nach oben

The International Journal of Advanced Manufacturing Technology

Erschienen in:

14.09.2021 | ORIGINAL ARTICLE

Generative design of truss systems by the integration of topology and shape optimisation

verfasst von: Marcus Watson, Martin Leary, Milan Brandt

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 3-4/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Generative design refers to the automated design of components through the use of computer-aided engineering (CAE) tools. This is an enabling technology which allows reduced lead times in component design, particularly for custom and unique parts; improved certification of components; efficient exploration of the design space; and results in optimised design outcomes. Topology optimisation (TO) is a systematic tool for defining efficient material distributions for given boundary conditions, whereas shape optimisation (SO) is useful for refining identified part geometries with respect to design constraints. These qualities make TO and SO suitable for automating the embodiment and detailed design phases respectively for use in generative design. However, integration of these tools is hindered by challenges in the parameterisation of TO solutions, which are typically manually resolved. This manual resolution imposes a limit on the rate in which data can be generated as well as the scope of the solution space. In response to this challenge, a novel parameterisation method is proposed to directly integrate topology and shape optimisation to achieve an automated design process. The benefits of the proposed method are reduced computational cost and improved automation, which is suitable for investigating a large number of potential solutions and selecting and refining an optimal design for a given loading condition. The proposed method has been used within this paper for the design of 2D trusses as a proof of concept.

Vorheriger Artikel Understanding the surface integrity of laser surface engineered tungsten carbide

Nächster Artikel New directions for inline inspection of automobile laser welds using non-destructive testing

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Frazer J (2002) Chapter 9 - Creative design and the generative evolutionary paradigm. In: Bentley PJ, Corne DW (eds) Creative Evolutionary Systems. Morgan Kaufmann, San Francisco, pp 253–274CrossRef

Feinstein JL (1989) Introduction to expert systems. J Policy Anal Manag 8(2):182–187CrossRef

Tan CF, Wahidin LS, Khalil SN, Tamaldin N, Hu J, Rauterberg M (2016) The application of expert system: a review of research and applications 11 2448-2453

Crilly N, Cardoso C (2017) Where next for research on fixation, inspiration and creativity in design? Des Stud 50:1–38. https://doi.org/10.1016/j.destud.2017.02.001CrossRef

Alcaide-Marzal J, Diego-Mas JA, Acosta-Zazueta G (2020) A 3D shape generative method for aesthetic product design. Des Stud 66:144–176. https://doi.org/10.1016/j.destud.2019.11.003CrossRef

Rodrigues E, Amaral AR, Gaspar AR, Gomes Á (2015) An approach to urban quarter design using building generative design and thermal performance optimization. Energy Procedia 78:2899–2904. https://doi.org/10.1016/j.egypro.2015.11.662CrossRef

Jiang L, Chen S, Sadasivan C, Jiao X (2017) Structural topology optimization for generative design of personalized aneurysm implants: design, additive manufacturing, and experimental validation, in 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) 6-8 Nov. 2017 2017, 9-13. https://doi.org/10.1109/HIC.2017.8227572

Lohan DJ, Dede EM, Allison JT et al (2017) Struct Multidiscip Optim 55(3):1063–1077. https://doi.org/10.1007/s00158-016-1563-6MathSciNetCrossRef

Oh S, Jung Y, Kim S, Lee I, Kang N (2019) Deep Generative design: integration of topology optimization and generative models. J Mech Des 141:1. https://doi.org/10.1115/1.4044229CrossRef

10.

Salta S, Papavasileiou N, Pyliotis K, Katsaros M (2020) Adaptable emergency shelter: a case study in generative design and additive manufacturing in mass customization era. Procedia Manufact 44:124–131. https://doi.org/10.1016/j.promfg.2020.02.213CrossRef

11.

Zimmermann L, Chen T, Shea K (2018) A 3D, performance-driven generative design framework: automating the link from a 3D spatial grammar interpreter to structural finite element analysis and stochastic optimization. Artificial Intel Eng Design Anal Manufact AIEDAM 32(2):189–199. https://doi.org/10.1017/S0890060417000324CrossRef

12.

Kielarova SW, Sansri S (2016) Shape optimization in product design using interactive genetic algorithm integrated with multi-objective optimization. In: Sombattheera C, Stolzenburg F, Lin F, Nayak A (eds) Multi-disciplinary Trends in Artificial Intelligence. Springer International Publishing, Cham, pp 76–86CrossRef

13.

Balling RJ, Briggs RR, Gillman K (2006) Multiple optimum size/shape/topology designs for skeletal structures using a genetic algorithm. J Struct Eng (New York, NY) 132(7):1158–1165. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:7(1158)CrossRef

14.

Troiano L, Birtolo C (2014) Genetic algorithms supporting generative design of user interfaces: examples. Inf Sci 259:433–451. https://doi.org/10.1016/j.ins.2012.01.006CrossRef

15.

Leary M (2019) Design for additive manufacturing, 1st edn. Elsevier Science Ltd, United States

16.

Di Nicolantonio M, Rossi E, Stella P (2020) Generative design for printable mass customization jewelry products. Adv Intel Syst Comput 975:143–152CrossRef

17.

Khan S, Awan MJ (2018) A generative design technique for exploring shape variations. Adv Eng Inform 38:712–724. https://doi.org/10.1016/j.aei.2018.10.005CrossRef

18.

Wannarumon S, Pradujphongphet P, Bohez ILJ (2014) An approach of generative design system: jewelry design application. IEEE Int Conf Indust Eng Eng Manag:1329–1333. https://doi.org/10.1109/IEEM.2013.6962626

19.

Wannarumon S, Pradujphongphet P, Bohez E (2015) New Interactive-generative design system: hybrid of shape grammar and evolutionary design - An Application of Jewelry Design. 302-313

20.

Krish S (2011) A practical generative design method. Comput Aided Des 43(1):88–100. https://doi.org/10.1016/j.cad.2010.09.009CrossRef

21.

Michell AGM (1904) LVIII The limits of economy of material in frame-structures. London Edinburgh Dublin Phil Mag J Sci 8(47):589–597. https://doi.org/10.1080/14786440409463229CrossRefMATH

22.

Bendsøe MP, Kikuchi N (1988) Generating optimal topologies in structural design using a homogenization method. Comput Methods Appl Mech Eng 71(2):197–224. https://doi.org/10.1016/0045-7825(88)90086-2MathSciNetCrossRefMATH

23.

Bendsøe MP, Sigmund O (1999) Material interpolation schemes in topology optimization. Arch Appl Mech 69(9):635–654. https://doi.org/10.1007/s004190050248CrossRefMATH

24.

Huang X, Xie M (2010) Evolutionary topology optimization of continuum structures: methods and applications. WileyCrossRef

25.

Wang MY, Wang X, Guo D (2003) A level set method for structural topology optimization. Comput Methods Appl Mech Eng 192(1):227–246. https://doi.org/10.1016/S0045-7825(02)00559-5MathSciNetCrossRefMATH

26.

GR DW, Greenberg H (1964) Automatic design of optimal structures. J Mecanique 3(1):25–52

27.

Bendsoe MPA, Sigmund O (2004) Topology optimization theory, methods, and applications, 2^nd Ed Corrected Printing. ed. Springer, Berlin Imprint: SpringerMATH

28.

K. Suresh, A 199-line Matlab code for Pareto-optimal tracing in topology optimization. 2010, pp. 665-679.

29.

Zegard T, Paulino GH (2014) GRAND — Ground structure based topology optimization for arbitrary 2D domains using MATLAB. Struct Multidiscip Optim 50(5):861–882. https://doi.org/10.1007/s00158-014-1085-zMathSciNetCrossRef

30.

Lin M-H, Tsai J-F, Yu C-S (2012) A review of deterministic optimization methods in engineering and management. Math Probl Eng 2012:1–15. https://doi.org/10.1155/2012/756023MathSciNetCrossRefMATH

31.

Horn BM, Lüthen H, Pfetsch ME, Ulbrich S (2017) Geometry and topology optimization of sheet metal profiles by using a branch-and-bound framework: Geometrie- und Topologieoptimierung für Blechprofile unter Verwendung eines Branch-and-Bound-Frameworks. Mater Werkst 48(1):27–40. https://doi.org/10.1002/mawe.201600762CrossRef

32.

Horst R, Tuy H (1990) Branch and Bound. In: Horst R, Tuy H (eds) Global Optimization: Deterministic Approaches. Springer, Berlin, pp 111–172CrossRef

33.

Niebling J, Eichfelder G (2019) A branch--and--bound-based algorithm for nonconvex multiobjective optimization. SIAM J Optim 29(1):794–821. https://doi.org/10.1137/18M1169680MathSciNetCrossRefMATH

34.

Scholz D (2012) The geometric branch-and-bound algorithm. In: Scholz D (ed) Deterministic Global Optimization: Geometric Branch-and-bound Methods and their Applications. Springer New York, New York, pp 15–24CrossRef

35.

Vieira DAG, Lisboa AC (2019) A cutting-plane method to nonsmooth multiobjective optimization problems. Eur J Oper Res 275(3):822–829. https://doi.org/10.1016/j.ejor.2018.12.047MathSciNetCrossRefMATH

36.

Patzold J, Schobel A (2020) Approximate cutting plane approaches for exact solutions to robust optimization problems. Eur J Oper Res 284(1):20–30. https://doi.org/10.1016/j.ejor.2019.11.059MathSciNetCrossRefMATH

37.

Liu A, Yang R, Quek TQS, Zhao M-j (2021) Two-stage stochastic optimization via primal-dual decomposition and deep unrolling. IEEE Trans Signal Process:1–1. https://doi.org/10.1109/TSP.2021.3079807

38.

Hamdi A (2005) Two-level primal–dual proximal decomposition technique to solve large scale optimization problems. Appl Math Comput 160(3):921–938. https://doi.org/10.1016/j.amc.2003.11.040MathSciNetCrossRefMATH

39.

Stolpe M (2015) Truss topology optimization with discrete design variables by outer approximation. J Glob Optim 61(1):139–163. https://doi.org/10.1007/s10898-014-0142-xMathSciNetCrossRefMATH

40.

Varvarezos DK, Grossmann IE, Biegler LT (1992) An outer-approximation method for multiperiod design optimization. Ind Eng Chem Res 31(6):1466–1477. https://doi.org/10.1021/ie00006a008CrossRef

41.

Csirmaz L Inner approximation algorithm for solving linear multiobjective optimization problems. Optimization, vol. ahead-of-print, no. ahead-of-print pp. 1-25. https://doi.org/10.1080/02331934.2020.1737692

42.

Yamada S, Tanino T, Inuiguchi M (2000) An inner approximation method for optimization over the weakly efficient set. J Glob Optim 16(3):197–217. https://doi.org/10.1023/A:1008336124425MathSciNetCrossRefMATH

43.

T. Liu, T. K. Pong, and A. Takeda, "A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems," 2017.

44.

Bunin GA (2016) Extended reverse-convex programming: an approximate enumeration approach to global optimization. J Glob Optim 65(2):191–229. https://doi.org/10.1007/s10898-015-0352-xMathSciNetCrossRefMATH

45.

Henderson D, Smith JC (2009) An exact reformulation-linearisation technique algorithm for solving a parameter extraction problem arising in compact thermal models. Optim Method Softw 24(4-5):857–870. https://doi.org/10.1080/10556780802616924MathSciNetCrossRefMATH

46.

Gergel VP (1997) A global optimization algorithm for multivariate functions with lipschitzian first derivatives. J Glob Optim 10(3):257–281. https://doi.org/10.1023/A:1008290629896MathSciNetCrossRefMATH

47.

Mockus J, Paulavičius R, Rusakevičius D, Šešok D, Žilinskas J (2017) Application of reduced-set pareto-lipschitzian optimization to truss optimization. J Glob Optim 67(1):425–450. https://doi.org/10.1007/s10898-015-0364-6MathSciNetCrossRefMATH

48.

Alexandropoulos S-AN, Pardalos PM, Vrahatis MN (2020) Dynamic search trajectory methods for global optimization. Ann Math Artif Intell 88(1-3):3–37. https://doi.org/10.1007/s10472-019-09661-7MathSciNetCrossRefMATH

49.

Hillermeier C, Mittelmann D, Bank RE (2001) Nonlinear multiobjective optimization: a generalized homotopy approach (International Series of Numerical Mathematics). Springer Basel AG, BaselCrossRef

50.

Floudas CA (2000) Deterministic global optimization theory, methods and applications, 1st ed. 2000. ed. (Nonconvex Optimization and Its Applications, 37). Springer, New York

51.

Jiang C, Han X, Guan FJ, Li YH (2007) An uncertain structural optimization method based on nonlinear interval number programming and interval analysis method. Eng Struct 29(11):3168–3177. https://doi.org/10.1016/j.engstruct.2007.01.020CrossRef

52.

Yildiz AR, Abderazek H, Mirjalili S (2020) A Comparative study of recent non-traditional methods for mechanical design optimization. Arch Comput Methods Eng 27(4):1031–1048. https://doi.org/10.1007/s11831-019-09343-xMathSciNetCrossRef

53.

Holland JH (2019) Adaptation in natural and artificial systems - an introductory analysis with applications to biology, control, and artificial I (Complex Adaptive Systems). The MIT Press

54.

Beyer H-G, Schwefel H-P (2002) Evolution strategies – a comprehensive introduction. Nat Comput 1(1):3–52. https://doi.org/10.1023/A:1015059928466MathSciNetCrossRefMATH

55.

Storn R, Price K (1997) Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328MathSciNetCrossRefMATH

56.

B. F. David, Artificial intelligence through simulated evolution," in Evolutionary Computation: The Fossil Record: IEEE, 1998, pp. 227-296.

57.

Langdon WB, Poli R (2002) Foundations of Genetic Programming. Springer, BerlinCrossRef

58.

Slowik A, Kwasnicka H (2020) Evolutionary algorithms and their applications to engineering problems. Neural Comput & Applic 32(16):12363–12379. https://doi.org/10.1007/s00521-020-04832-8CrossRef

59.

Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithms. PLoS One 10(5):e0122827–e0122827. https://doi.org/10.1371/journal.pone.0122827CrossRef

60.

Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization: artificial ants as a computational intelligence technique. IEEE Comput Intell Mag 1:28–39. https://doi.org/10.1109/CI-M.2006.248054CrossRef

61.

Yıldız AR (2009) A novel particle swarm optimization approach for product design and manufacturing. Int J Adv Manuf Technol 40(5):617–628. https://doi.org/10.1007/s00170-008-1453-1CrossRef

62.

Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906–2912. https://doi.org/10.1016/j.asoc.2012.04.013CrossRef

63.

Y. Xin-She and S. Deb, "Cuckoo search via Lévy flights," ed: IEEE, 2009, pp. 210-214.

64.

Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3(2):87–124. https://doi.org/10.1007/s11721-008-0021-5CrossRef

65.

Kirkpatrick S, Gelatt JCD, Vecchi MP (1983) Optimization by simulated annealing. Sc Am Assoc Adv Sci 220(4598):671–680. https://doi.org/10.1126/science.220.4598.671MathSciNetCrossRefMATH

66.

Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004CrossRefMATH

67.

Gentle JE (2013) Random number generation and Monte Carlo methods (Statistics and computing). Springer

68.

O. K. Erol and I. Eksin, "A new optimization method: Big Bang–Big Crunch," Adv Eng Softw (1992), vol. 37, no. 2, pp. 106-111, https://doi.org/10.1016/j.advengsoft.2005.04.005.

69.

Haifeng DU, Xiaodong WU, Jian Z (2006) “Small-World optimization algorithm for function optimization,” ed. Springer, Berlin

70.

S. Ruder, "An overview of gradient descent optimization algorithms," 2016.

71.

M. D. Zeiler, ADADELTA: an adaptive learning rate method. 2012.

72.

Liu S, Li Q, Liu J, Chen W, Zhang Y (2018) A realization method for transforming a topology optimization design into additive manufacturing structures. Engineering 4(2):277–285. https://doi.org/10.1016/j.eng.2017.09.002CrossRef

73.

Nana A, Cuillière J-C, Francois V (2017) Automatic reconstruction of beam structures from 3D topology optimization results. Comput Struct 189:62–82. https://doi.org/10.1016/j.compstruc.2017.04.018CrossRef

74.

Kumar AV, Gossard DC (1996) Synthesis of optimal shape and topology of structures. J Mech Des 118(1):68–74. https://doi.org/10.1115/1.2826858CrossRef

75.

Hsu Y-L, Hsu M-S, Chen C-T (2001) Interpreting results from topology optimization using density contours. Comput Struct 79(10):1049–1058. https://doi.org/10.1016/S0045-7949(00)00194-2CrossRef

76.

Hsu M-H, Hsu Y-L (2005) Interpreting three-dimensional structural topology optimization results. Comput Struct 83(4):327–337. https://doi.org/10.1016/j.compstruc.2004.09.005CrossRef

77.

Marsan AL, Dutta D (1996) Construction of a surface model and layered manufacturing data from 3D homogenization output. J Mech Des 118(3):412–418. https://doi.org/10.1115/1.2826901CrossRef

78.

Tang P-S, Chang K-H (2001) Integration of topology and shape optimization for design of structural components. Struct Multidiscip Optim 22(1):65–82. https://doi.org/10.1007/PL00013282CrossRef

79.

Papalambros PY, Chirehdast M (1990) An Integrated environment for structural configuration design. J Eng Des 1(1):73–96. https://doi.org/10.1080/09544829008901645CrossRef

80.

Chang KH, Tang PS (2001) Integration of design and manufacturing for structural shape optimization. Adv Eng Softw 32(7):555–567. https://doi.org/10.1016/S0965-9978(00)00103-4CrossRefMATH

81.

Koguchi A, Kikuchi N (2006) A surface reconstruction algorithm for topology optimization. Eng Comput 22(1):1–10. https://doi.org/10.1007/s00366-006-0023-0CrossRef

82.

Chacón JM, Bellido JC, Donoso A (2014) Integration of topology optimized designs into CAD/CAM via an IGES translator. Struct Multidiscip Optim 50(6):1115–1125. https://doi.org/10.1007/s00158-014-1099-6CrossRef

83.

Larsen S, Jensen CG (2009) Converting Topology optimization results into parametric CAD models. Comput Aided Design Appl 6(3):407–418. https://doi.org/10.3722/cadaps.2009.407-418CrossRef

84.

Lin CY, Chao LS (2000) Automated image interpretation for integrated topology and shape optimization. Struct Multidiscip Optim 20(2):125–137. https://doi.org/10.1007/s001580050144CrossRef

85.

Yi G, Kim NH (2017) Identifying boundaries of topology optimization results using basic parametric features. Struct Multidiscip Optim 55(5):1641–1654. https://doi.org/10.1007/s00158-016-1597-9CrossRef

86.

Cormen TH (2009) Introduction to algorithms, 3rd edn. MIT Press, Cambridge, MassMATH

87.

Zhang JYA, Ohsaki M (2015) Tensegrity structures form, stability, and symmetry. Springer Japan: Imprint: Springer, TokyoCrossRef

88.

Gonzalez RCA, Woods RE (2018) Digital image processing, 4th edn. Pearson, New York

89.

Sedgewick R, Wayne KD (2011) Algorithms, 4th edn. Addison-Wesley, Upper Saddle River

Titel: Generative design of truss systems by the integration of topology and shape optimisation
verfasst von: Marcus Watson
Martin Leary
Milan Brandt
Publikationsdatum: 14.09.2021
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 3-4/2022
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-07943-1

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3-4/2022

A novel sensitivity analysis of translational axis operation considering key component tolerances

Coordinate control of strip thickness-crown-tension based on inverse linear quadratic in tandem hot rolling mill

Development of an empirical process model for adjusted porosity in laser-based powder bed fusion of Ti-6Al-4V

Tool remaining useful life prediction using deep transfer reinforcement learning based on long short-term memory networks

Characterization of Thrust Force, Temperature, Chip Morphology and Power in Ultrasonic-assisted Drilling of Aluminium 6061

Investigation into deep hole drilling of austenitic steel with advanced tool solutions

Premium Partner

Marktübersichten