Computer-aided Design Model Research Articles

Radiologically derived 3D virtual models for neurosurgical planning

Three dimensional (3D) virtual models for neurosurgery have demonstrated substantial clinical utility, especially for neuro-oncological cases. Computer-aided design (CAD) modelling of radiological images can provide realistic and high-quality 3D models which neurosurgeons may use pre-operatively for surgical planning. 3D virtual models are useful as they are the basis for other models that build off this design. 3D virtual models are quick to segment but can also be easily added to normal neurosurgical and radiological workflow without disruption. Three anatomically complex neuro-oncology cases that were referred from a single institution by three different neurosurgeons were segmented and 3D virtual models were created for pre-operative surgical planning. A face-to-face interview was performed with the surgeons after the models were delivered to gauge the usefulness of the model in pre-surgical planning. All three neurosurgeons found that the 3D virtual model was useful for presurgical planning. Specifically, the virtual model helped in planning operative positioning, understanding spatial relationship between lesion and surrounding critical anatomy and identifying anatomy that will be encountered intra-operatively in a sequential manner. It provided benefit in Multidisciplinary team (MDT) meetings and patient education for shared decision making.3D virtual models are beneficial for pre-surgical planning and patient education for shared decision making for neurosurgical neuro-oncology cases. We believe this could be further expanded to other surgical specialties. The integration of 3D virtual models into normal workflow as the initial step will provide an easier transition into modalities that build off the virtual models such as printed, virtual, augmented and mixed reality models.

Interactive simulations as a tool for logistics and maintenance of IFMIF-DONES

Pre-configured virtual reality (VR) simulations of the logistics and maintenance processes have proven to be useful for identifying potential design issues as well as planning operations during an early design phase of facility. But VR simulations can also be used to deeply explore the feasibility of these procedures in a more interactive manner, so that we can identify potential risks and difficulty levels from early stages and study different maintenance strategies to assist the maintenance worker during these procedures. This article presents a framework to design and validate logistics and maintenance procedures in complex facilities, such as the International Fusion Materials Irradiation Facility DEMO Oriented Neutron Source (IFMIF-DONES). Our framework begins with a preparatory phase where essential information about the procedures and Computer-Aided Design (CAD) models is compiled into a comprehensive Virtualization Task Document (VTD). Differently from previous work, this VTD allows representation of parallel tasks. We implement the interactive version of the virtual environment, where the different maintenance and logistics equipment, as well as a virtual maintenance worker is controlled by the user (the person executing the interactive simulation). We have validated this interactive framework with two simulations for the installation process of the Superconducting Radio Frequency Linear accelerator (SRF Linac) modules in IFMIF-DONES. In one simulation (the automatic one), the procedures are reproduced as they are planned, while in the second simulation (the interactive one) the user freely controls the movements of the moving parts of the crane, grab and release plant equipment, move platforms, etc. Based on our simulations, the interactive version allows easier detection of potential points of collisions as well as more precise assessment of the difficulty of the tasks to be performed.

Edge illumination x-ray phase contrast simulations using the CAD-ASTRA toolbox.

Edge illumination x-ray phase contrast imaging (XPCI) provides increased contrast for low absorbing materials compared to attenuation images and sheds light on the material microstructure through dark field contrast. To apply XPCI in areas such as non-destructive testing and inline inspection, where scanned samples are increasingly compared to simulated reference images, accurate and efficient simulation software is required. However, currently available simulators rely on expensive Monte Carlo techniques or wave-optics frameworks, resulting in long simulation times. Furthermore, these simulators are often not optimized to work with computer-aided design (CAD) models, a common and memory-efficient method to represent manufactured objects, hindering their integration in an inspection pipeline. In this work, we address these shortcomings by introducing an edge illumination XPCI simulation framework built upon the recently developed CAD-ASTRA toolbox. CAD-ASTRA allows for the efficient simulation of x-ray projections from CAD models through GPU-accelerated ray tracing and supports ray refraction in a geometric optics framework. The edge illumination implementation is validated and its performance is benchmarked against GATE, a state-of-the-art Monte Carlo simulator, revealing a simulation speed increase of up to three orders of magnitude, while maintaining high accuracy in the resulting images.

A comparative analysis of CAD modeling approaches for design solution space exploration

Design solution space (DSS) exploration is a pivotal process for comprehending design challenges and identifying diverse solution alternatives based on varying requirements. Computer-aided design (CAD) approaches, such as parametric design, knowledge-based design, and generative design, have proven successful in DSS exploration. However, a comparative study evaluating their performance is lacking in the technical literature. This paper addresses this gap by conducting a comparative analysis of these approaches regarding their performance in exploring DSS. The research begins by providing an overview of parametric design, knowledge-based design, and generative design, establishing the foundation for the study. Six evaluation criteria are identified based on the DSS exploration process. A qualitative analysis is then conducted, considering these criteria, to objectively assess the performance of each modeling approach. The results highlight the strengths and weaknesses of each approach, revealing that DSS exploration success is directly tied to the quantity of implemented knowledge. The results also emphasize the complementarity of those approaches, as their strengths and weaknesses are based on different problem-solving logics, demonstrating the synergy that can be achieved through strategic combinations of them. Additionally, the paper discusses open issues related to DSS exploration, contributing valuable insights for future developments in this field.

Intensify Biodegradation Process of Polylactic Acid (PLA) Waste Generated from 3D Printing Activities

Additive manufacturing, or 3D printing, is a key technology driving Industry 4.0. via the formation of three-dimensional objects from a computer-aided design model which can be done through layer-by-layer technique. Polylactic acid (PLA) ranks as one of the most favored materials as a 3D printing filament. Despite its unique properties, PLA took about 12 weeks to biodegrade which is slow degradation leads to an increased rate of plastic pollution in the environment. The aim of this study was to provide an alternative method for bioplastic waste management through biodegradation process using potting mix soil at different incubation temperatures and times. The PLA was designed in coupon shapes and eventually, 3D printed, respectively. Consequently, potting mix soil was prepared in a container and coupon, as all PLA samples were then planted 7 cm from the soil surface. Eventually, PLA samples were subjected to biodegradation process in the soil at 25 °C and 50 °C. Subsequently, each sample was drawn from the soil at different incubation times up to 65 days. The results show that prolong incubation time has resulted in PLA coupon losing weight up to 10.4% and 1.4% for the incubation time of 25 °C and 50 °C, respectively. Meanwhile, the physical structure has deteriorated to powder form and the fragility decreases proportionally as prolonged incubation time. Functional groups analysis showed the functional groups altered after the biodegradation process of PLA and soil. Ergo, this method can be utilized for the community to execute self-degradation of their PLA waste generated even at their own facility.

A Finite-Element-Analysis-Based Feasibility Study for Optimizing Pantograph Performance Using Aluminum Metal Matrix Composites

A pantograph is a key component on the tops of trains that allows them to efficiently tap electricity from power lines and propel them. This study investigates the possibility of using metal matrix composites (MMCs), specifically aluminum MMCs, as a material for making pantograph parts regarding the dynamics of the train’s movement and external meteorological conditions. In this study, a computer-aided design (CAD) model is created using PTC Creo design software and moves to detailed finite element analysis (FEA) simulations executed by the ANSYS software suite. These simulations are important in examining how the dynamic performance of pantographs can vary. The incorporation of Al MMC materials into the structure of the pantograph resulted in significant improvements in structural robustness, with equal stress reduced by up to 0.18%. Similarly, aluminum MMC materials reduced the strain energy by 0.063 millijoules. The outcomes not only give a new perspective to the implementation of modern materials but also provide a breakthrough concept to improve efficiency and increase the service life of pantographs. This study marks a significant milestone in the theoretical development of essential train systems, furnishing eminent perspectives toward the tactical development of transportation infrastructure by suggesting new avenues for the smooth incorporation of smart materials in railway transportation, which would contribute to a more sustainable and reliable future.

Dimensional accuracy and precision and surgeon perception of additively manufactured bone models: effect of manufacturing technology and part orientation

BackgroundAdditively manufactured (AM) anatomical bone models are primarily utilized for training and preoperative planning purposes. As such, they must meet stringent requirements, with dimensional accuracy being of utmost importance. This study aimed to evaluate the precision and accuracy of anatomical bone models manufactured using three different AM technologies: digital light processing (DLP), fused deposition modeling (FDM), and PolyJetting (PJ), built in three different part orientations. Additionally, the study sought to assess surgeons’ perceptions of how well these models mimic real bones in simulated osteosynthesis.MethodsComputer-aided design (CAD) models of six human radii were generated from computed tomography (CT) imaging data. Anatomical models were then manufactured using the three aforementioned technologies and in three different part orientations. The surfaces of all models were 3D-scanned and compared with the original CAD models. Furthermore, an anatomical model of a proximal femur including a metastatic lesion was manufactured using the three technologies, followed by (mock) osteosynthesis performed by six surgeons on each type of model. The surgeons’ perceptions of the quality and haptic properties of each model were assessed using a questionnaire.ResultsThe mean dimensional deviations from the original CAD model ranged between 0.00 and 0.13 mm with maximal inaccuracies < 1 mm for all models. In surgical simulation, PJ models achieved the highest total score on a 5-point Likert scale ranging from 1 to 5 (with 1 and 5 representing the lowest and highest level of agreement, respectively), (3.74 ± 0.99) in the surgeons’ perception assessment, followed by DLP (3.41 ± 0.99) and FDM (2.43 ± 1.02). Notably, FDM was perceived as unsuitable for surgical simulation, as the material melted during drilling and sawing.ConclusionsIn conclusion, the choice of technology and part orientation significantly influenced the accuracy and precision of additively manufactured bone models. However, all anatomical models showed satisfying accuracies and precisions, independent of the AM technology or part orientation. The anatomical and functional performance of FDM models was rated by surgeons as poor.

Characterization and classification of EEG signals evoked by different CAD models

AbstractThe past two decades have witnessed dramatic advancement in computer‐aided design (CAD). However, development of human–computer interfaces (HCI) for CAD have not kept up with these advances. Windows, Icons, Menus, Pointer (WIMP) is still the mainly used interface for CAD applications which limits the naturalness and intuitiveness of the CAD modeling process. As a novel interface, Brain–computer interfaces (BCIs) have great potential in the application of CAD modeling. Utilizing BCIs, the user can create CAD models just by thinking about it in principle, because BCIs provide an end‐to‐end interaction channel between users and CAD models. However, current related studies are mainly limited to the existing BCIs paradigms, while ignoring the relationship between electroencephalogram (EEG) signals and CAD models, which largely increases the cognitive load on the users. In this study, we aimed to explore the potential of using BCI to create CAD models directly independent of the classical BCIs paradigms. For this purpose, EEG signals evoked by six basic CAD models (i.e., point, square, trapezoid, line, triangle, and circle) were collected from 28 participants. After preprocessing and sub‐trial principal components analysis (st‐PCA) of recorded data, the peak, mean and time‐frequency energy features were extracted from EEG signals. By applying the one‐way repeated measures analysis of variance, we demonstrated that there were significant differences among these EEG features evoked by different CAD models. These features from EEG electrode channels ranked by mutual information were then used to train a discriminant classifier of genetic algorithm‐based support vector machine. The empirical result showed that this classifier can discriminate the CAD models with an average accuracy of about 72%, which turns out that EEG based model generation is feasible, and provides the technical and theoretical basis for building a novel BCI for CAD modeling.

Design and analysis of serviceable cantilever fit snap in automotive plastic parts

Purpose Snap fits are crucial in automotive applications for rapid assembly and disassembly of mating components, eliminating the need for fasteners. This study aims to focus on designing and analyzing serviceable cantilever fit snap connections used in automobile plastic components. Snap fits are classified into permanent and semi-permanent fittings, with permanent fittings having a snap clipping angle between 0° and 5° and semi-permanent fittings having a clipping angle between 15° and 45°. Polypropylene random copolymer is chosen for its exceptional fatigue resistance and elasticity. Design/methodology/approach The design process includes determining dimensions, computing assembly, disassembly pressures and creating three-dimensional computer-aided design models. Finite element analysis (FEA) is used to evaluate the snap-fit mechanism’s stress, deformation and general functionality in operational scenarios. Findings The study develops a modified snap-fit mechanism with decreased bending stress and enhanced mating force optimization. The maximum bending stress during assembly is 16.80 MPa, requiring a mating force of 7.58 N, while during disassembly, it is 37.3 MPa, requiring a mating force of 16.85 N. The optimized parameters significantly improve the performance and dependability of the snap-fit mechanism. The results emphasize the need of taking into account both the assembly and disassembly processes in snap-fit design, because the research demonstrates greater forces during disassembly. The approach developed integrates FEA and design for assembly (DFA) concepts to provide a solution for improving the efficiency and reliability of snap-fit connectors in automotive applications. Originality/value The research paper’s distinctiveness comes from the fact that it presents a thorough and realistic viewpoint on snap-fit design, emphasizes material selection, incorporates DFA principles and emphasizes the specific requirements of both assembly and disassembly operations. These discoveries may enhance the efficiency, reliability and sustainability of snap-fit connections in plastic automobile parts and beyond. In conclusion, the idea that disassembly needs to be done with a lot more force than installation in a snap-fit design can have a good effect on buzz, squeak and rattle and noise, vibration and harshness characteristics in automobiles.

Novel Technique for the Identification of Hip Implants Using Artificial Intelligence

BackgroundThe anticipated growth of total hip arthroplasty will result in an increased need for revision total hip arthroplasty. Preoperative planning, including identifying current implants, is critical for successful revision surgery. Artificial intelligence (AI) is promising for aiding clinical decision-making, including hip implant identification. However, previous studies have limitations such as small datasets, dissimilar stem designs, limited scalability, and the need for AI expertise. To address these limitations, we developed a novel technique to generate large datasets, tested radiographically similar stems, and demonstrated scalability utilizing a no-code machine learning solution. MethodsWe trained, validated, and tested an automated machine learning-implemented convolutional neural network to classify 9 radiographically similar femoral implants with a metaphyseal-fitting wedge taper design. Our novel technique uses computed tomography-derived projections of a 3-dimensional scanned implant model superimposed within a computed tomography pelvis volume. We employed computer-aided design modeling and MATLAB to process and manipulate the images. This generated 27,020 images for training (22,957) and validation (4,063) sets. We obtained 786 test images from various sources. The performance of the model was evaluated by calculating sensitivity, specificity, and accuracy. ResultsOur machine learning model discriminated the 9 implant models with a mean accuracy of 97.4%, sensitivity of 88.4%, and specificity of 98.5%. ConclusionsOur novel hip implant detection technique accurately identified 9 radiographically similar implants. The method generates large datasets, is scalable, and can include historic or obscure implants. The no-code machine learning model demonstrates the feasibility of obtaining meaningful results without AI expertise, encouraging further research in this area.

Vibrational Analysis of a Splash Cymbal by Experimental Measurements and Parametric CAD-FEM Simulations

The present study encompasses a thorough analysis of the vibrations in a splash musical cymbal. The analysis is performed using a hybrid methodology that combines experimental measurements with parametric computer-aided design and finite element method simulations. Experimental measurements, including electronic speckle pattern interferometry, and impulse response measurements are conducted. The interferometric measurements are used as a reference for the evaluation of finite element method modal analysis results. The modal damping ratio is calculated via the impulse response measurements and is adopted by the corresponding simulations. Two different approximations are employed for the computer-aided design and finite element method models: one using three-point arcs and the other using lines to describe the non-smooth curvature introduced during manufacturing finishing procedures. The numerical models employing the latter approximation exhibit better agreement with experimental results. The numerical results demonstrate that the cymbal geometrical characteristics, such as the non-smooth curvature and thickness, greatly affect the vibrational behavior of the percussion instrument. These results are of valuable importance for the development of vibroacoustic numerical models that will accurately simulate the sound synthesis of cymbals.

Dynamic simulation of the differential gear in road vehicles

The differential gear is a mechanism that is part of the kinematic chain of the vehicle’s transmission. The differential gear has the role of dividing the self-propulsion power flow received from the main drive into two branches, each transmitted to a drive wheel, giving them the possibility to rotate with different angular velocities, depending on the driving conditions. The present work presents the possibilities of computer-aided design and 3D modeling of the main drive and differential gear, respectively the bevel pinion, the crown wheel, the planet pinions and the satellites, using Autodesk Inventor Professional. The 3D model of the main drive - differential gear assembly, considered as a mechanism, was subjected to a dynamic study, using the Dynamic Simulation module. This involves the modeling of the kinematic couplings, the definition of the drive movement parameters and the external loads, respectively the graphical visualization of the kinematic and kinetostatic results for the various components of the mechanism, in real operating conditions, during a kinematic cycle. At the same time, based on the obtained results, the analysis by the finite element method of the most critical component elements, under straight-line driving and turning conditions, is presented.

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.

Design and Analysis of UAV Profile for Agriculture and Surveying Application

This study represents the aerodynamic design of an Unmanned aerial vehicle intended for surveillance or agriculture with a maximum take weight of 125 kg. Weight estimation and constraint analysis were done based on the Mission profile. Design of Computer-Aided Design (CAD) models were generated for three different configurations using CATIA V5R20 as a high wing, mid-wing, and low wing. Flow analysis was done for the above configurations at various angles of attack. ANSYS 15 was used for the flow Analysis. A Tetrahedron element meshed the model with the minimum required orthogonal quality. Five microns were given to the initial layer height of the prism mesh. Spalart Allmaras model is used as the Turbulence model in the solver. The aerodynamic characteristics of the above configuration obtained from Computational Fluid Dynamics (CFD) results were compared with the DATCOM program and validated with the wind tunnel experimental test data. The open-circuited suction-type Subsonic wind tunnel was employed for the test. The aerodynamic properties for the angle of attack in the range of -2° to 14° angle of attack are calculated using a six-component balance. The study aims to find the Unmanned Aerial Vehicle (UAV) configuration based on the aerodynamic characteristics obtained from the CFD and DATCOM results. High-wing UAVs have better aerodynamic efficiency than the other two configurations.

Sketching feasibility of additively manufactured different size gradient conventional hollow capsular shells (HCSs) by selective laser sintering (SLS): From design to applications

Additive manufacturing (AM) is widely used to fabricate 3D printed objects from Computer-aided Design (CAD) prepared using the SolidWorks CAD modelling software. Different printing techniques are used to fabricate desired 3D objects; among all these techniques, it is widely accepted that SLS is one of the most effective methods of 3D printing for fabricating drug-loaded solid oral dosage forms (SODFs) in bulk quantities using the single-step process. Different SODFs, such as pills, miniprintlets, dual miniprintlets, and tablets, were fabricated with different sizes and shapes. In this study, for the first time, we introduce SLS-mediated hollow capsular shells (HCSs) with the help of the SLS 3D printing technique. This work aimed to explore the sinterability and feasibility of sketching HCSs using the SLS-mediated sintering technique with different marketed sizes of capsules ranging from 000 to 5. Here, we have utilized Kolliphor P 188 (KP 188) and Kollidon SR (KSR) in a 1:1 ratio as a matrix-forming agent and 1% charcoal as a laser absorption-enhancing material. In accordance with the CAD models, we have fabricated the gradient conventional different sizes of HCSs ranging from 000 to 5 using the constant printing parameters and composition. Fabricated all biobased HCSs were subjected to the assessment of mechanistic and physicochemical parameters using varied analytical tools. In the current study, tartrazine dye is used to assess the release pattern from HCSs, which resulted in the modified release pattern. The adapted approach will be the futuristic approach to replace animal-based gelatin capsules with pharmaceutical-grade polymer-based HCSs with a modified release with optimum mechanical strength.

Advancing Front Mesh Generation on Dirty Composite Surfaces

Computer-aided design (CAD) models usually contain many errors between neighboring surfaces, such as slivers, gaps, and overlaps. To clean up such models, virtual operations have been suggested to merge multiple neighboring CAD surfaces into a single composite surface. However, it remains a challenge to generate a quality mesh on thereby formed dirty composite surfaces. In this paper, we propose a novel advancing front technique (AFT) that can treat such composite surfaces by developing two new schemes to enhance the traditional AFT. Firstly, for each composite surface, we define a parametric plane by using a combined set of the tessellation on this composite surface. Simplicial complex augmentation framework reparameterization approach is suggested since it can treat tessellations containing gap and overlap after introducing a pre-processing step. Meanwhile, this approach can ensure a bijective mapping between the parametric and physical space. The front intersection check can thus be performed on the parametric plane robustly. Secondly, the indirect and direct approaches are alternatively employed to calculate ideal points in different circumstances. In the circumstance that the possible new element is completely contained in one single CAD surface, the ideal point is calculated on the intrinsic parametric plane of the surface; otherwise, the ideal point is directly calculated on the physical space. We avoid using the geometry defined on the tessellation since we prefer to getting a mesh respecting the original CAD model rather than its tessellation counterpart. Presently, the developed new schemes have been incorporated into our in-house surface mesher, and their efficiency and effectiveness have been demonstrated through a comparison with state-of-the-art commercial tools (e.g., COMSOL Multiphysics) and AFT algorithm, using CAD models of industry-level complexity.

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.

Improved algorithm of finding the next volume in direct CAD method for fusion neutronics analysis

The magnetic confinement fusion device, Tokamak, has a complex 3D geometry structure. As a result, the fusion neutronics geometry modeling process will be time-consuming and labor-intensive. Some researchers have improved modeling methods by using CAD (Computer Aided Design) models, but there is still a need to convert CAD models to CSG (Constructive Solid Geometry) or pre-process CAD models. Based on the existing direct CAD Monte Carlo approach, this paper creates a new algorithm for finding the next volume, which greatly simplifies or even omits the preprocessing process. By forming a grouping bounding box acceleration algorithm, the improved finding next volume performance is adequate to ensure precision and computation speed for complex 3D models. The results are verified by comparing them with a commercial Monte Carlo code, cosRMC, and an example of neutron fluence per particle on real CAD models of the HL-3 first wall and divertor shows a better applicability of this new algorithm.

Statistical modelling and process optimization of EDM electrode fabricated using pressureless microwave sintering and rapid tooling route

The fabrication of customized products necessitates specialized setups, such as tools and dies, capable of accommodating complex shapes. In the present work, rapid tooling, an indirect 3D printing process along with pressureless microwave sintering was employed to fabricate complex shaped electric discharge machine (EDM) electrode. Copper spherical particles were used as the material for sample fabrication, and central composite design technique was utilized to conduct experiments. The investigation focused on exploring the impact of microwave sintering machine parameters – sintering temperature, heating rate and holding time – on crucial electrode properties, including density, volumetric shrinkage and electrical conductivity. The dominance of the sintering temperature on responses was revealed over the heating rate and holding time. Additionally, the effect of interaction between heating rate and holding time on electrical conductivity and density showed less effect of large holding time at slower heating rate. To investigate the particle bonding behaviour, scanning electron micrographs of the fabricated samples were also studied. The adopted methodology effectively sintered the particles without the need for applying pressure. Using genetic algorithm (GA), a multi-objective optimization was conducted to achieve an optimized set of microwave sintering machine parameters, aiming for maximum density, electrical conductivity and minimum volumetric shrinkage. A density of 7.32 g/cc, volumetric shrinkage of 8.36% and electrical conductivity of 73.88% IACS was obtained from samples sintered at optimized sintering parameters. The dimensions of the fabricated electrodes and machined cavity were then compared with the computer-aided design model. The results revealed an efficient process for fabricating complex shaped EDM electrodes.

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.