Automatic multi-direction slicing algorithms for wire based additive manufacturing

One of the key challenges in Additive Manufacturing is to develop a robust algorithm to slice CAD models into a set of layers which requires minimal support structures. This paper reports the concept and implementation of a new strategy for multi-direction slicing of CAD models represented in STL format. Differing from the existing multi-direction slicing approaches that are mainly focused on finding an optimal volume decomposition strategy, this study presents a decomposition-regrouping method. The CAD model is firstly decomposed into sub-volumes using a simple curvature-based volume decomposition method. Then a depth-tree structure based on topology information is introduced to merge them into ordered groups for slicing. In addition, a model simplification step is introduced before CAD model decomposition to significantly enhance the capability of the proposed multi-direction strategy. The proposed strategy is shown to be simple and efficient on various tests parts especially for geometries with large number of holes.     

Self-supporting rhombic infill structures for additive manufacturing

Recent work has demonstrated that the interior material layout of a 3D model can be designed to make a fabricated replica satisfy application-specific demands on its physical properties, such as resistance to external loads. A widely used practice to fabricate such models is by layer-based additive manufacturing (AM), which however suffers from the problem of adding and removing interior supporting structures. In this paper, we present a novel method for generating application-specific infill structures on rhombic cells so that the resultant structures can automatically satisfy manufacturing requirements on overhang-angle and wall-thickness. Additional supporting structures can be avoided entirely in our framework. To achieve this, we introduce the usage of an adaptive rhombic grid, which is built from an input surface model. Starting from the initial sparse set of rhombic cells, via numerical optimization techniques an objective function can be improved by adaptively subdividing the rhombic grid and thus adding more walls in cells. We demonstrate the effectiveness of our method for generating interior designs in the applications of improving mechanical stiffness and static stability.     

Support slimming for single material based additive manufacturing

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.     

A multi-objective iterative learning control approach for additive manufacturing applications

Iterative learning control (ILC) is a method for improving the performance of stable, repetitive systems. Standard ILC is constructed in the temporal domain, with performance improvements achieved through iterative updates to the control signal. Recent ILC research focuses on reformulating temporal ILC into the spatial domain, where 2D convolution accounts for spatial closeness. This work expands spatial ILC to include optimization of multiple performance metrics. Performance objectives are classified into primary, complementary, competing, and domain specific objectives. New robustness and convergence criteria are provided. Simulation results validate flexibility of the spatial framework on a high-fidelity additive manufacturing system model.     

Lattice structure lightweight triangulation for additive manufacturing

Additive manufacturing offers new available categories of geometries to be built. Among those categories, one can find the well developing field of lattice structures. Attention has been paid on lattice structures for their lightweight and mechanical efficiency ratio, thus leading to more optimized mechanical parts for systems. However this lightness only holds true from a mass related point of view. The files sent to additive manufacturing machines are quite large and can go up to such sizes that machines can freeze and get into malfunction. This is directly related to the lattice structures tendency to be of a high geometric complexity. A large number of vertices and triangles are necessary to describe them geometrically, thus leading to larger file sizes. With the increasing use of lattice structures, the need for their files to be lighter is also rising. This paper aims at proposing a method for tessellating a certain category of such structures, using topologic and geometric criteria to generate as few as possible triangles, thus leading to lightweight files. The triangulation technique is driven by a chordal error that controls the deviation between the exact and tessellated structures. It uses interpolation, boolean as well as triangulation operators. The method is illustrated and discussed through examples from our prototype software.     

A modular flexible scalable and reconfigurable system for manufacturing of Microsystems based on additive manufacturing and e-printing

Digital manufacturing technologies [1] are gaining more and more importance as key enabling technologies in future manufacturing, especially when a flexible scalable manufacturing of small medium series of customized parts is required. The paper describes a new approach for design manufacturing of complex three dimensional components building on a combination of digital manufacturing technologies such as laminated objects manufacturing, laser and e-printing technologies. The micro component is made up of stacks of functionalized layers of polymer films. The concept is currently developed further in the project SMARTLAM [2], [3], funded by the European Commission. The manufacturing system is based on a flexible, scalable and modular equipment and application features approach which enables the manufacturing of different small size batches without tool or mask making in short time. Different modules can be combined by defined hardware and software interfaces. Avoiding time consumable and difficult programming caused by manufacturing a new conceptual approach a Function-Block Runtime (FORTE) executes generated control application platform-independently and coordinates component module functionalities. The control system is designed to integrate all processes as well as the base platform with features far beyond ordinary PLC systems. One aspect is the use of process data out of the data acquisition system to simulate and optimize the processes. These results are incorporated into the main machine control system. Another aspect is the vision system for flexible quality control and closed-loop positioning control with visual servoing.The paper shows the overall concept of SMARTLAM and exemplarily demonstrates the control system as well as the modular equipment approach by the example of the control system for alignment of different stacks and inspection system.     

A non-retraction path planning approach for extrusion-based additive manufacturing

Notwithstanding the widespread use and large number of advantages over traditional subtractive manufacturing techniques, the application of additive manufacturing technologies is currently limited by the undesirable fabricating efficiency, which has attracted attentions from a wide range of areas, such as fabrication method, material improvement, and algorithm optimization. As a critical step in the process planning of additive manufacturing, path planning plays a significant role in affecting the build time by means of determining the paths for the printing head's movement. So a novel path filling pattern for the deposition of extrusion–based additive manufacturing is developed in this paper, mainly to avoid the retraction during the deposition process, and hence the time moving along these retracting paths can be saved and the discontinuous deposition can be avoided as well. On the basis of analysis and discussion of the reason behind the occurrence of retraction in the deposition process, a path planning strategy called “go and back” is presented to avoid the retraction issue. The “go and back” strategy can be adopted to generate a continuous extruder path for simple areas with the start point being connected to the end point. So a sliced layer can be decomposed into several simple areas and the sub-paths for each area are generated based on the proposed strategy. All of these obtainable sub-paths can be connected into a continuous path with proper selection of the start point. By doing this, separated sub-paths are joined with each other to decrease the number of the startup and shutdown process for the extruder, which is beneficial for the enhancement of the deposition quality and the efficiency. Additionally, some methodologies are proposed to further optimize the generated non-retraction paths. At last, several cases are used to test and verify the developed methodology and the comparisons with conventional path filling patterns are conducted. The results show that the proposed approach can effectively reduce the retraction motions and is especially beneficial for the high efficient additive manufacturing without compromise on the part resistance.     

Stable matching of customers and manufacturers for sharing economy of additive manufacturing

With rapid advances in internet and computing technologies, sharing economy paves a new way for people to “share” assets and services with others that disrupts traditional business models across the world. Specifically, rapid growth of additive manufacturing (AM) enables individuals and small manufacturers to own machines and share under-utilized resources with others. Such a decentralized market calls upon the development of new analytical methods and tools to help customers and manufacturers find each other and further shorten the AM supply chain. This paper presents a bipartite matching framework to model the resource allocation among customers and manufacturers and leverage the stable matching algorithm to optimize matches between customers and AM providers. We perform a comparison study with Mix Integer Linear Programming (MILP) optimization as well as the first-come-first-serve (FCFS) allocation strategy for different scenarios of demand-supply configurations (i.e., from 50% to 500%) and system complexities (i.e., uniform parts and manufacturers, heterogeneous parts and uniform manufacturers, heterogeneous parts and manufacturers). Experimental results show that the proposed framework has strong potentials to optimize resource allocation in the AM sharing economy.     

Implicit slicing for functionally tailored additive manufacturing

One crucial component of the additive manufacturing software toolchain is a class of geometric algorithms known as “slicers.” The purpose of the slicer is to compute a parametric toolpath and associated commands, which direct an additive manufacturing system to produce a physical realization of a three-dimensional input model. Existing slicing algorithms operate by application of geometric transformations upon the input geometry in order to produce the toolpath. In this paper we introduce a new implicit slicing algorithm based on the computation of toolpaths derived from the level sets of arbitrary heuristics-based or physics-based fields defined over the input geometry. This enables computationally efficient slicing of arbitrarily complex geometries in a straight forward fashion. Additionally, the calculation of component “infill” (as a process control parameter) is explored due to its crucial effect on functional performance fields of interest such as strain and stress distributions. Several examples of the application of the proposed implicit slicer are presented. Finally, an example demonstrating improved structural performance during physical testing is presented. We conclude with remarks regarding the strengths of the implicit approach relative to existing explicit approaches, and discuss future work required in order to extend the methodology.     

Attribute driven process architecture for additive manufacturing

In additive manufacturing (AM) process, the manufacturing attributes are highly dependent upon the execution of hierarchical plan. Among them, material deposition plan can frequently interrupt the AM process due to tool-path changes, tool start-stop and non-deposition time, which can be challenging during free-form part fabrication. In this paper, the layer geometries for both model and support structure are analyzed to identify the features that create change in deposition modality. First, the overhanging points on the part surface are identified using the normal vector direction of the model surface. A k-th nearest point algorithm is implemented to generate the 3d boundary support contour which is used to construct the support structure. Both model and support structures are sliced and contours are evaluated. The layer contour, plurality, concavity, number of contours, geometric shape, size and interior islands are considered to generate an AM deposition model. The proposed model is solved for minimizing the change in deposition modality by maximizing the continuity and connectivity in the material deposition plan. Both continuity and connectivity algorithms are implemented for model and support structure for free-form object. The proposed algorithm provides the optimum deposition direction that results in minimum number of tool-path segments and their connectivity while minimizing contour plurality effect. This information is stored as a generic digital file format named Part Attributable Motion (PAM). A common application program interface (API) platform is also proposed in this paper, which can access the PAM and generate machine readable file for different existing 3D printers. The proposed research is implemented on three free-form objects with complex geometry and parts are fabricated. Also, the build time is evaluated and the results are compared with the available 3d printing software.     

Recursive symmetries for geometrically complex and materially heterogeneous additive manufacturing

We present a generative method for the creation of geometrically complex and materially heterogeneous objects. By combining generative design and additive manufacturing, we demonstrate a unique form-finding approach and method for multi-material 3D printing. The method offers a fast, automated and controllable way to explore an expressive set of symmetrical, complex and colored objects, which makes it a useful tool for design exploration and prototyping. We describe a recursive grammar for the generation of solid boundary surface models suitable for a variety of design domains. We demonstrate the generation and digital fabrication of watertight 2-manifold polygonal meshes, with feature-aligned topology that can be produced on a wide variety of 3D printers, as well as post-processed with traditional 3D modeling tools. To date, objects with intricate spatial patterns and complex heterogeneous material compositions generated by this method can only be produced through 3D printing.     

多视点云数据自动拼合算法

通过在被测物体加贴参考点，提出了以参考点的距离空间不变量为依据来搜索同构子图的方法来对多视中的标记参考点进行对应匹配；通过SVD的方法求解多视间的坐标转换矩阵以来最终达到多视间拼合。运用实例检验表明本文的算法快速、简洁，同时，拼合的鲁棒性也很好。在一定的拼合的精度下，提高了运行的速度和算法的稳定性。     

Using augmented reality to build digital twin for reconfigurable additive manufacturing system

The extrusion-based additive manufacturing (AM) processes are those where one or multiple tools (usually nozzles) are driven along predefined paths to deposit fabrication materials. They are usually inherently slow because solid contours have to be filled with mere single deposition lines of material. An intuitive way to improve the fabrication speed is to introduce multiple independent actuators for concurrent deposition of materials without collision among them. In this paper, a methodology of using augmented reality (AR) technique is presented to conveniently communicate the layout information between a reconfigurable AM system made of robotic arms and its corresponding digital twin for toolpath planning and simulation. A prototype system made of two desktops AM robotic arms is developed, and transformation matrices are derived to determine the spatial relation between different items in the system, including camera, markers, robotic arms and part substrate. Case studies are conducted to demonstrate the capability of this methodology in automatically retrieving layout information and assisting users to deploy pre-determined layout. The results show that the developed methodology enables rapid retrieval of position information from the physical system layout into the digital twin simulation and optimization and facilitates convenient deployment of an optimized layout determined in the digital twin into the physical system.     

Support structure constrained topology optimization for additive manufacturing

There is significant interest today in integrating additive manufacturing (AM) and topology optimization (TO). However, TO often leads to designs that are not AM friendly. For example, topologically optimized designs may require significant amount of support structures before they can be additively manufactured, resulting in increased fabrication and clean-up costs.In this paper, we propose a TO methodology that will lead to designs requiring significantly reduced support structures. Towards this end, the concept of ‘support structure topological sensitivity’ is introduced. This is combined with performance sensitivity to result in a TO framework that maximizes performance, subject to support structure constraints. The robustness and efficiency of the proposed method is demonstrated through numerical experiments, and validated through fused deposition modeling, a popular AM process.     

Towards an automated robotic arc-welding-based additive manufacturing system from CAD to finished part

Arc welding has been widely explored for additive manufacturing of large metal components over the last three decades due to its lower capital cost, an unlimited build envelope, and higher deposition rates. Although significant improvements have been made, an arc welding process has yet to be incorporated in a commercially available additive manufacturing system. The next step in exploiting “true” arc-welding-based additive manufacturing is to develop the automation software required to produce CAD-to-part capability. This study focuses on developing a fully automated system using robotic gas metal arc welding to additively manufacture metal components. The system contains several modules, including bead modelling, slicing, deposition path planning, weld setting, and post-process machining. Among these modules, bead modelling provides the essential database for process control, and an innovative path planning strategy fulfils the requirements of the automated system. A user friendly interface has been developed for non-experts to operate the developed system. Finally, a thin-walled aluminium structure has been fabricated automatically using only a CAD model as the informational input to the system. This exercise demonstrates that the developed system is a significant contribution towards the ultimate goal of producing a practical and highly automated arc-welding-based additive manufacturing system for industrial application.     

Generative adversarial network for early-stage design flexibility in topology optimization for additive manufacturing

Topology optimization has become a valuable design tool for structures to be fabricated by additive manufacturing (AM). However, during early stage design, parameters are frequently evolving, resulting in multiple similar TO runs. Especially when design for manufacturing principles expand the parameter space, this iterative process is computationally burdensome, and does not take advantage of redundant information in each study. We introduce a deep learning-based framework that learns latent similarities between runs in a training set to predict near optimal designs, enabling efficient wholistic understanding of the problem setup space, which includes both loading conditions and, for the first time in this study, manufacturing process configuration. Learning was achieved using a conditional generative adversarial network (cGAN) trained on a dataset of randomized boundary conditions, loadings, and AM build orientations, and the corresponding optimal structures obtained through overhang-filtered TO. cGAN predictions showed good agreement with true optima. For even greater accuracy, predictions can be post-processed by applying a small number of TO iterations. Manifold learning techniques were used to provide further insight, and we were able to conclude that the cGAN error generally increases with distance between the load and the boundary conditions or build plate. Interestingly, in 9% of test cases, the cGAN generated structures with compliances better than the corresponding TO-calculated structures, often by as much as 50 % with an average of 7.8 %. That some of these structures appeared qualitatively different in form suggests the potential value of the approach in other domains such as generative design, where a range of alternate near-optimal designs are used to guide the ideation process.     

Eco-friendly additive manufacturing of metals: Energy efficiency and life cycle analysis

Additive manufacturing (AM) of metal materials has attracted widespread attention and is shifting the conventional manufacturing landscape toward free-form processes. With increasing concerns about global sustainability, eco-consideration is highly encouraged to be integrated into AM processes. This review provides a comprehensive and timely discussion on the life cycle of metal parts fabricated through AM. The energy consumption required for raw metal material extraction and subsequent AM processes is analyzed. The eco-design and energy efficiency of metal AM are evaluated to reveal the role of manufacturing methods, machine subsystems, and post-processing modes in the eco-integration. AM-induced supply chain management, utilization, and recycling of the printed metal structure are also analyzed. Finally, a comprehensive life cycle assessment regarding the environmental, social, and economic impacts of metal AM is also addressed. Future directions of AM are also briefly discussed to provide insight and vision on the emerging field of additive eco-manufacturing.     

R-VPCG: RGB image feature fusion-based virtual point cloud generation for 3D car detection

Although 3D object detection methods based on feature fusion have made great progress, the methods still have the problem of low precision due to sparse point clouds. In this paper, we propose a new feature fusion-based method, which can generate virtual point cloud and improve the precision of car detection. Considering that RGB images have rich semantic information, this method firstly segments the cars from the image, and then projected the raw point clouds onto the segmented car image to segment point clouds of the cars. Furthermore, the segmented point clouds are input to the virtual point cloud generation module. The module regresses the direction of car, then combines the foreground points to generate virtual point clouds and superimposed with the raw point cloud. Eventually, the processed point cloud is converted to voxel representation, which is then fed into 3D sparse convolutional network to extract features, and finally a region proposal network is used to detect cars in a bird’s-eye view. Experimental results on KITTI dataset show that our method is effective, and the precision have significant advantages compared to other similar feature fusion-based methods.     

Multiple platforms design and product family process planning for combined additive and subtractive manufacturing

Industry 4.0 promotes the utilization of new exponential technologies such as additive manufacturing in responding to different manufacturing challenges. Among these, the integration of additive and subtractive manufacturing technologies can play an important role and be a game changer in manufacturing products. In addition, using product platforms improves the efficiency and responsiveness of manufacturing systems and is considered an enabler of mass customization. In this paper, a model to design multiple platforms that can be customized using additive and subtractive manufacturing to manufacture a product family cost-effectively is proposed. The developed model is used to determine the optimal number of product platforms, each platform design (i.e. its features set), the assignment of each platform to various product variants, and the macro process plans for customizing the platforms while minimizing the overall product family manufacturing cost.The multiple additive/subtractive platforms and their process plans are determined by considering not only the commonality between the product variants but also their various manufacturing cost elements and the customer demand of each variant. The design of multiple product family platforms and their process plans is NP-hard problem. A genetic algorithm-based model is developed to reduce the computational complexity and find optimal or near optimal solution. Two case studies are used to illustrate the developed multiple platform model. The model results were compared with a single platform model in literature and the results demonstrate the multiple platform model superiority in manufacturing product families in lower cost. The use of the developed model enables manufacturing product families cost efficiently and allows manufacturers to manage diversity in products and market demands.