Computer-Aided Design最新文献

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 layout represents a possible harness design. For layout generation, the proposed method utilizes the so-called routing graphs associated with the closed surfaces bounding the product. The developed methods are able to handle both — (1) On-Surface routing, when the wires are required to be constrained to the surface of the product, and (2) In-Air routing, when in addition to the surface the wires are also allowed to be embedded in product’s ambiance. For the final geometric embedding of the generated harnesses, we present an optimization-based methodology that determines the optimum lengths of the segments over which the wires should be bundled together. The results presented demonstrate the efficacy of the proposed approach through multiple realistic examples.

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 the results in collision detection against the popular Gilbert–Johnson–Keerthi (GJK) and Minkowski Portal Refinement (MPR) algorithms, which represent bodies using the support function. Results show that our algorithm has similar performance to both, providing penetration depth like MPR and, with better robustness, minimum distance like GJK. Our algorithm provides accurate and fast collision detection between implicitly modeled convex rigid bodies and is able to substitute existing algorithms in previous applications whenever the support function is replaced with the SDF.

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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 novel advancing front technique (AFT) that can treat such composite surfaces by developing two new schemes to enhance the traditional AFT. Firstly, for each composite surface, we define a parametric plane by using a combined set of the tessellation on this composite surface. Simplicial complex augmentation framework reparameterization approach is suggested since it can treat tessellations containing gap and overlap after introducing a pre-processing step. Meanwhile, this approach can ensure a bijective mapping between the parametric and physical space. The front intersection check can thus be performed on the parametric plane robustly. Secondly, the indirect and direct approaches are alternatively employed to calculate ideal points in different circumstances. In the circumstance that the possible new element is completely contained in one single CAD surface, the ideal point is calculated on the intrinsic parametric plane of the surface; otherwise, the ideal point is directly calculated on the physical space. We avoid using the geometry defined on the tessellation since we prefer to getting a mesh respecting the original CAD model rather than its tessellation counterpart. Presently, the developed new schemes have been incorporated into our in-house surface mesher, and their efficiency and effectiveness have been demonstrated through a comparison with state-of-the-art commercial tools (e.g., COMSOL Multiphysics) and AFT algorithm, using CAD models of industry-level complexity.

Traditional structural optimization design methods are based on the finite element analysis(FEA), which makes it difficult to construct a direct relationship between the design parameters and the design objective parameters in the structural design process. The FEA method needs to convert the models back and forth between the design model and the analysis or optimization model during the design process. It is a cumbersome and time-consuming work and also affects the analysis accuracy. We propose an integrated design method that seamlessly integrates process of design, simulation and optimization based on uniformity of design models, analysis models and optimization models by benefiting the advantages of volume parameterization and isogeometric analysis(IGA). The size parameters are input as high-level parameters, then the middle parameters are obtained through hierarchical mapping. Based on these parameters, the semantic feature framework composes of feature points, feature curves and feature surfaces and even feature volume is gradually constructed. By extracting paths and sections, the geometric feature framework is generated. The paths and sections are segmented to form the volume parametric sub-patches through volume parametric mapping. These sub-patches are merged into a whole volume parametric model that can be used for IGA and size driven deformation. Based on volume parametric model, a mathematical relationship is constructed between the design objective parameters and the size design parameters. Through the mathematical relationship, the sensitivity equations are derived for sensitivity analysis. Finally, an isogeometric size optimization process is complete. Thus, an integration of design process including geometric modeling, performance analysis, and structural optimization is achieved. Taking the maximum stiffness and the minimum stress as the size optimization objectives, the integrated design examples fall into four groups including single size optimization, multi sizes non-coupled optimization, multi sizes coupled optimization, and complex mechanical structure optimization. The optimization results prove that our method is effective, and it can be applied on complex mechanical parts. The designed results do not require reconstruction, thus achieving the integrated and optimized design of mechanical structures.

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 tessellation function which depends on each tile. This generates a succession of tessellation functions that will be the parameter of the same seed function. Finally, the fractal is generated by a weighted sum of the seed function evaluated on each value of the succession of parameters. If the sites used to generate the Voronoi tessellation are random, natural elements such as mountains, craters, lakes, etc. are generated; if they are deterministic, architectural and decorative elements are generated. In addition, the designers can control the morphology of the generated fractal by simply varying the metric.

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 points to approximate the provided data, with weights and knots remaining static. To enhance fitting precision and overcome this constraint, we present Full-LSPIA, an innovative LSPIA method that jointly optimizes weights and knots alongside control points adjustments for superior NURBS curves and surfaces creation. We achieve this by constructing an objective function that incorporates control points, weights, and knots as variables, and solving the resultant optimization problem. Specifically, control points are adjusted using LSPIA, while weights and knots are optimized through the LBFGS method based on the analytical gradients of the objective function with respect to weights and knots. Additionally, we present a knot removal strategy known as Decremental Full-LSPIA. This strategy reduces the number of knots within a specified error tolerance, and determines optimal knot locations. The proposed Full-LSPIA and Decremental Full-LSPIA maximize the strengths of LSPIA, with numerical examples further highlighting the superior performance and effectiveness of these methods. Compared to the classical LSPIA, Full-LSPIA offers greater fitting accuracy for NURBS curves and surfaces while maintaining the same number of control points, and automatically determines suitable weights and knots. Moreover, Decremental Full-LSPIA yields fitting results with fewer knots while maintaining the same error tolerance.

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 location with respect to case-specific information of the routing environment, e.g., zones to avoid. A deterministic and computationally effective cable harness routing algorithm has been developed to solve the routing problem and is used to generate a set of cable harness topology candidates and approximate the Pareto front. Our approach was tested against a stochastic and an exact solver and our routing algorithm generated objective function values better than the stochastic approach and close to the exact solver. Our algorithm was able to find solutions, some of them being proven to be near-optimal, for three industrial-sized 3D cases within reasonable time (in magnitude of seconds to minutes) and the computation times were comparable to those of the stochastic approach.

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 meshes formed by planar quadrilateral faces. The importance of this Izmestiev class is undisputed as it represents the first known flexible discrete surface – T-nets – which has been constructed by Graf and Sauer. Our algebraic approach yields a complete characterization of all flexible 3 × 3 quad meshes of the orthodiagonal involutive type up to some degenerated cases. It is shown that one has a maximum of 8 degrees of freedom to construct such mechanisms. This is illustrated by several examples, including cases which could not be realized using planar faces. We demonstrate the practical realization of the proposed mechanisms by building a physical prototype using stainless steel. In contrast to plastic prototype fabrication, we avoid large tolerances and inherent flexibility.

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 on a given initial surface forming the grid shell to be reinforced; then, we compute the height of the additional layer that maximizes its overall structural performance. Our method includes a new formulation to derive a smooth direction field that orients the quad remeshing and a novel algorithm that iteratively optimizes the height of the additional layer to minimize the structure’s compliance. We couple our optimization strategy with a set of constraints to improve buildability of the network and, simultaneously, preserve the initial surface. We showcase our method on a significant dataset of shapes to demonstrate its applicability to cases where free-form grid shells do not exhibit adequate structural performance due to their geometry.