Computer-Aided Design最新文献

We present a learning based framework for mesh quality improvement on unstructured triangular and quadrilateral meshes. Our model learns to improve mesh quality according to a prescribed objective function purely via self-play reinforcement learning with no prior heuristics. The actions performed on the mesh are standard local and global element operations. The goal is to minimize the deviation of the node degrees from their ideal values, which in the case of interior vertices leads to a minimization of irregular nodes.

Owing to the rapid development in additive manufacturing, the potential to fabricate intricate structures has become a reality, emphasizing the importance of designing structures conducive to additive manufacturing processes. A crucial consideration is the ability to design structures requiring no additional support during manufacturing. This paper employs implicit B-spline representations for self-supporting structure design by integrating a topology optimization model with self-supporting constraints derived analytically from the implicit representation. This analytical derivation for detecting overhang regions enables accurate and efficient calculation of constraints, outperforming other B-spline-based methods. Compared to the traditional voxel-based methods, the implicit B-spline representation significantly expedites the optimization process by reducing the number of design variables. Additionally, several acceleration techniques are implemented to enhance the efficiency of our method, allowing simulations of 3D models with millions of finite elements to be completed within one and half an hour, excelling other B-spline-based methods and voxel-based methods. Various numerical experiments validate its excellent performance, confirming the effectiveness and efficiency of the proposed algorithm.

Shell structures refer to structural elements that derive strength and load-bearing capacity from their thin and curved geometry. In practical applications, shell structures are commonly composed of multiple patches to represent intricate and diverse architectural configurations faithfully. Nevertheless, the design of multi-patch shell structures holds considerable promise. However, most of the previous work is devoted to the numerical analysis of multi-patch shell structures without further optimization design. The work proposes an inverse design framework, specifically focusing on multi-patch configurations based on Reissner–Mindlin theory. First, reparameterization and global refinement operations are employed on the provided multi-patch shell structures. Renumbering the indices of control points with shared degrees of freedom at the interface naturally ensures $C^0$-continuity between patches. Subsequently, this study investigates the amalgamation of Isogeometric Analysis (IGA) and the Solid Isotropic Material with Penalization (SIMP) method for topology optimization of shell structures. The proposed approach is validated through numerical examples, emphasizing its capacity to enhance multi-patch shell structure design, showcasing robustness and efficiency.

Designing the layout for complex electromechanical products involves the challenging task of automatically laying out cables. This challenge is particularly pronounced in the case of branch cables, which are more intricate due to their multiple connection terminals and branches. This paper presents a polynomial-time wiring algorithm based on dynamic programming to determine branching point locations in the layout design of cables, given the electrical definition of the wire harness. The method considers various engineering constraints, including obstacle avoidance, wall adherence, minimum bend radius, and gray areas. To validate our method, we implemented a branch cable auto-layout system through secondary development based on the UG platform. The experimental results indicate the effectiveness of our approach, demonstrating promising performance in terms of time and high-quality layouts. This showcases its potential for practical application in cable layout design for complex electromechanical systems.

The benefits of integrated design using the Model Based System Engineering (MBSE) approach in the design process of mechatronic systems have gradually become apparent. The automatic generation of simulation models and geometric models based on System Modeling Languages (SysML) models enables system engineers to swiftly analyze and simulate system performance, visually depict design outcomes, and expedite the product development process. Due to the current system modeling's lack of model integration and geometric design functions, this paper proposes an integrated design and simulation method for mechatronic systems that can carry out complete model synchronization and verification, rapid geometric solution generation, and visual representation. Furthermore, a corresponding model synchronization integration framework is established. This framework primarily encompasses system de-sign, system simulation, and geometric design, with its model integration method being model transformation and model synchronization. The paper concludes with an example of the design process of a quadruped robot to validate the framework and its supported methods, providing a reference for other system design and integration endeavors.

Lattice structures have been widely used in applications due to their superior mechanical properties. To fabricate such structures, a geometric processing step called triangulation is often employed to transform them into the STL format before sending them to 3D printers. Because lattice structures tend to have high geometric complexity, this step usually generates a large amount of triangles, a memory and compute-intensive task. This problem manifests itself clearly through large-scale lattice structures that have millions or billions of struts. To address this problem, this paper proposes to transform a lattice structure into an intermediate model called meta-mesh before undergoing real triangulation. Compared to triangular meshes, meta-meshes are very lightweight and much less compute-demanding. The meta-mesh can also work as a base mesh reusable for conveniently and efficiently triangulating lattice structures with arbitrary resolutions. A CPU+GPU asynchronous meta-meshing pipeline has been developed to efficiently generate meta-meshes from lattice structures. It shifts from the thread-centric GPU algorithm design paradigm commonly used in CAD to the recent warp-centric design paradigm to achieve high performance. This is achieved by a new data compression method, a GPU cache-aware data structure, and a workload-balanced scheduling method that can significantly reduce memory divergence and branch divergence. Experimenting with various billion-scale lattice structures, the proposed method is seen to be two orders of magnitude faster than previously achievable.

In this study, we present the bicubic Hermite element method (BHEM), a new computational framework devised for the elastodynamic simulation of thin-shell structures. The BHEM is constructed based on quadrilateral Hermite patches, which serve as a unified representation for shell geometry, simulation, collision avoidance, as well as rendering. Compared with the commonly utilized linear FEM, the BHEM offers higher-order solution spaces, enabling the capture of more intricate and smoother geometries while employing significantly fewer finite elements. In comparison to other high-order methods, the BHEM achieves conforming $C^1$ continuity for Kirchhoff–Love (KL) shells with minimal complexity. Furthermore, by leveraging the subdivision and convex hull properties of Hermite patches, we develop an efficient algorithm for ray-patch intersections, facilitating collision handling in simulations and ray tracing in rendering. This eliminates the need for laborious remodeling of the pre-existing surface as the conventional approaches do. We substantiate our claims with comprehensive experiments, which demonstrate the high accuracy and versatility of the proposed method.

The automated finite element analysis of complex CAD models using boundary-fitted meshes is rife with difficulties. Immersed finite element methods are intrinsically more robust but usually less accurate. In this work, we introduce an efficient, robust, high-order immersed finite element method for complex CAD models. Our approach relies on three adaptive structured grids: a geometry grid for representing the implicit geometry, a finite element grid for discretising physical fields and a quadrature grid for evaluating the finite element integrals. The geometry grid is a sparse VDB (Volumetric Dynamic B+ tree) grid that is highly refined close to physical domain boundaries. The finite element grid consists of a forest of octree grids distributed over several processors, and the quadrature grid in each finite element cell is an octree grid constructed in a bottom-up fashion. The resolution of the quadrature grid ensures that finite element integrals are evaluated with sufficient accuracy and that any sub-grid geometric features, like small holes or corners, are resolved up to a desired resolution. The conceptual simplicity and modularity of our approach make it possible to reuse open-source libraries, i.e. openVDB and p4est for implementing the geometry and finite element grids, respectively, and BDDCML for iteratively solving the discrete systems of equations in parallel using domain decomposition. We demonstrate the efficiency and robustness of the proposed approach by solving the Poisson equation on domains described by complex CAD models and discretised with tens of millions of degrees of freedom. The solution field is discretised using linear and quadratic Lagrange basis functions.

Triply periodic minimal surface (TPMS) is emerging as an important way of designing microstructures. However, there has been limited use of commercial CAD/CAM/CAE software packages for TPMS design and manufacturing. This is mainly because TPMS is consistently described in the functional representation (F-rep) format, while modern CAD/CAM/CAE tools are built upon the boundary representation (B-rep) format. One possible solution to this gap is translating TPMS to STEP, which is the standard data exchange format of CAD/CAM/CAE. Following this direction, this paper proposes a new translation method with error-controlling and $C^2$ continuity-preserving features. It is based on an approximation error-driven TPMS sampling algorithm and a constrained-PIA algorithm. The sampling algorithm controls the deviation between the original and translated models. With it, an error bound of $2ϵ$ on the deviation can be ensured if two conditions called $ϵ$-density and $ϵ$-approximation are satisfied. The constrained-PIA algorithm enforces $C^2$ continuity constraints during TPMS approximation, and meanwhile attaining high efficiency. A theoretical convergence proof of this algorithm is also given. The effectiveness of the translation method has been demonstrated by a series of examples and comparisons. The code will be open-sourced upon publication.

In this paper, we introduce TriClsNet, a novel learning-based network that reconstructs surfaces by reframing the triangle classification problem as a graph node classification problem. An improved graph-based triangle classification module is employed to aggregate information from neighboring triangles, effectively leveraging local neighborhood information and enhancing triangle classification accuracy. Additionally, a self-supervised learning branch is incorporated to predict point cloud normals, aiding our network in better learning local point cloud features. Furthermore, a new loss function is designed to guide our network in effective multi-task learning, encompassing both graph node classification and normal prediction. Comparative experimental results on ShapeNet demonstrate that our method can efficiently perform surface reconstruction, outperforming existing methods in the aspects of preserving surface details, reducing holes, and generalization.