EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 6 (2015) Int. J. Simul. Multisci. Des. Optim., 6 (2015) A9 Full HTML
Open Access
Issue
Int. J. Simul. Multisci. Des. Optim.
Volume 6, 2015
Article Number A9
Number of page(s) 13
DOI https://doi.org/10.1051/smdo/2016001
Published online 26 January 2016

© S. Guessasma et al., Published by EDP Sciences, 2016

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

Additive Manufacturing (AM) is a collection of versatile techniques of rapid prototyping that allow material design from 3D digital models [1–3]. The term AM comes also under different other nicknames such as direct digital manufacturing or solid freeform fabrication [4, 5]. AM is rated as one of the most promising technology for design [6], presented as a new industrial revolution [7], and a vector for creativity [8], impact [9] and interrogations [10]. The laying down of the material in different states including liquid, powder and fused material defines roughly categories of AM [2, 7, 11–13]. More accurate classifications do exist such as the ASTM F42 reported in reference [14]. Wide varieties of materials can be processed using additive manufacturing including metals [15–18], alloys [19–22], ceramics [23–26], polymers [27–30], composites [31–34], airy structures [35, 36] and multi-phase materials [37–39].

The main characteristic of AM is the reduced number of manufacturing steps that stands between the virtual design and the ready-to-use part [40]. In addition, one major advantage of AM reported in the literature is the ability to process complex shapes that are not easy to design using traditional ways such as extrusion and moulding [3, 41, 42]. AM potential, as a leading design technique, is enormous and the related applications are huge [7, 43–47]. Different printing techniques are used in the biomedical sector [41, 48] more particularly for tissue engineering [5, 35, 49–51]. Preform design of composites is evidently an inspiring topic for AM [31] because of the wide possibilities in structuring yarns and reinforcing composite structures [34]. Aerospace applications of AM are the most challenging because of the extreme performance that need to be achieved under fine scale monitoring and in-service validation [52, 53]. Recent contributions by NPU demonstrates the central role of topology optimisation in additive manufacturing for aerospace applications [54, 55]. Micro-fabrication technologies emerge also as an efficient way to produce functional micro-components for microelectronics systems [42]. The scaling down of AM is now possible thanks to cutting edge processes that allow material design at a very fine scale like with different types of lithography [55, 56].

The idea behind AM is the direct import of CAD (Computer-Aided-Design) object as machine instructions (Figure 1). The preferred way to achieve this import is the transformation of surface tessellation representing the geometry of the virtual part into a set of toolpaths. One starts from an STL or Standard Tessellation Language or STereoLithography file. All external boundaries and internal surfaces appear smooth and continuous using STL format. Generation of the toolpaths represents the first challenge and actually a limitation for AM [57]. The reason is that the building process in most 3D printing technologies relies on a successive layer-by-layer building process. So, starting from 3D space tessellation and ending with 2D building strategy is a first drawback. It is even worse when droplet based printing is considered because the fused matter is no more connected in any direction. Discontinuities may appear in all building directions (Figure 2) as a result of the layer-by-layer laying down process (Figure 3) [27]. The consequence of this appears to be the development of dimensional inaccuracy, inacceptable finishing state, structural and mechanical anisotropies, which are continuously addressed in many research contributions [12, 28, 58–64]. Anisotropy can be also inferred to the development of particular grain texture as revealed by laser melting deposition or arc welding alloying of metals [61, 65–68]. Reduction of anisotropy can be achieved by selecting the appropriate orientation of the virtual design [69–71]. Numerous papers mention strategies of common sense to build parts with acceptable performance, which are nearly equivalent or even superior to other design techniques. Scudino and others [67, 72] report that better ductility of metallic materials can be achieved using selective laser melting compared to casting as a result of the fine grain structure driven by AM. One particular feature highlighted in these contributions refers to avoid building the part along its largest surface. In other words, if the successive layers of the part exhibit a lack of cohesion, the large contact area between layers drives worse performance under tension. Some other strategies rely on reducing the lack of cohesion between layers or filaments by further processing of the real design. An example of radiation treatment is proposed in the work of Shaffer et al. [28].

thumbnail Figure 1.

The process chain in typical AM.

thumbnail Figure 2.

Typical examples of CAD objects transformed into collection of toolpaths using (a) Repetier and (b) CatalystEX software.

thumbnail Figure 3.

Laying down process of fused ABS polymer in typical FDM equipment.

In addition to the role of anisotropy, differences between the virtual and real design can be striking knowing that AM resolution is finite due to available tooling [64]. If we consider the example of fused deposition modelling, which is a popular AM technique [12], the toolpath generation is referred as collection of filament paths of finite dimensions (Figure 3). This has three main consequences on the real design: internal structural features may not be well captured depending on their size; discontinuities may appear depending on local curvature; and the surface finishing state may be limited due to rough profiles [62]. These limitations are illustrated in Figure 2, which highlights simple and complex geometries and the corresponding toolpath generation using two software, one is Repetier from Hot-World GmbH & Co, Germany and the other is CatalystEx from Stratasys Inc. Eden Prairie MN, USA.

All limitations mentioned earlier assume implicitly the role of defects induced by AM. These need to be faced in order to deliver a design representing, with much accuracy, the result of an optimisation procedure. These defects are related to the porosities that develop as a consequence of the discontinuous process of printing and other issues related to process errors [73]. A large number of contributions is dedicated to the evaluation of the effect of porosities in printed parts. One particular consequence of the role of porosity is that a large amount of them reduces the mechanical performance. Such reduction is represented by a theoretical linear decrease of stiffness with the increase porosity level (if limited stress transfer between layers is neglected). Under tension, porosities act as stress concentrators and may induce lower mechanical strength by enhancing the development of damage in the form of micro-cracks. Under compression, different considerations can be pointed out. Even if the porosities are closed during compressive loading, lateral expansion due to Poisson’s effect may cause failure driven by opening mode or shearing effects that are dramatically enhanced by the anisotropy [27, 58].

Porosity should not be considered systematically as a negative issue in AM since it can be a positive driving factor for permeability [74].

Another type of defects is the presence of support material trapped between internal surfaces. The material is needed to withstand the fragile printed structure during the printing process. While this material is studied to provide limited adhesion to the deposited materials, its residual amount contributes in increasing the weight of the structure and modifies the load bearing distributions. These two drawbacks alter the expected performance of the optimal design. In addition, none-optimised support deposition affects finishing state, material consumption, fabrication time, etc. [75]. Strategies exist to reduce the dependence of AM to the presence of a support material by operating smart or slimming support generation [75, 76]. For some strategies, the part orientation is continuously adapted during the processing [57]. Curved regions can be processed smoothly under continuous deposition mode and the presence of support material is no more needed. For other strategies, building complex shape without the support material relies on the intrinsic properties of the deposited material itself. These materials exhibit generally rapid cooling kinetics, which allow them to support their own weight and prevent the structure collapsing. This kind of strategies obviously limits the spectrum of materials that can be printed.

2 Optimisation in additive manufacturing

In this paper, our focus goes towards optimisation difficulties that are inferred to AM. With regards to the large number of disseminated works on the subject, common characteristics of optimisation in AM are highlighted in this section and described through selective literature work. In particular, several aspects of additive manufacturing can be optimized. Some of these aspects are related to design optimisation, more particularly topology optimisation. Some others like geometry accuracy, finishing state are tackled through process planning optimisation.

For most contributions, optimisation in AM is classically considered as a process parameter optimisation as it is the case for many design techniques [77, 78]. Raster angle, building direction, layer dimensions are some of the main parameters that find some interest in the literature. For instance, Garg et al. [79] present genetic programming approach as an intelligence tool to relate the AM process parameters to physical and structural outputs. While this is an important issue from the processing viewpoint, it is less attractive from a numerical analysis perspective, where strong and robust optimisation tools need to address more significant challenges. This does not diminish the purpose of the earlier approach. Accurate dimension, acceptable roughness and processing time are some of the important outcomes that justify the continuity of the research effort in this particular field. A typical example showing the importance of the process optimisation is provided in recent works [54, 55, 78]. The paper by Zhou et al. [78] introduces the concept of pixel blending to define the effect of neighbour pixels light intensity in solid freeform fabrication using photopolymerisation medium. The optimisation in such kind of studies is meaningful as the achievement of shape accuracy relies on the precise control of light intensity over a pixel-based image.

The paper by Yang and Zhao [3] report one of the most recent review on AM-enabled design, which is the closest subject to topology optimisation. In their review, design guidelines are exposed and the focus on structure optimisation methodology is explored through different contributions. This kind of methodologies needs to take care about the specificities of the design in terms of material combination, shape complexity and the targeted in-service performance. In the same review [3], the authors bring to our attention the possibilities of process combination involving more conventional or AM-based techniques [80–82]. We consider that this research direction associating various processes is out of the scope of this paper from an angle view of topology optimisation. However, the other considerations discussed there are central to the topology optimisation such as those related to design simplicity, material choice efficiency, improved multi-functionality, integrated technical solutions [83], etc. The concepts of multiphase material [37] and functionally graded materials [84, 85] emerge as a direct consequence to point by point material control in AM [86]. Also, designers are no more bonded by the tooling which needs to be a factor in the design with traditional processing [87]. This opens new chances for simplifying the design but also for increasing the creativity [7]. This particular point helps significantly the designers who need generally to reshape the result of the topology procedure to fit processing constrains like the development of particular tooling. A simple example would be the conversion of a material deposition probability into a graded material [4] instead of thresholding to solid or air phase [88]. In addition, if the conditions of blending at the microscale or nanoscale are met, the achievement of multi-material design is not a threat for the design but mostly an advantage because of the possibility to further improve performance at a lower material consumption rate [4].

Considerations related to topology optimisation are numerous [37, 88]. Some of them depend on the level of access to the AM processing technology itself which is not systematically provided by the commercial solutions. Here are some of these considerations that are central to the development of robust AM solutions:

  • Geometry of the CAD model: this is direct target of the topology optimisation, which needs to predict what would be the exact geometry that satisfies all model constrains. Successful examples of topology optimisation can be found in the literature for cellular materials [88, 89], multiphase materials [37], implants [90].

  • Path generation: this is an important parameter that affects geometry accuracy, cohesion in the part, residual stresses, and the finishing state [20, 63, 91]. Path generation needs to handle as much the change in process speed and the transition time at the borders [92]. A specific branch of research is dedicated to the optimisation of tool generation path including studies related to the improvement of the scanning mode [93, 94], geometry slicing strategies [95, 96], optimal material deposition [97, 98], multi-directional AM [99], tool path anticipation procedures [100].

  • Process selection: different technologies are developed to enhance the capabilities of AM like jet printing [26, 101], friction stir AM [102], welding based AM [94], ultrasonic AM [103], electrochemical AM [104], micro-plasma powder deposition [105], Solid freeform fabrication [106] and related variants such as selective laser sintering [107, 108], or directed light fabrication [20], selective infiltration manufacturing [109]). The outcomes of these technologies diverge. This reinforces specific aspects of AM like the material type, printing size, accuracy, speed, cost, etc. [110]. Figure 4 shows two examples of technologies applied for the design of airy structures, one based on FDM (uPrint® SE from Stratasys) and the other is a photolithography equipment (SPS350B from XJRP company). The earlier one is restricted to printing ABS polymer where the last one can process only photosensitive resins. The ability to control the process parameters is crucial to decide on the relevance of a particular AM process [111]. And perhaps, a badly performing design reflects only the lack of knowledge of the process. Thus, the mastering of the process parameters is a criterion for process selection.

    thumbnail Figure 4.

    Two examples of airy randomly structured polymers designed using (a) fused disposition modelling (uPrint equipment in GEPEA lab., university of Nantes, France) and (b) photolithography (SPS350B equipment in ESAC lab, NPU, Xian, China).

  • Process resolution: this is also an important aspect that guarantees the accuracy of the AM process [14, 112] and the development of appropriate scaling solutions for nano-, micro- and macro-features [38, 64, 113–116]. Some studies focus on slicing techniques to increase accuracy such as adaptive slicing proposed by Siraskar et al. [117] using volume decomposition by octants.

  • Feed material properties: rheological and phase change properties of the feed materials are essential to the success of AM building capabilities especially for achieving standards in material selection [118]. This is a continuous research direction aiming at increasing the spectrum of printable materials and optimising the intrinsic material properties for a better performance during fabrication step [14, 19, 119–121].

  • Support material: optimisation of the support material is now fully integrated in numerous commercial solutions, which require supporting of the part during processing. This optimisation relies on different options such as the smart deposition for which only reduced amount of support material is needed [76]. Some research works still contribute in this particular area to improve the spatial distribution of airy support material [75, 122].

The logic behind topology optimisation is illustrated in Figure 5. The geometry of the CAD model can be something to discover through the optimisation procedure or assisted by imaging tools such as micro-CT scanning [123]. Since the optimisation combines physical and geometrical constrains, numerical solutions need to be available to predict what would be the result of the part performance [14, 112]. This is generally addressed using finite element computation [53]. The numerical model needs to converge in all design situations within a short time because the process is meant to be repeated several times. Depending on the nature of the physical constrain, the numerical model can be more or less sophisticated. For instance, residual stress analysis requires most of the time solving a multi-physics problems [124, 125]. If numerical models are able to handle technological, physical, and geometrical constrains, design guidelines can be adopted by coupling the optimisation tool to decision making paradigms. Some studies show that AM process simulation is possible [125, 126] but the ultimate goal would be to bridge such realistic tools with the optimisation paradigm. Recent works prove unfortunately that we are far from such ideal situation [127].

thumbnail Figure 5.

(a) Classical view of topological optimisation, (b) Modified scheme with defect sensitive topology optimisation of AM.

Topology optimisation needs to cope with the specificities of AM. As this process generates a complex network of 3D defects, numerical models need to integrate the result of defect in the analysis as an implicit performance perturbation or explicit defect influence. This is the main difference between the two schemes presented in Figure 5. The classical scheme (Figure 5a) does not handle the defects induced by processing, which makes any deviation from the optimal virtual design a cause of failure. Numerical sizing in aerospace applications [128] is a typical example where such defects can be an issue to validate the final design in airframe development. In the second scheme (Figure 5b), the corrections introduced by the monitoring of the defects helps in guiding the optimisation tool towards the best realistic solution. This resolution is directly related to the AM tooling constrains, for instance the choice of the tip size. If the second scheme is used to consider appropriate selection for tooling options (like nozzle diameter), then such process parameter can be considered as a discrete variable. Optimal design can be searched in a larger space depending on the possibilities offered to select a certain number of available nozzles. Real-time control of AM like the optical tomography [129, 130], thermographic analysis [131] or ultrasonic monitoring [132, 133] helps in gaining valuable information about the structural defects that develop during AM processing and their direct consequence on failure of the designed part [73]. This is still a challenging issue as it appears that adequate non-destructive techniques are not yet fully available to evaluate properly AM part performance [52]. This situation can be improved through the development of standards which is still an ongoing process for the validation of testing techniques applicable to AM [134]. One of the most promising techniques to analyse microstructural defects in AM parts is X-ray micro-tomography [135]. This technique is able to provide precise information about the porous network induced by processing, surface roughness, part volume, amount of support material and any other microstructural defect [27]. As the technique relies on transformation of 2D projections into 3D image [136, 137], structural anisotropy effects can be quantified. Figure 6 shows two examples of defects revealed in ABS polymer printed using fused deposition modelling. Cross-section views refer to a dense block of ABS (30 × 30 × 30 mm3) analysed using X-ray micro-tomography. This block is oriented at 0° in the printing plateau, but we notice clearly the crossing of filaments at an angle of 45° and the presence of bounding layer. In Figure 6a, the resolution of the image is 1077 × 1062 × 1059 voxels, where a voxel is a graphical unit in 3D. The physical size of the voxel determines the accuracy of the structural defect evaluation. In Figure 5a, the voxel size is 30 μm. More information about the operating conditions can be found in reference [27]. Figure 5a shows the lack of cohesion between successive filaments and tendency to flattening because the filament diameter is tripled during the laying down of the fused matter. The subsequent porosity forms as a regular network of micro-sized defects, and appears to be highly connected. Also, residual support material can be found at the borders, which reveals difficulties of support material removal. The automatic cleaning process is generally followed by manual removal step to ensure that no residual support material is left behind. The second example highlighted in Figure 6b shows other imperfections that infiltrate the design of a two phase material. These imperfections are related to discontinuities of matter in the cell walls, the change of geometry due to design mismatch effects and the presence of support material trapped in closed pores of small size.

thumbnail Figure 6.

Microstructural defects in ABS polymer printed using FDM. Analysis is performed using X-ray micro-tomography. Two examples are shown one is (a) a dense ABS block and the other is (b) airy ABS materials exhibiting more than 60% of porosity.

In several contributions, the red links in Figure 5b are ignored as if the approximate result of the optimisation is unavoidably accepted. More recent contributions tackle such links by suggesting corrections of the design based on manufacturability considerations [64]. This is performed, however, independently from the optimisation tool itself and does not involve defects induced by processing.

3 Challenges for AM topology optimisation

One of the important issues that topology optimisation needs to address is the pertinence of the constitutive laws representing the behaviour of the printed materials. Unfortunately, material law implementation is not yet fully revisited leaving an open area for research in this direction [138]. A typical research direction would be to explore interfacial effect in terms of limited load transfer and damage kinetics at the light of results achieved for composite materials [139, 140]. This direction is fully justified by the fact that lack of performance is more associated to the weak adhesion between filaments. Thus, failure mechanisms are likely to be affected by the arrangement of such weak regions [27].

Another concern is the embedding of the microstructural details in the topology optimisation. During the past decades, this opportunity to tune microstructurally the design was out of reach because of computation resource limitations. Now, this is accessible at the cost of using efficient paradigms that avoid unrealistic configurations and constrain the search volume to design-effective solutions. Recent experimental achievements show the potential of AM to tune locally the performance of multi-material parts [39]. The next realistic step would be to promote this kind of experimental attempts to fully automated procedures. The role of microstructural details can be even determinant in hierarchical structures. Indeed, previous studies show large possibilities of airy arrangement using hybrid optimisation strategies [141]. One can imagine the large possibilities of pore connectivity tailoring driven by AM if micro-porosity is considered.

Topology optimisation is not yet ready to provide systematic process error detection for AM. We know that AM processes are exposed to inaccuracy in terms of geometry imperfections, volume mismatch, and undesirable surface texture. All these drawbacks can be properly addressed by a tool that apprehends the limits of the AM processing. Realistic designs with acceptable defects are better than ideal designs with unmeasurable bias. Figure 7 shows some clues about how topology optimisation can achieve a higher sensitivity to defects in AM. A better understanding of the AM defects is a matter of scaling down the numerical model to the size of heterogeneities that are the birth sites of the process-induced defects. Explicit implementation of discontinuities can be handled as well as lack of bonding between layers or filaments. Also more elaborated constitutive laws can be considered in order to take into account anisotropies that are subsequent to the rapid cooling and stretching of the matter.

thumbnail Figure 7.

Towards modelling of AM process.

A straight-A learning paradigm is not also accessible for topology optimisation. The near future developments will meet substantial use of in-situ or in-line monitoring procedures for AM [16, 142]. Topology optimisation can integrate some of these procedures to learn from the design. This requires the combination with another class of algorithms that are derived from artificial intelligence [143]. As an end, the optimal design can be performed using optimal process parameters, which saves a considerable amount of time.

A bigger tool for higher perspectives is what process managers expect from optimisation tools. Topology optimisation can be part of it if other considerations are handled carefully like cost effectiveness with a large material catalogue, material saving logics, automated process selection, scenarios of durability, recyclability and projections of life time. Fully automated decision making processes can be then launched starting from the idea of design to the post-mortem step of the AM part.

Acknowledgments

The authors would like to thank Omar Ben Hassana, Pierre Malige and Julien Grison, from IUT of Nantes, and Kaike Yang from ESAC lab, NPU for their technical assistance on the 3D printing process and mechanical testing. The authors gratefully acknowledge the International Campus on Safety and Intermodality in Transportation (CISIT), the Nord-Pas-de-Calais Region and the European Community (FEDER funds) for partly funding the X-ray tomography equipment. The authors are thankful to IBSM federation for the financial support and to the Nord-Pas-de-Calais Region (France) for funding Hedi Nouri’s post-doctoral grant (contract No 14002212). The second author, ZHANG Weihong, thanks the support by National Natural Science Foundation of China (11432011).

References

  1. Yan X, Gu P. 1996. A review of rapid prototyping technologies and systems. Computer-Aided Design, 28(4), 307–318. [CrossRef] [Google Scholar]
  2. Pham DT, Gault RS. 1998. A comparison of rapid prototyping technologies. International Journal of Machine Tools & Manufacture, 38(10–11), 1257–1287. [Google Scholar]
  3. Yang S, Zhao YF. 2015. Additive manufacturing-enabled design theory and methodology: a critical review. International Journal of Advanced Manufacturing Technology, 80(1–4), 327–342. [CrossRef] [Google Scholar]
  4. Chiu WK, Yu KM. 2008. Direct digital manufacturing of three-dimensional functionally graded material objects. Computer-Aided Design, 40(12), 1080–1093. [CrossRef] [Google Scholar]
  5. Shim JH, Kim AJ, Park JY, Yi N, Kang I, Park J, Rhie JW, Cho DW. 2013. Effect of solid freeform fabrication-based polycaprolactone/poly(lactic-co-glycolic acid)/collagen scaffolds on cellular activities of human adipose-derived stem cells and rat primary hepatocytes. Journal of Materials Science-Materials in Medicine, 24(4), 1053–1065. [CrossRef] [Google Scholar]
  6. Huang SH, Liu P, Mokasdar A, Hou L. 2013. Additive manufacturing and its societal impact: a literature review. International Journal of Advanced Manufacturing Technology, 67(5–8), 1191–1203. [Google Scholar]
  7. Zhai YW, Lados DA, Lagoy JL. 2014. Additive manufacturing: making imagination the major limitation. JOM, 66(5), 808–816. [CrossRef] [Google Scholar]
  8. Schelly C, Anzalone G, Wijnen B, Pearce JM. 2015. Open-source 3-D printing technologies for education: bringing additive manufacturing to the classroom. Journal of Visual Languages and Computing, 28, 226–237. [CrossRef] [Google Scholar]
  9. Gress DR, Kalafsky RV. 2015. Geographies of production in 3D: theoretical and research implications stemming from additive manufacturing. Geoforum, 60, 43–52. [CrossRef] [Google Scholar]
  10. Kietzmann J, Pitt L, Berthon P. 2015. Disruptions, decisions, and destinations: enter the age of 3-D printing and additive manufacturing. Business Horizons, 58(2), 209–215. [CrossRef] [Google Scholar]
  11. Shirazi SFS, Gharehkhani S, Mehrali M, Yarmand H, Metselaar HSC, Kadri NA, Abu Osman NA. 2015. A review on powder-based additive manufacturing for tissue engineering: selective laser sintering and inkjet 3D printing. Science and Technology of Advanced Materials, 16(3), 033502-1–033502-20. [Google Scholar]
  12. Turner BN, Gold SA. 2015. A review of melt extrusion additive manufacturing processes: II. Materials, dimensional accuracy, and surface roughness. Rapid Prototyping Journal, 21(3), 250–261. [CrossRef] [Google Scholar]
  13. Gong XB, Anderson T, Chou K. 2012. Review on powder-based electron beam additive manufacturing technology. Proceedings of the ASME/ISCIE International Symposium on Flexible Automation, Isfa 2012, St. Louis, Missouri, USA, 507–515. [Google Scholar]
  14. Huang Y, Leu MC, Mazumder J, Donmez A. 2015. Additive manufacturing: current state, future potential, gaps and needs, and recommendations. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(1), 014001-1–014001-10. [Google Scholar]
  15. Frazier WE. 2014. Metal additive manufacturing: a review. Journal of Materials Engineering and Performance, 23(6), 1917–1928. [Google Scholar]
  16. Tapia G, Elwany A. 2014. A review on process monitoring and control in metal-based additive manufacturing. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 136(6), 060801-1–060801-10. [CrossRef] [Google Scholar]
  17. Vayre B, Vignat F, Villeneuve F. 2012. Metallic additive manufacturing: state-of-the-art review and prospects. Mechanics & Industry, 13(2), 89–96. [CrossRef] [EDP Sciences] [Google Scholar]
  18. Nie B, Yang LM, Huang H, Bai S, Wan P, Liu J. 2015. Femtosecond laser additive manufacturing of iron and tungsten parts. Applied Physics A-Materials Science & Processing, 119(3), 1075–1080. [CrossRef] [Google Scholar]
  19. Finke S, Feenstra FK. 2002. Solid freeform fabrication by extrusion and deposition of semi-solid alloys. Journal of Materials Science, 37(15), 3101–3106. [CrossRef] [Google Scholar]
  20. Milewski JO, Dickerson PG, Nemec RB, Lewis GK, Fonseca JC. 1999. Application of a manufacturing model for the optimization of additive processing of Inconel alloy 690. Journal of Materials Processing Technology, 91(1–3), 18–28. [CrossRef] [Google Scholar]
  21. Xu W, Brandt M, Sun S, Elambasseril J, Liu Q, Latham K, Xia K, Qian M. 2015. Additive manufacturing of strong and ductile Ti-6Al-4V by selective laser melting via in situ martensite decomposition. Acta Materialia, 85, 74–84. [Google Scholar]
  22. Tang HP, Yang GY, Jia WP, He WW, Lu SL, Qian M. 2015. Additive manufacturing of a high niobium-containing titanium aluminide alloy by selective electron beam melting. Materials Science and Engineering A-Structural Materials Properties Microstructure and Processing, 636, 103–107. [CrossRef] [Google Scholar]
  23. Deckers J, Vleugels J, Kruthl JP. 2014. Additive manufacturing of ceramics: a review. Journal of Ceramic Science and Technology, 5(4), 245–260. [Google Scholar]
  24. Chappell WJ, Reilly C, Halloran J, Katehi LPB. 2003. Ceramic synthetic substrates using solid freeform fabrication. IEEE Transactions on Microwave Theory and Techniques, 51(3), 752–760. [CrossRef] [Google Scholar]
  25. Tang HH, Yen HC. 2015. Slurry-based additive manufacturing of ceramic parts by selective laser burn-out. Journal of the European Ceramic Society, 35(3), 981–987. [CrossRef] [Google Scholar]
  26. Gaytan SM, Cadena MA, Karim H, Delfin D, Lin Y, Espalin D, MacDonald E, Wicker RB. 2015. Fabrication of barium titanate by binder jetting additive manufacturing technology. Ceramics International, 41(5), 6610–6619. [CrossRef] [Google Scholar]
  27. Guessasma S, Belhabib S, Nouri H. 2015. Significance of pore percolation to drive anisotropic effects of 3D printed polymers revealed with X-ray µ-tomography and finite element computation. Polymer, 81, 29–36. [CrossRef] [Google Scholar]
  28. Shaffer S, Yang KJ, Vargas J, Di Prima MA, Voit W. 2014. On reducing anisotropy in 3D printed polymers via ionizing radiation. Polymer, 55(23), 5969–5979. [CrossRef] [Google Scholar]
  29. Hatzenbichler M, Geppert M, Seemann R, Stampfl J. 2013. Additive manufacturing of photopolymers using the texas instruments DLP lightcrafter. Emerging Digital Micromirror Device Based Systems and Applications V, 8618, 86180A-1–86180A-8. [CrossRef] [Google Scholar]
  30. Puppi D, Mota C, Gazzarri M, Dinucci D, Gloria A, Myrzabekova M, Ambrosio L, Chiellini F. 2012. Additive manufacturing of wet-spun polymeric scaffolds for bone tissue engineering. Biomedical Microdevices, 14(6), 1115–1127. [CrossRef] [Google Scholar]
  31. Quan Z, Wu A, Keefe M, Qin X, Yu J, Suhr J, Byun J-H, Kim B-S, Chou T-W. 2015. Additive manufacturing of multidirectional preforms for composites: opportunities and challenges. Materials Today, 18, 503–512. [CrossRef] [Google Scholar]
  32. Ning FD, Cong WL, Qiu JJ, Wei JH, Wang SR. 2015. Additive manufacturing of carbon fiber reinforced thermoplastic composites using fused deposition modeling. Composites Part B-Engineering, 80, 369–378. [Google Scholar]
  33. Gu DD, Chang F, Dai DH. 2015. Selective laser melting additive manufacturing of novel aluminum based composites with multiple reinforcing phases. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(2). [Google Scholar]
  34. Tekinalp HL, Kunc V, Velez-Garcia GM, Duty CE, Love LJ, Naskar AK, Blue CA, Ozcan S. 2014. Highly oriented carbon fiber-polymer composites via additive manufacturing. Composites Science and Technology, 105, 144–150. [CrossRef] [Google Scholar]
  35. Lee JS, Cha HD, Shim JH, Jung JW, Kim JY, Cho DW. 2012. Effect of pore architecture and stacking direction on mechanical properties of solid freeform fabrication-based scaffold for bone tissue engineering. Journal of Biomedical Materials Research Part A, 100A(7), 1846–1853. [CrossRef] [Google Scholar]
  36. Sa AME, Mello VM, Echavarria KR, Covill D. 2015. Adaptive voids primal and dual adaptive cellular structures for additive manufacturing. Visual Computer, 31(6–8), 799–808. [CrossRef] [Google Scholar]
  37. Takezawa A, Kobashi M, Kitamura M. 2015. Porous composite with negative thermal expansion obtained by photopolymer additive manufacturing. APL Materials, 3(7), 076103-1–076103-6. [CrossRef] [Google Scholar]
  38. Watjen AM, Gingter P, Kramer M, Telle R. 2014. Novel prospects and possibilities in additive manufacturing of ceramics by means of direct inkjet printing. Advances in Mechanical Engineering, 2014, 141346. [CrossRef] [Google Scholar]
  39. Sugavaneswaran M, Arumaikkannu G. 2014. Modelling for randomly oriented multi material additive manufacturing component and its fabrication. Materials & Design, 54, 779–785. [CrossRef] [Google Scholar]
  40. Herderick ED. 2015. Progress in additive manufacturing. JOM, 67(3), 580–581. [CrossRef] [Google Scholar]
  41. Giannatsis J, Dedoussis V. 2009. Additive fabrication technologies applied to medicine and health care: a review. International Journal of Advanced Manufacturing Technology, 40(1–2), 116–127. [CrossRef] [Google Scholar]
  42. Vaezi M, Seitz H, Yang SF. 2013. A review on 3D micro-additive manufacturing technologies. International Journal of Advanced Manufacturing Technology, 67(5–8), 1721–1754. [CrossRef] [Google Scholar]
  43. Venekamp NJR, Le Fever HT. 2015. Application areas of additive manufacturing from curiosity to application. IEEE Technology and Society Magazine, 34(3), 81–87. [CrossRef] [Google Scholar]
  44. Lipton JI, Cutler M, Nigi F, Cohen D, Lipson H. 2015. Additive manufacturing for the food industry. Trends in Food Science & Technology, 43(1), 114–123. [Google Scholar]
  45. Heinrich A, Maillard P, Suckow A, Grzesiak A, Sorg P, Berger U. 2015. Additive manufacturing – a new approach for individualized optical shape metrology. Optical Measurement Systems for Industrial Inspection Ix, 9525, 95251T-1–95251T-10. [CrossRef] [Google Scholar]
  46. Dickson MN. 2015. Soft strain sensors fabricated through additive manufacturing. MRS Bulletin, 40(6), 463. [CrossRef] [Google Scholar]
  47. Setaki F, Tenpierik M, Turrin M, van Timmeren A. 2014. Acoustic absorbers by additive manufacturing. Building and Environment, 72, 188–200. [CrossRef] [Google Scholar]
  48. Ho CMB, Ng SH, Yoon YJ. 2015. A review on 3D printed bioimplants. International Journal of Precision Engineering and Manufacturing, 16(5), 1035–1046. [CrossRef] [Google Scholar]
  49. Butscher A, Bohner M, Hofmann S, Gauckler L, Muller R. 2011. Structural and material approaches to bone tissue engineering in powder-based three-dimensional printing. Acta Biomaterialia, 7(3), 907–920. [CrossRef] [Google Scholar]
  50. Cox SC, Thornby JA, Gibbons GJ, Williams MA, Mallick KK. 2015. 3D printing of porous hydroxyapatite scaffolds intended for use in bone tissue engineering applications. Materials Science & Engineering C-Materials for Biological Applications, 47, 237–247. [CrossRef] [Google Scholar]
  51. Jiang CP, Chen YY, Hsieh MF, Lee HM. 2013. Solid freeform fabrication and in-vitro response of osteoblast cells of mPEG-PCL-mPEG bone scaffolds. Biomedical Microdevices, 15(2), 369–379. [CrossRef] [Google Scholar]
  52. Waller JM, Saulsberry RL, Parker BH, Hodges KL, Burke ER, Taminger KM. 2015. Summary of NDE of Additive Manufacturing Efforts in NASA. 41st Annual Review of Progress in Quantitative Nondestructive Evaluation, Vol. 34, 1650, 51–62. [Google Scholar]
  53. Zhu WJ, Zhao XL, Zhang W, Ren K, Zhang ZY, Li DC. 2014. Design and evaluation of fully configured models built by additive manufacturing. AIAA Journal, 52(7), 1441–1451. [CrossRef] [Google Scholar]
  54. Zhu J-H, Zhang W-H, Xia L. 2015. Topology optimization in aircraft and aerospace structures design. Archives of Computational Methods in Engineering, 1–28. [Google Scholar]
  55. Gu XJ, Zhu JH, Zhang WH. 2012. The lattice structure configuration design for stereolithography investment casting pattern using topology optimization. Rapid Prototyping Journal, 18(5), 353–361. [CrossRef] [Google Scholar]
  56. Engstrom DS, Porter B, Pacios M, Bhaskaran H. 2014. Additive nanomanufacturing – a review. Journal of Materials Research, 29(17), 1792–1816. [CrossRef] [Google Scholar]
  57. Chakraborty D, Reddy BA, Choudhury AR. 2008. Extruder path generation for curved layer fused deposition modeling. Computer-Aided Design, 40(2), 235–243. [CrossRef] [Google Scholar]
  58. Lee CS, Kim SG, Kim HJ, Ahn SH. 2007. Measurement of anisotropic compressive strength of rapid prototyping parts. Journal of Materials Processing Technology, 187, 627–630. [Google Scholar]
  59. Ahn SH, Montero M, Odell D, Roundy S, Wright PK. 2002. Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248–257. [Google Scholar]
  60. Lee PH, Chung H, Lee SW, Yoo J, Ko J. 2014. Review: dimensional accuracy in additive manufacturing processes. Proceedings of the ASME 9th International Manufacturing Science and Engineering Conference, 2014, Detroit, Michigan, USA, vol. 1, V001T04A045. [Google Scholar]
  61. Zhu YY, Tian XJ, Li J, Wang HM. 2015. The anisotropy of laser melting deposition additive manufacturing Ti-6.5Al-3.5Mo-1.5Zr-0.3Si titanium alloy. Materials & Design, 67, 538–542. [CrossRef] [Google Scholar]
  62. Rosa B, Mognol P, Hascoet JY. 2015. Laser polishing of additive laser manufacturing surfaces. Journal of Laser Applications, 27, S29102-1–S29102-7. [CrossRef] [Google Scholar]
  63. Pinto JM, Arrieta C, Andia ME, Uribe S, Ramos-Grez J, Vargas A, Irarrazaval P, Tejos C. 2015. Sensitivity analysis of geometric errors in additive manufacturing medical models. Medical Engineering & Physics, 37(3), 328–334. [CrossRef] [Google Scholar]
  64. Nelaturi S, Kim W, Kurtoglu T. 2015. Manufacturability feedback and model correction for additive manufacturing. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(2), 021015-1–021015-9. [CrossRef] [Google Scholar]
  65. Wang T, Zhu YY, Zhang SQ, Tang HB, Wang HM. 2015. Grain morphology evolution behavior of titanium alloy components during laser melting deposition additive manufacturing. Journal of Alloys and Compounds, 632, 505–513. [Google Scholar]
  66. Ma Y, Cuiuri D, Hoye N, Li HJ, Pan ZX. 2015. The effect of location on the microstructure and mechanical properties of titanium aluminides produced by additive layer manufacturing using in-situ alloying and gas tungsten arc welding. Materials Science and Engineering A-Structural Materials Properties Microstructure and Processing, 631, 230–240. [CrossRef] [Google Scholar]
  67. Hong C, Gu DD, Dai DH, Alkhayat M, Urban W, Yuan PP, Cao S, Gasser A, Weisheit A, Kelbassa I, Zhong ML, Poprawe R. 2015. Laser additive manufacturing of ultrafine TiC particle reinforced Inconel 625 based composite parts: tailored microstructures and enhanced performance. Materials Science and Engineering A-Structural Materials Properties Microstructure and Processing, 635, 118–128. [CrossRef] [Google Scholar]
  68. Carroll BE, Palmer TA, Beese AM. 2015. Anisotropic tensile behavior of Ti-6Al-4V components fabricated with directed energy deposition additive manufacturing. Acta Materialia, 87, 309–320. [CrossRef] [Google Scholar]
  69. Thrimurthulu K, Pandey PM, Reddy NV. 2004. Optimum part deposition orientation in fused deposition modeling. International Journal of Machine Tools & Manufacture, 44(6), 585–594. [CrossRef] [Google Scholar]
  70. Rua Y, Muren R, Reckinger S. 2015. Limitations of additive manufacturing on microfluidic heat exchanger components. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(3), 034504-1–034504-5. [Google Scholar]
  71. Carneiro OS, Silva AF, Gomes R. 2015. Fused deposition modeling with polypropylene. Materials & Design, 83, 768–776. [CrossRef] [Google Scholar]
  72. Scudino S, Unterdorfer C, Prashanth KG, Attar H, Ellendt N, Uhlenwinkel V, Eckert J. 2015. Additive manufacturing of Cu-10Sn bronze. Materials Letters, 156, 202–204. [CrossRef] [Google Scholar]
  73. Foster BK, Reutzel EW, Nassar AR, Dickman CJ, Hall BT. 2015. A brief survey of sensing for metal-based powder bed fusion additive manufacturing. Dimensional Optical Metrology and Inspection for Practical Applications Iv, 9489, 94890B-1–94890B-9. [CrossRef] [Google Scholar]
  74. Furumoto T, Koizumi A, Alkahari MR, Anayama R, Hosokawa A, Tanaka R, Ueda T. 2015. Permeability and strength of a porous metal structure fabricated by additive manufacturing. Journal of Materials Processing Technology, 219, 10–16. [CrossRef] [Google Scholar]
  75. Hu KL, Jin S, Wang CCL. 2015. Support slimming for single material based additive manufacturing. Computer-Aided Design, 65, 1–10. [CrossRef] [Google Scholar]
  76. Jin YA, He Y, Fu JZ. 2015. Support generation for additive manufacturing based on sliced data. International Journal of Advanced Manufacturing Technology, 80(9–12), 2041–2052. [CrossRef] [Google Scholar]
  77. Mohamed OA, Masood SH, Bhowmik JL. 2015. Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Advances in Manufacturing, 3(1), 42–53. [CrossRef] [Google Scholar]
  78. Zhou C, Chen Y, Waltz RA. 2009. Optimized mask image projection for solid freeform fabrication. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 131(6), 061004-1–061004-12. [Google Scholar]
  79. Garg A, Lam JSL, Savalani MM. 2015. A new computational intelligence approach in formulation of functional relationship of open porosity of the additive manufacturing process. International Journal of Advanced Manufacturing Technology, 80(1–4), 555–565. [CrossRef] [Google Scholar]
  80. Mun J, Yun BG, Ju J, Chang B. 2015. Indirect additive manufacturing based casting of a periodic 3D cellular metal – flow simulation of molten aluminum alloy. Journal of Manufacturing Processes, 17, 28–40. [CrossRef] [Google Scholar]
  81. Giannitelli SM, Mozetic P, Trombetta M, Rainer A. 2015. Combined additive manufacturing approaches in tissue engineering. Acta Biomaterialia, 24, 1–11. [CrossRef] [Google Scholar]
  82. Rogers CM, Morris GE, Gould TWA, Bail R, Toumpaniari S, Harrington H, Dixon JE, Shakesheff KM, Segal J, Rose FRAJ. 2014. A novel technique for the production of electrospun scaffolds with tailored three-dimensional micro-patterns employing additive manufacturing. Biofabrication, 6(3), 035003. [CrossRef] [Google Scholar]
  83. Newman ST, Zhu ZC, Dhokia V, Shokrani A. 2015. Process planning for additive and subtractive manufacturing technologies. CIRP Annals-Manufacturing Technology, 64(1), 467–470. [Google Scholar]
  84. Jin GQ, Li WD. 2013. Adaptive rapid prototyping/manufacturing for functionally graded material-based biomedical models. International Journal of Advanced Manufacturing Technology, 65(1–4), 97–113. [CrossRef] [Google Scholar]
  85. Zhou MY, Xi JT, Yan JQ. 2004. Modeling and processing of functionally graded materials for rapid prototyping. Journal of Materials Processing Technology, 146(3), 396–402. [CrossRef] [Google Scholar]
  86. Roper DA, Good BL, McCauley R, Yarlagadda S, Smith J, Good A, Pa P, Mirotznik MS. 2014. Additive manufacturing of graded dielectrics. Smart Materials and Structures, 23(4), 045029. [CrossRef] [Google Scholar]
  87. Becker R, Grzesiak A, Henning A. 2005. Rethink assembly design. Assembly Automation, 25(4), 262–266. [CrossRef] [Google Scholar]
  88. Zhang P, Toman J, Yu Y, Biyikli E, Kirca M, Chmielus M, To AC. 2015. Efficient design-optimization of variable-density hexagonal cellular structure by additive manufacturing: theory and validation. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(2), 021004-1–021004-8. [Google Scholar]
  89. Yang L, Harrysson O, West H, Cormier D. 2015. Mechanical properties of 3D re-entrant honeycomb auxetic structures realized via additive manufacturing. International Journal of Solids and Structures, 69–70, 475–490. [CrossRef] [Google Scholar]
  90. Chen JN, Ahmad R, Suenaga H, Li W, Sasaki K, Swain M, Li Q. 2015. Shape optimization for additive manufacturing of removable partial dentures – a new paradigm for prosthetic CAD/CAM. PLos One, 10(7), 0132552. [Google Scholar]
  91. Denlinger ER, Heigel JC, Michaleris P, Palmer TA. 2015. Effect of inter-layer dwell time on distortion and residual stress in additive manufacturing of titanium and nickel alloys. Journal of Materials Processing Technology, 215, 123–131. [CrossRef] [Google Scholar]
  92. Thompson B, Yoon HS. 2014. Efficient path planning algorithm for additive manufacturing systems. IEEE Transactions on Components Packaging and Manufacturing Technology, 4(9), 1555–1563. [CrossRef] [Google Scholar]
  93. Kim HC, Choi KH, Doh YH, Kim DS. 2009. Fabrication of parts and their evaluation using a dual laser in the solid freeform fabrication system. Journal of Materials Processing Technology, 209(10), 4857–4866. [CrossRef] [Google Scholar]
  94. Ding DH, Pan ZX, Cuiuri D, Li HJ. 2015. Wire-feed additive manufacturing of metal components: technologies, developments and future interests. International Journal of Advanced Manufacturing Technology, 81(1–4), 465–481. [Google Scholar]
  95. Ren L, Sparks T, Ruan JZ, Liou F. 2008. Process planning strategies for solid freeform fabrication of metal parts. Journal of Manufacturing Systems, 27(4), 158–165. [CrossRef] [Google Scholar]
  96. Paul R, Anand S. 2015. A new Steiner patch based file format for additive manufacturing processes. Computer-Aided Design, 63, 86–100. [CrossRef] [Google Scholar]
  97. Xu AP, Shaw LL. 2005. Equal distance offset approach to representing and process planning for solid freeform fabrication of functionally graded materials. Computer-Aided Design, 37(12), 1308–1318. [CrossRef] [Google Scholar]
  98. Ding DH, Pan ZX, Cuiuri D, Li HJ. 2015. A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). Robotics and Computer-Integrated Manufacturing, 31, 101–110. [Google Scholar]
  99. Song X, Pan YY, Chen Y. 2015. Development of a low-cost parallel kinematic machine for multidirectional additive manufacturing. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(2), 021005-1–021005-13. [CrossRef] [Google Scholar]
  100. Arntz K, Wegener M, Liu Y. 2015. Computer aided manufacturing supported process planning of additive manufacturing by laser deposition welding. Journal of Laser Applications, 27, S14002-1–S14002-5. [CrossRef] [Google Scholar]
  101. Seifert T, Sowade E, Roscher F, Wiemer M, Gessner T, Baumann RR. 2015. Additive manufacturing technologies compared: morphology of deposits of silver ink using inkjet and aerosol jet printing. Industrial & Engineering Chemistry Research, 54(2), 769–779. [CrossRef] [Google Scholar]
  102. Palanivel S, Nelaturu P, Glass B, Mishra RS. 2015. Friction stir additive manufacturing for high structural performance through microstructural control in an Mg based WE43 alloy. Materials & Design, 65, 934–952. [CrossRef] [Google Scholar]
  103. Monaghan T, Capel AJ, Christie SD, Harris RA, Friel RJ. 2015. Solid-state additive manufacturing for metallized optical fiber integration. Composites Part A-Applied Science and Manufacturing, 76, 181–193. [CrossRef] [Google Scholar]
  104. Sundaram MM, Kamaraj AB, Kumar VS. 2015. Mask-less electrochemical additive manufacturing: a feasibility study. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(2), 021006-1–021006-9. [CrossRef] [Google Scholar]
  105. Wang H, Jiang W, Valant M, Kovacevic R. 2003. Microplasma powder deposition as a new solid freeform fabrication process. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 217(12), 1641–1650. [CrossRef] [Google Scholar]
  106. Leong KF, Cheah CM, Chua CK. 2003. Solid freeform fabrication of three-dimensional scaffolds for engineering replacement tissues and organs. Biomaterials, 24(13), 2363–2378. [CrossRef] [PubMed] [Google Scholar]
  107. Cai K, Guo D, Huang Y, Yang JL. 2003. Solid freeform fabrication of alumina ceramic parts through a lost mould method. Journal of the European Ceramic Society, 23(6), 921–925. [CrossRef] [Google Scholar]
  108. Guo D, Cai K, Nan C, Li LT, Gul ZL. 2002. Gelcasting based solid freeform fabrication of piezoelectric ceramic objects. Scripta Materialia, 47(6), 383–387. [CrossRef] [Google Scholar]
  109. Sohn H, Yang DY. 2002. Selective infiltration of superheated micro-sized droplets of a tin-lead alloy into a preheated powder layer for a novel solid freeform fabrication process. Journal of Materials Science Letters, 21(21), 1717–1719. [CrossRef] [Google Scholar]
  110. Mancanares CG, Zancul ED, da Silva JC, Miguel PAC. 2015. Additive manufacturing process selection based on parts’ selection criteria. International Journal of Advanced Manufacturing Technology, 80(5–8), 1007–1014. [CrossRef] [Google Scholar]
  111. Price S, Cheng B, Lydon J, Cooper K, Chou K. 2014. On process temperature in powder-bed electron beam additive manufacturing: process parameter effects. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 136(6), 061019-1–061019-10. [CrossRef] [Google Scholar]
  112. Wei C, Dong JY. 2014. Development and modeling of melt electrohydrodynamic-jet printing of phase-change inks for high-resolution additive manufacturing. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 136(6), 061010-1–061010-7. [Google Scholar]
  113. Kang HW, Seol YJ, Cho DW. 2009. Development of an indirect solid freeform fabrication process based on microstereolithography for 3D porous scaffolds. Journal of Micromechanics and Microengineering, 19(1), 015011. [CrossRef] [Google Scholar]
  114. Mondrinos MJ, Dembzynski R, Lu L, Byrapogu VKC, Wootton DM, Lelkes PI, Zhou J. 2006. Porogen-based solid freeform fabrication of polycaprolactone-calcium phosphate scaffolds for tissue engineering. Biomaterials, 27(25), 4399–4408. [CrossRef] [Google Scholar]
  115. Choi H, Yang Y, Li XC. 2005. Experimental study on integration of laser-based additive/subtractive processes for meso/micro solid freeform fabrication. International Journal of Advanced Manufacturing Technology, 26(4), 335–341. [CrossRef] [Google Scholar]
  116. Yoshida H, Igarashi T, Obuchi Y, Takami Y, Sato J, Araki M, Miki M, Nagata K, Sakai K, Igarashi S. 2015. Architecture-scale human-assisted additive manufacturing. ACM Transactions on Graphics, 34(4), 88. [CrossRef] [Google Scholar]
  117. Siraskar N, Paul R, Anand S. 2015. Adaptive slicing in additive manufacturing process using a modified boundary octree data structure. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(1), 011007-1–011007-11. [CrossRef] [Google Scholar]
  118. Slotwinski JA, Garboczi EJ, Stutzman PE, Ferraris CF, Watson SS, Peltz MA. 2014. Characterization of metal powders used for additive manufacturing. Journal of Research of the National Institute of Standards and Technology, 119, 460–493. [Google Scholar]
  119. Sakar-Deliormanli A, Celik E, Polat M. 2008. Rheological behavior of PMN gels for solid freeform fabrication. Colloids and Surfaces A-Physicochemical and Engineering Aspects, 324(1–3), 159–166. [Google Scholar]
  120. Yen HC. 2015. Experimental studying on development of slurry-layer casting system for additive manufacturing of ceramics. International Journal of Advanced Manufacturing Technology, 77(5–8), 915–925. [CrossRef] [Google Scholar]
  121. Shiva S, Palani IA, Mishra SK, Paul CP, Kukreja LM. 2015. Investigations on the influence of composition in the development of Ni-Ti shape memory alloy using laser based additive manufacturing. Optics and Laser Technology, 69, 44–51. [CrossRef] [Google Scholar]
  122. Strano G, Hao L, Everson RM, Evans KE. 2013. A new approach to the design and optimisation of support structures in additive manufacturing. International Journal of Advanced Manufacturing Technology, 66(9–12), 1247–1254. [CrossRef] [Google Scholar]
  123. Cooper D, Thornby J, Blundell N, Henrys R, Williams MA, Gibbons G. 2015. Design and manufacture of high performance hollow engine valves by additive layer manufacturing. Materials & Design, 69, 44–55. [CrossRef] [Google Scholar]
  124. Zohdi TI. 2015. Modeling and simulation of cooling-induced residual stresses in heated particulate mixture depositions in additive manufacturing. Computational Mechanics, 56(4), 613–630. [CrossRef] [MathSciNet] [Google Scholar]
  125. Bai XW, Zhang HO, Wang GL. 2015. Modeling of the moving induction heating used as secondary heat source in weld-based additive manufacturing. International Journal of Advanced Manufacturing Technology, 77(1–4), 717–727. [CrossRef] [Google Scholar]
  126. Brant AM, Sundaram MM, Kamaraj AB. 2015. Finite element simulation of localized electrochemical deposition for maskless electrochemical additive manufacturing. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 137(1), 011018-1–011018-9. [CrossRef] [Google Scholar]
  127. Kranz J, Herzog D, Emmelmann C. 2015. Design guidelines for laser additive manufacturing of lightweight structures in TiAl6V4. Journal of Laser Applications, 27, S14001-1–S14001-16. [CrossRef] [Google Scholar]
  128. Grihon S, Krog L, Bassir D. 2011. Numerical optimization applied to structure sizing at AIRBUS: a multi-step process. International Journal for Simulation and Multidisciplinary Design Optimization, 3(4), 432–442. [CrossRef] [EDP Sciences] [Google Scholar]
  129. Zenzinger G, Bamberg J, Ladewig A, Hess T, Henkel B, Satzger W. 2015. Process monitoring of additive manufacturing by using optical tomography. 41st Annual Review of Progress in Quantitative Nondestructive Evaluation, Vol. 34, 1650, 164–170. [Google Scholar]
  130. Carl V. 2015. Monitoring system for the quality assessment in additive manufacturing. 41st Annual Review of Progress in Quantitative Nondestructive Evaluation, Vol. 34, 1650, 171–176. [Google Scholar]
  131. Krauss H, Zeugner T, Zaeh MF. 2015. Thermographic process monitoring in powderbed based additive manufacturing. 41st Annual Review of Progress in Quantitative Nondestructive Evaluation, Vol. 34, 1650, 177–183. [Google Scholar]
  132. Rieder H, Dillhofer A, Spies M, Bamberg J, Hess T. 2015. Ultrasonic online monitoring of additive manufacturing processes based on selective laser melting. 41st Annual Review of Progress in Quantitative Nondestructive Evaluation, Vol. 34, 1650, 184–191. [Google Scholar]
  133. Everton S, Dickens P, Tuck C, Dutton B. 2015. Evaluation of laser ultrasonic testing for inspection of metal additive manufacturing. Laser 3d Manufacturing Ii, 9353, 935316-1–935316-8. [Google Scholar]
  134. Monzon MD, Ortega Z, Martinez A, Ortega F. 2015. Standardization in additive manufacturing: activities carried out by international organizations and projects. International Journal of Advanced Manufacturing Technology, 76(5–8), 1111–1121. [CrossRef] [Google Scholar]
  135. Raguvarun K, Balasubramaniam K, Rajagopal P, Palanisamy S, Nagarajah R, Hoye N, Curiri D, Kapoor A. 2015. A study of internal structure in components made by additive manufacturing process using 3D X-ray tomography. 41st Annual Review of Progress in Quantitative Nondestructive Evaluation, Vol. 34, 1650, 146–155. [Google Scholar]
  136. Guessasma S, Nouri H. 2015. Compression behaviour of bread crumb up to densification investigated using X-ray tomography and finite element computation. Food Research International, 72, 140–148. [CrossRef] [Google Scholar]
  137. Ayadi A, Nouri H, Guessasma S, Roger F. 2015. An original approach to assess elastic properties of a short glass fibre reinforced thermoplastic combining X-ray tomography and finite element computation. Composite Structures, 125, 277–286. [CrossRef] [Google Scholar]
  138. Adamczak S, Bochnia J, Kaczmarska B. 2015. An analysis of tensile test results to assess the innovation risk for an additive manufacturing technology. Metrology and Measurement Systems, 22(1), 127–138. [Google Scholar]
  139. Guessasma S, Bassir DH. 2010. Identification of mechanical properties of biopolymer composites sensitive to interface effect using hybrid approach. Mechanics of Materials, 42(3), 344–353. [Google Scholar]
  140. Hbib M, Guessasma S, Bassir D, Benseddiq N. 2011. Interfacial damage in biopolymer composites reinforced using hemp fibres: finite element simulation and experimental investigation. Composites Science and Technology, 71(11), 1419–1426. [CrossRef] [Google Scholar]
  141. Guessasma S, Bassir D. 2010. Optimization of the mechanical properties of virtual porous solids using a hybrid approach. Acta Materialia, 58(2), 716–725. [Google Scholar]
  142. Xiong J, Zhang GJ. 2014. Adaptive control of deposited height in GMAW-based layer additive manufacturing. Journal of Materials Processing Technology, 214(4), 962–968. [CrossRef] [Google Scholar]
  143. Witherell P, Feng S, Simpson TW, Saint John DB, Michaleris P, Liu ZK, Chen LQ, Martukanitz R. 2014. Toward metamodels for composable and reusable additive manufacturing process models. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 136(6), 061025-1–061025-9. [CrossRef] [Google Scholar]

Cite this article as: Guessasma S, Zhang W, Zhu J, Belhabib S & Nouri H: Challenges of additive manufacturing technologies from an optimisation perspective. Int. J. Simul. Multisci. Des. Optim., 2015, 6, A9.

All Figures

thumbnail Figure 1.

The process chain in typical AM.
In the text
thumbnail Figure 2.

Typical examples of CAD objects transformed into collection of toolpaths using (a) Repetier and (b) CatalystEX software.
In the text
thumbnail Figure 3.

Laying down process of fused ABS polymer in typical FDM equipment.
In the text
thumbnail Figure 4.

Two examples of airy randomly structured polymers designed using (a) fused disposition modelling (uPrint equipment in GEPEA lab., university of Nantes, France) and (b) photolithography (SPS350B equipment in ESAC lab, NPU, Xian, China).
In the text
thumbnail Figure 5.

(a) Classical view of topological optimisation, (b) Modified scheme with defect sensitive topology optimisation of AM.
In the text
thumbnail Figure 6.

Microstructural defects in ABS polymer printed using FDM. Analysis is performed using X-ray micro-tomography. Two examples are shown one is (a) a dense ABS block and the other is (b) airy ABS materials exhibiting more than 60% of porosity.
In the text
thumbnail Figure 7.

Towards modelling of AM process.
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.