapid growth has resulted in exponential increase of 3D computer-aided design (CAD) models. Unfortunately, counterfeiting of intellectual property becomes a prominent threat as many 3D designs are accessible online, combined with the proliferation of cheap consumer 3D printers that enable malicious actors to produce non-authentic parts. State-of-the-art techniques to secure manufacturing processes mostly rely on watermarking, which embeds hidden information inside CAD models to prove ownership and authenticity. Nevertheless, such techniques tamper with the model itself, while existing attacks allow removing such watermarks altogether. To address these shortcomings, we integrate signal processing and cryptographic techniques and describe a tailored solution for CAD model ownership and supply chain management. Our approach generates unique identifiers for 3D designs using frequency-domain transforms and employs non-fungible tokens (NFTs) that persist on public distributed ledgers. Our NFTs are implemented on the Ethereum blockchain using smart contracts and their functionality is twofold: (a) authenticate the owner of a CAD model, and (b) enable ownership transfer. To validate our technique, we deployed our smart contract on Ethereum's proof-of-work Ropsten network and demonstrated the applicability of our methodology.

Generative Design of Progressive Die

The evolution of products, dictated by the necessity to survive in the market, requires multi manufacturing processes to be performed simultaneously. Progressive die is one type of tool in which several individual stations are arranged in sequence. Each station will perform one or more various operations on the part. It is widely used to manufacture parts for automobile and other industries. This process with the help of progressive die is a very inexpensive method for forming moderate to large volume parts while keeping labor and die costs down. This study is dedicated to providing a 3D Computer-Aided Design (CAD) model for simple and economical progressive die construction which is suitable for the mass production of an industrial bracket. Different forces and stress convoluted in performing metal operations were also calculated with an appropriate formula. Generative designing technologies are also applied to yield the most efficient design economically as well as productively without compromising the optimum features of the tool. Various contours representation of data obtained is also presented in this work.

Biomechanical comparison of a novel circular glenoid with peripheral ring fixation to a standard anchor pegged glenoid in total shoulder arthroplasty

BackgroundCurrent standard total shoulder pegged glenoid component designs have a high rate of loosening, which accounts for the most common long-term reason for surgical failure. Glenoid design, method of fixation, and implantation version all play a role in component stability. This study aimed to evaluate the biomechanical stability of a novel circular glenoid with peripheral ring fixation and compare to a standard anchor pegged glenoid both directly and to historical biomechanical data. Additionally, we compared the estimated bone removal for the glenoid design types. MethodsBiomechanical testing was performed to assess the circular glenoid with peripheral ring fixation with regards to stability and resistance to “rocking horse” effect, torsional loosening, resistance to torque dissociation, and axial pullout strength. This was compared directly (pullout strength) and to historical (“rocking horse”) biomechanical data obtained for a standard anchor pegged glenoid. A 3D Computer-Aided Design model of five different scapula sizes and shapes was also utilized to estimate volume bone removed when using implant specific prep instrumentation for each glenoid system. ResultsTensile displacement of the worst-case ring fixation component (size medium) after dynamic rocking was 0.060 mm requiring an average peak torque of 6.21N-m to dissociate the glenoid. The ring fixation component demonstrated a mean pullout strength of 1367 N compared with 645 N for the standard anchor pegged glenoid component. The ring fixation component revealed no loosening when placed under torsional stress. The peripheral ring fixation glenoid preparation on average removed 45% less bone than a standard anchor pegged glenoid. ConclusionThe circular glenoid with peripheral ring fixation demonstrated superior maximum pullout strength and more bone preservation with glenoid preparation when compared to the standard pear-shaped, pegged glenoid components. Compared to published standard pegged glenoid component controls, the glenoid with peripheral ring fixation demonstrated diminished “rocking horse” displacement. Despite the circular anchor ring design, there was no evidence of loosening under torsional stress. Initial biomechanical data is overall promising for this novel glenoid design.

Design of hexacopter and finite element analysis for material selection

PurposeThe purpose of this study is to offer a better and more effective hexacopter design for a 3 kg payload using finite element analysis (FEA), facilitating the use of different materials for different components that too without compromising strength.Design/methodology/approachA 3D computer-aided design (CAD) model of a hexacopter with a regular hexagonal frame is presented. Furthermore, a finite element model is developed to perform a structural analysis and determine Von Mises stress and strain values along with deformations of different components of the proposed hexacopter design.FindingsThe results establish that carbon fibre outperforms acrylonitrile butadiene (ABS) with respect to deformations. Within the permissible limits of the stress and strain values, both carbon fiber and ABS are suggested for different components. Thus, a proposed hexacopter offers lighter weight, high strength and low cost.Originality/valueThe use of different materials for different components is suggested by making use of static structural analysis. This encourages new research work and helps in developing new applications of hexacopter, and it has never been reported in literature. The suggested materials for the components of the hexacopter will prove to be suitable considering weight, strength and cost.

Automated Process Planning System for Machining Injection Molding Dies Using CAD Models of Product Shapes in STL Format

In this study, we developed a method for automatically generating computer-aided design (CAD) models of injection molding dies. The method only required 3D CAD models of products in the Standard Triangulated Language (STL) format as the input information. We also developed a system for automatically generating numerical control (NC) programs by automating the system process planning necessary for machining the injection molding dies. The method generated CAD models of the injection molding dies by dividing the STL files of the products into triangular meshes on a specified split plane. For injection molding dies with several free curved surfaces, we acquired the tool positions of a ball end mill (as approximated by a spherical shape) and flat drill (as approximated by a cylindrical shape) from the geometrical relationships of the triangles constituting the CAD model. We generated a CAD model of an injection molding die using the proposed method with respect to the CAD model of a product shape to verify the validity of the developed system. Then, we machined the product based on the NC programs and tool position. In addition, we injection molded a product with a machined die to mold it into its original product shape.

Effect of build orientation on the green tribological properties of multi-jet fusion manufactured PA12 parts

Additive manufacturing (AM) is a newly developed technology for manufacturing parts from three-dimensional (3D) computer-aided design (CAD) models by depositing a material layer on layer. Multi-jet fusion (MJF) is one of the AM technologies that can be used for manufacturing functional parts like gear, linear, bearing, etc., by polymers. In various applications, technical processes, design of tribological systems, optimum lubrication, and reduction of wear and friction are significant performance criteria to provide energy and material conservation. The development of green tribological applications offers essential solutions to problems of ecosystem pollution and energy from a global point of view to increase sustainability. In this article, the green tribological behavior of Polyamide 12 (PA12) parts, manufactured by MJF with different build orientations was evaluated by ball-on-disc tribological tests under different normal loads and lubricated environments. Mechanical profilometer and digital microscope were used to evaluate surface quality and morphology. Scanning electron microscope (SEM) was used to determine the samples’ wear characteristics. The maximum surface roughness was measured for the part having a build orientation of 45°. The maximum coefficient of friction value was determined as 0.355 for the sample with 45° orientation under 5 N load and dry environment. The maximum wear rate value was determined as 2.3249 × 10−4 mm3/Nmm for the sample with 0° orientation under 10 N load and dry state. The differences between build orientations should be considered when evaluating tribological properties. This article provides a new perspective to researchers and practitioners toward green tribology of polymer AM parts.

An investigation on tensile strength of 3D printed parts of Acrylonitrile butadiene styrene (ABS) material

Fused Deposition Modelling (FDM) components' mechanical properties reflect processing parameters' effectiveness. Tensile Strength’s parameter sequence of a FDM 3D-printed Acrylonitrile butadiene styrene (Abs) material specimen is examined. Design Expert tools were utilized to conduct a variance analysis (ANOVA) to assess tensile strength components and interactions. Four FDM processing parameters—layer thickness, raster angle, infill pattern, and infill width—are examined using the Taguchi DOE. The ASTM D638-compliant specimen measurements were compared to the 3D computer-aided design model. When there are ranges of processing parameters and no linear processing criteria, design of experiments (DOEs) become difficult to determine. Identifying the best processing parameters requires a certain number of specimens. Optimum tensile strength of ABS component was 9.22 MPa at 0.3 mm layer thickness, 60° raster angle, infill width of 0.25 mm.

A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

This paper proposes a stereo-vision-based method that detects and registers the positions and postures of muti-type, randomly placed miniature circuit breaker (MCB) components within scene point clouds acquired by a 3D stereo camera. The method is designed to be utilized in the flexible assembly of MCBs to improve the precision of gripping small-sized and complex-structured components. The proposed method contains the following stages: First, the 3D computer-aided design (CAD) models of the components are converted to surface point cloud models by voxel down-sampling to form matching templates. Second, the scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions. Third, point cloud features are extracted by Intrinsic Shape Signatures (ISSs) from the templates and the candidate-matching regions and described by Fast Point Feature Histogram (FPFH). We apply Sample Consensus Initial Alignment (SAC-IA) to the extracted features to obtain a rough matching. Fourth, fine registration is performed by employing Iterative Closest Points (ICPs) with a K-dimensional Tree (KD-tree) between the templates and the roughly matched targets. Meanwhile, Random Sample Consensus (RANSAC), which effectively solves the local optimal problem in the classic ICP algorithm, is employed to remove the incorrectly matching point pairs for further precision improvement. The experimental results show that the proposed method achieves spatial positioning errors smaller than 0.2 mm and postural estimation errors smaller than 0.5°. The precision and efficiency meet the requirements of the robotic flexible assembly for MCBs.

Study on the performance characteristics and failure analysis of exhaust valves

Abstract Exhaust valves are a type of valve that can commonly be found in the top section of the cylinder block in an engine. When failures occur on the exhaust valve, the engine has a high possibility to result engine failure or breakdown. This will affect the user where the user requires to spend a huge amount on repairing costs or engine replacement. Thus, this research aims to investigate the effect of the exhaust valve’s material on its performance and identify the appropriate material which could increase its fatigue life in a four-stroke internal combustion diesel engine. A three-dimension (3D) Computer-aided design model of the exhaust valve was generated by using SOLIDWORKS and undergo static structural analysis as well as thermal analysis. The measure variables are von-mises stress, total deformation, temperature, total heat flux and directional heat flux by using ANSYS Finite Element Analysis. Stainless steel 304 alloy and Aluminium-Silicon Carbide (Al-Sic) materials are the manipulated variable that was applied to the 3D CAD model exhaust valve to undergo all the analyses. Before performing all the analyses, the appropriate mesh method and mesh sizing was identified by referring to the mesh metrics spectrum and performing a mesh independence test. The results from the analysis were collected and tabulated for comparison and discussion purposes. From the analysis, the maximal thermal stress was determined in the valve stem region. By appointing Stainless Steel 304 alloy on the exhaust valve, the durability and the fatigue life are improved as the material can achieve a smaller value of temperature distribution, total heat flux, total deformation, and higher von-mises stress. This research provided a better understanding of the effect of an exhaust valve material on its performance characteristic.

Simplification of 3D CAD Model in Voxel Form for Mechanical Parts Using Generative Adversarial Networks

Most three-dimensional (3D) computer-aided design (CAD) models of mechanical parts, created during the design stage, have high shape complexity. The shape complexity required of CAD models reduces according to the field of application. Therefore, it is necessary to simplify the shapes of 3D CAD models, depending on their applications. Traditional simplification methods recognize simplification target shape based on a pre-defined algorithm. Such algorithm-based methods have difficulty processing unusual partial shapes not considered in the CAD model. This paper proposes a method that uses a network based on a generative adversarial network (GAN) to simplify the 3D CAD models of mechanical parts. The proposed network recognizes and removes simplification target shapes included in the 3D CAD models of mechanical parts. A 3D CAD model dataset was constructed to train the 3D CAD model simplification network. 3D CAD models are represented in voxel form in the dataset. Next, the constructed training dataset was used to train the proposed network. Finally, a 3D voxel simplification experiment was performed to evaluate the performance of the trained network. The experiment results showed that the network had an average error rate of 3.38% for the total area of the mechanical part and an average error rate of 14.61% for the simplification target area.