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Periodic implicit representation, design and optimization of porous structures using periodic B-splines Comput. Aided Des. (IF 4.3) Pub Date : 2024-03-11 Depeng Gao, Yang Gao, Hongwei Lin
Porous structures are intricate solid materials with numerous small pores, extensively used in fields like medicine, chemical engineering, and aerospace. However, the design of such structures using computer-aided tools is a time-consuming and tedious process. In this study, we propose a novel representation method and design approach for porous units that can be infinitely spliced to form a porous
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Robust Hole-Detection in Triangular Meshes Irrespective of the Presence of Singular Vertices Comput. Aided Des. (IF 4.3) Pub Date : 2024-02-28 Mauhing Yip, Annette Stahl, Christian Schellewald
In this work, we present a boundary and hole detection approach that traverses all the boundaries of an edge-manifold triangular mesh, irrespectively of the presence of singular vertices, and subsequently determines and labels all holes of the mesh. The proposed automated hole-detection method is valuable to the computer-aided design (CAD) community as all boundary-edges within the mesh are utilized
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A computational implementation of Vector-based 3D Graphic Statics (VGS) for interactive and real-time structural design Comput. Aided Des. (IF 4.3) Pub Date : 2024-02-20 Jean-Philippe JASIENSKI, Yuchi SHEN, Patrick Ole OHLBROCK, Denis ZASTAVNI, Pierluigi D'ACUNTO
This article presents a computational implementation for the Vector-based Graphic Statics (VGS) framework making it an effective CAD tool for the design of spatial structures in static equilibrium (VGS-tool). The paper introduces several key features that convert a purely theoretical graph and geometry based framework into a fully automated computational procedure, including the following new contributions:
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Homotopy Based Skinning of Spheres Comput. Aided Des. (IF 4.3) Pub Date : 2024-02-17 Marián Fabian, Pavel Chalmovianský, Martina Bátorová
This paper deals with surfaces covering a set of spheres, whose centers form polyhedra. We propose novel methods of skinning based on homotopic deformation for the considered case. A method starts with a regular surface with a simple construction which can be deformed in a many ways. We demonstrate some of them in a few examples. The method is compared to the existing solutions by the new approach
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A Novel Topological Method for Automated and Exhaustive Wire Harness Design Comput. Aided Des. (IF 4.3) Pub Date : 2024-02-13 Arun Rehal, Dibakar Sen
The current practice of manual wire harness design is labor-intensive, time-consuming, costly, and error-prone. In this paper, we present a methodology for completely automated wire harness design. We propose a topological approach that yields all the possible electrically admissible but topologically distinct harness system layouts that can be used to connect the specified terminals. Each generated
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Convex Body Collision Detection Using the Signed Distance Function Comput. Aided Des. (IF 4.3) Pub Date : 2024-02-02 Pedro López-Adeva Fernández-Layos, Luis F.S. Merchante
We present a new algorithm to compute the minimum distance and penetration depth between two convex bodies represented by their Signed Distance Function (SDF). First, we formulate the problem as an optimization problem suitable for arbitrary non-convex bodies, and then we propose the ellipsoid algorithm to solve the problem when the two bodies are convex. Finally, we benchmark the algorithm and compare
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Special issue editorial: Computational modeling, design and fabrication for textiles Comput. Aided Des. (IF 4.3) Pub Date : 2024-01-17 David E. Breen, James McCann
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Advancing Front Mesh Generation on Dirty Composite Surfaces Comput. Aided Des. (IF 4.3) Pub Date : 2024-01-06 Taoran Liu, Hongfei Ye, Jianjing Zheng, Yao Zheng, Jianjun Chen
Computer-aided design (CAD) models usually contain many errors between neighboring surfaces, such as slivers, gaps, and overlaps. To clean up such models, virtual operations have been suggested to merge multiple neighboring CAD surfaces into a single composite surface. However, it remains a challenge to generate a quality mesh on thereby formed dirty composite surfaces. In this paper, we propose a
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Isogeometric Size Optimization Design Based on Parameterized Volume Parametric Models Comput. Aided Des. (IF 4.3) Pub Date : 2024-01-06 Long Chen, Lele Zhang, Yanan Wu, Gang Xu, Baotong Li
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Design of Random and Deterministic Fractal Surfaces from Voronoi Cells Comput. Aided Des. (IF 4.3) Pub Date : 2024-01-03 Javier Rodríguez-Cuadrado, Jesús San Martín
We show a fractal surface generation method that, unlike other methods, generates both random and deterministic fractals that model natural and architectural elements. The method starts with a succession of sets of sites, which determine, by means of a metric, a succession of Voronoi tessellations of the region where the fractal is defined. For each element of the tessellation sequence we define a
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Full-LSPIA: A Least-Squares Progressive-Iterative Approximation Method with Optimization of Weights and Knots for NURBS Curves and Surfaces Comput. Aided Des. (IF 4.3) Pub Date : 2024-01-02 Lin Lan, Ye Ji, Meng-Yun Wang, Chun-Gang Zhu
The Least-Squares Progressive-Iterative Approximation (LSPIA) method offers a powerful and intuitive approach for data fitting. Non-Uniform Rational B-splines (NURBS) are a popular choice for approximation functions in data fitting, due to their robust capabilities in shape representation. However, a restriction of the traditional LSPIA application to NURBS is that it only iteratively adjusts control
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Flexible Kokotsakis Meshes with Skew Faces: Generalization of the Orthodiagonal Involutive Type Comput. Aided Des. (IF 4.3) Pub Date : 2023-12-23 Alisher Aikyn, Yang Liu, Dmitry A. Lyakhov, Florian Rist, Helmut Pottmann, Dominik L. Michels
In this paper, we introduce and study a remarkable class of mechanisms formed by a 3 × 3 arrangement of rigid quadrilateral faces with revolute joints at the common edges. In contrast to the well-studied Kokotsakis meshes with a quadrangular base, we do not assume the planarity of the quadrilateral faces. Our mechanisms are a generalization of Izmestiev’s orthodiagonal involutive type of Kokotsakis
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Bending-Reinforced Grid Shells for Free-form Architectural Surfaces Comput. Aided Des. (IF 4.3) Pub Date : 2023-12-23 Francesco Laccone, Nico Pietroni, Paolo Cignoni, Luigi Malomo
We introduce a new method for designing reinforcement for grid shells and improving their resistance to out-of-plane forces inducing bending. The central concept is to support the base network of elements with an additional layer of beams placed at a certain distance from the base surface. We exploit two main techniques to design these structures: first, we derive the orientation of the beam network
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Automatic Cable Harness Layout Routing in a Customizable 3D Environment Comput. Aided Des. (IF 4.3) Pub Date : 2023-12-28 T. Karlsson, E. Åblad, T. Hermansson, J.S. Carlson, G. Tenfält
Designing cable harnesses can be time-consuming and complex due to many design and manufacturing aspects and rules. Automating the design process can help to fulfil these rules, speed up the process, and optimize the design. To accommodate this, we formulate a harness routing optimization problem to minimize cable lengths, maximize bundling by rewarding shared paths, and optimize the cables’ spatial
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Defining metric-aware size-shape measures to validate and optimize curved high-order meshes Comput. Aided Des. (IF 4.3) Pub Date : 2023-12-19 Guillermo Aparicio-Estrems, Abel Gargallo-Peiró, Xevi Roca
We define a regularized size-shape distortion (quality) measure for curved high-order elements on a Riemannian space. To this end, we measure the deviation of a given element, straight-sided or curved, from the stretching, alignment, and sizing determined by a target metric. The defined distortion (quality) is suitable to check the validity and the quality of straight-sided and curved elements on Riemannian
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The deep neural network solver for B-spline approximation Comput. Aided Des. (IF 4.3) Pub Date : 2023-12-19 Zepeng Wen, Jiaqi Luo, Hongmei Kang
This paper introduces a novel unsupervised deep learning approach to address the knot placement problem in the field of B-spline approximation, called deep neural network solvers (DNN-Solvers). Given discrete points, the DNN acts as a solver for calculating knot positions in the case of a fixed knot number. The input can be any initial knots and the output provides the desirable knots. The loss function
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Fabrication-aware strip-decomposable quadrilateral meshes Comput. Aided Des. (IF 4.3) Pub Date : 2023-12-16 Ioanna Mitropoulou, Amir Vaxman, Olga Diamanti, Benjamin Dillenburger
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A Dual Neural Network Approach to Topology Optimization for Thermal-Electromagnetic Device Design Comput. Aided Des. (IF 4.3) Pub Date : 2023-12-05 Benjamin A. Jasperson, Michael G. Wood, Harley T. Johnson
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A Globalized and Preconditioned Newton-CG Solver for Metric-Aware Curved High-Order Mesh Optimization Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-30 Guillermo Aparicio-Estrems, Abel Gargallo-Peiró, Xevi Roca
We present a specific-purpose globalized and preconditioned Newton-CG solver to minimize a metric-aware curved high-order mesh distortion. The solver is specially devised to optimize curved high-order meshes for high polynomial degrees with a target metric featuring non-uniform sizing, high stretching ratios, and curved alignment — exactly the features that stiffen the optimization problem. To this
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Singularity structure simplification for hex mesh via integer linear program Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-23 Junyi Duan, Xiaopeng Zheng, Na Lei, Zhongxuan Luo
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Computational Segmentation of Timber Slabs with Free Column Placement Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-23 Luis Orozco, Hans Jakob Wagner, Anna Krtschil, Jan Knippers, Achim Menges
Modular floor slabs must be subdivided into prefabricable, transportable segments. This slab segmentation process conventionally uses a rectangular pattern, particularly for timber buildings. Regular segmentation patterns and strict column grids are ideal for rectangular building shapes, but restrict timber buildings to only some architectural uses, and are unideal for urban infill. Unfortunately,
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A Survey of Methods for Converting Unstructured Data to CSG Models Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-23 Pierre-Alain Fayolle, Markus Friedrich
The goal of this document is to survey existing methods for recovering or extracting CSG (Constructive Solid Geometry) representations from unstructured data such as 3D point-clouds or polygon meshes. We review and discuss related topics such as the segmentation and fitting of the input data. We cover techniques from solid modeling for the conversion of a polyhedron to a CSG expression and for the
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The Generation of 3D Surface Meshes for NURBS-Enhanced FEM Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-21 Xi Zou, Sui Bun Lo, Ruben Sevilla, Oubay Hassan, Kenneth Morgan
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Accurate Detection and Smoothness-Oriented Avoidance Method of Singularity in 5-Axis CNC Machining Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-14 Lei Wu, Jinting Xu, Hebing Xing, Yuwen Sun
As an inherent flaw in the kinematic chain mechanism of 5-axis machine tools, singularity can induce dramatic changes in machine axes motion and unfavorable fluctuations in feedrate. For effective singularity avoidance, it is desirable to first achieve accurate and efficient singularity detection and then eliminate the singularity without impairing tool orientation smoothness. This paper presents a
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Reconstruction and Preservation of Feature Curves in 3D Point Cloud Processing Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-08 Ulderico Fugacci, Chiara Romanengo, Bianca Falcidieno, Silvia Biasotti
Given a 3D point cloud, we propose a method for suitably resampling the cloud while reconstructing and preserving the feature curves to which some points are identified to belong. The first phase of our strategy enriches the cloud by approximating the curvilinear profiles outlined by the feature points with piece-wise polynomial parametric space curves through the use of the Hough transform. The second
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IF-TONIR: Iteration-free Topology Optimization based on Implicit Neural Representations Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-04 Jiangbei Hu, Ying He, Baixin Xu, Shengfa Wang, Na Lei, Zhongxuan Luo
Topology optimization holds great significance as a research topic in the field of mechanical engineering, aiming to design and optimize structures to achieve desired performance while adhering to specific constraints. However, its high computational complexity and iterative optimization process severely impact the efficiency, which presents substantial obstacles to its practical applications. To tackle
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Texture-Driven Adaptive Mesh Refinement with Application to 3D Relief Comput. Aided Des. (IF 4.3) Pub Date : 2023-11-04 Jiaze Li, Shengfa Wang, Eric Paquette
A high-quality 3D relief requires an appropriate refinement that has accurate carving boundaries with a limited number of added polygons. Most existing refinement methods cannot be applied to 3D reliefs directly, as they exhibit a mixture of problems such as not accurately following the texture contours, creating ill-shaped triangles, and excessively increasing the polygon count. We introduce an efficient
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Interface-Based Search and Automatic Reassembly of CAD Models for Database Expansion and Model Reuse Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-28 Lucas Vergez, Arnaud Polette, Jean-Philippe Pernot
This paper introduces a new framework for reassembling CAD models of mechanical parts. The generated CAD assemblies are well-constrained, with collision-free parts, and they are ready-to-use for downstream applications. First, input dead CAD models candidate for the reassembly are sorted following a part-by-part interface-based identification algorithm that is capable of characterizing each part according
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Fabric mechanical parameters for 3D cloth simulation in apparel CAD: A systematic review Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-30 Xiaoqun Dai, Yan Hong
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Taming Connectedness in Machine-Learning-Based Topology Optimization with Connectivity Graphs Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-26 Mohammad Mahdi Behzadi, Jiangce Chen, Horea T. Ilies
Despite the remarkable advancements in machine learning (ML) techniques for topology optimization, the predicted solutions often overlook the necessary structural connectivity required to meet the load-carrying demands of the resulting designs. Consequently, these predicted solutions exhibit subpar structural performance because disconnected components are unable to bear loads effectively and significantly
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Fast Reconstruction of Microstructures with Ellipsoidal Inclusions Using Analytical Descriptors Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-20 Paul Seibert, Markus Husert, Maximilian P. Wollner, Karl A. Kalina, Markus Kästner
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A Shape Derivative Approach to Domain Simplification Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-21 J. Hinz, O. Chanon, A. Arrigoni, A. Buffa
The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. This enables faster and more efficient simulations, without sacrificing accuracy in the regions of interest. More precisely, we consider an isogeometric
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Reducing the Number of Different Faces in Free-Form Surface Approximations Through Clustering and Optimization Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-21 Yuanpeng Liu, Ting-Uei Lee, Anooshe Rezaee Javan, Nico Pietroni, Yi Min Xie
Free-form structures are highly valued for their aesthetic appeal in architecture, but they typically comprise panels of many different shapes, which can pose great challenges for building construction. In this study, we aim to address this issue by proposing a novel clustering–optimization method to reduce the number of different n-gonal faces in free-form surface approximations. The method partitions
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A compact yet flexible design space for large-scale nonperiodic 3D woven composites based on a weighted game for generating candidate tow architectures Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-20 Zhen-Pei Wang, Brian N. Cox, Shemuel Joash Kuehsamy, Mark Hyunpong Jhon, Olivier Sudre, N. Sridhar, Gareth J. Conduit
Three-dimensional non-periodic woven composite preforms have sufficient design flexibility that tows can be aligned along principal loading paths even in shaped structural components with detailed local features. While this promises competitive performance, the feasible design space is combinatorially large, far beyond exhaustive search. Seeking a design space that is compact and easily searched yet
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Gradient design and fabrication methodology for interleaved self-locking kirigami panels Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-13 Hao Qiu, Yixiong Feng, Yicong Gao, Zhaoxi Hong, Jianrong Tan
Sandwich panels with excellent mechanical properties are widely used in the aerospace, architecture, and automobile industries. Kirigami-inspired structural designs are receiving increasing attention owing to the shape-induced functions and novel properties imparted by their folds and cuts. In this study, novel graded self-locking kirigami panels based on a tucked-interleaved pattern are developed
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Machine learning-driven optimization design of hydrogel-based negative hydration expansion metamaterials Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-11 Yisong Qiu, Hongfei Ye, Hongwu Zhang, Yonggang Zheng
Hydrogel-based negative hydration expansion (NHE) metamaterials are composite structures composed of responsive hydrogels and polymers, and their properties depend on their unique structures. In this paper, an optimization method based on the combination of the back-propagation neural network (BPNN) and the multi-population genetic algorithm (MPGA) is developed to rapidly design isotropic and anisotropic
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Extending Point-Based Deep Learning Approaches for Better Semantic Segmentation in CAD Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-10 Gerico Vidanes, David Toal, Xu Zhang, Andy Keane, Jon Gregory, Marco Nunez
Geometry understanding is a core concept of computer-aided design and engineering (CAD/CAE). Deep neural networks have increasingly shown success as a method of processing complex inputs to achieve abstract tasks. This work revisits a generic and relatively simple approach to 3D deep learning – a point-based graph neural network – and develops best-practices and modifications to alleviate traditional
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Multicomponent Topology Optimization Method Considering Stepwise Linear Assemblability with a Fictitious Physical Model Comput. Aided Des. (IF 4.3) Pub Date : 2023-10-06 R. Hirosawa, M. Noda, K. Matsushima, Y. Noguchi, T. Yamada
This paper proposes a multicomponent topology optimization method that considers assemblability. Generally, it is difficult to consider assemblability in topology optimization; however, in this study, we achieve it by introducing a fictitious physical model. To perform multicomponent topology optimization, the extended level set method is used to represent multiple components. First, the assembly constraints
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Algebraic 3D Graphic Statics with Edge and Vertex Constraints: A Comprehensive Approach to Extend the Solution Space for Polyhedral Form-Finding Comput. Aided Des. (IF 4.3) Pub Date : 2023-09-29 Yao Lu, Márton Hablicsek, Masoud Akbarzadeh
Built upon a previous algebraic framework for polyhedron-based 3D Graphic Statics (3DGS) that can numerically solve for a variety of dual diagrams given an input force or form diagram, this paper introduces an improved algebraic formulation that integrates edge lengths and vertex location constraints for better control over the results. Those constraints are realized by additional edge and vertex constraint
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Simultaneous Boundary and Interior Parameterization of Planar Domains Via Deep Learning Comput. Aided Des. (IF 4.3) Pub Date : 2023-09-25 Zheng Zhan, Wenping Wang, Falai Chen
In isogeometric analysis (IGA), domain parameterization plays a crucial role as it significantly impacts the results of subsequent numerical analyses. Previous literature has often treated domain parameterization as two separate processes: boundary correspondence and interior parameterization. However, this division tends to decrease the quality of the final parameterization. To address this issue
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Robust Reconstruction of Closed Parametric Curves by Topological Understanding with Persistent Homology Comput. Aided Des. (IF 4.3) Pub Date : 2023-09-04 Yaqi He, Jiacong Yan, Hongwei Lin
Curve reconstruction is a fundamental problem in reverse engineering, which has intrigued researchers for decades. In this paper, we propose a topological understanding based method for reconstructing parametric curves robustly from unorganized point clouds. Given a point cloud, we firstly understand the number of closed curves which need to be reconstructed using persistent homology. Then, by calculating
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Lightweight Geometric Compression Encoding for Additive Manufacturing Comput. Aided Des. (IF 4.3) Pub Date : 2023-08-25 Xin Zhao, Jinjie Huang
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Optimizing Cutting Sequences and Paths for Common-Edge Nested Parts Comput. Aided Des. (IF 4.3) Pub Date : 2023-08-18 Qirui Hu, Zhiwei Lin, Jianzhong Fu, Congcong Luan
Different from the normal nesting algorithm, common-edge nesting reduces the spacing between parts, improves the utilization rate of the sheet, and shortens the processing time. Although common-edge nested parts may be useful in theory, they are not practical for large-scale production due to their cutting limitations. Existing research either did not consider all common cutting constraints or used
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Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design Comput. Aided Des. (IF 4.3) Pub Date : 2023-08-16 Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed
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Towards a High Quality Shrink Wrap Mesh Generation Algorithm Using Mathematical Morphology Comput. Aided Des. (IF 4.3) Pub Date : 2023-08-12 Vijai Kumar Suriyababu, Cornelis Vuik, Matthias Möller
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DiffSVR: Differentiable Neural Implicit Surface Rendering for Single-View Reconstruction with Highly Sparse Depth Prior Comput. Aided Des. (IF 4.3) Pub Date : 2023-08-05 Artem Komarichev, Jing Hua, Zichun Zhong
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Generation of Homotopy Classes for Unconstrained 3D Wire Routing from Characteristic Loops Comput. Aided Des. (IF 4.3) Pub Date : 2023-08-03 Arun Rehal, Dibakar Sen
Routing of wire harnesses in complex machinery is a complicated problem. In this paper, we present an approach for computing all non-homotopic paths characteristic of all the homotopy classes associated with a pair of source–destination points embedded on the surface of the product. We introduce the notion of routing graphs, which are generated from specific non-trivial loops in the first homology
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A Class of Rational Quartic Splines and their Local Tensor Product Extensions Comput. Aided Des. (IF 4.3) Pub Date : 2023-08-01 Yuanpeng Zhu, Yunyi Tang
This work describes a new class of rational quartic splines with local shape parameters as well as their local tensor product extensions. The important local shape preserving properties of the new rational quartic spline curves, including monotonicity-preserving and convexity-preserving, are discussed in detail. With a simple knot vector, the resulting rational quartic spline curves are G2 continuous
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Computing smooth preferred feed direction fields with high material removal rates for efficient CNC tool paths Comput. Aided Des. (IF 4.3) Pub Date : 2023-07-19 Zirui Wang, Shibo Liu, Ligang Liu, Qiang Zou, Xiao-Ming Fu
We propose a novel method to compute smooth preferred feed direction fields with high material removal rates to construct CNC tool paths for five-axis machining with ball-end mills. At the core of our method are three new facts about the local maximum feed directions of the material removal rate to facilitate feed direction field optimization. According to these new facts, we develop a practical method
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Real-Time 3D Topological Braiding Simulation with Penetration-Free Guarantee Comput. Aided Des. (IF 4.3) Pub Date : 2023-07-22 Xinyu Lu, Pengbo Bo, Linqin Wang
We present an efficient approach for simulating 3D topological braiding while guaranteeing non-penetration. Our method combines eXtended Position-Based Dynamics (XPBD) with Incremental Potential Contact (IPC), leveraging XPBD’s efficiency, robustness, and numerical stability with IPC’s non-penetration capabilities. However, incorporating IPC introduces nonlinearity errors that result in instability
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Modeling and Analyzing Layup Gaps in the Trajectory Planning for Automated Tape Placement Comput. Aided Des. (IF 4.3) Pub Date : 2023-07-20 Peng Zhang, Lairong Yin, Zhenhua Zhou, Yonggang Tong, Wenming Feng
Composite materials are widely used in the aerospace industry thanks to their lightweight along with high stiffness and strength. Automated tape placement (ATP) is an important automated process used for the fabrication of large composite structures. In ATP, the layup paths of contiguous tapes are not parallel along their lengths due to the complex contour of the mandrel, which eventually introduces
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Guest Editorial: Proceedings of SPM 2023 Symposium Comput. Aided Des. (IF 4.3) Pub Date : 2023-07-20 Lucia Romani, Jianmin Zheng, Morad Behandish
Abstract not available
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Robust and Accurate Feature Detection on Point Clouds Comput. Aided Des. (IF 4.3) Pub Date : 2023-07-17 Zheng Liu, Xiaopeng Xin, Zheng Xu, Weijie Zhou, Chunxue Wang, Renjie Chen, Ying He
Geometric feature detection on surfaces is a crucial task for the characterization and understanding of geometry shapes. In this paper, we present a robust and reliable approach for accurately capturing local surface variations at different feature sizes within point clouds. To this end, we define a bilateral weighted centroid projection-based metric to quantify surface deviations. Based on the metric
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Global Continuous Toolpath Planning with Controllable Local Directions Comput. Aided Des. (IF 4.3) Pub Date : 2023-07-13 Yingxin Ma, Yuan Yao, Jinxiu Yang, Hang Zhang, Beishui Liao
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Subdivision-based IGA Coupled EIEQ Method for the Cahn–Hilliard Phase-Field Model of Homopolymer Blends on Complex Surfaces Comput. Aided Des. (IF 4.3) Pub Date : 2023-07-13
In this paper, we construct an IGA-EIEQ coupling scheme to solve the phase-field model of homopolymer blends on complex subdivision surfaces, in which the total free energy contains a gradient entropy with a concentration-dependent de-Gennes type coefficient and a non-linear logarithmic Flory–Huggins type potential. Based on the EIEQ method, we develop a fully-discrete numerical scheme with the superior
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Point Pair-Based Expression of Cutter Swept Envelopes in Five-Axis Milling Comput. Aided Des. (IF 4.3) Pub Date : 2023-06-26 Ye Ding, Yongxue Chen
This paper proposes an efficient method of formulating cutter swept envelopes in five-axis milling based on conformal geometric algebra. The surface of the rotary cutter is represented as a canal surface, as the envelope of a one-parameter family of spheres. The swept envelopes generated by the general rigid motion of the cutter can be regarded as the envelopes of a two-parameter family of spheres
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Automatic Design of Overflow System for Preventing Gas Defects by Considering the Direction of Molten Metal Flow Comput. Aided Des. (IF 4.3) Pub Date : 2023-06-14 Daichi Minamide, Ken’ichi Yano, Masahiro Sano, Takahiro Aoki
In die casting, gas defects occur if the molten metal entrains the gas inside the shot sleeve and mold and remains inside the product after filling. Therefore, an exhaust system such as an exhaust runner or overflow is generally designed at the die casting mold to discharge the gas-entrained molten metal outside the mold completely. In addition, an overflow has a broader designable area than an exhaust
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A Review of a B-spline based Volumetric Representation: Design, Analysis and Fabrication of Porous and/or Heterogeneous Geometries Comput. Aided Des. (IF 4.3) Pub Date : 2023-06-13 Gershon Elber
The needs of modern (additive) manufacturing (AM) technologies can no longer be satisfied by geometric modeling tools that are based on boundary representations (B-reps) - AM requires the representation and manipulation of interior heterogeneous fields and materials. Further, while the need for a tight coupling between design and analysis has been recognized as crucial almost since geometric modeling