Computer-Aided Design最新文献

Hyperbolic paraboloids, “hypars,” are special types of ruled surfaces. Their geometric properties provide them with loadbearing and stabilizing capacities, as well as distinct esthetic qualities. These attributes become evident in numerous applications in buildings, in many of which concrete or timber is used for the construction of the hypars. Hypars could also be relevant in the context of circular construction and design for disassembly, and the upcycling of construction waste. Due to the geometric simplicity of straight lines, which generate ruled surfaces, hypar-based structures can be designed and built with relatively simple means. They can consist of self-similar or even identical elements, which could facilitate their reuse.

Compared to other types of ruled surfaces, such as conoids, hypars have the advantage of being doubly ruled, meaning that structural grids of straight elements can be formed. This paper investigates another interesting property, which is the possibility of creating flat-quad meshes by diagonally connecting the intersection points of the generatrices. This property has been previously described by other scholars, some of which explored its applicability for glass-clad steel grid shells. In this research, we focus on its potential for segmented timber shells that can serve as stand-alone structures, or as modular and reusable building parts, such as façade or roof components. The reusability of such modular units could be achieved by using reversible joints between them.

More specifically, our research investigates the design space of construction systems based on such components via computational design and optimization algorithms, such as the memory limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm with automatic computation of the gradient, within the Julia programming environment. By applying principles and methods of differential geometry, we study hypars with irregular tilings, enabling the integration of panels with diverse proportions, shapes and sizes, as they can occur in wood production waste. By reducing construction waste, the work aims at reducing the negative environmental impact of the building construction sector. Moreover, irregular tilings could enable a more customized design of acoustic qualities and offer visual variety in segmented hypar based timber structures.

The here presented studies show that the proposed optimization method provides a good fit of many tiles to rhombi, particularly when the steepness is not too large. We also show that optimizing towards rectangles provides better results. Overall, the results support the initial assumption that irregular rulings could be a means of adapting to both homogeneous and diverse material stocks.

The circular hole structures on automotive engines possess stringent mechanical processing requirements, so it is of vital importance to perform quality inspections on all manufactured circular hole structures. The detection of circular holes on automotive engines presents a significant challenge due to their numerous, multi-scale, and irregular distribution. Additionally, the data pertaining to circular holes is often incomplete, further complicating the detection process. In this paper, we proposed a multi-scale and irregularly distributed circular hole detection method for engine cylinder blocks, which enables the efficient extraction of all hole feature points within the engine, thereby facilitating quality inspection. First, the utilization of compartmentalization analysis techniques enhances the perceptual capacity for internal hole features from various angles. Second, by employing curvature center contractility method, hole-wall points are contracted towards their circular center positions, further enhancing the identification accuracy of small holes and holes with missing data. The proposed method is tested on both synthetic data and raw data, and compared with existing extraction and circular hole fitting methods. The experiment results demonstrate that compared to other methods, our method achieves the best feature point detection accuracy and hole primitive parameter calculation accuracy. Notably, even in special situations such as those with insufficient hole points and rounded structures, our method maintains exceptional discriminative capability and stability.

Stochastic media are used to characterize materials with irregular structure and spatial randomness, and the remarkable macroscopic features of stochastic media are often determined by their internal microstructure. Hardware loads and computational burdens have always been a challenge for the reconstruction of large-volume materials. To tackle the aforementioned concerns, this paper proposes a learning model based on generative adversarial network that uses multiple 2D slice images to reconstruct 3D stochastic microstructures. The whole model training process requires only a 3D image of stochastic media as the training image. In addition, the attention mechanism captures cross-dimensional interactions to prioritize the learned features and improves the effectiveness of training. The model is tested on stochastic porous media with two-phase internal structure and complex morphology. The experimental findings demonstrate that utilizing multiple 2D images helps the model learn better and reduces the occurrence of overfitting, while greatly reducing the hardware loads of the model.

Optimizing heterogeneous elastic material distribution on a 3D part to achieve desired deformation behavior is an important task in computer-aided design and additive manufacturing. This paper presents a solution to this problem, which involves interactive design, automatic deformation generation, and optimization of spatial distribution of heterogeneous elastic materials. Our method improves previous techniques in three aspects. First, we incorporates a geometric deformation-based interactive design into FEM-based optimization, which makes the solution less dependent of initial guesses of Young’s modulus values and it more likely to produce the target design even with sparse user input of displacements and forces at a limited set of mesh vertices. Second, we formulate the problem as an $L_2$- or $L_0$-optimization problem. The $L_2$ formulation outputs smoothly varying heterogeneous material distribution that accommodates multiple functions within a single part. The $L_0$ formulation achieves the computation of sparse material distribution in one step, which is beneficial for additive manufacturing with multi-material printers. Third, we utilize the adjoint method to derive formulae for efficiently computing the gradient of the objective functions, making it possible to quickly solve the optimization problem in the full-dimensional space of materials, which was previously infeasible. The experiments demonstrate the robustness and efficiency of our approach.

Structural design is a search for the best trade-off between multiple architecture, engineering, and construction objectives, not only mechanical efficiency or construction rationality. Producing hybrid designs from single-objective optimal designs to explore multi-objective trade-offs is common in the design of structural forms, constrained to a single parametric design space. However, producing topological hybrids offers a more complex challenge, as a combinatorial problem that is not encoded as a finite set of real numbers but as an unbonded series of grammar rules. This paper presents a strategy for the generation of hybrid designs of quad-mesh pattern topologies for surface structures. Based on a quad-mesh grammar, an algebra is introduced to measure the distance between designs, find their similar features, and enumerate designs with different degrees of topological similarity. Structural design applications are shown to highlight the use of topologically hybrid designs as a surrogate for obtaining multi-objective trade-offs.

The digitalization of manufacturing processes and recent trends, such as the Industrial Metaverse, are continuously increasing in adoption in various critical industries, resulting in a surging demand for 3D CAD models and their exchange. Following this, it becomes necessary to protect the intellectual property of content designers in increasingly decentralized production environments where 3D assets are repeatedly shared online within the ecosystem. CAD models can be protected by traditional security methods such as watermarking, which embeds additional information into the file. Nevertheless, malicious actors may find ways to remove the information from a file. To authenticate and protect 3D models without relying on additional information, we propose a robust 3D perceptual hash generated based on the prevalent geometric features. Furthermore, our geometry-based approach generates compact and tamper-resistant fingerprints for a 3D model by projecting multiple spherical sliced layers of intersection points into cluster distances. The resulting hash links the 3D model to an owner, supporting the detection of counterfeits. The approach was benchmarked for similarity search and evaluated against established state-of-the-art shape retrieval techniques. The results show promising resistance against arbitrary transformations and manipulations, with our approach detecting 25.6% more malicious tampering attacks than the baseline.

Least-squares progressive iterative approximation (LSPIA) is an effective tool for fitting data points with curves and surfaces in CAD/CAM, due to its intuitive geometric meaning and its suitability for handling mass data. However, the classic LSPIA method for blending curves and surfaces has a slow convergence rate and takes a long CPU execution time, since the spectral radius of its iteration matrix is near to 1. To achieve a reduction in CPU execution time, this paper presents a distributed least-squares progressive iterative approximation (DLSPIA) method by dividing the collocation matrix into some blocks, which are called processors. The proposed method distributes the computation of the control points progressively in a way that each processor is responsible for a block of the whole point set. Combining the information obtained from the previous processors with that of the current processor, the DLSPIA method can progressively and quickly approximate the least-squares fitting result of the original data set block by block via distributed computation. And the algorithm converges within a finite number of iterations. Furthermore, the iterative formulae for blending surface fitting are expressed in matrix form, which can avoid the computation of the matrix Kronecker product to reduce the CPU execution time. Several numerical examples are presented to demonstrate the superiority of the proposed method compared with the previous methods.

In this study, we present a novel method for modeling the canopy surface of an umbrella. Our approach involves representing the area between the ribs on the canopy as a trimmed bilinear patch. Furthermore, we conduct an in-depth exploration of various differential geometric properties of the umbrella surface. We introduce a method for unfolding the canopy surface onto a plane, which serves as a valuable technique for fabricating a cardboard template to accurately cut canopy fabrics. To validate the effectiveness of our geometric modeling method, we apply it to several umbrella models, showcasing its practical application and benefits.

Methods that use neural networks for synthesizing 3D shapes in the form of a part-based representation have been introduced over the last few years. These methods represent shapes as a graph or hierarchy of parts and enable a variety of applications such as shape sampling and reconstruction. However, current methods do not allow easily regenerating individual shape parts according to user preferences. In this paper, we investigate techniques that allow the user to generate multiple, diverse suggestions for individual parts. Specifically, we experiment with multimodal deep generative models that allow sampling diverse suggestions for shape parts and focus on models which have not been considered in previous work on shape synthesis. To provide a comparative study of these techniques, we introduce a method for synthesizing 3D shapes in a part-based representation and evaluate all the part suggestion techniques within this synthesis method. In our method, which is inspired by previous work, shapes are represented as a set of parts in the form of implicit functions which are then positioned in space to form the final shape. Synthesis in this representation is enabled by a neural network architecture based on an implicit decoder and a spatial transformer. We compare the various multimodal generative models by evaluating their performance in generating part suggestions. Our contribution is to show with qualitative and quantitative evaluations which of the new techniques for multimodal part generation perform the best and that a synthesis method based on the top-performing techniques allows the user to more finely control the parts that are generated in the 3D shapes while maintaining high shape fidelity when reconstructing shapes.

Cardiac simulators can assist in the diagnosis of heart disease and enhance human understanding of this leading cause of mortality. The coupling of multiphysics, such as electrophysiology and active–passive mechanics, in the simulation of the heart poses challenges in utilizing existing methodologies for real-time applications. The low efficiency of physically-based simulation is mostly caused by the need for electrical-stress conduction to use tiny time steps in order to prevent numerical instability. Additionally, the mechanical simulation experiences sluggish convergence when dealing with significant deformation and stiffness, and there are also concerns regarding volume inversion. We provide a coupling physics model that transforms the active–passive dynamics into multiphysics solving constraints, aiming at boosting the real-time efficiency of the cardiac electromechanical simulation. The multiphysics processes are initially divided into two levels: cell-level electrical stimulation and organ-level electrical-stress diffusion/conduction. This separation is achieved by employing operator splitting in combination with the quasi-steady-state method, which simplifies the system equations. Next, utilizing spatial discretization, we employ the matrix-free conjugate gradient approach to solve the electromechanical model, therefore improving the efficiency of the simulation. The experimental results illustrate that our simulation model is capable of replicating intricate cardiac physiological phenomena, including 3D spiral waves and rhythmic contractions. Moreover, our model achieves a significant advancement in real-time computation while maintaining a comparable level of accuracy to current methods. This improvement is advantageous for interactive medical applications.