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Research Article

Integrated vehicle chassis fabricated by wire and arc additive manufacturing: structure generation, printing radian optimisation, and performance prediction

ORCID Icon, ORCID Icon, , ORCID Icon, &
Article: e2301483 | Received 03 Oct 2023, Accepted 29 Dec 2023, Published online: 10 Jan 2024

References

  • Gibson I, Rosen D, Stucker B. Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing. Cham: Springer; 2015.
  • Thompson MK, Moroni G, Vaneker T, et al. Design for additive manufacturing: trends, opportunities, considerations, and constraints. CIRP Ann. 2016;65(2):737–760. doi:10.1016/j.cirp.2016.05.004
  • Vaneker T, Bernard A, Moroni G, et al. Design for additive manufacturing: framework and methodology. CIRP Ann. 2020;69(2):578–599. doi:10.1016/j.cirp.2020.05.006
  • Aage N, Andreassen E, Lazarov BS, et al. Giga-voxel computational morphogenesis for structural design. Nature. 2017;550(7674):84–86. doi:10.1038/nature23911
  • Smith CC, Gilbert M, et al. Application of discontinuity layout optimization to plane plasticity problems. P Roy Soc A-Math Phy. 2007;463:2461–2484. doi:10.1098/rspa.2013.0009
  • Wang Z, Zhang Y, Bernard A, et al. A constructive solid geometry-based generative design method for additive manufacturing. Addit Manuf. 2021;41:101952. doi:10.1016/j.addma.2021.101952
  • Wang YQ, Wang A. On the topological yarn structure of 3-D rectangular and tubular braided preforms. Compos Sci Technol. 1994;51(4):575–586. doi:10.1016/0266-3538(94)90090-6
  • Wu J, Aage N, Westermann R, et al. Infill optimization for additive manufacturing-approaching bone-like porous structures. Ieee T Vis Comput Gr. 2018;24(2):1127–1140. doi:10.1109/TVCG.2017.2655523
  • Guo X, Zhang W, Zhong W. Doing topology optimization explicitly and geometrically—a new moving morphable components based framework. J Appl Mech. 2014;81(8):081009. doi:10.1115/1.4027609
  • Bai J, Zuo W. Hollow structural design in topology optimization via moving morphable component method. Struct Multidiscip O. 2020;61(1):187–205. doi:10.1007/s00158-019-02353-0
  • Zhang W, Yu TX, Xu J. Uncover the underlying mechanisms of topology and structural hierarchy in energy absorption performances of bamboo-inspired tubular honeycomb. Extreme Mech Lett. 2022;52:101640. doi:10.1016/j.eml.2022.101640
  • Jayanath S, Achuthan A. A computationally efficient finite element framework to simulate additive manufacturing processes. J Manuf Sci E-T Asme. 2018;140(4). doi:10.1115/1.4039092
  • Rai A, Helmer H, Körner C. Simulation of grain structure evolution during powder bed based additive manufacturing. Addit manuf. 2016: 13. doi:10.1016/j.addma.2016.10.007
  • Lu L-X, Sridhar N, Zhang Y-W. Phase field simulation of powder bed-based additive manufacturing. Acta Mater. 2018;144:801–809. doi:10.1016/j.actamat.2017.11.033
  • Rodgers TM, Madison JD, Tikare V. Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo. Comp Mater Sci. 2017;135:78–89. doi:10.1016/j.commatsci.2017.03.053
  • Zinovieva O, Zinoviev A, Ploshikhin V. Three-dimensional modeling of the microstructure evolution during metal additive manufacturing. Comp Mater Sci; 141:207–220. doi:10.1016/j.commatsci.2017.09.018
  • Liu J, To AC. Quantitative texture prediction of epitaxial columnar grains in additive manufacturing using selective laser melting. Addit Manuf. 2017;16:58–64. doi:10.1016/j.addma.2017.05.005
  • Rappaz M, Gandin C-A. Probabilistic modelling of microstructure formation in solidification processes. Acta Metall Mater. 1993;41:345–360. doi:10.1016/0956-7151(93)90065-Z
  • Gandin CA, Rappaz M, Tintillier R. Three-dimensional probabilistic simulation of solidification grain structures: application to superalloy precision castings. Metall Trans A. 1993;24(2):467–479. doi:10.1007/BF02657334
  • Koepf JA, Soldner D, Ramsperger M, et al. Numerical microstructure prediction by a coupled finite element cellular automaton model for selective electron beam melting. Comp Mater Sci. 2019;162:148–155. doi:10.1016/j.commatsci.2019.03.004
  • Lian Y, Gan Z, Yu C, et al. A cellular automaton finite volume method for microstructure evolution during additive manufacturing. Mater Des. 2019;169(6):107672. doi:10.1016/j.matdes.2019.107672
  • Zinoviev A, Zinovieva O, Ploshikhin V, et al. Evolution of grain structure during laser additive manufacturing. Simulation by a cellular automata method. Mater Des. 2016;106:321–329. doi:10.1016/j.matdes.2016.05.125
  • Kaji F, Jinoop AN, Zardoshtian A, et al. Robotic laser directed energy deposition-based additive manufacturing of tubular components with variable overhang angles: adaptive trajectory planning and characterization. Addit Manuf. 2023;61(7):103366. doi:10.1016/j.addma.2022.103366
  • Guidetti X, Balta EC, Nagel Y, et al. Stress flow guided non-planar print trajectory optimization for additive manufacturing of anisotropic polymers. Addit Manuf. 2023;72(5):103628. doi:10.1016/j.addma.2023.103628
  • Huang Y, Fang G, Zhang T, et al. Turning-angle optimized printing path of continuous carbon fiber for cellular structures. Addit Manuf. 2023;68:103501. doi:10.1016/j.addma.2023.103501
  • Jin Y-a, He Y, Fu J-z, et al. Optimization of tool-path generation for material extrusion-based additive manufacturing technology. Addit Manuf. 2014;1-4:32–47. doi:10.1016/j.addma.2014.08.004
  • Bartlett JL, Croom BP, Burdick J, et al. Revealing mechanisms of residual stress development in additive manufacturing via digital image correlation. Addit Manuf. 2018;22:1–12. doi:10.1016/j.addma.2018.04.025
  • Wu Q, Li D-P. Analysis and X-ray measurements of cutting residual stresses in 7075 aluminum alloy in high speed machining. Int J Precis Eng Man. 2014;15(8):1499–1506. doi:10.1007/s12541-014-0497-4
  • Kim M-S, Lee S-H, Jung J-G, et al. Prediction of grain structure in direct-chill cast Al–Zn–Mg–Cu billets using cellular automaton-finite element method. Prog Nat Sci-Mater. 2021;31(3):434–441. doi:10.1016/j.pnsc.2021.05.003
  • Song Y, Jiang H, Zhang L, et al. Integrated model for describing the microstructure evolution of the inoculated Al-Zn-Mg-Cu alloys in continuous solidification. Results Phys. 2021;26(2):104465. doi:10.1016/j.rinp.2021.104465
  • Sun J, Hensel J, Köhler M, et al. Residual stress in wire and arc additively manufactured aluminum components. J Manuf Process. 2021;65:97–111. doi:10.1016/j.jmapro.2021.02.021
  • Fernandes RR, van de Werken N, Koirala P, et al. Experimental investigation of additively manufactured continuous fiber reinforced composite parts with optimized topology and fiber paths. Addit Manuf. 2021;44(15):102056. doi:10.1016/j.addma.2021.102056
  • He L, Gilbert M, Song X. A Python script for adaptive layout optimization of trusses. Struct Multidiscip O. 2019;60(2):835–847. doi:10.1007/s00158-019-02226-6
  • Xia L, Bi M, Wu J, et al. Integrated lightweight design method via structural optimization and path planning for material extrusion. Addit Manuf. 2023;62(10):103387. doi:10.1016/j.addma.2022.103387
  • Mohebbi MS, Ploshikhin V. Implementation of nucleation in cellular automaton simulation of microstructural evolution during additive manufacturing of Al alloys. Addit Manuf. 2020;36:101726. doi:10.1016/j.addma.2020.101726
  • Zhu M, Stefanescu D. Virtual front tracking model for the quantitative modeling of dendritic growth in solidification of alloys. Acta Mater. 2007;55(5):1741–1755. doi:10.1016/j.actamat.2006.10.037
  • Meng G, Gong Y, Zhang J, et al. Multi-scale simulation of microstructure evolution during direct laser deposition of Inconel718. Int J Heat Mass Transfer. 2022;191:122798. doi:10.2139/ssrn.4017183
  • Rolchigo MR, LeSar R. Modeling of binary alloy solidification under conditions representative of additive manufacturing. Comp Mater Sci. 2018;150:535–545. doi:10.1016/j.commatsci.2018.04.004
  • Zhang Y, Zhang J. Modeling of solidification microstructure evolution in laser powder bed fusion fabricated 316L stainless steel using combined computational fluid dynamics and cellular automata. Addit manuf. 2019: 28. doi:10.1016/j.addma.2019.06.024
  • Chandra S, Tan X, Narayan RL, et al. A generalised hot cracking criterion for nickel-based superalloys additively manufactured by electron beam melting. Addit Manuf. 2020: 37. doi:10.1016/j.addma.2020.101633
  • Derekar KS, Ahmad B, Zhang X, et al. Effects of process variants on residual stresses in wire arc additive manufacturing of aluminum alloy 5183. J Manuf Sci E-T Asme. 2022;144(7):1–35. doi:10.1115/1.4052930
  • Yamamoto K, Luces JVS, Shirasu K, et al. A novel single-stroke path planning algorithm for 3D printers using continuous carbon fiber reinforced thermoplastics. Addit Manuf. 2022;55(2):102816. doi:10.1016/j.addma.2022.102816
  • Zegard T, Paulino G. GRAND — ground structure based topology optimization for arbitrary 2D domains using MATLAB. Struct Multidiscip Optim. 2014;50:861–882. doi:10.1007/s00158-014-1085-z
  • Hu Z, Xu P, Pang C, et al. Microstructure and mechanical properties of a high-ductility Al-Zn-Mg-Cu aluminum alloy fabricated by wire and arc additive manufacturing. J Mater Eng Perform. 2022;31(8):6459–6472. doi:10.1007/s11665-022-06715-6
  • Park JK, Ardell AJ. Microstructures of the commercial 7075 Al alloy in the T651 and T7 tempers. Metall Trans A. 1983;14(10):1957–1965. doi:10.1007/BF02662363
  • Luo Z, Zhao Y. A survey of finite element analysis of temperature and thermal stress fields in powder bed fusion additive manufacturing. Addit Manuf. 2018;21:318–332. doi:10.1016/j.addma.2018.03.022
  • Cambon C, Bendaoud I, Rouquette S, et al. A WAAM benchmark: from process parameters to thermal effects on weld pool shape, microstructure and residual stresses. Mater Today Commun. 2022;33:104235. doi:10.1016/j.mtcomm.2022.104235
  • Zhang J, Liou F, Seufzer W, et al. A coupled finite element cellular automaton model to predict thermal history and grain morphology of Ti-6Al-4V during direct metal deposition (DMD). Addit Manuf. 2016;11; doi:10.1016/j.addma.2016.04.004
  • Fathi-Hafshejani P, Soltani-Tehrani A, Shamsaei N, et al. Laser incidence angle influence on energy density variations, surface roughness, and porosity of additively manufactured parts. Addit Manuf. 2022;50(11):102572. doi:10.1016/j.addma.2021.102572
  • Yu Y, Kenevisi M, Yan W, et al. Modeling precipitation process of Al-Cu alloy in electron beam selective melting with a 3D cellular automaton model. Addit Manuf. 2020;36:101423. doi:10.1016/j.addma.2020.101423
  • Teferra K, Rowenhorst D. Optimizing the cellular automata finite element model for additive manufacturing to simulate large microstructures. Acta Mater. 2021;213(6):116930. doi:10.1016/j.actamat.2021.116930
  • Diourté A, Bugarin F, Bordreuil C, et al. Continuous three-dimensional path planning (CTPP) for complex thin parts with wire arc additive manufacturing. Addit Manuf. 2021;37:101622. doi:10.1016/j.addma.2020.101622
  • Kalman RE. A new approach to linear filtering and prediction problems. J Basic Eng. 1960;82(1):35–45. doi:10.1115/1.3662552
  • Nault IM, Ferguson G, Nardi A. Multi-axis tool path optimization and deposition modeling for cold spray additive manufacturing. Addit Manuf. 2020;38(3):101779. doi:10.1016/j.addma.2020.101779
  • Wan Q, Yang W, Wang L, et al. Global continuous path planning for 3D concrete printing multi-branched structure. Addit Manuf. 2023;71(3):103581. doi:10.1016/j.addma.2023.103581
  • Suzuki T, Fukushige S, Tsunori M. Load path visualization and fiber trajectory optimization for additive manufacturing of composites. Addit Manuf. 2019;31:100942. doi:10.1016/j.addma.2019.100942