Volume 20, Issue 3 (March 2020)                   Modares Mechanical Engineering 2020, 20(3): 689-699 | Back to browse issues page

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Kazemi M, Rahimi A. Improving the Surface Roughness, Material Consumption and Abrasion Ability of Additive Manufacturing Products by Segmentation Design. Modares Mechanical Engineering 2020; 20 (3) :689-699
URL: http://mme.modares.ac.ir/article-15-28710-en.html
1- Mechanical Engineering Department, Amirkabir University of Technology, Tehran, Iran
2- Mechanical Engineering Department, Amirkabir University of Technology, Tehran, Iran , rahimi@aut.ac.ir
Abstract:   (4163 Views)
Additive manufacturing technology significantly simplifies the production of complex 3D parts directly by the computer-aided design model. However, additive manufacturing processes have unique flexibility. They still have restrictions that don’t allow engineers to generate some specific geometric shapes, easily. Some of these restrictions are the consumption of materials to supports, the poor abrasion resistance and the inferior surface finish of some surfaces with certain angles. One of the methods to overcome these problems is designing by segmentation. The proposed methodology consists of two steps: 1) segmenting of the 3D model and 2) exploring the best orientation for each segment. In the first step, engineers consider the possible number of segments and the connection method of segments. In this paper, a series of segments is obtained by recognition of features and separating them with one or more appropriate planes. In the second step, the best fabrication orientation should be chosen. The criterion for optimization is that the support volume, abrasion ability, and surface roughness should be minimum. The operation is performed automatically by the algorithm created based on principles of the Particle swarm optimization algorithm using visual C#. Experimental tests show that segmentation design improves additive manufacturing processes from the aspects of material consumption, abrasion volume, and surface quality. This paper presents an original approach to improving the efficiency of additive manufacturing technologies that make the additive manufacturing closer to maturity.
Full-Text [PDF 1332 kb]   (1434 Downloads)    
Article Type: Original Research | Subject: Build add-on
Received: 2018/12/28 | Accepted: 2019/07/14 | Published: 2020/03/1

References
1. Ilinkin I, Janardan R, Majhi J, Schwerdt J, Smid M, Sriram R. A decomposition-based approach to layered manufacturing. Computational Geometry. 2002;23(2):117-151. [Link] [DOI:10.1016/S0925-7721(01)00059-1]
2. Qiang L, Kucukkoc I, Zhang DZ. Production planning in additive manufacturing and 3D printing. Computers & Operations Research. 2017;83:157-172. [Link] [DOI:10.1016/j.cor.2017.01.013]
3. Akhoundi B, Behravesh AH. Effect of filling pattern on the tensile and flexural mechanical properties of FDM 3D Printed products. Experimental Mechanics. 2019;59(6):883-897. [Link] [DOI:10.1007/s11340-018-00467-y]
4. Kazemi M, Rahimi AR. Supports effect on tensile strength of the stereolithography parts. Rapid Prototyping Journal. 2015;21(1):79-88. [Link] [DOI:10.1108/RPJ-12-2012-0118]
5. Gibson I, Rosen D, Stucker B. Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing. Johnson Matthey Technology Review. 2015;59(3):193-198. [Link] [DOI:10.1595/205651315X688406]
6. Rosen DW, editors. Design for additive manufacturing: A method to explore unexplored regions of the design space. In 18th Solid Freeform Fabrication Symposium, 2007 Aug 1, Atlanta, Georgia. Austin: University of Texas at Austin; 2007. pp. 402-415. [Link]
7. Thomas DS, Gilbert SW. Costs and cost effectiveness of additive manufacturing. Gaithersburg: National Institute of Standards and Technology; 2014. [Link] [DOI:10.6028/NIST.SP.1176]
8. Akhoundi B, Behravesh AH, Bagheri Saed A. An innovative design approach in three-dimensional printing of continuous fiber-reinforced thermoplastic composites via fused deposition modeling process: In-melt simultaneous impregnation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2019 Apr. [Link] [DOI:10.1177/0954405419843780]
9. Chan CK, Tan ST. Volume decomposition of CAD models for rapid prototyping technology. Rapid Prototyping Journal. 2005;11(4):221-34. [Link] [DOI:10.1108/13552540510612910]
10. Xie Y, Chen X. Support-free interior carving for 3D printing. Visual Informatics. 2017;1(1):9-15. [Link] [DOI:10.1016/j.visinf.2017.01.002]
11. Rodrigue H, Rivette M. An assembly-level design for additive manufacturing methodology. Unknown City: InsIDE-Virtual Concept; 2010. [Link]
12. Kaji F, Barari A. Evaluation of the surface roughness of additive manufacturing parts based on the modelling of cusp geometry. IFAC-PapersOnLine. 2015;48(3):658-663. [Link] [DOI:10.1016/j.ifacol.2015.06.157]
13. Emami MM, Barazandeh F, Yaghmaie F. Scanning-projection based stereolithography: Method and structure. Sensors and Actuators A: Physical. 2014;218:116-124. [Link] [DOI:10.1016/j.sna.2014.08.002]
14. Krause FL, Ciesla M, Klocke F, Wirtz H, Ulbrich A. Improving rapid prototyping processing speeds by adaptive slicing. 6th European Conference on Rapid Prototyping and Manufacturing. Nottingham: Univercity of Nottingham; 1997 pp. 31-36. [Link]
15. Mangan AP, Whitaker RT. Partitioning 3D surface meshes using watershed segmentation. IEEE Transactions on Visualization and Computer Graphics. 1999;5(4):308-321. [Link] [DOI:10.1109/2945.817348]
16. Medellín H, Lim T, Corney J, Ritchie JM, Davies JB. Automatic subdivision and refinement of large components for rapid prototyping production. Journal of Computing and Information Science in Engineering. 2007;7(3):249-258. [Link] [DOI:10.1115/1.2753162]
17. Hao J, Fang L, Williams RE. An efficient curvature-based partitioning of large-scale STL models. Rapid Prototyping Journal. 2011;17(2):116-127. [Link] [DOI:10.1108/13552541111113862]
18. Luo L, Baran I, Rusinkiewicz S, Matusik W. Chopper: partitioning models into 3D-printable parts [Internet]. New York: Princeton; 2012 [Unknown Cited]. Available from: https://gfx.cs.princeton.edu/pubs/Luo_2012_CPM/. [Link] [DOI:10.1145/2366145.2366148]
19. Hu R, Li H, Zhang H, Cohen-Or D. Approximate pyramidal shape decomposition. ACM Transactions on Graphics (TOG). 2014;33(6). [Link] [DOI:10.1145/2661229.2661244]
20. Oh Y, Behdad S. Assembly based part design to improve the additive manufacturing productivity: process time, cost and surface roughness. ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2016 Aug 21-24, Charlotte, USA. New York: American Society of Mechanical Engineers; 2016. [Link] [DOI:10.1115/DETC2016-59652]
21. Sabourin E, Houser SA, Helge Bøhn J. Adaptive slicing using stepwise uniform refinement. Rapid Prototyping Journal. 1996;2(4):20-26. [Link] [DOI:10.1108/13552549610153370]
22. Moroni G, Syam WP, Petrò S. Functionality-based part orientation for additive manufacturing. Procedia CIRP. 2015;36:217-222. [Link] [DOI:10.1016/j.procir.2015.01.015]
23. Qian X, Dutta D. Feature based fabrication in layered manufacturing. Journal of Mechanical Design. 2001;123(3):337-345. [Link] [DOI:10.1115/1.1377282]
24. Michael J, Carthy Mc, editors. What are feature interactions. Proceedings of the 1996 ASME Design Engineering Technical Conference and Computers in Engineering Conference, 1996 Aug 18-22, Irvine, California. New York: American Society of Mechanical Engineers; 1996. [Link]
25. Kennedy J, Eberhart R, editors. Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, 1995 Nov 27-1 Dec, Perth, WA, Australia. Neuherberg: International Conference on Neural Networks; 2002. [Link]
26. Reeves PE, Cobb RC. Surface deviation modeling of LMT processes- A comparative analysis. Proceedings of the Fifth European Conference on Rapid Prototyping and Manufacturing, 1995 Jun, Helsinki, Finland. England: British Library Board; 1996. PP. 59-77. [Link]
27. Byun HS, Lee KH. Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making. Robotics and Computer-Integrated Manufacturing. 2006;22(1):69-80. [Link] [DOI:10.1016/j.rcim.2005.03.001]
28. Arora JS. Introduction to optimum design. 4th edition. Amsterdam: Elsevier; 2017. [Link]
29. Golbon-Haghighi MH, Saeidi-Manesh H, Zhang G, Zhang Y. Pattern Synthesis for the Cylindrical Polarimetric Phased Array Radar (CPPAR). Progress in Electromagnetics Research. 2018;66:87-98. [Link]
30. Ghorpade A, Dashroa Y, Tiwari MK, Karunakaran KP. Introducing hierarchical particle swarm optimization to optimal part orientation in fused deposition modeling. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; 2006 Sep 10-13; Philadelphia, Pennsylvania. New York: American Society of Mechanical Engineers; 2008. pp. 233-240. [Link]
31. Sonnenberg M. Force and effort analysis of unfastening actions in disassembly processes [Dissertation]. New Jersey: Institute of Technology; 2001. [Link]

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