Computer-aided Design Model Research Articles

New CAD modeling approach in Geant4 with half-space CSG

Geant4 offers constructive solid geometry (CSG) for modeling detector geometries, but representing intricate computer-aided design (CAD) structures can be cumbersome. This article presents a novel approach that overcomes this limitation. A new CSG solid type called “half-space solid” enables the equivalent representation of complex CAD solids within Geant4. An automatic program utilizes optimized decomposition algorithms to convert CAD solids into half-space solids. The geometry description markup language (GDML) has been extended to accommodate the half-space solid type alongside the development of interfaces for exporting converted geometries and their subsequent import into Geant4. These advancements establish a fully automated workflow for converting CAD geometries into CSG-based representations suitable for Geant4 simulations. The reliability of the half-space solid-based modeling approach has been verified through comparisons with established Geant4 solids for both simple shapes and a complex fusion reactor model. The excellent agreement obtained from these comparisons demonstrates the efficiency of this new approach.

A parametric and feature-based CAD dataset to support human-computer interaction for advanced 3D shape learning

3D shape learning is an important research topic in computer vision, in which the datasets play a critical role. However, most of the existing 3D datasets use voxels, point clouds, mesh, and B-rep, which are not parametric and feature-based. Thus they can not support the generation of real-world engineering computer-aided design (CAD) models with complicated shape features. Furthermore, they are based on 3D geometry results without human-computer interaction (HCI) history. This work is the first to provide a full parametric and feature-based CAD dataset with a selection mechanism to support HCI in 3D learning. First, unlike existing datasets, mainly composed of simple features (typical sketch and extrude), we devise complicated engineering features, such as fillet, chamfer, mirror, pocket, groove, and revolve. Second, different from the monotonous combination of features, we invent a select mechanism to mimic how human focuses on and selects a particular topological entity. The proposed mechanism establishes the relationships among complicated engineering features, which fully express the design intention and design knowledge of human CAD engineers. Therefore, it can process advanced 3D features for real-world engineering shapes. The experiments show that the proposed dataset outperforms existing CAD datasets in both reconstruction and generation tasks. In quantitative experiment, the proposed dataset demonstrates better prediction accuracy than other parametric datasets. Furthermore, CAD models generated from the proposed dataset comply with semantics of the human CAD engineers and can be edited and redesigned via mainstream industrial CAD software.

AN EXPERIMENTAL RIGHT ATRIUM PLATFORM TO ASSESS RECIRCULATION IN HEMODIALYSIS CATHETERS

Hemodialysis (HD) is a treatment supporting decreased kidney function, via a catheter inserted into the heart’s Right Atrium (RA). Recirculation is a source of inefficiency for treatment, where blood is dialyzed again due to poor catheter design. Lab-testing is still relatively unexplored, hence, a mechanical testing system was designed with the intention of providing a consistent and repeatable environment for testing HD catheters. System geometry was composed using a Computer-Aided Design (CAD) model of a heart, with the RA scaled to appropriate dimensions, and a PolyDiMethylSiloxane (PDMS) model produced through 3D printing and negative wax casting. Pulsatile blood flow was mimicked by peristaltic pumps driving a blood analogue (BA). Recirculation was induced by adding dyed BA to the system via the catheter and measured using a colorimeter. The developed platform was initially evaluated using two catheters, demonstrating the capability to accurately replicate atrial hemodynamic conditions. Two step-tipped catheters, A and B, were tested at 350 ml/min, producing recirculation values of 13.11% and 18.58%, respectively. The results exhibit the ability of the system developed to evaluate HD catheter performance, with the potential to explore a wider range of tip geometries relevant to clinical preference. Furthermore, this advancement towards an anatomically accurate lab-based test system could be paired with computational methods to progress the evaluation of such medical devices and enhance their development.

Prototype pipeline modelling using interval scanning point clouds

AbstractWith the aid of computer aided design (CAD) and building information modelling (BIM), as-built to as-designed comparison has seen many developments in improving the workflow of manufacturing and construction tasks. Recently, evolution has been centred around automation of scene interpretation from three-dimensional (3D) scan data. The scope of this paper is to assess assemblies as the installation process progresses and inferring if arising deviations are problematic (complex task). The adequacy of utilising unorganised point clouds to compliance check are trialled with a real life down-scaled prototype model in conjunction with a synthetic dataset. This work aims to highlight areas where large rework could be avoided, in part by the detection of potential clashes of components early in the pipeline installation process. With the help of an extracted model in the form of a point cloud generated from a scanned physical model and a 3D CAD model representing the nominal geometry, an operator can be made visually aware of potential deviations and component clashes during a pipeline assembly process.

Development of idealized human aortic models for in vitro and in silico hemodynamic studies.

The aorta, a central component of the cardiovascular system, plays a pivotal role in ensuring blood circulation. Despite its importance, there is a notable lack of idealized models for experimental and computational studies. This study aims to develop computer-aided design (CAD) models for the idealized human aorta, intended for studying hemodynamics or solid mechanics in both in vitro and in silico settings. Various parameters were extracted from comprehensive literature sources to evaluate major anatomical characteristics of the aorta in healthy adults, including variations in aortic arch branches and corresponding dimensions. The idealized models were generated based on averages weighted by the cohort size of each study for several morphological parameters collected and compiled from image-based or cadaveric studies, as well as data from four recruited subjects. The models were used for hemodynamics assessment using particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Two CAD models for the idealized human aorta were developed, focusing on the healthy population. The CFD simulations, which align closely with the PIV measurements, capture the main global flow features and wall shear stress patterns observed in patient-specific cases, demonstrating the capabilities of the designed models. The collected statistical data on the aorta and the two idealized aorta models, covering prevalent arch variants known as Normal and Bovine types, are shown to be useful for examining the hemodynamics of the aorta. They also hold promise for applications in designing medical devices where anatomical statistics are needed.

Data-driven simulation-assisted-Physics learned AI (DPAI) for heat diffusion in large grain polycrystalline materials

In this paper, we propose Data-driven simulation-assisted Physics-learned Artificial Intelligence (DPAI), a deep-learning algorithm to simulate heat diffusion in large-grain polycrystalline materials. The DPAI model captures the spatio-temporal representation of heat diffusion in the material from input sequences from the training dataset. The training dataset consists of various temperature plots of polycrystalline materials taken from Finite Element (FE) simulations having varying numbers of grains oriented in random directions with a single-point heat source at the center. The arbitrary plane of the 3D microstructure of these materials is represented using 2D Voronoi tessellations. Voronoi configurations are used to model the geometry of the 2D Computer-Aided Design (CAD) model. Each cell of the Voronoi tessellation represents one grain of the microstructure. This CAD model is used as an input to the FE for solving heat diffusion equations. To model the near-realistic material anisotropy and accurately measure temperature differences at cell boundaries, a smaller mesh size is required in FE modeling, which takes considerable solver time. Therefore, the proposed Deep learning model significantly reduces the computational time while maintaining accuracy as compared to conventional numerical techniques. After training, the effectiveness of the trained DPAI model is examined by modeling larger domain problems involving a greater number of grains and varying material properties. The simulation result is qualitatively compared with the experiment. A scaled-up version of the microstructure is represented using Unidirectional Carbon Fiber laminate. The laminate is heated with a point heat source and the temperature plots are captured using Infrared Camera.

Open wedge high tibial osteotomy alters patellofemoral joint kinematics: A multibody simulation study.

Changes in lower limb alignment after open-wedge high tibial osteotomy (owHTO) influence joint kinematics. The aim of this study was to investigate the morphological and kinematic changes of the knee joint, in particular the patellofemoral joint, using a multibody simulation model. OwHTO with an open tibial wedge of 6-12 mm (1 mm intervals) was virtually performed on each of 13 three-dimensional (3D) computer-aideddesign models (CAD models) derived from computer tomography scans of full-leg cadaver specimens. For each owHTO, an individual biomechanical simulation model was built and knee flexion from 5° to 100° was simulated using a multibody simulation model of the native knee. Morphologic and alignment parameters as well as tibiofemoral and patellofemoral kinematic parameters were evaluated. Almost linear changes in tibial tuberosity trochlea groove(TT-TG) (0.42 mm/1 mm wedge height) were observed which led to pathological values (TT-TG > 20 mm) in 3 out of 13 knees. Furthermore, a 6 mm increase in osteotomy wedge height increased lateral patellofemoral rotation by 0.8° (range: 0.39° to 1.11°) and led to a lateral patellar translation of 0.8 mm (range: 0.37-3.11 mm) on average. Additionally, valgisation led to a medial translation of the tibia and a decrease in the degree of tibial internal rotation during knee flexion of approximately 0.3°/1 mm increase in osteotomy wedge height. The increase in TT-TG and the biomechanical effects observed influence patellofemoral tracking, which may increase retropatellar pressure and are potential risk factors for the development of anterior knee pain.

Studying Biomolecular Protein Complexes via Origami and 3D-Printed Models.

Living organisms are constructed from proteins that assemble into biomolecular complexes, each with a unique shape and function. Our knowledge about the structure-activity relationship of these complexes is still limited, mainly because of their small size, complex structure, fast processes, and changing environment. Furthermore, the constraints of current microscopic tools and the difficulty in applying molecular dynamic simulations to capture the dynamic response of biomolecular complexes and long-term phenomena call for new supplementary tools and approaches that can help bridge this gap. In this paper, we present an approach to comparing biomolecular and origami hierarchical structures and apply it to comparing bacterial microcompartments (BMCs) with spiral-based origami models. Our first analysis compares proteins that assemble the BMC with an origami model called "flasher", which is the unit cell of an assembled origami model. Then, the BMC structure is compared with the assembled origami model and based on the similarity, a physical scaled-up origami model, which is analogous to the BMC, is constructed. The origami model is translated into a computer-aided design model and manufactured via 3D-printing technology. Finite element analysis and physical experiments of the origami model and 3D-printed parts reveal trends in the mechanical response of the icosahedron, which is constructed from tiled-chiral elements. The chiral elements rotate as the icosahedron expands and we deduce that it allows the BMC to open gates for transmembrane passage of materials.

Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.

Optimizing TCAD Model and Temperature-Dependent Analysis of Pt/AlN Schottky Barrier Diodes for High-Power and High-Temperature Applications

This research presents a comprehensive investigation and optimization of the Pt/AlN Schottky Barrier diode (SBD) using technology computer-aided design (TCAD) modeling. The study explores the electrical characteristics of AlN SBDs with various metal contacts, including Aluminum (Al), Silver (Ag), Tungsten (W), Gold (Au), Nickel (Ni), and Platinum (Pt). Through the comparative analyses of different metal/AlN Schottky contacts, the Pt/AlN structure emerges as the most promising due to its superior barrier height and lower leakage current. At [Formula: see text]K, the diode demonstrates a barrier height of 2.72[Formula: see text]V, a nearly ideal leakage current of 0.046[Formula: see text]pA, and a breakdown voltage of 363[Formula: see text]V. The research extends to examining the temperature-dependent electrical behavior of Pt/AlN Schottky diodes, particularly for high-power and high-temperature applications. Analysis carried out across temperatures ranging from [Formula: see text]K to [Formula: see text]K reveals a trend of increasing ON resistance and consistently lower leakage current with rising temperature. Importantly, the study indicates that the impact of temperature on the barrier height and breakdown voltage of the diode is negligible, thus rendering it suitable for high-temperature operation. Leveraging the unique properties of AlN as an ultra-wide bandgap material within the III-V compound semiconductor family, this research provides valuable insights into the potential applications of Pt/AlN Schottky contact. The study highlights that the Pt/AlN Schottky contact is effective not only for high-power, high-temperature SBDs but also as superior metal/semiconductor gate contacts for field-effect transistors (FETs). Their suitability is attributed to their ability to handle high voltages, minimize reverse leakage current, and demonstrate improved thermal stability.

BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry

This paper presents BrepGen , a diffusion-based generative approach that directly outputs a Boundary representation (B-rep) Computer-Aided Design (CAD) model. BrepGen represents a B-rep model as a novel structured latent geometry in a hierarchical tree. With the root node representing a whole CAD solid, each element of a B-rep model (i.e., a face, an edge, or a vertex) progressively turns into a child-node from top to bottom. B-rep geometry information goes into the nodes as the global bounding box of each primitive along with a latent code describing the local geometric shape. The B-rep topology information is implicitly represented by node duplication. When two faces share an edge, the edge curve will appear twice in the tree, and a T-junction vertex with three incident edges appears six times in the tree with identical node features. Starting from the root and progressing to the leaf, BrepGen employs Transformer-based diffusion models to sequentially denoise node features while duplicated nodes are detected and merged, recovering the B-Rep topology information. Extensive experiments show that BrepGen advances the task of CAD B-rep generation, surpassing existing methods on various benchmarks. Results on our newly collected furniture dataset further showcase its exceptional capability in generating complicated geometry. While previous methods were limited to generating simple prismatic shapes, BrepGen incorporates free-form and doubly-curved surfaces for the first time. Additional applications of BrepGen include CAD autocomplete and design interpolation. The code, pretrained models, and dataset are available at https://github.com/samxuxiang/BrepGen.

A family of folding single-loop metamorphic mechanisms for aerospace manipulators: Synthesis, network, and analysis

Guided by predetermined degrees of freedom for different desired tasks, the type synthesis of single-loop metamorphic mechanisms (SLMMs) has long posed a challenge. Owing to the complexity and coupling of their overconstraints and the variability of constraints arising from bifurcation motions, it is difficult to ensure geometric coordination and synchronous motion for a network of multiple SLMMs. Here, we propose an approach to type synthesize SLMMs using the atlas method. A novel family of SLMMs is further synthesized. The mobility analysis of a selected 7R SLMM is carried out via screw theory. The kinematics and bifurcation phenomena are analyzed and verified through numerical simulation using computer-aided design models. To evaluate the performance of the SLMMs, the mapping relationship between key design parameters and performance indicators, such as the grasping angle, folding angle, and mechanical advantage, is studied. A networking method is then proposed by considering the geometric coordination conditions and variation of the overconstraints of SLMMs. Prototypes of an SLMM and manipulator with optimal geometrical parameters are developed to verify the feasibility and correctness of the proposed method and kinematic analysis.

Synthesis and Characterization of Furan-Based Methacrylate Oligomers Containing the Imine Functional Group for Stereolithography.

Herein, a furan-based methacrylate oligomer (FBMO) featuring imine functional groups was synthesized for application in stereolithography. The preparation involved the imination reaction of 5-hydroxymethylfurfural (5-HMF) and amino ethanol. Utilizing 5-HMF as a sustainable building block for furan-based polymers, FBMO was formulated and subsequently integrated into photosensitive resin formulations along with methacrylate-containing diluents, such as PEGDMA and TEGDMA. The synthesized furan-based methacrylate oligomers underwent comprehensive characterization using FTIR, 1H NMR spectroscopy, and size exclusion chromatography. The impact of methacrylate-containing diluents on various properties of the formulated resins and the resulting 3D-printed specimens was systematically evaluated. This assessment included an analysis of rheological behavior, printing fidelity, mechanical properties, thermal stability, surface morphology, and cytotoxicity. By adjusting the ratios of FBMO to methacrylate-containing diluents within the range of 50:50 to 90:10, the viscosity of the resulting resins was controlled to fall within 0.04 to 0.28 Pa s at a shear rate of 10 s-1. The 3D-printed specimens exhibited precise conformity to the computer-aided design (CAD) model and demonstrated compressive moduli ranging from 0.53 ± 0.04 to 144 ± 6.70 MPa, dependent on the resin formulation and internal structure. Furthermore, cytotoxicity assessments revealed that the 3D-printed specimens were noncytotoxic to porcine chondrocytes. In conclusion, we introduce a new strategy to prepare the furan-based methacrylate oligomer (FBMO) and 3D-printed specimens with adjustable properties using stereolithography, which can be further utilized for appropriate applications.

Discretization of Non-uniform Rational B-Spline (NURBS) Models for Meshless Isogeometric Analysis

We present an algorithm for fast generation of quasi-uniform and variable-spacing nodes on domains whose boundaries are represented as computer-aided design (CAD) models, more specifically non-uniform rational B-splines (NURBS). This new algorithm enables the solution of partial differential equations within the volumes enclosed by these CAD models using (collocation-based) meshless numerical discretizations. Our hierarchical algorithm first generates quasi-uniform node sets directly on the NURBS surfaces representing the domain boundary, then uses the NURBS representation in conjunction with the surface nodes to generate nodes within the volume enclosed by the NURBS surface. We provide evidence for the quality of these node sets by analyzing them in terms of local regularity and separation distances. Finally, we demonstrate that these node sets are well-suited (both in terms of accuracy and numerical stability) for meshless radial basis function generated finite differences discretizations of the Poisson, Navier-Cauchy, and heat equations. Our algorithm constitutes an important step in bridging the field of node generation for meshless discretizations with isogeometric analysis.

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.

Recent Trends and Technologies in rapid prototyping and its Inclination towards Industry 4.0

Prototyping technology is becoming vital in the business as a means of cutting costs and manufacturing time. At present, reverse engineering and rapid prototyping are important technologies that enhance prototype development. The traditional approaches require various intricate processes, such as selective heat sintering (SHS), digital-light-processing printer (DLP), remote distributed rapid prototyping model (RDRP), Stereo Lithography (STL) models, and reconstructing computer-aided design (CAD) models from scanned point data and these approaches has limitations in terms of time-consuming and expert knowledge required for automation. This study aims to explore the significance of Industry 4.0 and its impact on rapid prototyping. The study also addresses rapid prototyping in computer network architecture; digital-light-processing printers (DLP) in rapid prototyping, and software-defined network (SDN) networks in the context of rapid prototyping. Along with this powder bed fusion (PBF) method and electron beam melting (EBM) are included in the manuscript. Based on our exploration, the study suggested vital recommendations for the advancement in rapid prototyping using Industry 4.0

Evaluation and Improvement of a Prosthetic Hand Product Using Biomechanics and Empathy Map Approach

The Panangan Prosthetic Hand is a transradial prosthesis that can be operated with both amputated and non-amputated arms. Several users have reported some degree of exhaustion and discomfort when using this prosthesis. Users may also be dissatisfied because this product has not met some of their specific needs and desires. To evaluate the product, tests were conducted by measuring muscle activity using an electromyography (EMG) sensor and assessing discomfort levels using the Borg CR10 scale during specific activities performed with the prosthesis. Additionally, interviews were conducted using an empathy map to profile the participants. The test results showed high muscle activities and discomfort levels in certain tasks, such as pouring water from a drink box. Based on these findings and the participants' profiles, their needs were identified, and solutions for improving the product were designed. Subsequently, these solutions were developed into a three-dimensional computer-aided design (3D CAD) model prototype.

SimPLE, a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects.

Existing robotic systems have a tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single task, lacking "precise generalization," or the ability to solve many tasks without compromising on precision. This paper explores solutions for precise and general pick and place. In precise pick and place, or kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement, which can facilitate further manipulation. We propose SimPLE (Simulation to Pick Localize and placE) as a solution to precise pick and place. SimPLE learns to pick, regrasp, and place objects given the object's computer-aided design model and no prior experience. We developed three main components: task-aware grasping, visuotactile perception, and regrasp planning. Task-aware grasping computes affordances of grasps that are stable, observable, and favorable to placing. The visuotactile perception model relies on matching real observations against a set of simulated ones through supervised learning to estimate a distribution of likely object poses. Last, we computed a multistep pick-and-place plan by solving a shortest-path problem on a graph of hand-to-hand regrasps. On a dual-arm robot equipped with visuotactile sensing, SimPLE demonstrated pick and place of 15 diverse objects. The objects spanned a wide range of shapes, and SimPLE achieved successful placements into structured arrangements with 1-mm clearance more than 90% of the time for six objects and more than 80% of the time for 11 objects.

A nonconforming surface mesh generation method by binary tree

Computer-aided engineering (CAE) has emerged as an indispensable tool for facilitating engineering practices and driving industrial innovation. However, the insufficient quality and efficiency of discretizing complex computer-aided design (CAD) models significantly impede the advancement of CAE calculation accuracy and automation. The presence of “dirty” geometry leads to the fact that it is almost impossible to generate a traditional conforming mesh without geometry repair. In most instances, automating geometry repair proves to be even more challenging than meshing. To cope with intricate CAD model with “dirty” geometry, a novel binary tree surface subdivision method (BSSM) is proposed for automatically generated conforming and nonconforming meshes directly on CAD models. In contrast to the conventional conforming mesh, the nonconforming mesh allows for hanging nodes, thereby eliminating the limitations of the mesh conformance. This facilitates rapid transitions in mesh size for automatic mesh generation when dealing with “dirty” geometry. A series of numerical models employing BSSM are presented in this study. Results reveal that BSSM can automatically, efficiently and reliably generate high level quality mesh for arbitrary structures.

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery.

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These "markerless" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.