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Numerical investigation of the effect of topology optimisation methods parameters in the topology quality, the strength, and the computational cost

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The literature abounds with many distinct topology optimisation methods, many of which share common parameter configurations. This study demonstrates that alternative parameter configurations may produce better results than common parameters. Additionally, we try to answer two fundamental questions: identifying the most effective topology optimisation method and determining the optimal parameter selection within this optimisation method. In order to respond to these questions, we conducted a comparative and objective analysis of topology optimisation methods. Design/methodology/approach: This paper evaluates four prominent topology optimisation methodologies, SIMP, RAMP, BESO, and LSM, based on three essential criteria: structural strength, topology quality, and computational cost. We conducted an in-depth examination of 12,500 topology optimisation results spanning a broad range of critical parameter values. These outcomes were generated using MATLAB codes. In the meantime, we comprehensively compared our findings with the existing literature on this subject. Findings: As predicted, our chosen parameters had a substantial effect on the topology quality, structural strength, and computational cost of the topology optimisation outcomes. Across the 12,500 results, many parameter combinations appeared to produce favourable results compared to conventional parameters commonly found in the existing literature. Research limitations/implications: This study focuses exclusively on four specific topology optimisation methods; however, its findings may be extrapolated to apply to other methodologies. Additionally, while it extensively examines the effects of parameters on topology quality, strength, and computational cost, it does not encompass an exploration of these parameters' impacts on other performance criteria. Originality/value: Novel parameter configurations for topology optimisation have been identified, yielding enhanced outcomes in terms of topology quality, structural strength, and computational efficiency.
Rocznik
Strony
55--71
Opis fizyczny
Bibliogr. 44 poz.
Twórcy
  • National High School of Electricity and Mechanics, University Hassan II, Casablanca, Morocco
  • Laboratory of Mechanics, Production, and Industrial Engineering, EST, University Hassan II, Casablanca, Morocco
autor
  • National High School of Electricity and Mechanics, University Hassan II, Casablanca, Morocco
autor
  • Laboratory IMII, FST, University Hassan I, Settat, Morocco
Bibliografia
  • 1. B. Badiru, V.V Valencia, D. Liu (eds), Additive manufacturing handbook: product development for the defense industry, CRC Press, Boca Raton, 2017.
  • 2. J. Zhu, H. Zhou, C. Wang, L. Zhou, S. Yuan, W. Zhang, A review of topology optimization for additive manufacturing: Status and challenges, Chinese Journal of Aeronautics 34/1 (2021) 91-110. DOI: https://doi.org/10.1016/j.cja.2020.09.020
  • 3. T. Wohlers, T. Gornet, History of additive manufacturing, Wohlers Report, Wohlers Associates, Fort Collins, 2014.
  • 4. A.C. Taylor, S. Beirne, G. Alici, G.G. Wallace, System and process development for coaxial extrusion in fused deposition modelling, Rapid Prototyping Journal 23/3 (2017) 543-550. DOI: https://doi.org/10.1108/RPJ-10-2015-0141
  • 5. I. Yadroitsev, I. Yadroitsava, A. du Plessis, E. MacDonald, Fundamentals of Laser Powder Bed Fusion of Metals, Elsevier, Amsterdam, 2021. DOI: https://doi.org/10.1016/C2020-0-01200-4
  • 6. Y. Zheng, Y. Wang, R.K. Chen, S. Deshpande, N.S. Nelson, S.R. Buchman, A.J. Shih, Tissue transformation mold design and stereolithography fabrication, Rapid Prototyping Journal 23/1 (2017) 162-168. DOI: https://doi.org/10.1108/RPJ-10-2015-0133
  • 7. N. Muthuram, P. Sriram Madhav, D. Keerthi Vasan, M.E. Mohan, G. Prajeeth, A review of recent literatures in poly jet printing process, Materials Today: Proceedings 68/6 (2022) 1906-1920. DOI: https://doi.org/10.1016/j.matpr.2022.08.090
  • 8. M. Ziaee, N.B. Crane, Binder jetting: A review of process, materials, and methods, Additive Manufacturing 28 (2019) 781-801. DOI: https://doi.org/10.1016/j.addma.2019.05.031
  • 9. A. Pilipović, Sheet lamination, in: J. Izdebska-Podsiadły (ed), Polymers for 3D Printing: Methods, Properties, and Characteristics, William Andrew Publishing, Norwich, 2022, 127-136. DOI: https://doi.org/10.1016/B978-0-12-818311-3.00008-2
  • 10. A. Dass, A. Moridi, State of the Art in Directed Energy Deposition: From Additive Manufacturing to Materials Design, Coatings 9/7 (2019) 418. DOI: https://doi.org/10.3390/coatings9070418
  • 11. O. Lkadi, M. Nassraoui, O. Bouksour, An Overview on Additive Manufacturing : Technologies , Materials and Applications, Uncertainties and Reliability of Multiphysical Systems 6/2 (2022) 1-15 (in French). DOI: https://doi.org/10.21494/ISTE.OP.2022.0881
  • 12. O. Sigmund, K. Maute, Topology optimization approaches: A comparative review, Structural and Multidisciplinary Optimization 48/6 (2013) 1031-1055. DOI: https://doi.org/10.1007/s00158-013-0978-6
  • 13. M.P. Bendsoe, N. Kikuchi, Generating optimal topologies in structural design using a homogenization method, Computer Methods in Applied Mechanics and Engineering 71/2 (1988) 197-224. DOI: https://doi.org/10.1016/0045-7825(88)90086-2
  • 14. M.P. Bendsoe, O. Sigmund, Material interpolation schemes in topology optimization, Archive of Applied Mechanics 69/9-10 (1999) 635-654. DOI: https://doi.org/10.1007/S004190050248
  • 15. M. Stolpe, K. Svanberg, An alternative interpolation scheme for minimum compliance topology optimization, Structural and Multidisciplinary Optimization 22/2 (2001) 116-124. DOI: https://doi.org/10.1007/s001580100129
  • 16. X. Dai, P. Tang, X. Cheng, M. Wu, A variational binary level set method for structural topology optimization, Communications in Computational Physics 13/5 (2013) 1292-1308. DOI: https://doi.org/10.4208/cicp.160911.110512a
  • 17. Y.M. Xie, G.P. Steven, A simple evolutionary procedure for structural optimization, Computers and Structures 49/5 (1993) 885-896. DOI: https://doi.org/10.1016/0045-7949(93)90035-C
  • 18. L. Xia, Q. Xia, X. Huang, Y.M. Xie, Bi-directional Evolutionary Structural Optimization on Advanced Structures and Materials: A Comprehensive Review, Archives of Computational Methods in Engineering 25/2 (2018) 437-478. DOI: https://doi.org/10.1007/s11831-016-9203-2
  • 19. W. Zhang, Y. Zhou, J. Zhu, A comprehensive study of feature definitions with solids and voids for topology optimization, Computer Methods in Applied Mechanics and Engineering 325 (2017) 289-313. DOI: https://doi.org/10.1016/j.cma.2017.07.004
  • 20. X. Guo, W. Zhang, W. Zhong, Doing topology optimization explicitly and geometrically: A new moving morphable components based framework, Frontiers in Applied Mechanics 81/8 (2014) 31-32. DOI: https://doi.org/10.1142/9781783266852_0016
  • 21. W. Zhang, J. Chen, X. Zhu, J. Zhou, D. Xue, X. Lei, X. Guo, Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach, Computer Methods in Applied Mechanics and Engineering 322 (2017) 590-614. DOI: https://doi.org/10.1016/j.cma.2017.05.002
  • 22. G. Kazakis, N.D. Lagaros, Topology Optimization Based Material Design for 3D Domains Using MATLAB, Applied Sciences 12/21 (2022) 10902. DOI: https://doi.org/10.3390/app122110902
  • 23. M. Abdi, R. Wildman, I. Ashcroft, Evolutionary topology optimization using the extended finite element Method and isolines, Engineering Optimization 46/5 (2014) 628-647. DOI: https://doi.org/10.1080/0305215X.2013.791815
  • 24. K. Liu, A. Tovar, An efficient 3D topology optimization code written in Matlab, Structural and Multidisciplinary Optimization 50/6 (2014) 1175-1196. DOI: https://doi.org/10.1007/s00158-014-1107-x
  • 25. N. Nima, A. Iman, H. Iman, K. Mehmet Metin, A new hybrid method for size and topology optimization, Journal of Civil Engineering and Management 23/2 (2017) 252-262. DOI: https://doi.org/10.3846/13923730.2015.1075420
  • 26. L. Xia, D. Da, J. Yvonnet, Topology optimization for maximizing the fracture resistance of quasi-brittle composites, Computer Methods in Applied Mechanics and Engineering 332 (2018) 234-254. DOI: https://doi.org/10.1016/j.cma.2017.12.021
  • 27. N. Noii, H.A. Jahangiry, H. Waisman, Level-set topology optimization for Ductile and Brittle fracture resistance using the phase-field method, Computer Methods in Applied Mechanics and Engineering 409 (2023) 115963. DOI: https://doi.org/10.1016/j.cma.2023.115963
  • 28. F. Wang, B.S. Lazarov, O. Sigmund, On projection methods, convergence and robust formulations in topology optimization, Structural and Multidisciplinary Optimization 43/6 (2011) 767-784. DOI: https://doi.org/10.1007/s00158-010-0602-y
  • 29. Z. Liu, J.G. Korvink, R. Huang, Structure topology optimization: Fully coupled level set method via FEMLAB, Structural and Multidisciplinary Optimization 29/6 (2005) 407-417. DOI: https://doi.org/10.1007/s00158-004-0503-z
  • 30. G. Allaire, O. Pantz, Structural optimization with FreeFem++, Structural and Multidisciplinary Optimization 32/3 (2006) 173-181. DOI: https://doi.org/10.1007/s00158-006-0017-y
  • 31. V.J. Challis, A discrete level-set topology optimization code written in Matlab, Structural and Multidisciplinary Optimization 41/3 (2010) 453-464. DOI: https://doi.org/10.1007/s00158-009-0430-0
  • 32. W. Hunter, Predominantly solid-void three-dimensional topology optimisation using open source software, MSc Thesis, University of Stellenbosch, Stellenbosch, 2009.
  • 33. T. Sokol, A 99 line code for discretized Michell truss optimization written in Mathematica, Structural and Multidisciplinary Optimization 43/2 (2011) 181-190. DOI: https://doi.org/10.1007/s00158-010-0557-z
  • 34. C. Talischi, G.H. Paulino, A. Pereira, I.F.M. Menezes, PolyTop: a Matlab implementation of a general topology optimization framework using unstructured polygonal finite element meshes, Structural and Multidisciplinary Optimization 45/3 (2012) 329-357. DOI: https://doi.org/10.1007/s00158-011-0696-x
  • 35. E. Andreassen, A. Clausen, M. Schevenels, B.S. Lazarov, O. Sigmund, Efficient topology optimization in MATLAB using 88 lines of code, Structural and Multidisciplinary Optimization 43/1 (2011) 1-16. DOI: https://doi.org/10.1007/s00158-010-0594-7
  • 36. M. Otomori, T. Yamada, K. Izui, S. Nishiwaki, Matlab code for a level set-based topology optimization method using a reaction diffusion equation, Structural and Multidisciplinary Optimization 51/5 (2015) 1159-1172. DOI: https://doi.org/10.1007/s00158-014-1190-z
  • 37. N. Olhoff, On optimum design of structures and materials, Meccanica 31/2 (1996) 143-161. DOI: https://doi.org/10.1007/BF00426257
  • 38. Abdi, I. Ashcroft, R.D. Wildman, Design optimisation for an additively manufactured automotive component, International Journal of Powertrains 7/1-3 (2018) 142-161. DOI: https://doi.org/10.1504/IJPT.2018.090371
  • 39. D. Yago, J. Cante, O. Lloberas-Valls, J. Oliver, Topology Optimization Methods for 3D Structural Problems: A Comparative Study, Archives of Computational Methods in Engineering 29/3 (2022) 1525-1567. DOI: https://doi.org/10.1007/s11831-021-09626-2
  • 40. M.P. Bendsoe, O. Sigmund, Topology optimization: theory, methods, and applications, Topology optimization: theory, methods, and applications, 2004. DOI: https://doi.org/10.1007/978-3-662-05086-6
  • 41. A. Makrizi, B. Radi, A. El Hami, Solution of the Topology Optimization Problem Based Subdomains Method, Applied Mathematical Sciences 2/41 (2008) 2029-2045.
  • 42. O. Ibhadode, Z. Zhang, A. Bonakdar, E. Toyserkani, IbIPP for topology optimization—An Image-based Initialization and Post-Processing code written in MATLAB, SoftwareX 14 (2021) 100701. DOI: https://doi.org/10.1016/j.softx.2021.100701
  • 43. L. Xia, P. Breitkopf, Concurrent topology optimization design of material and structure within FE2 nonlinear multiscale analysis framework, Computer Methods in Applied Mechanics and Engineering 278 (2014) 524-542. DOI: https://doi.org/10.1016/j.cma.2014.05.022
  • 44. G. Allaire, F. Jouve, A.-M. Toader, Structural optimization using sensitivity analysis and a level-set method, Journal of Computational Physics 194/1 (2004) 363-393. DOI: https://doi.org/10.1016/j.jcp.2003.09.032
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cc903608-0fb2-4a1e-b927-b5cf952c22e9
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