Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

A review of geometry representation and processing methods for cartesian and multiaxial robot-based additive manufacturing

Nowadays, robot-based additive manufacturing (RBAM) is emerging as a potential solution to increase manufacturing flexibility. Such technology allows to change the orientation of the material deposition unit during printing, making it possible to fabricate complex parts with optimized material distribution. In this context, the representation of parts geometries and their subsequent processing become aspects of primary importance. In particular, part orientation, multiaxial deposition, slicing, and infill strategies must be properly evaluated so as to obtain satisfactory outputs and avoid printing failures. Some advanced features can be found in commercial slicing software (e.g., adaptive slicing, advanced path strategies, and non-planar slicing), although the procedure may result excessively constrained due to the limited number of available options. Several approaches and algorithms have been proposed for each phase and their combination must be determined accurately to achieve the best results. This paper reviews the state-of-the-art works addressing the primary methods for the representation of geometries and the subsequent geometry processing for RBAM. For each category, tools and software found in the literature and commercially available are discussed. Comparison tables are then reported to assist in the selection of the most appropriate approaches. The presented review can be helpful for designers, researchers and practitioners to identify possible future directions and open issues

Symmetry-based decomposition for optimised parallelisation in 3D printing processes

Current research in 3D printing focuses on improving printing performance through various techniques, including decomposition, but targets only single printers. With improved hardware costs increasing printer availability, more situations can arise involving a multitude of printers, which offers substantially more throughput in combination that may not be best utilised by current decomposition approaches. A novel approach to 3D printing is introduced that attempts to exploit this as a means of significantly increasing the speed of printing models. This was approached as a problem akin to the parallel delegation of computation tasks in a multi-core environment, where optimal performance involves computation load being distributed as evenly as possible. To achieve this, a decomposition framework was designed that combines recursive symmetric slicing with a hybrid tree-based analytical and greedy strategy to optimally minimise the maximum volume of subparts assigned to the set of printers. Experimental evaluation of the algorithm was performed to compare our approach to printing models normally (“in serial”) as a control. The algorithm was subjected to a range of models and a varying quantity of printers in parallel, with printer parameters held constant, and yielded mixed results. Larger, simpler, and more symmetric objects exhibited more significant and reliable improvements in fabrication duration at larger amounts of parallelisation than smaller, more complex, or more asymmetric objects

Design of optimal truss components for fabrication via multi-axis additive manufacturing

Additive manufacturing (AM) has developed rapidly in recent years and has the potential to enable fabrication of structurally optimized components that have hitherto been impossible to manufacture. When the degree of design freedom is high it can be observed that structurally optimized components are often truss-like in form, such that truss topology (or ‘layout’) optimization methods can be used to rapidly and directly identify optimal forms. However, these forms are often geometrically complex, and the presence of overhanging elements means that they generally need to be manufactured with support structures when traditional 3-axis AM machines are employed. To reduce, or even completely eliminate, the need for support structures, multi-axis AM machines with 5 or more axes can instead be used. In this contribution a novel process-aware truss layout optimization strategy tailored for multi-axis AM machines is proposed, which involves combining curved printing surface identification with truss layout and geometry optimization. Due to the non-linear and non-convex nature of the resulting optimization formulation, two strategies are proposed: (i) performance-based, to obtain highly materially-efficient structures, though which have as little need for support structures as possible; (ii) printability-based, to obtain fully self-supportable structures, though which consume as little material as possible. Several examples are presented to demonstrate the effectiveness of the proposed approach. It is shown that fully self-supporting optimized structures can often be identified, with little or no sacrifice in terms of structural performance

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044