Computer-aided Design Data Research Articles

Comparison of Early Postoperative Stress Distribution around Short and Tapered Wedge Stems in Femurs with Different Femoral Marrow Cavity Geometries Using Finite Element Analysis.

In total hip arthroplasty (THA), the ideal stem length remains uncertain; different stem lengths are used in different cases or institutions. We aimed to compare the stress distributions of cementless tapered wedges and short stems in femurs with different femoral marrow geometries and determine the appropriate fit. Finite element models were created and analyzed using HyperMesh and LS-DYNA R11.1, respectively. The 3-dimensional shape data of the femurs were extracted from computed tomography images using the RETOMO software. Femurs were divided into 3 groups based on the Dorr classification. The computer-aided design data of cementless tapered wedge-type and short stems were used to select the appropriate size. In the finite element analysis, the loading condition of the femur was assumed to be walking. Volumes of interest (VOIs) were placed within the femur model at the internal and external contact points of the stem based on Gruen zones. The average stresses and strain energy density (SED) of the elements included in each VOI were obtained from the preoperative and postoperative models. The von Mises stress and SED distributions of the cementless tapered wedge and short stems were similar in their respective Dorr classifications. In both stems, the von Mises stress and SED after THA were lower than before THA. The von Mises stress and SED of the cementless tapered wedge stem were higher than those of short stems. Cementless tapered wedge-type stems tended to have lower rates of change than short stems; however, Dorr C exhibited the opposite trend. In the Dorr classification comparison, the von Mises stress and SED were greater for both stems in the order of Dorr C > Dorr B > Dorr A, from Zone 2 to Zone 6. In Dorr A and B, the short stem exhibited a natural stress distribution closer to the preoperative femur than the tapered wedge stem; however, in Dorr C, the short stem may have a greater effect on stress distribution, suggesting that it may cause greater effects, such as fracture in the early postoperative period, than other Dorr types.

Computer-Aided Design and Multimodal Data Fusion in Green Building Planning

Computer-Aided Design and Multimodal Data Fusion in Green Building Planning

Simulation-based data reduction and data processing for sheet metal forming in the hybrid twin framework

In sheet metal forming, the interaction between virtual models and the real world remains challenging. Process simulations can exhibit significant errors, and reliable measurements are often scarce during early production stages. This study presents a hybrid twin framework that systematically unifies computer-aided design, simulation, and measurement data in an adaptive manner. Central to this framework is a reverse engineering algorithm that reconstructs and transforms the geometry of deep-drawn components from optical scan data into B-spline surfaces. The algorithm demonstrated high precision, indicating its suitability for process control and geometric analysis. The hybrid twin framework integrates virtual data from simulations and real-world data, as evidenced by a sensor concept for inline surface measurement. The framework ensures robust and redundant measurement concepts by estimating complete geometries from a few systematically preselected measuring points. This adaptive approach permits continuous updates and extensions to the database, accommodating both sparse inline signals and offline inspection data. This framework provides a conceptual model for integrating direct feedback interactions between virtual and physical environments, thereby enhancing the precision of analytical and predictive models in sheet metal forming processes.

Four-dimensional Printing: An Evolution in Making

There are promising applications of four-dimensional (4D) printing in the medical field. The need is to identify the research status and explore where this new set of technologies effectively can be deployed. Research articles are searched from Scopus by keywords as ‘4D printing’, ‘medical’, ‘dental’ and undertaken a bibliometric analysis of the identified papers. This study states that 4D printing is the latest technology that creates innovation and addresses complex medical problems. The paper briefly describes the 4D printing and details its difference from three-dimensional (3D) printing technology. The paper identified that research is carried out on 4D printing, but decidedly less publication is available in the medical field reporting the application of this technology. The main limitation is that it requires extensive investment and support for transformation. 4D printing is to provide benefits to medical practitioners especially in the areas not covered by 3D printing technologies. 4D printing helps to create a 3D physical object by adding smart material layer by layer through computer-operated computer-aided design data. It adds a dimension of transformation over time where printed products are sensitive to parameters such as temperature, humidity and time. This technology can provide extensive support in the medical field, especially with better and smart medical implants, tools and devices. Now, doctors and researchers can explore with 4D printing technology to provide better service to the patient.

Product Design Cost Estimation for Make-to-Order Industry: A Machine Learning Approach

This research addresses the need for accurate design cost estimation in the Make-To-Order (MTO) industry. The complexity of product customization is key to differentiation. While many studies focus on manufacturing cost estimation, few explore design cost estimation. To improve the accuracy of design cost estimation, this research proposes a new cost driver based on design features available in Computer-Aided Design (CAD) data. The design feature is analyzed to the actual industry cost using machine learning methods, including Artificial Neural Networks (ANNs) and Support Vector Regression (SVR). The cost drivers identified as significant consisted of twenty-six 3D CAD features and four 2D CAD features. The results showed that the ANN models outperformed the SVR models in correctly estimating product design costs, as evidenced by the high R2values in the training and testing phases. The proposed method allows early identification of cost drivers, a significant advantage at the order initiation stage when detailed design features are often ambiguous. The novelty of this research is the use of 3D CAD technology for cost estimation, which quantifies costs based on product design complexity, providing valuable insights into the impact of design adjustments on costs early in the design process. Doi: 10.28991/ESJ-2024-08-03-022 Full Text: PDF

Experimental Study of the Tensile Behavior of Structures Obtained by FDM 3D Printing Process.

Fused Deposition Modelling (FDM) is one of the layer-based technologies that fall under the umbrella term "Additive Manufacturing", where the desired part is created through the successive layer-by-layer addition process with high accuracy using computer-aided design data. Additive manufacturing technology, or as it is commonly known, 3D (three-dimensional) printing, is a rapidly growing sector of manufacturing that is incorporated in automotive, aerospace, biomedical, and many other fields. This work explores the impact of the Additive Manufacturing process on the mechanical proprieties of the fabricated part. To conduct this study, the 3D printed tensile specimens are designed according to the ASTM D638 standards and printed from a digital template file using the FDM 3D printer Raise3D N2. The material chosen for this 3D printing parameter optimization is Polylactic acid (PLA). The FDM process parameters that were studied in this work are the infill pattern, the infill density, and the infill cell orientation. These factors' effects on the tensile behavior of printed parts were analyzed by the design of experiments method, using the statistical software MINITAB2020.

‘5D PRINTING’ -THE NEW ERA IN DENTISTRY! -A NARRATIVE REVIEW

3D printing technology has a wide range of applications in various industries. It is widely used to produce complex 3D structures, but it has some limitations such as a limited amount of material, etc., which have been overcome with the introduction of 4D printing technology. In 4D printing, which involves time as a function and the combination of smart materials, this enables properties such as changing form and function.[1]Five-dimensional (5D) printing is a new branch of additive manufacturing (AM) with great potential to solve problems in engineering, medicine, dentistry and other related fields. It is the latest technological advancement used to produce complex and intricately shaped products, implants and devices with much better physical properties than those obtained by three-dimensional (3D) printing. The concept of 5D printing originated from William Yerazunis of the American University of Mitsubishi Electric Research Laboratories (MERL). In 5D printing, the printing plate also moves with the printing head during the printing process. in 3D printing techniques.[1]four-dimensional (4D) printing is the concept of using a smart material that can change the shape of a printed object over time as the temperature changes. [2] One of the advantages of 5D printing is the use of 25% less material than 3D printing. Five-dimensional printing is all about efficiently manufacturing this complex and curved structure with maximum strength. Using computer-aided design (CAD) data to produce super strong dental implants, orthodontic brackets, crowns, aligners, bridges and appliances. This CAD data is created using the dentist's 3D scanner / different design software.[1] 5D printing of five axes: [1], 1.X-axis ;2. Y-axis;3. Z axis; 4. Movable print head and 5. Movable printing base.5D printing is an advanced manufacturing technology that builds on the concept of 3D printing and adds customization options and features. While 3D printing requires the creation of three-dimensional objects layer by layer, 5D printing allows additional functions or features to be added to a printable object, often dynamically or responsively. its role in dentistry may be limited due to the newness of the technology.

BrepMFR: Enhancing machining feature recognition in B-rep models through deep learning and domain adaptation

Feature Recognition (FR) plays a crucial role in modern digital manufacturing, serving as a key technology for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP) and Computer-Aided Manufacturing (CAM) systems. The emergence of deep learning methods in recent years offers a new approach to address challenges in recognizing highly intersecting features with complex geometric shapes. However, due to the high cost of labeling real CAD models, neural networks are usually trained on computer-synthesized datasets, resulting in noticeable performance degradation when applied to real-world CAD models. Therefore, we propose a novel deep learning network, BrepMFR, designed for Machining Feature Recognition (MFR) from Boundary Representation (B-rep) models. We transform the original B-rep model into a graph representation as network-friendly input, incorporating local geometric shape and global topological relationships. Leveraging a graph neural network based on Transformer architecture and graph attention mechanism, we extract the feature representation of high-level semantic information to achieve machining feature recognition. Additionally, employing a two-step training strategy under a transfer learning framework, we enhance BrepMFR's generalization ability by adapting synthetic training data to real CAD data. Furthermore, we establish a large-scale synthetic CAD model dataset inclusive of 24 typical machining features, showcasing diversity in geometry that closely mirrors real-world mechanical engineering scenarios. Extensive experiments across various datasets demonstrate that BrepMFR achieves state-of-the-art machining feature recognition accuracy and performs effectively on CAD models of real-world mechanical parts.

Thermo-mechanical properties of ABS/stainless steel composite using FDM

Fused deposition modelling (FDM) process is the most common and traditional additive manufacturing methods for producing complicated three-dimensional (3D) samples from computer-aided design data at a cheaper cost than alternative methods. However, when compared to other common plastic production processes, such as injection moulding, FDM produced parts results in low mechanical properties. Hence, the objective of this study is to produce composite filament using stainless steel (SS) as filler material to enhance the mechanical properties of ABS. ABS is a petroleum based thermoplastic that commonly used in FDM. The production of metal/polymer composites utilising ABS as the matrix and varied SS powder compositions was studied in this work. ABS/SS composite filaments containing 5, 10 wt% SS powder were developed in this experiment to compare with pure ABS for the FDM process. The result shows that the higher the composition of SS powder the lower the ultimate tensile strength and yield strength where 10 wt% SS shows 38.42 MPa in ultimate tensile strength while for yield strength it shows 24.32 MPa. Meanwhile, 10 wt% SS filament has better elongation which is 18.28 % compare to pure ABS that is 9.89 % and 13.3 % for 5 wt% SS. Thus, the percentage of filler plays an important role in determining the properties of ABS.

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

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

BRepGAT: Graph neural network to segment machining feature faces in a B-rep model

Abstract In recent years, there have been many studies using artificial intelligence to recognize machining features in three-dimensional models in the computer-aided design (CAD)/computer-aided manufacturing field. Most of these studies converted the original CAD data into images, point clouds, or voxels for recognition. This led to information loss during the conversion process, resulting in decreased recognition accuracy. In this paper, we propose a novel deep learning model called the boundary representation graph attention network (BRepGAT) to segment faces in an original boundary representation (B-rep) model containing machining features. We define descriptors that represent information about the faces and edges of the B-rep model from the perspective of feature recognition. These descriptors are extracted from the B-rep model and transformed into homogeneous graph data, which are then passed to graph networks. BRepGAT recognizes machining features from the graph data input. Our experimental results using the MFCAD18++ dataset showed that BRepGAT achieved state-of-the-art recognition accuracy (99.1%). Furthermore, BRepGAT showed relatively robust performance on other datasets besides MFCAD18++.

Clinical Study of 14 Cases of Bone Augmentation with Selective Laser Melting Titanium Mesh Plates.

Additive manufacturing techniques are being used in the medical field. Orthopedic hip prostheses and denture bases are designed and fabricated based on the patient's computer-aided design (CAD) data. We attempted to incorporate this technique into dental implant bone augmentation. Surgical simulation was performed using patient data. Fourteen patients underwent bone augmentation using a selective laser melting (SLM) titanium mesh plate. The results showed no evidence of infection in any of the 14 patients. In 12 patients, only one fixation screw was used, and good results were obtained. The SLM titanium mesh plate was good adaptation in all cases, with bone occupancy greater than 90%. The average bone resorption of the marginal alveolar bone from the time of dental implant placement to the time of the superstructure placement was 0.69 ± 0.25 mm. Implant superstructures were placed in all cases, and bone augmentation with SLM titanium mesh plates was considered a useful technique.

Computer-aided design, syntheses, and ITC binding data of novel flavanone derivatives for use as potential inhibitors of the papain-like protease of COVID-19

The papain-like protease (PLpro) of SARS-CoV-2 (COVID-19) is a high-profile drug target for treating COVID-19 due to its critical role in making essential proteins crucial in viral replication and host immune sensing. The development of small molecule inhibitors of PLpro is an area of ongoing research and interest. To investigate the development of PLpro inhibitors, a series of novel flavanone derivatives were designed usingin silicodocking against the papain-like protease of COVID-19. The most promising targets were synthesized and structurally characterized by NMR and mass spectrometry. Using isothermal calorimetry studies, two synthesized derivatives were found to bind PLpro in the low micromolar to nanomolar range.

A framework for flexible and reconfigurable vision inspection systems

Reconfiguration activities remain a significant challenge for automated Vision Inspection Systems (VIS), which are characterized by hardware rigidity and time-consuming software programming tasks. This work contributes to overcoming the current gap in VIS reconfigurability by proposing a novel framework based on the design of Flexible Vision Inspection Systems (FVIS), enabling a Reconfiguration Support System (RSS). FVIS is achieved using reprogrammable hardware components that allow for easy setup based on software commands. The RSS facilitates offline software programming by extracting parameters from real images, Computer-Aided Design (CAD) data, and rendered images using Automatic Feature Recognition (AFR). The RSS offers a user-friendly interface that guides non-expert users through the reconfiguration process for new part types, eliminating the need for low-level coding. The proposed framework has been practically validated during a 4-year collaboration with a global leading automotive half shaft manufacturer. A fully automated FVIS and the related RSS have been designed following the proposed framework and are currently implemented in 7 plants of GKN global automotive supplier, checking 60 defect types on thousands of parts per day, covering more than 200 individual part types and 12 part families.

Application of Macro Element Method (MEM) for faster automotive crash safety design during concept stage

The non-linear nature of vehicle crash safety simulations using the Finite Element Method (FEM) usually requires a significant investment in computational time and resources, in addition to the requirement of full CAD (Computer Aided Design) data availability, which is challenging to obtain during the concept stage. The current work explores the usage of Macro Element Method (MEM) based on simplified Super Beam Element (SBE), for which the shape functions are developed based on experimental observations rather than algebraic equations. MEM crash analysis run time doesn’t exceed several seconds and is one of the underutilised analytical models which can be used to quickly explore new design directions during the early concept stage of the vehicle and has higher potential to speed up the product development process. In this paper, initially, a calibration study was done to validate the MEM at a component level to compare the results with FEM and experimental values. FEM and MEM simulation models were developed and evaluated for a metallic thin-walled square frusta with varying semi-apical angles subjected to axial compression. The results were compared, and it was found that MEM results show more than 97% correlation compared to FEM and experimental results. Subsequently, a design modification was introduced in the component and the results show close degree of correlation between MEM and FEM. Similar process was followed for full vehicle level simulations, such as full width frontal crash, side pole crash analyses to understand the robustness of the method in applying it to product development environment.

Drug design education in China.

With the emergence of innovative technologies, including combinatorial chemistry, high-throughput screening, computer-aided drug design (CADD), artificial intelligence (AI) and big data, the importance of drug design in the field of drug discovery and development is increasing. Additionally, education in drug design plays an important role in the training of pharmaceutical talent. Starting with undergraduates, cultivating pharmaceutical design thinking, developing innovation and creativity, and establishing an interdisciplinary knowledge system will not only provide students with a solid knowledge basis but also promote the development of the pharmaceutical industry in China. This article presents the current status of pharmaceutical education and the distribution of drug design courses in China and summarizes the employment prospects of graduates, thus providing valuable references and evidence for global pharmaceutical design education.

Effect of build angle on the dimensional accuracy of monolithic zirconia crowns fabricated with the nanoparticle jetting technique

The build angle is an essential parameter in additive manufacturing. Its effect on the dimensional accuracy of zirconia restorations fabricated using the nanoparticle jetting (NPJ) technique is unknown. The purpose of this in vitro study was to evaluate the effect of the build angle on the dimensional accuracy of monolithic zirconia complete crowns fabricated by using NPJ. Standardized artificial right maxillary incisors and mandibular first molars were prepared for ceramic complete crowns. In total, 100 monolithic zirconia crowns were fabricated using NPJ at build angles of 0, 45, 90, 135, and 180 degrees (n=10/angle for incisors and molars). The dimensional accuracies in the external, marginal, and intaglio regions were determined by superimposing the scanned data and computer-aided design data on the crowns. Root mean square (RMS) values were used to analyze the accuracy of the zirconia crowns overall and at the external, marginal, and intaglio surfaces. The Shapiro-Wilk test was used to examine the normality of data distribution. Differences among test groups were assessed using a 1-way analysis of variance and the post hoc least significant difference test (α=.05). Significant differences were found in the accuracy of monolithic zirconia incisor and molar complete crowns in the external, marginal, and intaglio regions among the 5 build angles (P<.05). For incisors, the external RMS value was lowest for a build angle of 45 degrees (18.2 ±3.0µm), the marginal and intaglio RMS values were lowest for a build angle of 135 degrees (47.4 ±10.7 and 26.5 ±6.1µm, respectively), and the overall RMS values did not differ significantly among the 5 build angles (P>.05). For molars, build angles of 0 degrees and 180 degrees yielded the lowest RMS values overall (22.3 ±1.5 and 21.8 ±3.2µm, respectively) and in the external (23.2 ±2.9 and 22.3 ±2.5µm, respectively) and intaglio (22.2 ±3.7 and 21.2 ±4.6µm, respectively) regions. No significant difference was observed in the marginal area among the 5 build angles (P>.05). The overall RMS values reflecting dimensional accuracy for the 5 build angles were between 23.5 and 26.7µm for incisors and 21.8 and 26.2µm for molars. The dimensional accuracy of monolithic zirconia crowns fabricated by using NPJ was affected by the build angle and was within clinically acceptable limits.

Special Issue on Recent Trends in Additive Manufacturing

Additive manufacturing (AM) has undergone rapid development in the past decade. Owing to its capacity to produce complex and functional parts in various industries, it has been recognized as a remarkable scientific and industrial technique. It is used to enhance weight-saving production and reduce the number of parts, with metal-based AM techniques in particular being recognized as the most promising AM techniques developed thus far. This is because of their high potential for direct production through the selective solidification of metal materials from three-dimensional, computer-aided design data. The medical, aerospace, and part molding industries are some of the many expected to reap particular benefit from the ability to produce high-quality models with reduced manufacturing costs and lead times. The objective of this special issue is to collect recent research works focused on recent trends in AM. This issue includes 7 papers covering the following topics: - Metal-based powder bed fusion using a laser beam (PBF-LB/M) - Wire and arc-based AM (WAAM) - Fused deposition modeling (FDM) This special issue is expected to help readers understand the recent trends in AM, leading in turn to further research on AM. We deeply appreciate the contributions of all authors and thank the reviewers for their incisive efforts.

Additive manufacturing of titanium and nickel- based superalloys: A review

This paper reviews on the rapidly emerging manufacturing technology known as additive manufacturing (AM) which utilizes computer-aided design (CAD) data to produce three-dimensional objects by building up layers of material. One of the most promising areas of application for additive manufacturing is in the use of Titanium and Ni-based superalloys for aerospace and biomedical applications. These superalloys have exceptional strength and corrosion resistance, making them ideal for use in parts that are exposed to high temperatures and harsh environments. AM allows for the precise control of the microstructure of these materials, leading to improved mechanical properties and performance. Additionally, AM could be utilized to produce internal structures and complex shapes that would be hard or unattainable to achieve with conventional manufacturing methods. A detailed discussion of applications of AM in biomedical, aerospace, nuclear and automobile industries which deal with Titanium and Ni-based superalloys is the primary focus of this paper.

Neural networks vs. splines: advances in numerical extruder design

In this paper, we present a novel approach to geometry parameterization that we apply to the design of mixing elements for single-screw extruders. The approach uses neural networks of a specific architecture to automatically learn an appropriate parameterization. This stands in contrast to the so far common user-defined parameterizations. Geometry parameterization is crucial in enabling efficient shape optimization as it allows for optimizing complex shapes using only a few design variables. Recent approaches often utilize computer-aided design (CAD) data in conjunction with spline-based methods where the spline’s control points serve as design variables. Consequently, these approaches rely on the design variables specified by the human designer. This approach results in a significant amount of manual tuning to define a suitable parameterization. In addition, despite this effort, many times the optimization space is often limited to shapes in close proximity to the initial shape. In particular, topological changes are usually not feasible. In this work, we propose a method that circumvents this dilemma by providing low-dimensional, yet flexible shape parametrization using a neural network, which is independent of any computational mesh or analysis methods. Using the neural network for the geometry parameterization extends state-of-the-art methods in that the resulting design space is not restricted to user-prescribed modifications of certain basis shapes. Instead, within the same optimization space, we can interpolate between and explore seemingly unrelated designs. To show the performance of this new approach, we integrate the developed shape parameterization into our numerical design framework for dynamic mixing elements in plastics’ extrusion. Finally, we challenge the novel method in a competitive setting against current free-form deformation-based approaches and demonstrate the method’s performance even at this early stage.