Computer-aided Design File Research Articles

Orthogonal Agent Comprising a Nitrile N-Oxide and a Phenylcarbamate for Facile Molecular Integration on Styrne-Butadiene Resin.

The covalent attachment of poly(ethylene glycol) (PEGylation) to materials minimizes non-specific fouling of the material surface with biocomponents. While the PEGylation reaction on polar surfaces is widely used and regarded as a common technique, the PEGylation on less polar polymers and elastomers is extremely difficult due to the absence of reactive points with PEG terminus. Herein, the design and synthesis of an orthogonal agent with a nitrile N-oxide and a phenyl carbamate that can mediate between an alkene and an amine are reported. The ligation capacity of the orthogonal agent is demonstrated through the model reaction to connect between 1-hexene and 4-methoxybenzylamine and the grafting reaction of PEG onto poly(styrene-co-butadiene) (SB) resin. The surface characteristics of PEGylated SB film are evaluated by X-ray photoelectron spectroscopy (XPS) and contact angle measurements. Because SB resin is frequently used as a 3D-printing polymer, the present study indicates that the orthogonal agent can be applicable to the surface modification of 3D-printed objects precisely manufactured by using a computer-aided design (CAD) file in the future.

Development of a multibody model for go-karts considering frame flexibility

AbstractThis study focuses on the optimization dynamics of racing go-karts, which is heavily influenced by the frame's stiffness. Lacking suspensions and differentials, go-karts rely on the frame stiffness for wheel balancing and skid prevention by lifting the inner rear wheel during turns. Utilizing a rigid-flexible model in MSC Software ADAMS View, validated by frame deformation measurements, this research integrates finite element analysis with multibody techniques. The model, leverages computer aided design files for frame geometry and employs finite element analysis for frame validation. It facilitates evaluating go-kart dynamics through simulations, aiding in maneuver testing and design optimization. This approach provides a comprehensive framework for advancing go-kart designs.

CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods

Computer-aided design (CAD) files are used to create digital designs for various structures – from the smallest chips in the high-tech industry to large-scale buildings and bridges in the civil engineering space. We found that most exploits and malicious payloads are deployed through Auto List Processing (AutoLISP) source code (LSP) or Fast Load AutoLISP (FAS) files, which are non-executable files (NEFs) containing scripts in the AutoLISP language that are native to AutoCAD; While antivirus software is capable of detecting many malicious CAD files, the potential to improve protection by using a dedicated machine learning (ML) based detection solution remains, especially against unknown and sophisticated CAD malware. In this study, we are the first to propose designated feature extraction methods and a robust framework aimed at the detection of known and unknown AutoLISP malware using ML algorithms. To accomplish this, we examined the structure, functionality, and ecosystems of AutoLISP files and collected the largest known representative collection of LSP files consisting of 6418 malicious and benign files (labeled and verified). We then explored the use of two novel static-analysis-based feature extraction methods (knowledge-based and structural) designated for LSP files to extract a discriminative set of informative features, which can subsequently be used by ML models to detect malicious LSP files. These two feature extraction methods serve as the basis of the proposed detection framework, whose performance we comprehensively compare to both widely used antiviruses and baseline ML models based on existing feature extraction methods, including MinHash, Bidirectional Encoder Representations from Transformers (BERT), and n-gram. Our results highlight our methods' contributions to the detection of unknown AutoLISP malware and demonstrate their ability to outperform existing methods. The best performance in the task of unknown malicious LSP file detection was obtained by the Artificial Neural Networks (ANN) model trained on 100 knowledge-based features, which obtained a true positive rate (TPR) of 99.49% with a false positive rate (FPR) of 0.57%. Our framework's role in explainability is also highlighted, as we also present the prominent features that contribute most to the model's detection capabilities; this information can be used for explainability purposes. We conclude by evaluating the proposed framework's ability to detect a malicious file from an unknown AutoLISP malware family and by evaluating our framework on an additional independent test set that originated from another source, scenarios that are often faced by malware detection solutions.

Effect of wound dressing porosity and exudate viscosity on the exudate absorption: In vitro and in silico tests with 3D printed hydrogels

Exudate absorption is a key parameter for proper wound dressing performance. Unlike standardized tests that consider exudate viscosity close to that of water, patients' exudates vary greatly in composition and, therefore, viscosity. This work aimed to investigate the effects of exudate viscosity and pore size of hydrogel-like dressings on the exudate absorption rate to establish rational criteria for the design of dressings that can meet the personalized needs of wound treatment. Computer-aided design (CAD) was used for Digital Light Processing (DLP) 3D printing of hydrogels with 0%, 30% and 60% porosity. The hydrogels were characterized in detail, and the absorption of two simulated exudate fluids (SEFs) was video-recorded. The same CAD files were used to develop in silico models to simulate exudate uptake rate. Both in vitro data and in silico modeling revealed that low-viscosity SEF penetrates faster through relatively small hydrogel pores (approx. 400 μm) compared to larger pores (approx. 1100 μm) due to capillary forces. However, in vitro vertical uptake took longer than when simulated using CAD design due to lateral fluid absorption through the pore walls in the hydrogel bulk. Distortions of hydrogel channels (micro-CT images) and lateral fluid absorption should be both considered for in silico simulation of SEF penetration. Overall, the results evidenced that porous hydrogel dressings allow rapid penetration (within a few seconds) and hosting of exudates, especially for pore size <1 mm. This information may be useful for design criteria of wound dressings with adequate fluid handling and drug release rate.

A spectral finite element Reissner–Mindlin shell formulation with NURBS-based geometry definition

A curved non-isoparametric Reissner–Mindlin shell element is developed for analyzing thin-walled structures. The standard kinematic description of the element requires the calculation of the director vector. To address this demand accurately, similar to isogeometric analysis (IGA), the geometry is defined by utilization of the non-uniform rational B-splines (NURBS) imported directly from computer-aided design (CAD) files. Then, shape functions of the Legendre spectral element method (SEM) are used to interpolate the displacements. Consequently, the shell director vector and Jacobian of the transformation are calculated properly according to the presented formulation. On the other hand, in Legendre SEM combined with Gauss–Lobatto–Legendre quadrature, the integration points and the element nodes coincide. Thus, the easily computable local coordinate systems at the integration points can be used directly as nodal basis systems. A separate calculation of nodal basis systems at control points, which is the source of either complexity or error in IGA shells, is not required. Given the condition number of the stiffness matrix in the developed method, super high-order elements can also be used. Very high order p-refined elements are used in addition to h-refinement of the mesh to show the capability of higher order elements to analyze problems without mesh refinement. The validity and convergence rate of the method are investigated and verified through various cases of h- and p-refinement in challenging obstacle course problems.

Computer-aided design files as a learning tool in dental anatomy.

Computer-aided design files as a learning tool in dental anatomy.

Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.

In nondestructive evaluation (NDE), accurately characterizing defects within components relies on accurate sizing and localization to evaluate the severity or criticality of defects. This study presents for the first time a deep learning (DL) methodology using 3-D U-Net to localize and size defects in carbon fiber reinforced polymer (CFRP) composites through volumetric segmentation of ultrasonic testing (UT) data. Using a previously developed approach, synthetic training data, closely representative of experimental data, was used for the automatic generation of ground truth segmentation masks. The model's performance was compared to the conventional amplitude 6 dB drop analysis method used in the industry against ultrasonic defect responses from 40 defects fabricated in CFRP components. The results showed good agreement with the 6 dB drop method for in-plane localization and excellent through-thickness localization, with mean absolute errors (MAEs) of 0.57 and 0.08 mm, respectively. Initial sizing results consistently oversized defects with a 55% higher mean average error than the 6 dB drop method. However, when a correction factor was applied to account for variation between the experimental and synthetic domains, the final sizing accuracy resulted in a 35% reduction in MAE compared to the 6 dB drop technique. By working with volumetric ultrasonic data (as opposed to 2-D images), this approach reduces preprocessing (such as signal gating) and allows for the generation of 3-D defect masks which can be used for the generation of computer-aided design files; greatly reducing the qualification reporting burden of NDE operators.

Automation of trimming die design inspection by zigzag process between AI and CAD domains

Quality control using artificial intelligence (AI) has led to considerable improvements in the manufacturing industry. However, inspection of computer-aided design (CAD) drawing to prevent potential errors is still performed manually by engineers. The design inspection which involves sequential and repetitive processes is labor-intensive, time-consuming, and prone to human error. In this study, we developed an automatic design inspection system for CAD data by integrating AI module and CAD software. The target product of this study is automobile trimming die design. The AI modules replace the engineers’ judgment, and the CAD software performs the operations requested by the AI modules. The design inspection is completed through a zigzag interaction between the AI modules and CAD software by a one-click operation without the intervention of experts. In the zigzag interaction process, 3D CAD files are converted to 2D images for interaction from the CAD software to the AI modules, and the output of the AI modules is transferred to the CAD software through VBA macros. The AI modules are designed to be CAD-independent and data-efficient; therefore, they can be easily adapted to other CAD software and achieve a high performance, even when trained with a few trimming dies. The length measurement error is only 2.4 % on average, which is at the 14th place out of 18 grades according to ISO 286–1:1988 standard tolerance. Additionally, the inspection time is reduced to approximately one-fifth of the manual inspection time by experts.

Digital Manufacturing of Functional Ready-to-Use Microfluidic Systems.

Digital manufacturing (DM) holds great potential for microfluidics, but requirements for embedded conduits and high resolution beyond the capability of common manufacturing equipment, and microfluidic systems' dependence on peripheralshave limited its adoption. Capillaric circuits (CCs) are structurally encoded, self-contained microfluidic systems that operate and self-fill via precisely tailored hydrophilicity. CCs are heretofore hydrophilized in a plasma chamber, but which offers only transient hydrophilicity, lacks reproducibility, and limits CC design to open surface channels subsequently sealed with tape. Here, the additive DM of monolithic, fully functional, and intrinsically hydrophilic CCs is reported. CCs are 3D printed with commonly available light-engine-based 3D printers using poly(ethylene glycol)diacrylate-based ink co-polymerized with hydrophilic acrylic acid crosslinkers and optimized for hydrophilicity and printability. A new, robust capillary valve design and embedded conduits with circular cross-sections that prevent bubble trapping are presented, interwoven circuit architectures created, and CC use illustrated with an immunoassay. Finally, the external paper capillary pumps are eliminated by directly embedding the capillary pump in the chip as a porous gyroid structure, realizing fully functional, monolithic CCs. Thence, a digital file can be made into a CC by commonly available 3D printers in less than 30min enabling low-cost, distributed DM of fully functional ready-to-use microfluidic systems.

BioIn-Tacto: A compliant multi-modal tactile sensing module for robotic tasks

We present the open-source design and fabrication of a compliant multimodal tactile sensing module. The sensing module design presented here enables robotic end-effectors to sense contact properties like pressure and vibration and estimate a quaternion that represents the deformation due to contact. We elaborated the module’s compliant structure fabrication process to use only 3D printed molds and a vacuum chamber, making it accessible to a broad range of roboticists. The process also allows production batches of up to five modules and easy deployment to robotic hands. The repository associated contains packages that support the Robot Operating System (ROS) to perform data collection and visualization. All hardware Computer-Aided Design (CAD) files and software source codes have been released and can be easily assembled and modified. The sensing module proposed in this paper uses off-the-shelf Microelectromechanical (MEMs) barometers and Magnetic, Angular Rate and Gravity (MARG) systems. Those components may be replaced by alternatives depending on the sensor’s availability, constraints related to the module’s size, or robotic application without prejudice to sensor functionality.

Mean Flow Characteristics Downstream of a Vortex Tube Separator Array

Vortex tube separator (VTS) arrays are used to filter foreign particles from turboshaft engine intakes. Arrays may consist of hundreds of separators that share a common surrounding plenum, potentially allowing for significant interactions between the devices. However, most VTS studies have been limited to individual separators acting alone. The current study first involves an experimental investigation of the fluid flow emerging from the outlets of a VTS array installed in a wind tunnel. Particle image velocimetry is used to measure the flowfield at planes , , and downstream of the outlets, where is the inlet tube diameter of a separator. The results indicate that the emerging vortices quickly merge into a single vortex with high circumferential velocity concentrated near the edges of the duct, and that a central recirculation zone (CRZ) develops. This is in contrast to the flow from a single VTS, which does not have a clear CRZ. Comparisons to similar combustion swirler flows lead to the hypothesis that VTS arrays may uniquely generate fast-developing CRZs that produce strong axial flow gradients near or even within some VTS outlets. Such gradients will alter the filtration performance of affected VTSs, which is highly sensitive to flow rates. To explain the onset of recirculation that is exclusive to the array configuration, the framework of a momentum integral model has been developed and is presented here. Additionally, computer-aided design files of the experimental geometry along with inlet/outlet boundary conditions are being made available for any future computational validation studies of this complex flowfield.

Effect of printing strategies on the properties of product in selective Sorel cement activation

PurposeParticles bed binding by selective cement activation (SCA) method is a computer-aided manufacturing (CAM) technique used to produce cementitious elements. A computer-aided design file is sliced to generate G-codes before printing. This paper aims to study the effect of key input parameters for slicer software on the final properties of printed products.Design/methodology/approachThe one factor at a time (OFAT) methodology is used to investigate the impact of selected parameters on the final properties of printed specimens, and the causes for the variations in outcomes of each variable are discussed.FindingsFiner aggregates can generate a more compact layer, resulting in a denser product with higher strength. Fluid pressure is directly determined by voxel rate (rV); however, high pressures enable better fluid penetration control for fortified products; for extreme rVs, residual voids in the interfaces between successive layers and single-line primitives impair mechanical strength. It was understood that printhead movement along the orientation of the parts in the powder bed improved the mechanical properties.Originality/valueThe design of experiment (DOE) method assesses the influence of process parameters on various input printing variables at the same time. As the resources are limited, a fractional factorial plan is carried out on a subset of a full factorial design; hence, providing physical interpretation behind changes in each factor is difficult. OFAT aids in analyzing the effect of a change in one factor on output while all other parameters are kept constant. The results assist engineers in properly considering the influence of variable variations for future DOE designs.

What’s in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models Through User-Provided Names in Computer Aided Design Files

Abstract Semantic knowledge of part-part and part-whole relationships in assemblies is useful for a variety of tasks from searching design repositories to the construction of engineering knowledge bases. In this work, we propose that the natural language names designers use in computer aided design (CAD) software are a valuable source of such knowledge, and that large language models (LLMs) contain useful domain-specific information for working with this data as well as other CAD and engineering-related tasks. In particular, we extract and clean a large corpus of natural language part, feature, and document names and use this to quantitatively demonstrate that a pre-trained language model can outperform numerous benchmarks on three self-supervised tasks, without ever having seen this data before. Moreover, we show that fine-tuning on the text data corpus further boosts the performance on all tasks, thus demonstrating the value of the text data which until now has been largely ignored. We also identify key limitations to using LLMs with text data alone, and our findings provide a strong motivation for further work into multi-modal text-geometry models. To aid and encourage further work in this area we make all our data and code publicly available.

Electron Target Cooling Analyses of the KIPT ADS Using MCNP and Ansys Fluent

This study presents multiphysics analyses of the electron target cooling system of the accelerator-driven system (ADS) of the Kharkiv Institute of Physics and Technology (KIPT) using MCNP and Fluent computer programs. MCNP has been used to transport electrons, gammas, and neutrons, and to calculate the energy deposition in the target materials. The MCNP mesh-tally data have been imported into Fluent by a C subroutine that has been compiled and linked to Fluent as a user-defined function. The KIPT ADS is located in Ukraine and was in operation until February 2022. The Fluent model is based on the computer-aided design files from the manufacturing process of the target assembly. The Fluent results for the reference case match very well the literature results obtained by STAR-CCM+ during the design phase. Other cases that differ from the reference one have been analyzed; in these cases, it is assumed a malfunction of the electron accelerator or of the water cooling system. The target cooling system operates normally for all the analyzed cases except when the inlet water mass flow rate is decreased. The transient analysis showed that the target cooling system can operate for 180 s with full power when the inlet water mass flow rate is decreased down by 75%.

A Geant4 shielding design for the first US carbon multi-ion hybrid synchrotron facility

The Mayo Clinic Florida Integrated Oncology Building will be the home of the first spot-scanning only carbon/proton hybrid therapy system by Hitachi, Ltd. It will provide proton beams up to kinetic energies of 230 MeV and carbon beams up to 430 MeV n−1 for clinical deployment. To provide adequate radiation protection, the Geant4 (v10.6) Monte Carlo toolkit was utilized to quantify the ambient dose equivalent at a 10 mm depth (H*(10)) for photons and neutrons. To perform accurate calculations of the ambient dose equivalent, three-dimensional computer-aided design files of the entire planned facility were imported into Geant4, as well as certain particle system components such as the bending magnets, fast Faraday cup, and gantry. Particle fluence was scored using 60 cm diameter spheres, which were strategically placed throughout areas of interests. Analytical calculations were performed as first-pass design checks. Major shielding slabs were optimized using Geant4 simulations iteratively, with more than 20 alternative designs evaluated within Geant4. The 430 MeV n−1 carbon beams played the most significant role in concrete thickness Requirements. The primary wall thickness for the carbon fixed beam room is 4 meters. The presence of the proton gantry structure in the simulation caused the ambient dose equivalent to increase by around 67% at the maze entrance, but a decrease in the high energy beam transport corridor. All shielding primary and secondary goals for clinical operations were met per state regulation and national guidelines.

Monte Carlo modeling of Halcyon and Ethos radiotherapy beam using CAD geometry: validation and IAEA-compliant phase space

Objective. A Monte Carlo (MC) model of a Halcyon and Ethos (Varian Medical Systems, a Siemens Healthineers Company) radiotherapy beam was validated and field-independent phase space (PHSP) files were recorded above the dual-layer multileaf collimators (MLC). Approach. The treatment head geometry was modeled according to engineering drawings and the dual-layer MLC was imported from CAD (computer-aided design) files. The information for the incident electron beam was achieved from an iterative electromagnetic solver. The validation of the model was performed by comparing the dose delivered by the square MLC fields as well as complex field measurements. Main results. An electron phase space was generated from linac simulations and achieved improved MC results. The output factors for square fields were within 1% and the largest differences of 5% were found in the build-up region of PDDs and the penumbra region of profiles. With the more complicated MLC-shaped field (Fishbone), the largest differences of up to 8% were found in the MLC leaf tip region due to the uncertainty of the MLC positioning and the mechanical leaf gap value. The impact of the collimator rotation on the PHSP solution has been assessed with both small and large fields, confirming negligible effects on in-field and out-of-field dose distributions. Significance. A computational model of the Halcyon and Ethos radiotherapy beam with a high accuracy implementation of the MLC was shown to be able to reproduce the radiation beam characteristics with square fields and more complex MLC-shaped fields. The field-independent PHSP files that were produced can be used as an accurate treatment head model above the MLC, and reduce the time to simulate particle transport through treatment head components.

Additive Manufacturing for Fabrication of Point-of-Care Therapies in Austere Environments.

Known as the "golden hour," survival of most critically injured patients is highly dependent on providing the required treatment within the first hour of injury. Recent technological advances in additive manufacturing (also known as three-dimensional [3D] printing) allow for austere deployment and point-of-care rapid fabrication of a variety of medical supplies, including human tissues and bioactive bandages, in prolonged field care scenarios. In this pilot project, our aim was to investigate the ability to 3D print a range of potential biomedical supplies and solutions in an austere field environment. We specifically designed and fabricated novel surgical tools, bioactive bandages, objects (screw and anatomic models), and human meniscal tissue in an austere African desert environment. A total of seven packages were sent using a commercial carrier directly to the end destination. A multi-tool ruggedized 3D printer was used as the manufacturing platform for all objects fabricated downrange. Human mesenchymal stem cells were shipped for 3D bioprinting of human menisci and bioactive bandages. Design and fabrication for all 3D-printed products utilized computer-aided design (CAD) tools. Initial shipment from a single U.S. site to the sub-Saharan Africa location was relatively prompt, taking an average of 4.7 days to deliver three test packages. However, the actual delivery of the seven packages from Orlando, FL, to the same sub-Saharan Africa site took an average of 16 days (range 7-23 days). The ruggedized printer successfully fabricated relevant medical supplies using biocompatible filament, bioink hydrogels, and stem cell-loaded bioinks. This prototype did not, however, have the capacity to provide a sterile environment. A multi-material complete bandage was 3D printed using polyamide polyolefin and cellulose, live cells, neomycin salve, and adhesive. The bandage, wound covering backing, and adhesive backing print took under 2 min to 3D print. Surgical instrument CAD files were based on commercially available medical-grade stainless-steel instruments. The screw CAD file was downloaded from the NIH 3D Print Exchange website. The prints of the two surgical tools and screw using thermoplastic material were successful. Menisci, relatively complex forms of the cartilage, were 3D bioprinted with a gel that held their form well after printing and were then solidified slightly using a cross-linking solution. After 2 min of solidification, it was possible to remove and handle the menisci. The current and future challenges of prolonged field care need to be addressed with new techniques, training, and technology. Ruggedized, deployable 3D printers allow for the direct fabrication of medical tools, supplies, and biological solutions for austere use. Delivery of packages can vary, and attention to routes and location is key, especially for transit of time-sensitive perishable supplies such as live cells. The significance of this study provides the real possibility to 3D print "just-in-time" medical solutions tailored to the need of an individual service member in any environment. This is a potentially exciting opportunity to bring critical products to the war front.

Embracing Anisotropy – Opportunities and Challenges to the Functional Application of Advanced Manufacturing of Energetic Materials

Embracing Anisotropy – Opportunities and Challenges to the Functional Application of Advanced Manufacturing of Energetic Materials

Smart Cloud Collocation: Geometry-Aware Adaptivity Directly From CAD

Computer Aided Design (CAD) is widely used in the creation and optimization of various industrial systems and processes. Transforming a CAD geometry into a computational discretization that be used to solve PDEs requires care and a deep knowledge of the selected computational method. In this article, we present a novel integrated collocation scheme based on smart clouds. It allows us to transform a CAD geometry into a complete point collocation model, aware of the base geometry, with minimum effort. For this process, only the geometry of the domain, in the form of a STEP file, and the boundary conditions are needed. We also introduce an adaptive refinement process for the resultant smart cloud using an a posteriori error indication. The scheme can be applied to any 2D or 3D geometry, to any PDE and can be applied to most point collocation approaches. We illustrate this with the meshfree Generalized Finite Difference (GFD) method applied to steady linear elasticity problems. We further show that each step of this process, from the initial discretization to the refinement strategy, is connected and is affected by the approach selected in the previous step, thus requiring an integrated scheme where the whole solution process should be considered at once. • CAD files are used to generate smart point clouds without any intermediate step. • Exact normal vectors and boundary conditions are based on the CAD geometry. • ZZ-type error indicators are the basis for model adaptivity and are quick to compute. • Model adaptivity speeds up the convergence rate of the solution. • Model adaptivity is based on the exact CAD geometry.

3D printing of hierarchically micro/nanostructured electrodes for high-performance rechargeable batteries.

3D printing, also known as additive manufacturing, is capable of fabricating 3D hierarchical micro/nanostructures by depositing a layer-upon-layer of precursor materials and solvent-based inks under the assistance of computer-aided design (CAD) files. 3D printing has been employed to construct 3D hierarchically micro/nanostructured electrodes for rechargeable batteries, endowing them with high specific surface areas, short ion transport lengths, and high mass loading. This review summarizes the advantages and limitations of various 3D printing methods and presents the recent developments of 3D-printed electrodes in rechargeable batteries, such as lithium-ion batteries, sodium-ion batteries, and lithium-sulfur batteries. Furthermore, the challenges and perspectives of the 3D printing technique for electrodes and rechargeable batteries are put forward. This review will provide new insight into the 3D printing of hierarchically micro/nanostructured electrodes in rechargeable batteries and promote the development of 3D printed electrodes and batteries in the future.