Computer-Aided Design最新文献

SITF: A Self-Supervised Iterative Training Framework for Point Cloud Denoising SITF：用于点云去噪的自监督迭代训练框架

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-10-18 DOI: 10.1016/j.cad.2024.103812

Zhiyong Su , Changchang Wang , Kun Jiang , Kai Jiang , Weiqing Li

{"title":"SITF: A Self-Supervised Iterative Training Framework for Point Cloud Denoising","authors":"Zhiyong Su , Changchang Wang , Kun Jiang , Kai Jiang , Weiqing Li","doi":"10.1016/j.cad.2024.103812","DOIUrl":"10.1016/j.cad.2024.103812","url":null,"abstract":"<div><div>Despite existing supervised point cloud denoising methods having made great progress, they require paired ideal noisy-clean datasets for training which is expensive and impractical in real-world applications. Moreover, they may perform the denoising process multiple times with fixed network parameters for better denoising results at test time. To address above issues, this paper proposes a self-supervised iterative training framework (SITF) for point cloud denoising, which only requires single noisy point clouds and a noise model. Given an off-the-shelf denoising network and original noisy point clouds, firstly, an intermediate noisier-noisy dataset is created by adding additional noises from the known noise model to noisy point clouds (i.e. learning targets). Secondly, after training on the noisier-noisy dataset, the denoising network is employed to denoise the original noisy point clouds to obtain the learning targets for the next iteration. The above two steps are iteratively and alternatively performed to get a better and better trained denoising network. Furthermore, to get better learning targets for the next round, this paper also proposes a novel iterative denoising network (IDN) architecture of stacked source attention denoising modules. The IDN explicitly models the iterative denoising process internally within a single network via reforming the given denoising network. Experimental results show that existing supervised networks trained through the SITF can achieve competitive denoising results and even outperform supervised networks under high noise conditions. The source code can be found at: <span><span>https://github.com/VCG-NJUST/SITF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50632,"journal":{"name":"Computer-Aided Design","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-Level High-Resolution Structural Topology Optimization with Equilibrated Cells 利用平衡单元进行两级高分辨率结构拓扑优化

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-10-17 DOI: 10.1016/j.cad.2024.103811

Rafael Merli , Antolín Martínez-Martínez , Juan José Ródenas , Marc Bosch-Galera , Enrique Nadal

{"title":"Two-Level High-Resolution Structural Topology Optimization with Equilibrated Cells","authors":"Rafael Merli , Antolín Martínez-Martínez , Juan José Ródenas , Marc Bosch-Galera , Enrique Nadal","doi":"10.1016/j.cad.2024.103811","DOIUrl":"10.1016/j.cad.2024.103811","url":null,"abstract":"<div><div>In today’s industry, the rapid evolution in the design and development of optimized mechanical components to meet customer requirements represents a significant challenge for companies. These companies seek efficient solutions to enhance their products in terms of stiffness and strength. One powerful approach is Topology Optimization, which aims to determine the optimal material distribution within a predefined domain to maximize the overall component’s stiffness. Achieving high-resolution solutions is also crucial for accurately defining the final material distribution. While standard Topology Optimization tools can propose optimal solutions for entire components, they struggle with small-scale details (such as trabecular structures) due to prohibitive computational costs. To address this issue, our proposed approach introduces a two-level topology optimization methodology considering density-based techniques. The proposed methodology includes three steps: The first one subdivides the whole component in cells and generates a coarse optimized low-definition material distribution, assigning a different density to each cell. Since the output stresses from the coarse problem are not equilibrated into each cell, they must not be directly used in the fine level. Thus, the second step uses the equilibrating traction recovery approach to convert the cell nodal forces into equilibrated lateral tractions over the cell boundary. Finally, taking as input data the densities from the coarse optimization and imposing these lateral tractions as Neumann boundary conditions, each cell is optimized at fine level. The main goal of this work is to efficiently solve high-resolution topology optimization problems using a two-level mechanically-continuous method, which would be unaffordable with standard computing facilities and the current techniques.</div></div>","PeriodicalId":50632,"journal":{"name":"Computer-Aided Design","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SplineGen: Approximating unorganized points through generative AI SplineGen：通过生成式人工智能逼近无组织点

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-10-10 DOI: 10.1016/j.cad.2024.103809

Qiang Zou, Lizhen Zhu, Jiayu Wu, Zhijie Yang

{"title":"SplineGen: Approximating unorganized points through generative AI","authors":"Qiang Zou, Lizhen Zhu, Jiayu Wu, Zhijie Yang","doi":"10.1016/j.cad.2024.103809","DOIUrl":"10.1016/j.cad.2024.103809","url":null,"abstract":"<div><div>This paper presents a learning-based method to solve the traditional parameterization and knot placement problems in B-spline approximation. Different from conventional heuristic methods or recent AI-based methods, the proposed method does not assume ordered or fixed-size data points as input. There is also no need for manually setting the number of knots. Parameters and knots are generated in an associative way to attain better parameter-knot alignment, and therefore a higher approximation accuracy. These features are attained by using a new generative model SplineGen, which casts the parameterization and knot placement problems as a sequence-to-sequence translation problem. It first adopts a shared autoencoder model to learn a 512-D embedding for each input point, which has the local neighborhood information implicitly captured. Then these embeddings are autoregressively decoded into parameters and knots by two associative decoders, a generative process automatically determining the number of knots, their placement, parameter values, and their ordering. The two decoders are made to work in a coordinated manner by a new network module called internal cross-attention. Once trained, SplineGen demonstrates a notable improvement over existing methods, with one to two orders of magnitude increase in approximation accuracy on test data.</div></div>","PeriodicalId":50632,"journal":{"name":"Computer-Aided Design","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimal surface-guided higher-order mesh generation for CAD models 为 CAD 模型生成最小曲面引导的高阶网格

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-10-01 DOI: 10.1016/j.cad.2024.103810

Kaixin Yu , Bohan Wang , Xuejuan Chen , Ying He , Jianjun Chen

引用次数: 0

Boundary recognition of ship planar components from point clouds based on trimmed delaunay triangulation 基于修剪三角测量法的点云船舶平面部件边界识别

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-09-27 DOI: 10.1016/j.cad.2024.103808

Puhao Lei , Zhen Chen , Runli Tao , Jun Li , Yuchi Hao

引用次数: 0

eCAD-Net: Editable Parametric CAD Models Reconstruction from Dumb B-Rep Models Using Deep Neural Networks eCAD-Net：利用深度神经网络从呆板的 B-Rep 模型重建可编辑的参数化 CAD 模型

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-09-24 DOI: 10.1016/j.cad.2024.103806

Chao Zhang , Arnaud Polette , Romain Pinquié , Gregorio Carasi , Henri De Charnace , Jean-Philippe Pernot

引用次数: 0

Numerical integration on 2D/3D arbitrary domains: Adaptive quadrature/cubature rule for domains with curved boundaries 二维/三维任意域的数值积分：具有弯曲边界的域的自适应正交/余角规则

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-09-24 DOI: 10.1016/j.cad.2024.103807

Nafiseh Niknejadi, Bijan Boroomand

{"title":"Numerical integration on 2D/3D arbitrary domains: Adaptive quadrature/cubature rule for domains with curved boundaries","authors":"Nafiseh Niknejadi, Bijan Boroomand","doi":"10.1016/j.cad.2024.103807","DOIUrl":"10.1016/j.cad.2024.103807","url":null,"abstract":"<div><div>This paper introduces an efficient quadrature rule for domains with curved boundaries in 2D/3D. Building upon our previous work focused on polytopes (Comput. Methods Appl. Mech. Engrg. 403 (2023) 115,726), we extend this method to handle volume/boundary integration on domains with general configurations and boundaries. In this method, we approximate a generic function using a finite number of orthogonal polynomials, and we obtain the coefficients of these polynomials through the integration points. The physical domain is enclosed by a fictitious rectangular/cuboidal domain, where a tensor-product of Gauss quadrature points is primarily considered. To locate the integration points that are strictly within the domain under consideration (e.g., the physical 3D domain itself or its mapped boundaries), we form a system of algebraic equations whose dimensions depend solely on the number of polynomials, not the number of quadrature points which may be significantly larger. This allows us to construct a full-rank square coefficient matrix, leading to the uniqueness of the solution, and the system of equations is then solved through a straightforward inverse process. To evaluate the integral of the polynomials, we transform the integration over the domain under consideration into an equivalent integration along the domain's boundaries using the divergence theorem. For 2D cases, we perform the boundary integration using Gauss points along the curved lines. In 3D cases, we provide an efficient algorithm for computing the boundary integrals over curved surfaces. We present several integration problems involving two and three-dimensional curved regions to demonstrate the accuracy and efficiency of the proposed method.</div></div>","PeriodicalId":50632,"journal":{"name":"Computer-Aided Design","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-part kinematic constraint prediction for automatic generation of CAD model assemblies using graph convolutional networks 利用图卷积网络自动生成 CAD 模型装配的多部分运动学约束预测

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-09-21 DOI: 10.1016/j.cad.2024.103805

Lucas Vergez, Arnaud Polette, Jean-Philippe Pernot

{"title":"Multi-part kinematic constraint prediction for automatic generation of CAD model assemblies using graph convolutional networks","authors":"Lucas Vergez, Arnaud Polette, Jean-Philippe Pernot","doi":"10.1016/j.cad.2024.103805","DOIUrl":"10.1016/j.cad.2024.103805","url":null,"abstract":"<div><div>This paper presents a machine learning-based approach to predict kinematic constraints between CAD models that have potentially never been assembled together before. During the learning phase, the algorithm is trained to predict the next-possible-constraints between a set of parts candidate to the assembly. Assemblies are represented in a new graph-based formalism that is capable of capturing features associated with parts, interfaces between parts and constraints between them. Using such a multi-level feature extraction strategy coupled to a state-by-state graph decomposition, the approach does not need to be trained on a large database. This formalism is used to model both the network input and output where the next-possible-constraints appear after evaluation. The core of the approach relies on a series of networks based on a link-prediction encoder–decoder architecture, integrating the capabilities of several convolutional networks trained in an end-to-end manner. A decision-making algorithm is added to post-process the output and drive the prediction process in finding one among the set of next-possible-constraints. This process is repeated until no more constraints can be added. The experimental results show that the proposed approach outperforms state-of-the-art methods on such assembly tasks. Although the state-by-state assembly algorithm is iterative, it still takes into account the whole set of parts as well as the whole set of constraints already predicted, and this makes it possible to handle constraint cycles, which is generally not possible when not considering multiple parts as input.</div></div>","PeriodicalId":50632,"journal":{"name":"Computer-Aided Design","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient evaluation of Bernstein-Bézier coefficients of B-spline basis functions over one knot span 在一个节点跨度上高效评估 B-样条曲线基函数的伯恩斯坦-贝塞尔系数

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-09-19 DOI: 10.1016/j.cad.2024.103804

Filip Chudy, Paweł Woźny

引用次数: 0

Computational design of asymptotic geodesic hybrid gridshells via propagation algorithms 通过传播算法计算设计渐近大地混合网格壳

IF 3 3区计算机科学

Computer-Aided Design Pub Date : 2024-09-12 DOI: 10.1016/j.cad.2024.103800

Bolun Wang , Maryam Almaskin , Helmut Pottmann

引用次数: 0