CAD System Research Articles

Recycling-oriented analysis of domestic vacuum cleaner

A study of recyclability of a selected product, based on analyses conducted using an original application for the assessment of recyclability of products. The term ‘recycling’ has been defined and the role of recyclability in the process of eco-design specified. Some general recycling-oriented assessment methods have been discussed. The method used in the study, based on a recycling-oriented product model (RmW) and an agent-based system, has been described. The analysis has been applied to a domestic vacuum cleaner, considering the specificity of the industry. A model of the vacuum cleaner has been created in a 3D CAD system and extended with recyclability parameters to support a comprehensive assessment for easiness of dismantling and reusability of materials. Results of the calculations performed by the system have been analysed in order to characterize recycling properties of the cleaner.

Cad Modelling of Combination Tool for End Mill and Reamer

Abstract: CAD model quality in parametric design scripts largely determines the position of inflexibility and rigidity of a 3D model (how easy it's to alter the figure) as well as its reusability (the capability to use being figure in other surrounds and operations). In the environment of mechanical CAD systems, the nature of the point- grounded parametric modelling paradigm, which is grounded on parent – child interdependencies between features, allows a wide selection of approaches for creating a specific model. In this project involves designing and fabrication of the combination tool to perform desire operation. This combination tool can perform two operation which is End milling, Reaming, this can be done in such manner that the lower part that is end mill will remove the material from previously drilled hole then the second stage of operation commence in which the reamer will finish off the small tiny particle in order to achieve desired flawless finish. The use of the combined cutting tools to optimize the productivity of fabrication & operative time by reducing the time to change the tools. Design of the tool has been done with the assistance of some software which is Catia, Blender. Keywords: Combination tool, End milling, Reaming, Catia.

A Machine Learning Based Framework for Heart Disease Detection

Even in rural parts of many nations, coronary heart disease has emerged as the main cause of mortality. More than 23 million people will die from cardiovascular disease by 2030, according to the World Health Organization (WHO). With the use of cardiovascular disease prediction, healthcare practitioners may check the characteristics necessary for diagnosis, such as blood pressure and diabetes, which are vital. Many data mining methods are currently used in the medical industry, but additional research is needed to evaluate how well these categorization approaches function in real-world settings. The project's purpose is to quickly identify the best candidates for constructing heart disease prediction models, and to do it in a timely manner. The goal of this study is to increase the accuracy of cardiac disease prediction by addressing and overcoming the issues in the area (CVDs). CAD systems, which help physicians make choices, are often developed as a result of breakthroughs in machine learning technology. The categorization and prediction of cardiac disease are discussed in this article. The algorithms explored include ANN, KNN, and CNN. To conduct the evaluation, we utilised the UCI Cleveland database. For these algorithms, a thorough evaluation of the utility and consistency of data mining approaches found that CNNs performed best. Usefulness and consistency of data mining techniques for these algorithms revealed that the CNN was the most reliable process.

Studying the Role of Cerebrovascular Changes in Different Compartments in Human Brains in Hypertension Prediction

Hypertension is a major cause of mortality of millions of people worldwide. Cerebral vascular changes are clinically observed to precede the onset of hypertension. The early detection and quantification of these cerebral changes would help greatly in the early prediction of the disease. Hence, preparing appropriate medical plans to avoid the disease and mitigate any adverse events. This study aims to investigate whether studying the cerebral changes in specific regions of human brains (specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying the vascular changes occurring over the entire brain’s vasculature. This was achieved by proposing a computer-aided diagnosis system (CAD) to predict hypertension based on cerebral vascular changes that occur at the anterior compartment, the posterior compartment, and the whole brain separately, and comparing corresponding prediction accuracy. The proposed CAD system works in the following sequence: (1) an MRA dataset of 72 subjects was preprocessed to enhance MRA image quality, increase homogeneity, and remove noise artifacts. (2) each MRA scan was then segmented using an automatic adaptive local segmentation algorithm. (3) the segmented vascular tree was then processed to extract and quantify hypertension descriptive vascular features (blood vessels’ diameters and tortuosity indices) the change of which has been recorded over the time span of the 2-year study. (4) a classification module used these descriptive features along with corresponding differences in blood pressure readings for each subject, to analyze the accuracy of predicting hypertension by examining vascular changes in the anterior, the posterior, and the whole brain separately. Experimental results presented evidence that studying the vascular changes that take place in specific regions of the brain, specifically the anterior compartment reported promising accuracy percentages of up to 90%. However, studying the vascular changes occurring over the entire brain still achieve the best accuracy (of up to 100%) in hypertension prediction compared to studying specific compartments.

Flexoelectric nanostructure design using explicit topology optimization

Flexoelectricity is the coupling between polarization and strain gradient. As the large strain gradient leads to a strong flexoelectric effect, the design of flexoelectric nano-structure via topology optimization has seen growing attentions. In the present work, an explicit topology optimization framework is proposed for flexoelectric structures design. To achieve this purpose, the Moving Morphable Void (MMV)-based approach is employed in the context of Isogeometric Analysis (IGA) combined with the Trimming Surface Analysis (TSA). Energy conversion factor and effective electric polarizability are respectively optimized to improve the flexoelectric performance of the nanostructure. Performing design under the explicit framework coupled with IGA can bring several advantages. Due to the use of NURBS basis functions of IGA, the required continuity in the approximation of the PDEs of flexoelectricity can be satisfied straightforwardly. Furthermore, with the use of the TSA technique, the occurrence of weak/gray material, which may cause numerical instability in flexoelectricity design, can be avoided. Also due to the explicit geometry description in MMV, the optimized result can be imported to the CAD system directly, which is significant for nanoscale structure from manufacturing perspective. Several representative numerical examples for topology optimization of flexoelectric structures are presented to demonstrate the effectiveness and advantages of the proposed approach.

Resistance properties and adaptation mechanism of cadmium in an enriched strain, Cupriavidus nantongensis X1T

Bacterial adaption to heavy metal stress is a complex and comprehensive process of multi-response regulation. However, the mechanism is largely unexplored. In this study, cadmium (Cd) resistance and adaptation mechanism in Cupriavidus nantongensis X1T were investigated. Strain X1T could resist the stress of 307 mg/L Cd2+ and remove 70% Cd2+ in 48 h. Spectroscopic analyses suggested interactions between Cd2+ with C-N, -COOH, and -NH ligands of extracellular polymeric substances. Whole-genome sequencing found that the resistance of Cd2+ in strain X1T was caused by the joint action of Czc and Cad systems. Cd2+ at 20 mg/L elicited differential expression of 1157 genes in strain X1T. In addition to the reported effects of uptake, adsorption, effluxion, and accumulation system, the oxidative stress system, Type-VI secretory protein system, Fe-S protein synthesis, and cysteine synthesis system in strain X1T were involved in the Cd2+ resistance and accumulation. The intracellular accumulation content of Cd2+ in strain X1T was higher than the extracellular adsorption content made strain X1T to be an important resource strain in the bioremediation of Cd-contaminated sewage. The results provide a theoretical network for understanding the complex regulatory system of bacterial resistance and adaptation of Cd against stressful environments.

Possibilities of analysis of porous structures using industrial computed tomography

This research examined the surface and internal structure of lattice samples with the use of computed tomography. The CAD system PTC Creo Parametric 8 was used to create a 3D model of a lattice structure. For research within this paper, two samples of ABS building material were produced with the use of FDM technology. One of them was destructively tested, i.e., deformed by pressure. The untested sample contained, in addition to base material,P400SR supporting material while the tested sample only contained ABS construction material. Individual samples were subjected to microCT scanning to perform samples' analysis of dimensional and shape characteristics and their porosity. The paper describes the procedure and methodology of individual problem-solving steps. Our basic requirement was to determine surface for software reconstruction as it affects all analyzes being performed. Since the unloaded sample also contains support material, the surface area for the base material only had to be determined and support material, while reconstructing the sample, be removed. Due to higher support material density, direct segmentation ofbase material was impossible. In the end, the porosity analysis of samples was created with the use of VGStudio MAX software.

Towards ubiquitous data access service of 3D part library in cloud scheme with host independence

Most current part libraries are developed and deployed in specific usage scenario or CAD systems, which inevitably brings obstacles in information sharing and exchanging of parts. To reduce repeated development and provide a uniform platform for designers in different sites, a 3D part library in cloud scheme is presented in this paper. Host programs with predetermined specification can access the part data through an adapter according to their customized requests with uniform interfaces, which constructs a ubiquitous service. To realize host independence, the part models are created in a modeler with native ACIS, and then, they are converted into 3D files in different formats for practical needs; finally, these models are imported into CAD systems or other platforms in real designs. The framework is composed of three components, namely, as PLS (part library service) provider, PLS adapters, and PLS hosts. PLS provider is the kernel of 3D data access service in cloud scheme, while PLS adapters serve as the bridges that connect PLS provider and hosts, and the PLS can be grafted on various applications including current mainstream CAD systems as an embedded module or run on the websites or even mobile terminals. The PLS provider is deployed and maintained on cloud, and designers can acquire remote part information within a local ongoing project. In the detailed construction of this part library, diversiform knowledge in part parameters and structures is implanted to define the geometry and rule constraints for the 3D modeling, with which the backstage has the ability of conveniently editing the information in the part library for better upgrade and contrapuntally services. This concept has been implemented within a PaaS framework to provide the ubiquitous 3D part data accessing route, which has been successfully applied in a large number of enterprises, and accumulates considerable practical cases.

High-performance medical image secret sharing using super-resolution for CAD systems

High-performance medical image secret sharing using super-resolution for CAD systems

Применение нейронных сетей при построении нелинейных моделей полевых транзисторов

The nonlinear model of a field-effect transistor based on the mathematical apparatus of the theory of artificial neural networks was developed. The main feature of this model is the possibility of training neural networks used to approximate the current-voltage characteristic and the gate-drain and gate-source capacitances of a nonlinear transistor model by using optimization algorithms built into popular microwave CAD systems. This makes it possible to use the well-known advantages of neural networks in the problems of function approximation to increase the reliability of the results of non-linear modeling of microwave devices based on field-effect transistors.

Automated method for real-time AMD screening of fundus images dedicated for mobile devices.

Aged macular degeneration (AMD) leads to a progressive decline in visual acuity until reaching blindness. It is considered as an irreversible pathology where an early diagnosis remains crucial. However, the lack of ophthalmologists, the permanent increase in elderly people, and their limited mobility involves a delay in AMD diagnosis. In this paper, we propose an automated method for AMD screening. The proposed processing pipeline consists in applying the well-known Radon transform to the macula region in order to model the AMD lesions even with a moderate quality of smartphone-captured fundus images. Thereby, the relevant features are carefully selected, related to the main proprieties of drusens, and then provided to an SVM classifier. The implementation of the method into a smartphone associated to a fundus image capturing device leads to a mobile CAD system that performs higher performance AMD screening. Within this framework and to achieve a real-time implementation, an optimization approach is suggested in order to reduce the processing workload. The evaluation of our method is carried out through the three public STARE, REFUGE, and RFMiD databases. A 4-fold cross-validation approach is used to evaluate the method performance where accuracies of 100%, 95.2%, and 94.3% are respectively obtained with STARE, REFUGE, and RFMiD databases. Comparisons with the state-of-the-art methods in the literature are done. Thereafter, the robustness of the proposed method was evaluated and proved. We note that 100% accuracy was preserved despite the use of degraded quality fundus images as noisy and blurred. Moreover, the propounded method was implemented in S7-Edge and S9 Smartphone devices, where the execution times of 19 and 15 milliseconds were respectively achieved, which proves the AMD real-time detection. Taking advantage of its mobility, cost-effective, detection performance, and reduced execution time, our proposed method seems a good solution for real-time AMD screening on mobile devices.

Distributed Computing Collaborative CAD System Based on COM / DCOM

Distributed Computing Collaborative CAD System Based on COM / DCOM

Mammogram breast cancer CAD systems for mass detection and classification: a review

Although there is an improvement in breast cancer detection and classification (CAD) tools, there are still some challenges and limitations that need more investigation. The significant development in machine learning and image processing techniques in the last ten years affected hugely the development of breast cancer CAD systems especially with the existence of deep learning models. This survey presents in a structured way, the current deep learning-based CAD system to detect and classify masses in mammography, in addition to the conventional machine learning-based techniques. The survey presents the current publicly mammographic datasets, also provides a dataset-based quantitative comparison of the most recent techniques and the most used evaluation metrics for the breast cancer CAD systems. The survey provides a discussion of the current literature and emphasizes its pros and limitations. Furthermore, the survey highlights the challenges and limitations in the current breast cancer detection and classification techniques.

Overview of Service-Oriented Architecture Implementations for Calculating the Impact of External Influences on all Output Functions of an Electronic Circuit

The principles of building information systems based on service-oriented architecture are considered. The advantages of using this architectural approach, as well as a model of the component composition of the system, are described. A description of various technologies for building software using a service-oriented approach based on web services is given. The main limitations of using the CORBA technology standard for writing distributed applications are determined. The technology of building web services based on the SOAP data exchange protocol is described in the context of using the ESB service bus for the interaction of heterogeneous services in a single information system. The architectural style of microservice organization of distributed software is considered as a way to increase the granularity of the system in order to ensure the best scalability and fault tolerance of the system. Methods for constructing the mathematical support of web services for distributed schematic CAD systems for calculating the influence of external influences (changes in temperature, radiation, etc.) on all output functions of the simulated circuit are presented. Parameters of two-terminal type R, L, and C and transmission parameters of dependent current or voltage sources controlled by potential or current variables are taken as variable values of the components.

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

AbstractThe cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately.

Machine Vision based Intelligent Breast Cancer Detection

Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.

AUTOMATION OF PARTIAL TASKS IN THE DESIGN OF ROBOTIC ARMS

The design of robotic arms is a demanding process, especially in terms of the demands placed on developers' knowledge and experience and the time required for the design. Therefore, the development aimed to create procedures for possible automation of some of the individual tasks of the robotic arm design process, which are further applied in the development of a software tool for the automation of partial tasks in the design of robotic arms RobotDesigner. The benefits and functions of RobotDesigner are demonstrated in the example of optimization of a simple manipulator with 5 degrees of freedom for a given manipulation task. The optimization results show that the use of this software tool significantly speeds up and streamlines the sub-tasks of design and optimization of arms. By using iterative processes and artificial intelligence, using similar software tools and connecting them to CAD systems, it will not only be possible in the future to fully automate the robot arm design process but also to generate and compare different structures and design solutions to find the most optimal for the task, which the robot will perform.

Review of Design Techniques Applied to Electrical Installation Projects: a Comparison of CAD and BIM Tools

In various sectors and industries, the use of digital design drawings has enabled the collection and visualization of relevant job data. The emergence of the CAD system changed several branches of the design segment and until today it is one of the main methods used. However, the evolution of society and technology culminated in the development of the BIM methodology, seen by many as promising in the design of projects, as it allows graphic improvements while integrating the project participants in the different phases of the work. Such an approach becomes information focused by exploring parametric and 3D representations of virtual models. In this article, both CAD and BIM contributions to the industry are raised through a case study of a residential electrical project developed by the company CGWorks.

Modeling of geometric correspondences and transformations via geometry constructions language

Geometric correspondences and transformations can be applied in solutions to numerous problems of geometric modeling, mainly in order to create models of curves and surfaces. Among the tools of CAD systems, the geometric transformations are presented in the form of a limited set of editing tools, array tools, tools providing formation of 3D solids and surfaces through motion. In practice, this set of tools restricts application of the wide capabilities of geometric transformations and correspondences. In this regard, it is proposed to model the geometric transformations and correspondences via the geometry construction language. The elements of the geometric transformation theory are analyzed. The geometric transformations are proposed to be created on par with other objects of the geometric model. The commands of the geometry constructions language enabling construction of the transformation objects are considered, their application is discussed. Solutions to practical problems of surface modeling via the geometry constructions language with the use of geometric transformation are demonstrated on examples. The proposed approach allows us to facilitate modeling of curves and surfaces in CAD systems, if said curves and surfaces can be acquired through geometric transformation or correspondence that can be defined via a certain constructive algorithm.

THE CURRENT STATE AND PROSPECTS OF THE USE OF DISTANCE LEARNING INSTRUMENTS DURING STUDY SHIP ENGINEERING

The paper is focused on identifying the advantages and disadvantages of remote education systems in terms of modern requirements to practical skills of future professionals, especially in the engineering field. Based on the analysis of the shortcomings of the already existing distance education models in the context of the pandemic, the need for a fundamentally new remote training scheme has been substantiated. The proposed model allows the students and interns of the shipbuilding profile to master the theoretical backgrounds of shipboard design and engineering and complete all practical tasks, acquiring practical skills in specialized packages of up-to-date shipbuilding CAD systems entirely remotely.This model was tested using a peer-to-peer data exchange technology between three modern information platforms: Moodle, CADMATIC™ and TeamViewer. Advantages of this distance learning model were identified: the possibility of acquiring practical skills in modern computer-aided design systems from anywhere globally through direct interaction of the learner and the teacher in real-time. An appropriate engineering education approach has been developed based on the presented model, enabling the student to acquire needed practical skills and initial experience in ship design using modern three-dimensional CAD in remote mode. The described course prepares the trainees for work at shipbuilding design and production companies in initial engineering positions, namely in ship hull design, structural digital twins, and production information development. A pilot project, "Development and testing of specialised multimedia courses in advanced ship engineering", was implemented at the "Neoteric Naval Engineering Institute" of the Admiral Makarov National University of Shipbuilding. The project results show the high efficiency of using the proposed engineers training model considering modern requirements of employers to future designers and the commercial safety requirements of shipbuilding CAD systems suppliers. The relevance of the results obtained is justified both in the context of the COVID-19 pandemic and beyond.