Computer-Aided Design最新文献

We present a method to resolve visual artifacts of a state-of-the-art iso-surface extraction algorithm by generating feature-preserving surface patches for isolated arbitrarily complex, single voxels without the need for further adaptive subdivision. In the literature, iso-surface extraction from a 3D voxel grid is limited to a single sharp feature per minimal unit, even for algorithms such as Cubical Marching Squares that produce feature-preserving surface reconstructions. In practice though, multiple sharp features can meet in a single voxel. This is reflected in the triple dexel model, which is used in simulation of CNC manufacturing processes. Our approach generalizes the use of normal information to perfectly preserve multiple sharp features for a single voxel, thus avoiding visual artifacts caused by state-of-the-art procedures.

Shell structures with high stiffness-to-weight ratios are desirable in various engineering applications. Topology optimization serves as a popular and effective tool for generating optimal shell structures. The solid isotropic material with penalization (SIMP) method is often chosen because of its simplicity and convenience. However, SIMP method is typically integrated with conventional Finite Element Analysis (FEA) which has limitations in computational accuracy. Achieving high accuracy with FEA necessitates a substantial number of elements, leading to computational burdens. In addition, the discrete representation of the material distribution function may result in rough boundaries. Owing to these limitations, this paper proposes an Isogeometric Analysis (IGA) based SIMP method for optimizing the topology of shell structures based on Reissner–Mindlin theory. This method uses Non-Uniform Rational B-Splines (NURBS) to represent both the shell structure and the material distribution function with the same basis functions, allowing for higher accuracy and smoother boundaries. The optimization model takes compliance as the objective function with a volume fraction constraint and the coefficients of the density function as design variables, resulting in an optimized shell structure defined by the material distribution function. To obtain fairing boundaries of the holes in the optimized shell structure, further process is conducted by fitting the boundaries with fair B-spline curves automatically. Furthermore, the proposed IGA-SIMP framework is applied to generate porous shell structures by imposing different local volume fraction constraints. Numerical examples are provided to demonstrate the feasibility and efficiency of the IGA-SIMP method, showing that it outperforms the FEA-SIMP method and produces smoother boundaries.

Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which are capable of taking precise account of real working conditions and enabling first-time-right control. As part of such a smart-control strategy, this paper introduces a machine learning-based approach capable of accurately predicting a priori the 3D coverage of a part according to a scan configuration given as input, i.e. predicting before scanning it which areas of the part will be acquired for real. This corresponds to a paradigm shift, where coverage estimation no longer relies on theoretical visibility criteria, but on rules learned from a large amount of data acquired in real-life conditions. The proposed 3D Scan Coverage Prediction Network (3DSCP-Net) is based on a 3D feature encoding and decoding module, which is capable of taking into account the specifics of the scan configuration whose impact on the 3D coverage is to be predicted. To take account of real working conditions, features are extracted at various levels, including geometric ones, but also features characterising the way structured-light projection behaves. The method is thus able to incorporate inter-reflection and overexposure issues into the prediction process. The database used for the training was built using an ad-hoc platform specially designed to enable the automatic acquisition and labelling of numerous point clouds from a wide variety of scan configurations. Experiments on several parts show that the method can efficiently predict the scan coverage, and that it outperforms conventional approaches based on purely theoretical visibility criteria.

The generation of support for 3D models toward 3D printing is a highly challenging task that is of great need in many additive manufacturing processes. In this work, we explore the use of multiresolution geometric lattices to generate support with controlled contact locations. That is, with bounds on the maximal distance between adjacent local support points. A variety of end-user controls over the synthesized support are provided, such as the angular slopes in the model that are provided with support and/or controls on the dimensions and sizes of the support lattice tiles. These controls are augmented with the option of an automated optimization via a direct link to analysis. We demonstrate this proposed lattice approach for support synthesis on several 3D models of different types.

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.