3D CAD Model Research Articles

Geometry reconstruction for additive manufacturing: From G-CODE to 3D CAD model

In the last decades, the flourishing of Additive Manufacturing (AM) promoted innovative design solutions in many different sectors. Despite the numerous advantages of AM technology, there are still open challenges in the field. In Fused Deposition Modelling (FDM) structures the layer-by-layer manufacturing process induces anisotropy in the material properties of the structures. The correct characterization of the mechanical properties is fundamental in the design and development stages but at the same time difficult to achieve. The experimental approach can be extremely long and expensive. An alternative is the use of an accurate numerical approach and performing a Finite Element Analysis (FEA) of the geometry which is effectively printed. However, to the best of the authors' knowledge, there is not a common and well-established procedure to reconstruct the real geometry which is generated after the slicing process. In this paper, starting from the information provided by the G-CODE, an easy-to-use, and reproducible methodology to reconstruct the printed geometry is presented. The performance of the innovative approach is evaluated via qualitative observations by referring to several case studies. The results are thoroughly analysed, and future trends and research needs are highlighted.

Impact Behavior Analysis of 3-D Printed Honeycomb Structures

The purpose of this paper is to evaluate the behaviour of 3D printed honeycomb structures under low velocity impact loading for their use in energy absorption applications. Additive manufacturing technologies are part of a growing field that represents high interest for industries such as aerospace, automotive and naval. This paper aims to determine the mechanical properties of a 3D printed polymer - Polylactic Acid (PLA) manufactured by FDM (Fused Deposition Modelling) technology. In this regard, first the material is characterized by low velocity impact dynamic experimental tests. A finite Element Analysis (FEA) is performed in LS-Dyna software in order to validate the results. The samples were manufactured by varying the infill percent to investigate the influence of different parameters on a batch of samples for every configuration. The 3D CAD modelling for impact tests samples were performed in Catia V5. Among wide range of cellular structures, honeycomb non-auxetic hexagonal cell pattern was selected in this study, assuring high strength/weight ratio. The amount of energy absorbed during the impact, the failure and degradation of the impacted specimens were monitored, following the analysis of experimental and numerical data. A fair agreement was obtained between experimental and numerical results, showing that honeycomb developed lightweight structures exhibits a proper energy absorption capacity, with a mechanism of release similar to metal or composite materials honeycombs.

Hybrid design and prototyping of metamaterials and metasurfaces

ABSTRACT Metamaterials are engineered materials conceived and designed to achieve very special or even unique physical properties, which depend on the designed micro or nanostructures, more than on the chemical composition of the raw materials employed for their fabrication. Normally metamaterials are made of periodic repetitions of unit cells or Boolean combinations of lattices or porous building blocks. Metasurfaces are the quasi-two-dimensional version of metamaterials and are generally applied to controlling electromagnetic and acoustic waves reaching them. Metamaterials are mainly created through high-precision additive manufacturing technologies, while metasurfaces are normally obtained using micromanufacturing techniques from the electronics industry and laser patterning methods. Consequently, the potential benefits and industrial applications of multi-scale or hierarchical metastructures, which could be obtained by merging metamaterials and metasurfaces, remain unexplored. Through the innovative combination of 3D CAD modelling resources and specific tools for computational mapping of topographical 2D images this study validates the possibility of texturing the building blocks and unit cells of metamaterials, hence reaching designs with interwoven metamaterials and metasurfaces. These microtextured lattices are additively manufactured, using two-photon polymerisation, to demonstrate the feasibility of bridging the gap between metamaterials and metasurfaces and analyse current challenges and potential applications of these digital materials.

MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis

AbstractWith the rapid development of data‐driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain‐Specific Language, allowing users to select scenes from the commercial indoor scene database, synthesize scenes for different tasks with customized rules, and render various types of imagery data, such as color images, geometric structures, semantic labels. Our system eases the difficulty of customizing massive scenes for different tasks and relieves users from manipulating fine‐grained scene configurations by providing user‐controllable randomness using multilevel samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks. The project page is at https://coohom.github.io/MINERVAS.

Structural and modal analysis of gas turbine blade using ansys

Gas turbine technology has advanced significantly during the last twenty years. The advancement in materials technology, new coatings, and novel cooling techniques are driving the expansion. Material selection is critical since turbomachinery design is complicated, and material performance is directly connected to efficiency. The materials should be able to withstand high vibrations, high temperature, and pressure. In this work, the maximum stress and the deformation induced due to pressure were determined to study the vibrational response, mode shapes, and natural frequencies. Four different materials have been considered, namely Inconel 617, Titanium alloy, Rhenium alloy, and Nimonic 80A. A suitable material for a gas turbine blade was suggested after analyzing these materials. The 3D CAD model is modeled in CATIA V5 and analysis is carried out using FEM-based software ANSYS

기계학습을 이용한 3D CAD 모델 형상과 경계조건의 유형 식별

In this paper, we propose a machine learning approach for classifying 3D CAD models with boundary conditions. We adopted an extended voxel model to represent a CAD model with its boundary conditions, and to construct the training data. By considering 7 types of part families and 3 types of boundary conditions, we generated 320 similar CAD models for each part family and assigned 3 different boundary conditions to each CAD model, which produces 960 datasets for the part family. We used multi-layer perceptron (MLP) and convolutional neural network (CNN) as machine learning models, which classify the combination type of a given CAD model with its boundary conditions. Using TensorFlow, we trained and tested the models, and compared their performance. We considered the MLP models made of three hidden layers and the CNN models made of two convolutional, two pooling, and three hidden layers. We also conducted a grid search to find the proper number of nodes in hidden layers. From experimental results, we found that the CNN models are better in accuracy than the MLP models. If further enhanced, the proposed approach is expected to become a useful tool for similar case search from archive CAE models.

Retrieval of a 3D CAD Model of a Transformer Substation Based on Point Cloud Data

When constructing a three-dimensional model of a transformer substation, it is critical to quickly find the 3D CAD model corresponding to the current point cloud data from a large number of transformer substation model libraries (due to the complexity and variety of models in the model base). In response to this problem, this paper proposes a method to quickly retrieve a 3D CAD model. Firstly, a 3D CAD model that shares the same size as the current point cloud model bounding box is extracted from the model library by the double-layer bounding box screening method. Then, the selected 3D CAD model is finely compared with the point cloud model by the multi-view method. The 3D CAD model that has the highest degree of corresponding to the point cloud data is acquired. The proposed algorithm, compared to other similar methods, has the advantages of retrieval accuracy and high efficiency.

INTERFACE DEVELOPMENT BETWEEN THE 3D CAD SOFTWARE AND THE STRUCTURAL STRENGTH ASSESSMENT SOFTWARE FOR EFFICIENT

As the ship design has been increasing complexity with the need to drive optimization and performance at the same time, the use of various computer-aided software during the ship design process is no longer an option for these days. However, most software basically supports their own proprietary file format which can only be used by their own software, and also requires to reiterate modeling the ship design which had already been used in other software. Often these repetitive works become a major hinder to streamline the design process due to the huge cost of time and effort and may have potential risk for human error. In this regard, interfaces to increase productivity between software for data reusability were developed, but the available data was limited in 2D CAD-based. However, as 3D CAD is adopted in the shipyards, it became possible to access and utilize the 3D modeling data containing various information of the ship design. We have developed the interface function that can carry out hull structural strength evaluation for classification approval very quickly and efficiently based on the 3D CAD model. In this paper, Korean Register (KR) present the interface between 3D CAD software, NAPA Designer, and classification’s structural strength assessment software, SeaTrust-HullScan [1], that can dramatically improve design productivity.

High Precision Fertilizer Applicator For Industrial Plantation: Discrete Element Method Simulation And Prototyping

We designed a high-precision fertilizer applicator to distribute fertilizer with the required dose at every specified point. The dose necessary aims to meet the nutritional needs of plants, so that plant growth is more effective while reduce waste. The fertilizer applicator was designed to have an auger and drive system that can discharge the fertilizer precisely. A 3D CAD model of the applicator was created to conduct a discrete element simulation to predict the discharge fertilizer process. Experiment was carried out to validate the simulation model. The experiment utilized a small-scale applicator prototype manufactured by 3D printing. A small electric actuator controlled by Arduino microcontroller was installed to rotate the auger. The auger can push and release the fertilizer precisely depending on the rotation's number. Both simulation and experiment results were compared and showed a small error of 6.42%. The results show our designed fertilizer applicator have good accuracy and precision, which indicates the applicator was suitable to distribute fertilizer uniformly.

CFD Simulation of Tapered Coil by Using Silicon Dioxide and Zinc Oxide Nanofluid with Water and Ethylene Glycol as Its Base Fluid

Abstract: Helical coils are very essential for various heat exchangers, nuclear reactors and in chemical engineering because of large quantity of heat transfer in a small space with high heat transfer rates and slight residence time dispersals even it suffers through a disadvantage of larger pressure drop. The curvature creates secondary flow arrangement which is perpendicular to main axial stream path. The secondary flow has insignificant capability to improve heat transfer owed to mingling of the fluid. The objective of this work is to compare and increase the pressure drop inside tapered helical coil heat exchanger in copper tube to succeed a better and more quantitative insight into heat transfer process by increasing secondary flow inside the coil and this can allow proper intermixing of the fluid and to maintain desired flow rate inside the helical coil. I work on CFD to analyze the helical coil by using ANSYS 15. Generation of 3D CAD model of aluminium and copper tapered helical tube of diameter (d) 10 mm keeping PCD 50 mm, pitch 20 mm , tapered angle 20 and length of 500 mm by using SOLIDWORKS and exporting to the IGES format and then import in ANSYS fluent 15

SketchCleanNet — A deep learning approach to the enhancement and correction of query sketches for a 3D CAD model retrieval system

Search and retrieval remains a major research topic in several domains, including computer graphics, computer vision, engineering design, etc. A search engine requires primarily an input search query and a database of items to search from. In engineering, which is the primary context of this paper, the database consists of 3D CAD models, such as washers, pistons, connecting rods, etc. A query from a user is typically in the form of a sketch, which attempts to capture the details of a 3D model. However, sketches have certain typical defects such as gaps, over-drawn portions (multi-strokes), etc. Since the retrieved results are only as good as the input query, sketches need cleaning-up and enhancement for better retrieval results.In this paper, a deep learning approach is proposed to improve or clean the query sketches. Initially, sketches from various categories are analysed in order to understand the many possible defects that may occur. A dataset of cleaned-up or enhanced query sketches is then created based on an understanding of these defects. Consequently, an end-to-end training of a deep neural network is carried out in order to provide a mapping between the defective and the clean sketches. This network takes the defective query sketch as the input and generates a clean or an enhanced query sketch. Qualitative and quantitative comparisons of the proposed approach with other state-of-the-art techniques show that the proposed approach is effective. The results of the search engine are reported using both the defective and enhanced query sketches, and it is shown that using the enhanced query sketches from the developed approach yields improved search results.

Posture Evaluation Based on Forward Kinematics and Inverse Kinematics of Parallel Link Type Machine Tool

Parallel mechanisms with multiple links have been expected to be used in machining because they are higher in rigidity, accuracy, and output power than series mechanisms, such as industrial robots. However, unlike conventional machine tools, which consist of linear and rotary axes, parallel mechanisms have a large number of error factors. In the parallel link mechanism, there is no guide surface that physically guarantees linearity, and all accuracy is determined by the operating performance of the composite axes. This makes it difficult to identify any error factors. Therefore, a kinematics model is devised, and the behavior of the tool tip is checked by inputting the encoder information during the actual operation of a specific axis. Based on the results, we evaluate the machining characteristics of the target machine tool. The target machine tool in this study is a 5-axis machine tool that combines a 3-DOF parallel mechanism consisting of three linear motion axes and a 2-DOF serial mechanism consisting of two rotary axes. In our previous research, we tried to build a forward kinematics model. Although its prediction accuracy was insufficient, it was possible to actually identify the cause of the defect in the quality of the machined surface using the servo position information of the kinematics machine. However, we have not been able to construct an inverse kinematics model that is suitable for calculating the correction position command value to improve the quality of the machined surface. In this study, based on the shape creation theory, we devise and evaluate the kinematics model of a robotic machine tool that has a parallel mechanism. As a result of comparing the kinematics model with the 3D-CAD model in order to evaluate the accuracy of the former, it was confirmed that the proposed method has high simulation accuracy. Then, machining tests were carried out to evaluate the machining accuracy by measuring, based on proposed kinematics model, the machined surfaces in order to identify the mechanism that affects the texture of the machined surface. In addition, we performed a circle interpolation to confirm the effects of reversing the motion of each drive axis on the behavior of the tool tip. As a result, it is considered that the linear motion axis has a large effect on the behavior of the tool tip on the quadrant glitch of each drive axis. It was also found that the effects of the 1st- and 3rd-axes on the behavior of the tool tip are different from those of the 2nd-axis.

DEF

We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven methods, which reduce this problem to feature classification, we propose to regress a scalar field representing the distance from point samples to the closest feature line on local patches. Our approach is the first that scales to massive point clouds by fusing distance-to-feature estimates obtained on individual patches. We extensively evaluate our approach against related state-of-the-art methods on newly proposed synthetic and real-world 3D CAD model benchmarks. Our approach not only outperforms these (with improvements in Recall and False Positives Rates), but generalizes to real-world scans after training our model on synthetic data and fine-tuning it on a small dataset of scanned data. We demonstrate a downstream application, where we reconstruct an explicit representation of straight and curved sharp feature lines from range scan data. We make code, pre-trained models, and our training and evaluation datasets available at https://github.com/artonson/def.

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.

Design of a 3D Printed Open Source Humanoid Robot

Nowadays, humanoid robots with great capabilities used in a variety of purposes to serve humans have become an integral part of our lives. In this study, we have developed a low-cost humanoid robot that can be fabricated with an open-source 3D printer. Firstly, the 3D-CAD model of the humanoid robot was created using source codes of the “InMoov” project which is originated by Gael Langevin who works as a designer on his project since 2012. The humanoid robot involves approximately 685 parts built from the PLA (Poly-Lactic Acid) raw material. After that, a new electronics system based on the embedded controllers which have been controlled with the python programming language has been designed. This robotic platform controlled with the help of voice commands has the capability to communicate with people. Furthermore, a skilled prosthetic hand controlled according to the commands from a smart glove, can grasp and holds objects, have been specially developed in this study. When comparing to the existing commercial humanoid robots, this humanoid robot developed as specific has a substructure which is not only low cost but also open to new improvements as well.

Integrated Interface System of STEP Data Models for the Tool Path Generation

The STEP (STandard for the Exchange of Product model data) is a worldwide standard for exchanging products between diverse computer systems and industrial applications. STEP files (ISO 10303) contain 3D data in the universal data format with a boundary representation (B-rep) that allows the exchange of data models between CAD, CAM, and CNC systems. Traditionally, G-code generation relies on CAM software as an add-on, which incurs significant installation costs. The purpose of this paper is to describe the development of an Integrated Interface System (I2S) for Tool Path Generation (TPG) for the CNC milling operations utilizing neutral STEP files.The algorithm was interpreted in Hypertext Preprocessor (PHP) programming and produced a computer interface system that is capable to convert STEP file geometric data to G-code format.The geometric features of the sample block were modeled using a 3D CAD model. The result was validated using machining simulation, and the machining is performed on a three-axis CNC milling machine with a PC-based controller.In modern manufacturing systems based on Open Architecture Controller (OAC), the development of the I2S provides an alternative to generating machining tool paths and G-code by intercepting CAM software.

Employing a Decision Support System (DSS) application system to modify a non-standard wall-mounting electrical panel using a 3D CAD modelling

Flexibility in the design process becomes an absolute necessity in a smart factory in the era of industrial manufacturing 4.0. CAD technology with a 3D modeling capability serves as a backbone in the design process in a manufacturing industry. Here, iLogic serving as an add-on of Inventor software will be combined with a Decision Support System (DSS) to build a DSS application system, namely "Panel Auto System". This system will help an engineer modify a 3D model wall-mounting panel to fulfill a non-standard order from his or her customers. This 3D-model database from various parts of the wall-mounting panel is generated from the 3D modeling. Then, by employing the if/then/else rules in the iLogic, the system can modify a 3D model wall-mounting panel data standard and serves as a geometric 3D model based on the customer’s order. The system works well and has been validated. The efficiency of the modeling process using the "Panel Auto System" increases its time efficiency up to 97,6 % compared to a manual process. Therefore, this system can increase the engineer’s productivity in the design of an in-wall mounting panel, and the customers can get the order of their 3D model faster.

Blitz Vision: Development of a New Full-Electric Sports Sedan Using QFD, SDE and Virtual Prototyping

In this paper, industrial design structure (IDeS) is applied for the development of two new full-electric sports sedan car proposals that go by the names Blitz Vision AS and Retro. With a deep analysis of the trends dominating the automotive industry, a series of product requirements was identified using quality function deployment (QFD). The results of such analysis led to the definition of the technical specifications of the product via benchmarking (BM) and top-flop analysis (TFA). The product architecture was then defined by making use of a modular platform chassis capable of housing a variety of vehicle bodyworks. The structured methodology of stylistic design engineering (SDE) was used. This can be divided in six phases: (1) stylistic trends analysis; (2) sketches; (3) 2D CAD drawings; (4) 3D CAD models; (5) virtual prototyping; (6) solid stylistic model. The chassis of the CAD model was verified structurally by means of FEM analysis, whereas the drag coefficients of the two vehicle proposals were compared with one of the main competitor’s vehicles via CFD simulations. The resulting car models are both aesthetically appealing and can be further developed, leading eventually to the production stage. This proves the effectiveness of IDeS and SDE in car design.

Manufacturing Polymer Model of Anatomical Structures with Increased Accuracy Using CAx and AM Systems for Planning Orthopedic Procedures

Currently, medicine uses typical industrial structure techniques, including reverse engineering, data processing, 3D-CAD modeling, 3D printing, and coordinate measurement techniques. Taking this into account, one can notice the applications of procedures used in the aviation or automotive industries based on the structure of Industry 4.0 in the planning of operations and the production of medical models with high geometric accuracy. The procedure presented in the publication shortens the processing time of tomographic data and increases the reconstruction accuracy within the hip and knee joints. The procedure allows for the partial removal of metallic artifacts from the diagnostic image. Additionally, numerical models of anatomical structures, implants, and bone cement were developed in more detail by averaging the values of local segmentation thresholds. Before the model manufacturing process, additional tests of the PLA material were conducted in terms of its strength and thermal properties. Their goal was to select the appropriate type of PLA material for manufacturing models of anatomical structures. The numerical models were divided into parts before being manufactured using the Fused Filament Fabrication technique. The use of the modifier made it possible to change the density, type of filling, number of counters, and the type of supporting structure. These treatments allowed us to reduce costs and production time and increase the accuracy of the printout. The accuracy of the manufactured model geometry was verified using the MCA-II measuring arm with the MMDx100 laser head and surface roughness using a 3D Talyscan 150 profilometer. Using the procedure, a decrease in geometric deviations and amplitude parameters of the surface roughness were noticed. The models based on the presented approach allowed for detailed and meticulous treatment planning.

Modelling and Analysis of CNC Milling Machine Bed with UHM CFRP composite Material

Abstract: Structural materials are used in a machine tool have a key role in defining the productivity, accuracy and Size proportions of the part manufactured by machine tool. The conventional structural materials like cast iron and Stainless or carbon steel at high operating speeds but used in precision machine tools grow positional errors due to the vibrations transferred into the Machine tool structure. Faster cutting speeds can be achieve only by structure which has good damping characteristics and high stiffness. Clearly the life of a machine tool is proportional to the levels of vibration Produce by machine. The further Work is carried out to Study the deformation and natural frequency using Static analysis and Modal analysis respectively. The bed in machine tool Took Major role in the precision in components. It is one of the greatest vital tool structures which is absorb the vibrations resultant from the cutting operation. To analyse the bed for possible material changes and Structure changes that could increase stiffness, decrease weight, expand damping characteristics and segregate natural frequency at the operating range. So main enthusiasm behind the impression to go for a composite Material in model involving ultra-High Modulus Carbon Fibre Reinforced Polymer Composite Material (UHM CFRP). In a carbon has good stiffness and strength properties but its absences in damping requirements. On the other hand, polymers, though its deficiencies in strength but, good damping in characteristics and it is easily holding the carbon fibres. So, this is makes greatest combination of materials in an appropriate manner. In this work, a machine tool’s bed is selected for the analysis static loads. Then study is carried out to decrease the weight of the machine tool’s bed without affecting its structural rigidity. The 3D CAD model of the bed has been created by using commercial 3D modelling software and analyses were carried out using ANSYS 2021 R2. Keywords: Composite material, hybrid structures, Milling machine bed, UHM CFRP.