Manufacturing-oriented topology optimization has been extensively studied the past two decades, in particular for the conventional manufacturing methods, for example, machining and injection molding ...or casting. Both design and manufacturing engineers have benefited from these efforts because of the close-to-optimal and friendly-to-manufacture design solutions. Recently, additive manufacturing (AM) has received significant attention from both academia and industry. AM is characterized by producing geometrically complex components layer-by-layer, and greatly reduces the geometric complexity restrictions imposed on topology optimization by conventional manufacturing. In other words, AM can make near-full use of the freeform structural evolution of topology optimization. Even so, new rules and restrictions emerge due to the diverse and intricate AM processes, which should be carefully addressed when developing the AM-specific topology optimization algorithms. Therefore, the motivation of this perspective paper is to summarize the state-of-art topology optimization methods for a variety of AM topics. At the same time, this paper also expresses the authors’ perspectives on the challenges and opportunities in these topics. The hope is to inspire both researchers and engineers to meet these challenges with innovative solutions.
This paper presents a new method to fabricate 3D models on a robotic printing system equipped with multi-axis motion. Materials are accumulated inside the volume along curved tool-paths so that the ...need of supporting structures can be tremendously reduced - if not completely abandoned - on all models. Our strategy to tackle the challenge of tool-path planning for multi-axis 3D printing is to perform two successive decompositions, first volume-to-surfaces and then surfaces-to-curves. The volume-to-surfaces decomposition is achieved by optimizing a scalar field within the volume that represents the fabrication sequence. The field is constrained such that its iso-values represent curved layers that are supported from below, and present a convex surface affording for collision-free navigation of the printer head. After extracting all curved layers, the surfaces-to-curves decomposition covers them with tool-paths while taking into account constraints from the robotic printing system. Our method successfully generates tool-paths for 3D printing models with large overhangs and high-genus topology. We fabricated several challenging cases on our robotic platform to verify and demonstrate its capabilities.
This paper introduces a perceptual model for determining 3D printing orientations. Additive manufacturing methods involving low-cost 3D printers often require robust branching support structures to ...prevent material collapse at overhangs. Although the designed shape can successfully be made by adding supports, residual material remains at the contact points after the supports have been removed, resulting in unsightly surface artifacts. Moreover, fine surface details on the fabricated model can easily be damaged while removing supports. To prevent the visual impact of these artifacts, we present a method to find printing directions that avoid placing supports in perceptually significant regions. Our model for preference in 3D printing direction is formulated as a combination of metrics including area of support, visual saliency, preferred viewpoint and smoothness preservation. We develop a training-and-learning methodology to obtain a closed-form solution for our perceptual model and perform a large-scale study. We demonstrate the performance of this perceptual model on both natural and man-made objects.
In layer-based additive manufacturing (AM), supporting structures need to be inserted to support the overhanging regions. The adding of supporting structures slows down the speed of fabrication and ...introduces artifacts onto the finished surface. We present an orientation-driven shape optimizer to slim down the supporting structures used in single material-based AM. The optimizer can be employed as a tool to help designers to optimize the original model to achieve a more self-supported shape, which can be used as a reference for their further design. The model to be optimized is first enclosed in a volumetric mesh, which is employed as the domain of computation. The optimizer is driven by the operations of reorientation taken on tetrahedra with ‘facing-down’ surface facets. We formulate the demand on minimizing shape variation as global rigidity energy. The local optimization problem for determining a minimal rotation is analyzed on the Gauss sphere, which leads to a closed-form solution. Moreover, we also extend our approach to create the functions of controlling the deformation and searching for optimal printing directions.
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•Optimize the shape of a designed model into a ‘self-supported’ state for AM.•Global shape of a model is preserved by minimizing the energy of rigidity.•A closed-form solution for minimal rotation to drive the optimization.•Tackle the shape optimization problem for reducing supporting structures.
The fabrication flexibility of 3D printing has sparked a lot of interest in designing structures with spatially graded material properties. In this paper, we propose a new type of density graded ...structure that is particularly designed for 3D printing systems based on filament extrusion. In order to ensure high-quality fabrication results, extrusion-based 3D printing requires not only that the structures are self-supporting, but also that extrusion toolpaths are continuous and free of self-overlap. The structure proposed in this paper, called CrossFill, complies with these requirements. In particular, CrossFill is a self-supporting foam structure, for which each layer is fabricated by a single, continuous and overlap-free path of material extrusion. Our method for generating CrossFill is based on a space-filling surface that employs spatially varying subdivision levels. Dithering of the subdivision levels is performed to accurately reproduce a prescribed density distribution. We demonstrate the effectiveness of CrossFill on a number of experimental tests and applications.
•A novel self-supporting space-filling surface which supports spatially graded density.•A scheme for refining the structure to match a prescribed density distribution.•An algorithm for merging the toolpath of an infill structure with the input model’s boundary so as to retain continuity.
Kinematics of Soft Robots by Geometric Computing Fang, Guoxin; Matte, Christopher-Denny; Scharff, Rob B. N ...
IEEE transactions on robotics,
2020-Aug., 2020-8-00, Letnik:
36, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Robots fabricated with soft materials can provide higher flexibility and, thus, better safety while interacting in unpredictable situations. However, the usage of soft material makes it challenging ...to predict the deformation of a continuum body under actuation and, therefore, brings difficulty to the kinematic control of its movement. In this article, we present a geometry-based framework for computing the deformation of soft robots within the range of linear material elasticity. After formulating both manipulators and actuators as geometry elements, deformation can be efficiently computed by solving a constrained optimization problem. Because of its efficiency, forward and inverse kinematics for soft manipulators can be solved by an iterative algorithm with a low computational cost. Meanwhile, components with multiple materials can also be geometrically modeled in our framework with the help of a simple calibration. Numerical and physical experimental tests are conducted on soft manipulators driven by different actuators with large deformation to demonstrate the performance of our approach.
Volumetric spline parameterization and computational efficiency are two main challenges in isogeometric analysis (IGA). To tackle this problem, we propose a framework of computation reuse in IGA on a ...set of three-dimensional models with similar semantic features. Given a template domain, B-spline based consistent volumetric parameterization is first constructed for a set of models with similar semantic features. An efficient quadrature-free method is investigated in our framework to compute the entries of stiffness matrix by Bézier extraction and polynomial approximation. In our approach, evaluation on the stiffness matrix and imposition of the boundary conditions can be pre-computed and reused during IGA on a set of CAD models. Examples with complex geometry are presented to show the effectiveness of our methods, and efficiency similar to the computation in linear finite element analysis can be achieved for IGA taken on a set of models.
•A framework of computation reuse in IGA on a set of three-dimensional models with similar semantic features.•An efficient quadrature-free method to compute the entries of stiffness matrix.•Compared with IGA-Galerkin method, up to 15.4 times speedup can be observed on problems with large number of degree-of-freedom.
Adaptive slicing is an important computational task required in the layer-based manufacturing process. Its purpose is to find an optimal trade-off between the fabrication time (number of layers) and ...the surface quality (geometric deviation error). Most of the traditional adaptive slicing algorithms are computationally expensive or only based on local evaluation of errors. To tackle these problems, we introduce a method to efficiently generate slicing plans by a new metric profile that can characterize the distribution of deviation errors along the building direction. By generalizing the conventional error metrics, the proposed metric profile is a density function of deviation errors, which measures the global deviation errors rather than the in-plane local geometry errors used in most prior methods. Slicing can be efficiently evaluated based on metric profiles in contrast to the expensive computation on models in boundary-representation. An efficient algorithm based on dynamic programming is proposed to find the best slicing plan. Our adaptive slicing method can also be applied to models with weighted features and can serve as the inner loop to search the best building direction. The performance of our approach is demonstrated by experimental tests on different examples.
•A new profile-based adaptive slicing framework is presented with good properties of accurate and efficient.•An optimization algorithm based on dynamic programming is presented to find the best slicing plan for an input CAD model.•The analysis taken in our algorithm is based on a metric profile that can be generated by GPU-accelerated techniques within seconds.•The formulation can be easily extended to integrate different commonly used error metrics, as well as the user specified salience.
This article presents a novel framework to realize proprioception and closed-loop control for soft manipulators. Deformations with large elongation and large bending can be precisely predicted using ...geometry-based sensor signals obtained from the inductive springs and the inertial measurement units (IMUs) with the help of machine learning techniques. Multiple geometric signals are fused into robust pose estimations, and a data-efficient training process is achieved after applying the strategy of sim-to-real transfer. As a result, we can achieve proprioception that is robust to the variation of external loading and has an average error of 0.7% across the workspace on a pneumatic-driven soft manipulator. The realized proprioception on soft manipulator is then contributed to building a sensor-space-based algorithm for closed-loop control. A gradient-descent solver is developed to drive the end-effector to achieve the required poses by iteratively computing a sequence of reference sensor signals. A conventional controller is employed in the inner loop of our algorithm to update actuators (i.e., the pressures in chambers) for approaching a reference signal in the sensor-space. The systematic function of closed-loop control has been demonstrated in tasks like path following and pick-and-place under different external loads.
3D printing techniques such as Fused Deposition Modeling (FDM) have enabled the fabrication of complex geometry quickly and cheaply. Objects are produced by filling (a portion of) the 2D polygons of ...consecutive layers with contour-parallel extrusion toolpaths. Uniform width toolpaths consisting of inward offsets from the outline polygons produce over- and underfill regions in the center of the shape, which are especially detrimental to the mechanical performance of thin parts. In order to fill shapes with arbitrary diameter densely the toolpaths require adaptive width. Existing approaches for generating toolpaths with adaptive width result in a large variation in widths, which for some hardware systems is difficult to realize accurately. In this paper we present a framework which supports multiple schemes to generate toolpaths with adaptive width, by employing a function to decide the number of beads and their widths. Furthermore, we propose a novel scheme which reduces extreme bead widths, while limiting the number of altered toolpaths. We statistically validate the effectiveness of our framework and this novel scheme on a data set of representative 3D models, and physically validate it by developing a technique, called back pressure compensation, for off-the-shelf FDM systems to effectively realize adaptive width.
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•A framework to generate contour-parallel toolpaths that minimize under- and overfill.•A width distribution scheme for reducing underfill and extrusion width variation.•A back pressure compensation approach for effective realization of adaptive width.
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