基于参数曲面的增材制造保形晶格结构生成方法

晶格结构因其具备特殊的机械性能,已成为增材制造复杂结构设计和制造的重要研究领域之一。 针对传统的模型晶格结构生成方法需要通过对参数化建模的晶格结构网格进行裁剪或对其网格进行保形变形 实现,且生成效率较低的问题,提出一种基于参数曲面的增材制造保形晶格结构生成方法,实现晶格结构对曲 面空间的适应和高效生成。首先,基于一种矩阵方法完成晶格结构骨架的表达和构造。其次,利用点与曲面、 曲线与曲面、曲面与曲面所形成的 3 种封闭空间,对晶格结构骨架进行保形变形,使晶格结构适应曲面空间。 最后,采用基于晶格结构骨架的网格生成和拼接方法,生成保形晶格结构的网格模型。通过组件应用架构(CAA) 二次开发方法提取计算机辅助三维交互应用软件(CATIA)模型的参数曲面,实现保形晶格结构模型的高效生成, 并表现出良好的曲面空间适应性,证明该方法具有一定工程价值。     

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.     

孔隙表征参数驱动的TPMS多孔结构建模

为了设计符合工程设计参数要求的多孔结构模型,提出一种孔隙表征参数驱动的多孔结构建模思路,并以增材制造制备成形.首先,针对三周期极小化曲面(TPMS)的4种常用类型(P/D/G/I-WP),研究了TPMS数学参数对各类孔隙表征参数(孔隙率、比表面积和孔径大小)的影响,并建立了相关参数之间的映射关系模型;接着,提出了若干基于孔隙表征参数的TPMS多孔结构设计方法,包括单设计参数法、多设计参数法、嵌套设计法;最后,给出了相关的工程应用案例并进行Micro-CT测试实验.实验结果表明,所设计模型的孔隙表征参数可控,增材制造成形的模型样件受工艺影响,其精度存在一定偏差.     

Concurrent lattice infill with feature evolution optimization for additive manufactured heat conduction design

Additive manufacturing (AM) eliminates many of the geometric restrictions in conventional manufacturing, and hence complex geometry, such as lattice structures, can be produced with little additional cost. AM designs based on lattice structuring have become increasingly popular as it possesses tunable properties and can be designed to be self-supporting easily. For these reasons, lattice infill recently has been actively studied and a variety of lattice structure topology optimization methods have been developed. On the other hand, lattice infill cannot span the design domain when there are functional features in the mechanical design (e.g. assembly holes and cooling channels). Also, the geometric form of these functional features need to be maintained and cannot be replaced by the lattice structure. Thus far, lattice structure topology optimization considers these features fixed in space without design freedom and obviously, this treatment lacks overall optimality. To fill this critical gap, this work combines the feature evolution into the variable-density lattice structure topology optimization framework, which leads to a concurrent lattice density and feature layout optimization method. Parametric level set functions are employed for the feature representation and R-functions are adopted to combine the density and level set fields. Sensitivity information is calculated on both the lattice densities and feature parameters, in order to solve the problem through a unified gradient-based approach. Several 3D numerical examples are provided to demonstrate the efficiency and robustness of the proposed method.     

Efficient size and shape optimization of truss structures subject to stress and local buckling constraints using sequential linear programming

The advance in digital fabrication technologies and additive manufacturing allows for the fabrication of complex truss structure designs but at the same time posing challenging structural optimization problems to capitalize on this new design freedom. In response to this, an iterative approach in which Sequential Linear Programming (SLP) is used to simultaneously solve a size and shape optimization sub-problem subject to local stress and Euler buckling constraints is proposed in this work. To accomplish this, a first order Taylor expansion for the nodal movement and the buckling constraint is derived to conform to the SLP problem formulation. At each iteration a post-processing step is initiated to map a design vector to the exact buckling constraint boundary in order to facilitate the overall efficiency. The method is verified against an exact non-linear optimization problem formulation on a range of benchmark examples obtained from the literature. The results show that the proposed method produces optimized designs that are either close or identical to the solutions obtained by the non-linear problem formulation while significantly decreasing the computational time. This enables more efficient size and shape optimization of truss structures considering practical engineering constraints.     

骨组织支架的计算机辅助设计方法综述

随着先进的计算机辅助设计和增材制造技术的快速发展,使得制造具有复杂几何结构的骨组织支架成为可能。根据骨组织支架功能设计要求,从几何形态的角度出发将其结构分为规则性多孔结构和不规则多孔结构两大类,并综述了骨组织支架的设计方法,特别强调了两种适合增材制造的设计方法,即三周期极小曲面(TPMS)和拓扑优化。针对骨组织支架结构设计面临的技术挑战,展望了骨组织支架设计方法的可能发展趋势。     

A big data-driven framework for sustainable and smart additive manufacturing

From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.     

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.     

Process planning strategies for solid freeform fabrication of metal parts

Process planning of additive manufacturing of metals is a research interest because of the applications of solid freeform fabrication of metal parts in industry. The strategy is to transform the model of the part into the combinations of 2D layers that will be deposited using different fabrication methods. Process planning for metal deposition in this paper consists of three major modules: spatial decomposition, slicing of the part, and toolpath generation for every slicing layer. Algorithmic improvements are proposed and implemented for these major modules. For spatial decomposition, 3D part decomposition based on modular boundary models and centroidal axis extraction methods are combined to decompose parts more robustly and reliably. For generating slicing layers, a planning process for building non-uniform layers is investigated to greatly increase the variety of the parts that can be manufactured without the need of support structure. For toolpath generation methods, optimization of the generated toolpath is studied especially for complex thin-wall structures to ensure the deposition quality. Experiments were carried out to evaluate the improvements of the major modules of process planning strategies for rapid manufacturing.     

An ontology-oriented knowledge methodology for process planning in additive layer manufacturing

The goal of this research is the generation of a novel knowledge with process-oriented ontology and the informal model. With regard to the proposed ontology, it establishes an improvement to related ontologies because it involves the demands of fabrication engineering and, specifically, the layer-upon-layer manufacturing planning process with various AM systems. Generally, task of AM planning indicates to make repeated and essential decisions which are always on the basis of the engineers’ knowledge and experience in additive manufacturing. Hence, it is a suitable field towards the execution of a knowledge-based engineering system. To represent the knowledge at an upper tier, the IDEF0 diagrams is introduced for identifying the sequence of tasks contained in the AM planning. They are a vital resources for defining the sequence of tasks and the messages flow. Afterward, these messages are analyzed thoroughly by applying schematic graphs, and then they are categorized into knowledge segments. Eventually, each knowledge segment is further divided into knowledge entities. At the same time, the relationships among them are also defined.Meanwhile, knowledge modeling involved generating an ontology of design feature which is utilized as a general information model in both computer-aided design and process planning, expression of fabrication criteria that depict the basis and properties for picking fabrication parameters. In a first method, the ontology has been examined utilizing an essential activity in AM planning: the task related to the confirmation of parameters over component quality. In this task, decisions have to be made in the orientation, slicing and the other process parameters. In this research, the differences between geometric and dimensional tolerance fabrication is considered in the generated methodology. The knowledge needed to aid all decisions is expressed utilizing the proposed ontology.     

Complex concrete structures: Merging existing casting techniques with digital fabrication

Over the course of the 20th century, architectural construction has gone through intense innovation in its material, engineering and design, radically transforming the way buildings were and are conceived. Technological and industrial advances enabled and challenged architects, engineers and constructors to build increasingly complex architectural structures from concrete. Computer-aided design and manufacturing (CAD/CAM) techniques have, more recently, rejuvenated and increased the possibilities of realizing ever more complex geometries. Reinforced concrete is often chosen for such structures as almost any shape can be achieved when placed into a formwork. However, most complex forms generated with these digital design tools bear little relation to the default modes of production used in concrete construction today. A large gap has emerged between the possibilities offered by the digital technology in architectural design and the reality of the building industry, where actually no efficient solutions exist for the production of complex concrete structures. This paper presents construction methods that unfold their full potential by linking digital design, additive fabrication and material properties and hence allow accommodating the construction of complex concrete structures. The emphasis is set on the on-going research project Smart Dynamic Casting (SDC) where advanced material design and robotic fabrication are interconnected in the design and fabrication process of complex concrete structures. The proposed fabrication process is belonging to an emerging architectural phenomenon defined first as Digital Materiality by Gramazio & Kohler (2008) or more recently as Material Ecologies by Neri Oxman  [1].     

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.     

Fabrication of relief-topographic surfaces with a one-step UV-lithographic process

This paper reports on a study of a methodology for fabrication of arbitrarily shaped silicon structures using technologies common to standard IC manufacturing processes. Particular emphasis is put on the design and use of halftone transmission masks for the lithography step required in the fabrication process of mechanical, optical or electronics components. The design and experimental investigation of gray-tone masks was supported by lithography simulation. Results are presented for both, simulated gray-tone patterns as well as experimental profiles.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.     

Time-synchronizing control of self-organizing shop floors for networked manufacturing

In networked manufacturing systems, shop floors that are geographically dispersed can coordinate autonomously to complete the fabrication and assembly of products. It is a type of self-organizing production process, in which the scheduling of those shop floors must be synchronized, in terms of time and quantity. In this paper, we propose a time-synchronizing control policy for self-organizing shop floors based on the (max, +) system theory, prove the convergence of the synchronization algorithm, and verify the effectiveness of the algorithm by numerical experiments. Besides, a method of implementing the synchronizing control system based on the radio frequency identification (RFID) technologies is also proposed briefly.     

Data-driven generative design for mass customization: A case study

Generative design provides a promising algorithmic solution for mass customization of products, improving both product variety and design efficiency. However, the current designer-driven generative design formulates the automated program in a manual manner and has insufficient ability to satisfy the diverse needs of individuals. In this work, we propose a data-driven generative design framework by integrating multiple types of data to improve the automation level and performance of detail design to boost design efficiency and improve user satisfaction. A computational workflow including automated shape synthesis and structure design methods is established. More specifically, existing designs selected based on user preferences are utilized in the shape synthesis for creating generative models. For structural design, user-product interaction data gathered by sensors are used as inputs for controlling the spatial distributions of heterogeneous lattice structures. Finally, the proposed concept and workflow are demonstrated with a bike saddle design with a personalized shape and inner structures to be manufactured with additive manufacturing.     

Flow-based fabrication: An integrated computational workflow for design and digital additive manufacturing of multifunctional heterogeneously structured objects

Structural hierarchy and material organization in design are traditionally achieved by combining discrete homogeneous parts into functional assemblies where the shape or surface is the determining factor in achieving function. In contrast, biological structures express higher levels of functionality on a finer scale through volumetric cellular constructs that are heterogeneous and complex. Despite recent advancements in additive manufacturing of functionally graded materials, the limitations associated with computational design and digital fabrication of heterogeneous materials and structures frame and limit further progress. Conventional computer-aided design tools typically contain geometric and topologic data of virtual constructs, but lack robust means to integrate material composition properties within virtual models. We present a seamless computational workflow for the design and direct digital fabrication of multi-material and multi-scale structured objects. The workflow encodes for and integrates domain-specific meta-data relating to local, regional and global feature resolution of heterogeneous material organizations. We focus on water-based materials and demonstrate our approach by additively manufacturing diverse constructs associating shape-informing variable flow rates and material properties to mesh-free geometric primitives. The proposed workflow enables virtual-to-physical control of constructs where structural, mechanical and optical gradients are achieved through a seamless design-to-fabrication tool with localized control. An enabling technology combining a robotic arm and a multi-syringe multi nozzle deposition system is presented. Proposed methodology is implemented and full-scale demonstrations are included.     

A kinematics-aware part clustering approach for part integration using additive manufacturing

Part integration is to integrate parts to be a fabrication and assembly unit. It can effectively reduce the fabrication and assembly unit quantity of a product and has been deemed as an effective way to promote the productivity of manufacturing. Although additive manufacturing (AM) has great potential to further promote the part integration for any product (assembly) model, part integration works using AM at present are often ad hoc, human-dependent and time-consuming. One main cause for this problem is that determining which parts in an assembly model can be integrated to be a fabrication and assembly unit automatically is still very difficult, especially when the model has kinematics (inner relative motions embodied by kinematic joints). In this paper, a novel part clustering approach is proposed, based on which, an input assembly model can smartly cluster all its parts to fewer sub-assembly models (each of them fits being integrated to be a fabrication and assembly unit in AM) according to its kinematics. To ensure that the input model after part integration can effectively realize its kinematics using AM, the criteria for part clustering are first defined. Accompanying with the criteria, the methods to determine the kinematics-related fabrication orientation for each part are proposed based on heuristic rules. Then, to make an accurate and efficient part clustering, an attributed part kinematic graph is put forward according to the above criteria. After that, by breaking through the detection automation challenges in sealing support structure and assembly feasibility, an efficient optimization objective function is defined based on the above criteria and graph. Finally, integrating a new adaptive perturbation strategy into the particle swarm optimization algorithm to avoid premature convergence, a novel graph-based part clustering optimization method is designed to cluster all the parts of the input model to be a high-quality (optimized) set of the above-mentioned sub-assembly models. Experiments and analyses are presented to verify the advantages of the proposed approach. Besides, complying with the general guidelines in AM, the proposed approach provides great potential to maximize part integration using AM in a wider application.     

高校飞行器制造工程专业的综合实验课程教学研究

针对高校飞行器制造工程专业的综合实验课程教学,论文提出了设置飞机 数字化设计制造一体化技术实验、飞机钣金精密塑性热成形工艺教学实验、飞机数字化柔性 装配教学实验及飞机复合材料构件设计制造一体化技术等四大实验系列共14 个实验的教学 设想。这些实验既涉及传统的飞机制造工艺,也包括最新的飞机研制信息化手段。完成上述 实验可提高本专业学生的动手能力,加深对飞行器产品制造过程各项知识点的理解。     

A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing

This work falls within the scope of computer-aided optimal design, and aims to integrate the topology optimization procedures and recent additive manufacturing technologies (AM). The elimination of scaffold supports at the topology optimization stage has been recognized and pursued by many authors recently. The present paper focuses on implementing a novel and specific overhang constraint that is introduced inside the topology optimization problem formulation along with the regular volume constraint. The proposed procedure joins the design and manufacturing processes into a integrated workflow where any component can directly be manufactured with no requirement of any sacrificial support material right after the topology optimization process. The overhang constraint presented in this work is defined by the maximum allowable inclination angle, where the inclination of any member is computed by the Smallest Univalue Segment Assimilating Nucleus (SUSAN), an edge detection algorithm developed in the field of image analysis and processing. Numerical results on some benchmark examples, along with the numerical performances of the proposed method, are introduced to demonstrate the capacities of the presented approach.