3D Computer Aided Design Model Research Articles

Simplification of 3D CAD Model in Voxel Form for Mechanical Parts Using Generative Adversarial Networks

Most three-dimensional (3D) computer-aided design (CAD) models of mechanical parts, created during the design stage, have high shape complexity. The shape complexity required of CAD models reduces according to the field of application. Therefore, it is necessary to simplify the shapes of 3D CAD models, depending on their applications. Traditional simplification methods recognize simplification target shape based on a pre-defined algorithm. Such algorithm-based methods have difficulty processing unusual partial shapes not considered in the CAD model. This paper proposes a method that uses a network based on a generative adversarial network (GAN) to simplify the 3D CAD models of mechanical parts. The proposed network recognizes and removes simplification target shapes included in the 3D CAD models of mechanical parts. A 3D CAD model dataset was constructed to train the 3D CAD model simplification network. 3D CAD models are represented in voxel form in the dataset. Next, the constructed training dataset was used to train the proposed network. Finally, a 3D voxel simplification experiment was performed to evaluate the performance of the trained network. The experiment results showed that the network had an average error rate of 3.38% for the total area of the mechanical part and an average error rate of 14.61% for the simplification target area.

A Lineage-Based Referencing DSL for Computer-Aided Design

3D Computer-Aided Design (CAD) modeling is ubiquitous in mechanical engineering and design. Modern CAD models are programs that produce geometry and can be used to implement high-level geometric changes by modifying input parameters. While there has been a surge of recent interest in program-based tooling for the CAD domain, one fundamental problem remains unsolved. CAD programs pass geometric arguments to operations using references, which are queries that select elements from the constructed geometry according to programmer intent. The challenge is designing reference semantics that can express programmer intent across all geometric topologies achievable with model parameters, including topologies where the desired elements are not present. In current systems, both users and automated tools may create invalid models when parameters are changed, as references to geometric elements are lost or silently and arbitrarily switched. While existing CAD systems use heuristics to attempt to infer user intent in cases of this undefined behavior, this best-effort solution is not suitable for constructing automated tools to edit and optimize CAD programs. We analyze the failure modes of existing referencing schemes and formalize a set of criteria on which to evaluate solutions to the CAD referencing problem. In turn, we propose a domain-specific language that exposes references as a first-class language construct, using user-authored queries to introspect element history and define references safely over all paths. We give a semantics for fine-grained element lineage that can subsequently be queried; and show that our language meets the desired properties. Finally, we provide an implementation of a lineage-based referencing system in a 2.5D CAD kernel, demonstrating realistic referencing scenarios and illustrating how our system safely represents models that cause reference breakage in existing CAD systems.

Comparisons of Emotional Responses, Flow Experiences, and Operational Performances in Traditional Parametric Computer-Aided Design Modeling and Virtual-Reality Free-Form Modeling

Three-dimensional (3D) computer-aided design (CAD) is a vital tool for visualizing design ideas. While conventional parametric CAD modeling is commonly used, emerging virtual reality (VR) applications in 3D CAD modeling require further exploration. This study contrasts the emotional response, flow experience, and operational performance of design novices using VR free-form modeling (Gravity Sketch 3D) and conventional parametric CAD modeling (SolidWorks). We arranged two representative tasks for 30 participants: modeling an exact geometric shape (a cube) and a creative shape (a mug). We measured emotional response and flow experience through scales, and gathered operational performance, and further insights through semistructured interviews. The findings reveal more positive and intense emotional responses to VR free-form modeling, but its overall flow experience did not exceed expectations. No significant differences were found in concentration, time distortion sense, or control between the two techniques. Comparing modeling tasks, VR free-form modeling showed promising operational performance for early ideation, whereas conventional parametric CAD modeling proved to be more effective in 3D digitization of known shapes.

Lightweight Model-Based Weld Line Generation and Its Applications to Support the Construction of Ships and Offshore Plants

Welding is the most expensive process in building ships and offshore plants. Therefore, the quantity of welding material should be calculated for the subsections (cells) of the blocks for efficient work planning, and welding paths must be generated for welding automation. Three-dimensional (3D) computer-aided design (CAD) models have been used for this work. However, relevant information regarding welding is often omitted, and a separate database and interface to this database must be developed. In this study, a method of lightweight model-based weld line generation is proposed, followed by the calculation of bead length for welding material quantity estimation and welding path generation. Experiments were performed on various test cases of curved parts and blocks. The proposed method accurately generated weld lines, calculated bead length, and generated welding paths in a short time of approximately 1 s.

An investigation on mechanical properties of 3D pen fused zones for additive manufactured parts

Additive manufacturing has been one of the most used techniques in the recent years because of its capabilities to fabricate complex structures as required by customer and industrial need from a 3D computer-aided design model without the usage of any tooling, dies and heavy machinery makes it a step ahead in the present manufacturing techniques. In the current study the author’s focus on the welding or joining of additive manufactured Polylactic acid (PLA) parts made by Fused Deposition Modeling (FDM). There are several techniques for welding these additive manufactured parts. This study mainly focuses on the joining of 3D printed PLA parts using a 3D pen and investigations on its mechanical properties experimentally. It is a very cheap and effective technique when compared to the other welding methods. This could overcome the drawback of small bed size in most 3D printers by joining smaller parts and it can also be used for repairing the defects caused during the 3D printing. Moreover the experimental testing of the mechanical properties also confirmed that the tensile, flexural and impact strength of 3D pen welded specimens retrieved above 70% of the strength to the original PLA specimen proving it to be a very effective method.

A Pose-Normalization Method for Casting Voxel Models Using Second-Order Central Moment Matrix

Within the framework of casting process design, the efficient retrieval of three-dimensional (3D) computer-aided design (CAD) models could result in significant time and cost savings. However, this technique still suffers from inefficiency and inaccuracy because of the wide variety of casting models’ poses. In this study, a method for normalizing the poses of casting models is proposed. This method constructs the transformation matrix through the eigendecomposition of the second-order central moment matrix calculated from the voxel casting model. Then the transformation matrix is applied to the casting model to get a normalized pose. An assessment approach for pose normalization is also suggested in the study, which measures the distance between poses normalized based on multiple poses of the same model. The study demonstrates that the pose-normalization approach reliably transforms distinct poses of the same model into a unified pose. The mean distance between normalized poses is 0.016 and the minimum distance is 0.010. The method’s effects improve when the voxel size reduces.

A novel method based on a convolutional graph neural network for manufacturing cost estimation

With the widespread application of mass customization strategy, estimating the manufacturing cost of products to provide suitable references for the quotations of products can assist enterprises to adapt to the competitive market. Moreover, estimating the manufacturing cost of products in the design stage can assist designers in optimizing product designs. With the continuous development of deep learning, studies on part manufacturing cost estimation based on deep learning have started in recent years. However, the existing deep learning-based methods ignore the part’s precision information (e.g. roughness, tolerance) that is important to the manufacturing cost, making them less practical. To this end, how to take the precision information of the parts into account in the manufacturing cost estimation to make deep learning-based methods more applicable is a difficulty that needs to be solved. In this context, an innovative convolutional graph neural network (ConvGNN)-based manufacturing cost estimation approach that considers precision information is suggested. Specifically, an attribute graph that is based on machining features and includes precision information is defined to represent the 3D computer-aided design (CAD) model of a part first. Then, a novel ConvGNN framework named Cost Estimation Network (CEN) is constructed that combines spectral-based convolutional layers and spatial-based convolutional layers. The input of CEN is the attribute graph of a part, while the output is the manufacturing cost. After being trained with historical data and an improved loss function based on Root Mean Squared Log Error (RMSLE), CEN can be applied to estimate manufacturing costs. In addition, since the deep learning model is treated as a black box, a modified gradient-weight class activation mapping (Grad-CAM) process is developed to explain the rationale of the manufacturing cost decision and differentiate the degree of influence of various machining features on the cost of parts. In experimental studies, the computer numerical control (CNC) machined rotary parts are used as examples to verify the feasibility and effectiveness of the presented approach.

Three-Dimensional Measurements of the Sinus Tarsi and Tarsal Canal in Pediatric Flexible Flatfeet using Weightbearing CT Scans

Category: Hindfoot; Other Introduction/Purpose: When the hindfoot is in varus the tarsal canal becomes visible on a lateral Xray (XR) and has been referred to as a 'target sign'. However, the sinus tarsi (ST) and tarsal canal (TC) are not easily visible radiographically when associated with a flatfoot. The dimensions of both the ST and TC in normal and flatfeet are unknown, but if understood, could help with the design and implantation of an arthroereisis, a commonly performed procedure for correction of pediatric flatfoot deformity. Knowing the size of the TC will also reduce postoperative complications of arthroereisis such as stiffness, resulting from too large an implant. The aim of this study was to take three-dimensional (3D) measurements of the TC in pediatric flatfeet under weightbearing (WB) conditions. Methods: This was a prospective IRB approved study in pediatric patients with flexible flatfeet and asymptomatic children without flatfeet who were reviewed and analyzed. Using 3D computer-aided design (CAD) models from weightbearing CT (WBCT) scans, the following parameters were measured: the widest diameter of the posteromedial tarsal canal (WDPTC), the narrowest supero- inferior diameter of the tarsal canal (NSDTC), the length of the tarsal sinus, (LTS), the length of the tarsal canal (LTC) and the tarsal sinus and canal volume (TSCV). Correlations between patient age, Meary's angle, foot and ankle offset (FAO), and each of the above measured parameters were investigated. Both weight bearing and non-weight bearing CT scans were used for study to investigate the effect of bearing weight on the above-mentioned parameters. (Figures 1, 2). Results: Twenty-two children with flexible flatfeet (age range 9-14) and fourteen children with no foot deformity (age 9-15) were included for study. With the WBCT the TSCV decreased by 20% in comparison with non-weightbearing evaluation. The TSCV, the widest diameter of the anterolateral tarsal sinus (WDATS) and LTC decreased in flatfeet both under WB and NWB conditions compared with the control group. A positive linear correlation was found between the TSCV and patient age (r = 0.7307, P < 0.0001), while negative linear correlations were found between the TSCV and FAO (r = -0.5188, P < 0.0001) and Meary's angle (r = -0.3132, P = 0.0019). The tarsal sinus and tarsal canal volume significantly decreased in the flatfoot group during weight bearing, while the volume of the tarsal canal remained unchanged. Older age was not associated with either a wider tarsal canal or its orientation. Conclusion: Although the tarsal sinus and canal volume was positively associated with patient age, there was a negative correlation with both the FAO and Meary's angle. These findings may be very relevant in future study of the subtalar joint in both children and adults, and clearly have an implication for treatment of the pediatric flatfoot with arthroereisis.

Machining feature recognition based on a novel multi-task deep learning network

• Propose a novel learning-based machining feature recognition method that can effectively determine the faces that constitute machining features. • Construct a novel multi-task deep learning network based on point cloud data that can realize machining feature segmentation, machining feature identification, and bottom face identification simultaneously. • Propose a novel loss function that can be applied to the tasks of multi-label classification in deep learning. • Promote the application of deep learning in the manufacturing industry. Machining feature recognition is an essential task to realize the integration of computer-aided design (CAD), process planning (CAPP), and manufacturing (CAM) systems. In general, traditional rule-based machining feature recognition approaches are inflexible and computationally expensive. Moreover, it's not easy to design rules for intersecting machining features. To this end, various learning-based approaches were proposed in recent years. However, some of the existing learning-based approaches are complicated, time-consuming, and hard to deal with intersecting machining features, while some only approximately locate the position of the machining features rather than accurately segmenting them from the parts, which is not conducive to subsequent process planning. To address the above issues, a novel multi-task network named Associatively Segmenting and Identifying Network (ASIN) based on point cloud data is proposed for machining feature recognition. The proposed network performs three tasks: clusters the part faces with high similarities into machining features whose type are unidentified, predicts the semantic class (e.g. hole, pocket) for each face to identify the type of each machining feature, and identifies the bottom faces of the machining features. Through the three tasks, ASIN can realize machining feature segmentation, machining feature identification, and bottom face identification simultaneously. The final machining feature recognition results are obtained by combining the results of the above three tasks. After being trained with a large labeled point cloud dataset of 3D CAD models and a novel loss function, ASIN can be used to recognize machining features. Experimental results demonstrate that the proposed approach can effectively segment the machining features by determining the faces that compose them, and performs well in recognizing intersecting machining features.

3D convolutional neural network for machining feature recognition with gradient-based visual explanations from 3D CAD models

In the manufacturing industry, all things related to a product manufactured are generated and managed with a three-dimensional (3D) computer-aided design (CAD) system. CAD models created in a 3D CAD system are represented as geometric and topological information for exchange between different CAD systems. Although 3D CAD models are easy to use for product design, it is not suitable for direct use in manufacturing since information on machining features is absent. This study proposes a novel deep learning model to recognize machining features from a 3D CAD model and detect feature areas using gradient-weighted class activation mapping (Grad-CAM). To train the deep learning networks, we construct a dataset consisting of single and multi-feature. Our networks comprised of 12 layers classified the machining features with high accuracy of 98.81% on generated datasets. In addition, we estimated the area of the machining feature by applying Grad-CAM to the trained model. The deep learning model for machining feature recognition can be utilized in various fields such as 3D model simplification, computer-aided engineering, mechanical part retrieval, and assembly component identification.

851 3D Printing Technology in Microsurgical Reconstruction of the Maxilla and Mandible at Great Ormond Street Hospital (GOSH)

Abstract Aim Since its development, three-dimensional (3D) printing has been used successfully in many industries, however its use in surgery is still in its infancy. We present two cases where 3D printing technology was used to aid surgical planning and guide free-flap microsurgical reconstruction. Method A free fibula flap was performed following a hemi-maxillectomy for a mucoepidermoid carcinoma of the palate. 3D-technology was used to design the position of the osteotomies, osteosynthesis and osseo-integrated dental implants by our in-house 3D technician. A 3D-printed cutting guide was created for the fibula and a custom-made titanium plate was designed for the maxillary reconstruction. Similar 3D-technology was used in a free DCIA flap mandibular reconstruction following resection of a mandibular ameoblastoma. Lower limb CTA showed bilateral peronea arteria magna. Thus, a DCIA flap was performed. A 3D computer-aided design-model for the surgical procedure was created allowing for precise measurement of iliac crest bone and cutting guides were created to aid mandibular resection and ensure accurate bone harvest. Results Both patients underwent successful reconstruction, the flaps maintained good perfusion with strong and consistent doppler signals with healthy skin paddles post-operatively. The patients recovered well; one remains in the immediate post-operative period and one-month follow-up of the DCIA flap showed good position of the bone flap with reasonable movement of the mandible. Conclusions 3D technology has the potential to improve the quality of craniofacial reconstructions. This technology allows the reconstructive surgeon to improve accuracy, demonstrate complex anatomy, plan and execute complex surgical plans with improved functional and aesthetic outcomes.

Digital Reconstruction of Fragmented Cultural Heritage Assets: The Case Study of the Dacian Embossed Disk from Piatra Roșie

The most peculiar characteristic of a cultural heritage is represented by its uniqueness. To ensure that an object is preserved against environmental deterioration, vandal attacks, and accidents, modern Cultural Heritage documentation involves 3D scanning technologies. In the case of fragmented artefacts, the digitization process represents an essential prerequisite for facilitating an accurate 3D reconstruction. The aim of this research paper is to present a framework that enables an accurate digital reconstruction of fragmented or damaged artefacts using ornament stencils obtained from 3D scan data. The proposed framework has been applied for the richly adorned ornaments of the fragmented Dacian embossed disk from Piatra Roșie. The case study makes use of the 3D dataset acquired, using a structured light scanner to extract vector displacement maps, which are then applied to the 3D computer-aided design (CAD) model. The output of the framework includes a proposed digital reconstruction of the aurochs fragmented Dacian embossed disk, as well as the ornaments’ stencils database. The proposed framework addresses problems that are associated with 3D reconstruction processes, such as self-intersections, non-manifold geometry, 3D model topology, and file format interoperability. Finally, the resulting 3D reconstruction has been integrated within virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications, as well as computer-aided manufacturing (CAM) based on additive manufacturing to facilitate the dissemination of the results.

Performance Analysis of Localization Algorithms for Inspections in 2D and 3D Unstructured Environments Using 3D Laser Sensors and UAVs

One of the most relevant problems related to Unmanned Aerial Vehicle’s (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to unstructured scenarios where objects of considerable size are present, such as a truck, a wind tower, an airplane, a building, etc. The presented methods require a previously developed Computer-Aided Design (CAD) model of the main object to be inspected. The first approach is based on an occupancy map built from a horizontal projection of this CAD model and the Adaptive Monte Carlo Localization (AMCL) algorithm to reach convergence by considering the likelihood field observation model between the 2D projection of 3D sensor data and the created map. The second approach uses a point cloud prior map of the 3D CAD model and a scan-matching algorithm based on the Iterative Closest Point Algorithm (ICP) and the Unscented Kalman Filter (UKF). The presented approaches have been extensively evaluated using simulated as well as previously recorded real flight data. We focus on aircraft inspection as a test example, but our results and conclusions can be directly extended to other applications. To support this assertion, a truck inspection has been performed. Our tests reflected that creating a 2D or 3D map from a standard CAD model and using a 3D laser scan on the created maps can optimize the processing time, resources and improve robustness. The techniques used to segment unexpected objects in 2D maps improved the performance of AMCL. In addition, we showed that moving around locations with relevant geometry after take-off when running AMCL enabled faster convergence and high accuracy. Hence, it could be used as an initial position estimation method for other localization algorithms. The ICP-NL method works well in environments with elements other than the object to inspect, but it can provide better results if some techniques to segment the new objects are applied. Furthermore, the proposed ICP-NL scan-matching method together with UKF performed faster, in a more robust manner, than NDT. Moreover, it is not affected by flight height. However, ICP-NL error may still be too high for applications requiring increased accuracy.

Influence of the femoral entry point for intramedullary alignment in total knee arthroplasty: A computer-aided design approach.

Objectives The aim of this study was to simulate different entry points and investigate potential angulation errors of the intramedullary device and resulting changes in the distal femoral cut using a computer-aided design (CAD) approach.Materials and methods We used a CAD approach to simulate various distal femoral entry points for intramedullary instrumentation. Simulations were performed on (i) a commercially available three-dimensional (3D) CAD model of a human femur (DigitalFemur) and (ii) a digital 3D model of an analogue large femur model produced using a coordinate measuring machine (FaroFemur). Divergent insertion points medial, lateral, anterior and posterior to the ideal position were simulated. Angulation deviations of the resulting positions of the intramedullary rod were measured and changes in the anatomical-mechanical axis angle were calculated. Differences between the two simulation models were quantified.Results The ideal entry point in the FaroFemur was 9.9 mm anterior and 4.3 mm medial to the apex of the intercondylar notch, and 9.2 mm anterior and 3.6 mm medial in the DigitalFemur. A medial entry point increased the angle between the anatomical femoral axis and the alignment rod in the FaroFemur and DigitalFemur (with 5 mm displacement 2.510° and 2.363°, respectively; with 10 mm displacement 3.239° and 3.283°, respectively). In contrast, a lateral entry point decreased the angle between the anatomical femoral axis and the alignment rod (with 5 mm displacement 2.267° and 2.262°, respectively; with 10 mm displacement 3.158° and 3.731°, respectively). An anterior entry point changed the angle between the anatomical femoral axis and the alignment rod towards extension (1.802° in the FaroFemur; 2.142° in the DigitalFemur), while a posterior entry point generated a deviation toward flexion (2.045° in the FaroFemur; 2.055° in the DigitalFemur). The mean difference between the two models was 0.108±0.121° with the highest difference for anterior displacement.Conclusion Minor deviations of the entry point for intramedullary instrumentation during total knee arthroplasty can result in malalignment of several degrees.

Application of TLS Method in Digitization of Bridge Infrastructures: A Path to BrIM Development

Over the past years, bridge inspection practices and condition assessments were predicated upon long-established manual and paper-based data collection methods which were generally unsafe, time-consuming, imprecise, and labor-intensive, influenced by the experience of the trained inspectors involved. In recent years, the ability to turn an actual civil infrastructure asset into a detailed and precise digital model using state-of-the-art emerging technologies such as laser scanners has become in demand among structural engineers and managers, especially bridge asset managers. Although advanced remote technologies such as Terrestrial Laser Scanning (TLS) are recently established to overcome these challenges, the research on this subject is still lacking a comprehensive methodology for a reliable TLS-based bridge inspection and a well-detailed Bridge Information Model (BrIM) development. In this regard, the application of BrIM as a shared platform including a geometrical 3D CAD model connected to non-geometrical data can benefit asset managers, and significantly improve bridge management systems. Therefore, this research aims not only to provide a practical methodology for TLS-derived BrIM but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction. This methodology was further verified and demonstrated via a case study on a cable-stayed bridge called Werrington Bridge, located in New South Wales (NSW), Australia. In this case, the process of extracting a precise 3D CAD model from TLS data using the sliced-based method and a workflow to connect non-geometrical information and develop a BrIM are elaborated. The findings of this research confirm the reliability of using TLS and the sliced-based method, as approaches with millimeter-level geometric accuracy, for bridge inspection subjected to precise 3D model extraction, as well as bridge asset management and BrIM development.

Identification of particular hydrodynamic parameters for a modular type 4 DOF underwater vehicle by means of CFD method

PurposeThe development of robust control algorithms for the position, velocity and trajectory control of unmanned underwater vehicles (UUVs) depends on the accuracy of their mathematical models. Accuracy of the model is determined by precise estimation of the UUV hydrodynamic parameters. The purpose of this study is to determine the hydrodynamic forces and moments acting on an underwater vehicle with complex body geometry and moving at low speeds and to achieve the accurate coefficients associated with them.Design/methodology/approachA three-dimensional (3D) computer-aided design (CAD) model of UUV is designed with one-to-one dimensions. 3D fluid flow simulations are conducted using computational fluid dynamics (CFD) software programme in the solution of Navier Stokes equations for laminar and turbulent flow analysis. The coefficients depending on the hydrodynamic forces and moments are determined by the external flow analysis using the CFD programme. The Flow Simulation k-ε turbulence model is used for the transition from laminar flow to turbulent flow. Hydrodynamic properties such as lift and drag coefficients and roll and yaw moment coefficients are calculated. The parameters are compared with the coefficient values found by experimental methods.FindingsAlthough the modular type UUV has a complex body geometry, the comparative results of the experiments and simulations confirm that the defined model parameters are accurate and close to the actual experimental values. In the proposed k-ε method, the percentage error in the estimation of drag and lifting coefficients is decreased to 4.2% and 8.39%, respectively.Practical implicationsThe model coefficients determined in this study can be used in high-level control simulations which leads to the development of robust real-time controllers for complex-shaped modular UUVs.Originality/valueThe Lucky Fin UUV with 4 degrees of freedom is a specific design and its CAD model is first extracted. Verification of simulation results by experiments is generally less referenced in studies. However, it provides more precise parameter identification of the model. Proposed study offers a simple and low-cost experimental measurement method for verification of the hydrodynamic parameters. The extracted model and coefficients are worthwhile references for the analysis of modular type UUVs.

Partial Shape Recognition of Boundary-Representation Models using Deep Neural Networks.

Shape recognition of the 3D shapes in computer-aided design (CAD) can help automate the product modelling and reduce the time to develop a new product. In this paper, in order to recognize partial shapes of 3D CAD models such as bolts and fillets, we propose a shape recognition method based on graph-neural-networks, which learns geometrical and topological information from data of annotated 3D CAD models. By using the graph of face adjacencies of the BRep model as input to the learning model, it is possible to predict recognition target directly from the BRep models. We applied this method to machine part data and verified its effectiveness. We demonstrate that our method can classify the BRep models in FabWave dataset with higher accuracy than the methods based on the point clouds.

Performance Study of Buried Pipelines under Static Loads

The possibility of servicing lifelines such as highways, railways, pipelines, and tunnels is of great social importance. The characteristic that separates the buried pipeline from other structures is that its dimensions are very long compared to its other dimensions. Ground vibrations caused by earthquakes, construction activities, traffic, explosions, and machinery can damage these structures. Lifeline integrity can be compromised in two ways: (1) direct damage due to excessive dynamic loading of the lifeline, and (2) indirect damage due to soil failures such as liquefaction, slope instability, and differential settlements. 3D printing (also known as additive manufacturing) is an advanced manufacturing process that can automatically produce complex geometric shapes from a 3D computer-aided design model without tools, molds, or fixtures. This automated manufacturing process has been applied in diverse industries today because it can revolutionize the construction industry with expected benefits. This research study on the performance of buried pipelines under static loads to the structure's safety against the possible development of progressive failure. This research study includes a numerical study, where it was studied many parameters to value the performance of the pipeline. The parameters are (a) the material of the pipeline (steel, traditional concrete, and 3D concrete printed), (b) the thickness of the pipeline (20, 30, and 40 mm), and (c) soil type (moist sandy soil, saturated sandy soil, moist cohesive soil, and saturated cohesive soil). Different results were obtained depending on the type of soil where all pipelines materials' behavior was similar in the case of moist soil. Doi: 10.28991/CEJ-2022-08-01-01 Full Text: PDF

Finite element analysis and experimental validation of polycentric prosthetic knee

The objective of this work is to perform simulation and experimental validation of a polycentric prosthetic knee through finite element analysis. The prosthetic knee may be subjected to a load of 4.7 to 5.6 times body weight during various activities which are taken into consideration during its design with a higher factor of safety. Static strength test has been performed to ensure its structural integrity as per the International Organization for Standardization ISO 10328:2006 standard. ANSYS 20.1 workbench has been used for its simulation to study the stress distribution, after obtaining accurate dimensioning of knee components and generating a 3D computer-aided design model. An experimental structural strength test has been performed to validate the findings of the simulation. The results indicate that the tested prosthetic knee sample is mildly sufficient to outstrip the static strength test. The validation result has confirmed the accuracy of the finite element model. The mean absolute error percentage between finite element-based simulation and the experimental result is estimated to be 10%.

Dataset and method for deep learning-based reconstruction of 3D CAD models containing machining features for mechanical parts

ABSTRACT Three-dimensional (3D) computer-aided design (CAD) model reconstruction techniques are used for numerous purposes across various industries, including free-viewpoint video reconstruction, robotic mapping, tomographic reconstruction, 3D object recognition, and reverse engineering. With the development of deep learning techniques, researchers are investigating the reconstruction of 3D CAD models using learning-based methods. Therefore, we proposed a method to effectively reconstruct 3D CAD models containing machining features into 3D voxels through a 3D encoder–decoder network. 3D CAD model datasets were built to train the 3D CAD model reconstruction network. For this purpose, large-scale 3D CAD models containing machining features were generated through parametric modeling and then converted into a 3D voxel format to build the training datasets. The encoder–decoder network was then trained using these training datasets. Finally, the performance of the trained network was evaluated through 3D reconstruction experiments on numerous test parts, which demonstrated a high reconstruction performance with an error rate of approximately 1%.