Design and Manufacturing Strategies for Fused Deposition Modelling in Additive Manufacturing:A Review

Although several research works in the literature have focused on studying the capabilities of additive manufacturing(AM) systems, few works have addressed the development of Design for Additive Manufacturing(DfAM) knowledge,tools, rules, and methodologies, which has limited the penetration and impact of AM in industry. In this paper a comprehensive review of design and manufacturing strategies for Fused Deposition Modelling(FDM) is presented.Consequently, several DfAM strategies are proposed and analysed based on existing research works and the operation principles, materials, capabilities and limitations of the FDM process. These strategies have been divided into four main groups: geometry, quality, materials and sustainability. The implementation and practicality of the proposed DfAM is illustrated by three case studies. The new proposed DfAM strategies are intended to assist designers and manufacturers when making decisions to satisfy functional needs, while ensuring manufacturability in FDM systems.Moreover, many of these strategies can be applied or extended to other AM processes besides FDM.

Optimum Weight Design of Functionally Graded Material Gears

Traditional gear weight optimization methods consider gear tooth number, module, face width or other dimension parameters of gear as design variables. However, due to the complicated form and geometric features peculiar to the gear, there will be large amounts of design parameters in gear design, and the influences of gear parameters changing on gear trains, transmission system and the whole equipment have to be taken into account, which increases the complexity of optimization problem. This paper puts forward to apply functionally graded materials（FGMs） to gears and then conduct the optimization. According to the force situation of gears, the material distribution form of FGM gears is determined. Then based on the performance parameters analysis of FGMs and the practical working demands for gears, a multi-objective optimization model is formed. Finally by using the goal driven optimization（GDO） method, the optimal material distribution is achieved, which makes gear weight and the maximum deformation be minimum and the maximum bending stress do not exceed the allowable stress. As an example, the applying of FGM to automotive transmission gear is conducted to illustrate the optimization design process and the result shows that under the condition of keeping the normal working performance of gear, the method achieves in greatly reducing the gear weight. This research proposes a FGM gears design method that is able to largely reduce the weight of gears by optimizing the microscopic material parameters instead of changing the macroscopic dimension parameters of gears, which reduces the complexity of gear weight optimization problem.

Development of Texture Mapping Approaches for Additively Manufacturable Surfaces

manufacturing(AM)technologies have been recognized for their capability to build complex components and hence have ofered more freedom to designers for a long time.The ability to directly use a computer-aided design(CAD)model has allowed for fabricating and realizing complicated components,monolithic design,reducing the number of components in an assembly,decreasing time to market,and adding performance or comfort-enhancing functionalities.One of the features that can be introduced for boosting a component functionality using AM is the inclusion of surface texture on a given component.This inclusion is usually a difcult task as creating a CAD model resolving fne details of a given texture is difcult even using commercial software packages.This paper develops a methodology to include texture directly on the CAD model of a target surface using a patch-based sampling texture synthesis algorithm,which can be manufactured using AM.Input for the texture generation algorithm can be either a physical sample or an image with heightmap information.The heightmap information from a physical sample can be obtained by 3D scanning the sample and using the information from the acquired point cloud.After obtaining the required inputs,the patches are sampled for texture generation according to non-parametric estimation of the local conditional Markov random feld(MRF)density function,which helps avoid mismatched features across the patch boundaries.While generating the texture,a design constraint to ensure AM producibility is considered,which is essential when manufacturing a component using,e.g.,Fused Deposition Melting(FDM)or Laser Powder Bed Fusion(LPBF).The generated texture is then mapped onto the surface using the developed distance and angle preserving mapping algorithms.The implemented algorithms can be used to map the generated texture onto a mathematically defned surface.This paper maps the textures onto fat,curved,and sinusoidal surfaces for illustration.After the texture mapping,a stereolithography(STL)model is generated with the desired texture on the target surface.The generated STL model is printed using FDM technology as a fnal step.

A Direct Toolpath Constructive Design Method for Controllable Porous Structure Configuration with a TSP-based Sequence Planning Determination

The inherent capabilities of additive manufacturing(AM)to fabricate porous lattice structures with controllable structural and functional properties have raised interest in the design methods for the production of extremely in-tricate internal geometries.Current popular methods of porous lattice structure design still follow the traditional flow,which mainly consists of computer-aided design(CAD)model construction,STereoLithography(STL)model conversion,slicing model acquisition,and toolpath configuration,which causes a loss of accuracy and manufac-turability uncertainty in AM preparation stages.Moreover,toolpath configuration relies on a knowledge-based approach summarized by expert systems.In this process,geometrical construction information is always ignored when a CAD model is created or constructed.To fully use this geometrical information,avoid accuracy loss and ensure qualified manufacturability of porous lattice structures,this paper proposes a novel toolpath-based con-structive design method to directly generate toolpath printing file of parametric and controllable porous lattice structures to facilitate model data exchange during the AM preparation stages.To optimize the laser jumping route between lattice cells,we use a hybrid travelling salesman problem(TSP)solver to determine the laser jumping points on contour scans.Four kinds of laser jumping orders are calculated and compared to select a minimal laser jumping route for sequence planning inside lattice cells.Hence,the proposed method can achieve high-precision lattice printing and avoid computational consumption in model conversion stages from a geomet-rical view.The optical metallographic images show that the shape accuracy of lattice patterns can be guaranteed.The existence of“grain boundaries”brought about by the multi-contour scanning strategy may lead to different mechanical properties.

Integration of Strain Sensors on Additively Manufactured Implantable Devices

The development of personalized healthcare is rapidly growing thanks to the support of low-power electronics,advanced fabrication processes and secured data transmission protocols.Long-acting drug delivery systems able to sustain the release of therapeutics in a controllable manner can provide several advantages in the treatment of chronic diseases.Various systems under development control drug release from an implantable reservoir via concentration driven diffusion through nanofluidic membranes.Given the high drug concentration in the reservoir,an inward osmotic fluid transport occurs across the membrane,which counters the outward diffusion of drugs.The resulting osmotic pressure buildup may be sufficient to cause the failure of implants with associated risks to patients.Confidently assessing the osmotic pressure buildup requires testing in vivo.Here,using metal and polymer AM(additive manufacturing)processes,we designed and developed implantable drug reservoirs with embedded strain sensors to directly measure the osmotic pressure in drug delivery implants in vitro and in vivo.

Optimization of fused deposition modeling process parameters:a review of current research and future prospects

Fused deposition modeling （FDM） is one of the most popuIar additive manufacturing technologies for various engineering applications. FDM process has been introduced commercially in early 1990s by Stratasys Inc., USA. The quality of FDM processed parts mainly depends on careful selection of process variables. Thus, identifica- tion of the FDM process parameters that significantly affect the quality of FDM processed parts is important. In recent years, researchers have explored a number of ways to improve the mechanical properties and part quality using various experimental design techniques and concepts. This article aims to review the research carried out so far in determining and optimizing the process parameters of the FDM process. Several statistical designs of experiments and optimization techniques used for the determination of optimum process parameters have been examined. The trends for future FDM research in this area are described.