SFF-Oriented Modeling and Process Planning of Functionally Graded Materials Using a Novel Equal Distance Offset Approach

This paper deals with the modeling and process planning of solid freeform fabrication (SFF) of 3D functionally graded materials (FGMs). A novel approach of representation and process planning of FGMs, termed as equal distance offset (EDO), is developed. In EDO, a neutral arbitrary 3D CAD model is adaptively sliced into a series of 2D layers. Within each layer, 2D material gradients are designed and represented via dividing the 2D shape into several sub-regions enclosed by iso-composition contours. If needed, the material composition gradient within each of sub-regions can be further determined by applying the equal distance offset algorithm to each sub-region. Using this approach, an arbitrary-shaped 3D FGM object with linear or non-linear composition gradients can be represented and fabricated via suitable SFF machines.Mechanical Engineerin

Procedural function-based modelling of volumetric microstructures

We propose a new approach to modelling heterogeneous objects containing internal volumetric structures with size of details orders of magnitude smaller than the overall size of the object. The proposed function-based procedural representation provides compact, precise, and arbitrarily parameterised models of coherent microstructures, which can undergo blending, deformations, and other geometric operations, and can be directly rendered and fabricated without generating any auxiliary representations (such as polygonal meshes and voxel arrays). In particular, modelling of regular lattices and cellular microstructures as well as irregular porous media is discussed and illustrated. We also present a method to estimate parameters of the given model by fitting it to microstructure data obtained with magnetic resonance imaging and other measurements of natural and artificial objects. Examples of rendering and digital fabrication of microstructure models are presented

Designing heterogeneous porous tissue scaffolds for additive manufacturing processes

A novel tissue scaffold design technique has been proposed with controllable heterogeneous architecture design suitable for additive manufacturing processes. The proposed layer-based design uses a bi-layer pattern of radial and spiral layers consecutively to generate functionally gradient porosity, which follows the geometry of the scaffold. The proposed approach constructs the medial region from the medial axis of each corresponding layer, which represents the geometric internal feature or the spine. The radial layers of the scaffold are then generated by connecting the boundaries of the medial region and the layer's outer contour. To avoid the twisting of the internal channels, reorientation and relaxation techniques are introduced to establish the point matching of ruling lines. An optimization algorithm is developed to construct sub-regions from these ruling lines. Gradient porosity is changed between the medial region and the layer's outer contour. Iso-porosity regions are determined by dividing the subregions peripherally into pore cells and consecutive iso-porosity curves are generated using the isopoints from those pore cells. The combination of consecutive layers generates the pore cells with desired pore sizes. To ensure the fabrication of the designed scaffolds, the generated contours are optimized for a continuous, interconnected, and smooth deposition path-planning. A continuous zig-zag pattern deposition path crossing through the medial region is used for the initial layer and a biarc fitted isoporosity curve is generated for the consecutive layer with C-1 continuity. The proposed methodologies can generate the structure with gradient (linear or non-linear), variational or constant porosity that can provide localized control of variational porosity along the scaffold architecture. The designed porous structures can be fabricated using additive manufacturing processes

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

Design for additive manufacturing: Trends, opportunities, considerations, and constraints

The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry

Design for additive manufacturing: trends, opportunities, considerations, and constraints

© 2016 CIRP. The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry

Optimal Design of Functionally Graded Parts

Several additive manufacturing processes are capable of fabricating three-dimensional parts with complex distribution of material composition to achieve desired local properties and functions. This unique advantage could be exploited by developing and implementing methodologies capable of optimizing the distribution of material composition for one-, two-, and three-dimensional parts. This paper is the first effort to review the research works on developing these methods. The underlying components (i.e., building blocks) in all of these methods include the homogenization approach, material representation technique, finite element analysis approach, and the choice of optimization algorithm. The overall performance of each method mainly depends on these components and how they work together. For instance, if a simple one-dimensional analytical equation is used to represent the material composition distribution, the finite element analysis and optimization would be straightforward, but it does not have the versatility of a method which uses an advanced representation technique. In this paper, evolution of these methods is followed; noteworthy homogenization approaches, representation techniques, finite element analysis approaches, and optimization algorithms used/developed in these studies are described; and most powerful design methods are identified, explained, and compared against each other. Also, manufacturing techniques, capable of producing functionally graded materials with complex material distribution, are reviewed; and future research directions are discussed

Optimal Design of Process Parameters During Laser Direct Metal Deposition of Multi-Material Parts

During the past few years, the need for multi-material parts or heterogeneous objects (HOs) has surfaced with the rapid growth of laser technology, material science and additive manufacturing techniques. Direct Metal Deposition (DMD) process, a metal based additive manufacturing technique, can locally deposit dissimilar metal powders to produce HOs as needed. While some theoretical and experimental studies have been conducted to investigate the DMD process, there are still some challenges such as the process parameters design, optimization, and adjustment during the fabrication of HOs that have not been well elucidated. This dissertation aims at developing the manufacturing science needed to design a laser additive manufacturing system capable of mixing two or more dissimilar powders to manufacture heterogeneous meta-materials objects. This research would enable moving beyond rapid “prototyping†into the realm of functional heterogeneous metal based additive manufacturing (HMAM). Therefore, the objective of this research is to develop the science needed to support the design and manufacture of HOs, placing materials where needed, when needed, in the proportions specified by the design, and combining them in-situ to achieve significant performance enhancements. The dissertation starts by showing the whole picture of the design process, then identify where the challenges and improvement opportunities rest. The whole DMD system design includes the geometrical design of the powder delivering nozzles, the optimal design of the process parameters when depositing dissimilar materials, and the control or planning of the process parameters during the DMD fabrication of HOs. The Laser Engineered Net Shaping (LENSTM) system developed at Sandia and commercialized by Optomec® Inc. is referred to and used to implement the research. An Artificial Neural Network (ANN) based method is proposed using FEM (Finite Element Method) as simulation tool to find the optimal geometry of the injection nozzles in order to maximize the process efficiency. Then, a mathematical model-based design method is proposed combining a multi-objective optimization algorithm to optimize the process parameters including the injection angles, injection velocities, and injection nozzle diameters for the two materials, as well as the laser power and the scanning speed. Finally, a comprehensive study investigating the relationship between the desired part\u27s composition and the process parameters is conducted to fabricate a part with precise composition compared to the heterogeneous components design information. This dissertation provides a better understanding of the physical process in the DMD manufacturing of HOs. This work would help design the whole DMD system, and make it a more efficient, more precise and more flexible process