AccScience Publishing / ESAM / Volume 2 / Issue 3 / DOI: 10.36922/ESAM026290013
Submit to ESAM
Cite this article
29
Views
Related Info Links
Google Scholar
More by Authors Links
Phuong Tran
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
REVIEW ARTICLE

Additive manufacturing: A design-led innovation approach for tertiary education and research training

Phuong Tran1* ,  Tian Lan1 ,  Erich Rutz2,3,4 ,  Jianhu Shen1 ,  Yun Lu Tee1
Show Less
1 School of Engineering, RMIT University, Victoria , Australia
2 Department of Paediatrics, Bob Dickens Orthopaedic Center for Education and Research, University of Melbourne, Parkville, Victoria , Australia
3 Department of Orthopaedics, The Royal Children’s Hospital, Melbourne, Victoria , Australia
4 Murdoch Children’s Research Institute, Melbourne, Victoria , Australia
ESAM 2026, 2(3), 026290013 https://doi.org/10.36922/ESAM026290013
Received: 15 July 2026 | Revised: 24 August 2026 | Accepted: 1 September 2026 | Published online: 10 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

Identified as a core technology in the Fourth Industrial Revolution, additive manufacturing (AM or 3D printing) has seen considerable industry growth, which has created a significant need for a skilled workforce. Furthermore, AM education and professional training are also crucial in the dissemination and widespread adoption of this technology beyond conventional manufacturing barriers. To address this demand, various educational institutions have undertaken different initiatives to incorporate AM components into their interdisciplinary engineering curricula. AM is not just about instrumentation and materials, but also involves design, programming, and validation skills. While most AM training courses focus on general knowledge and hands-on laboratory training, there is a lack of innovation frameworks led by digital design, which is arguably one of the core elements of AM technology. In this review, the authors examine global representative AM training and education programs. Additionally, institutional efforts to address this demand are presented through undergraduate and graduate courses employing problem- and project-based pedagogies to equip students with hands-on AM experiences and digital design-led innovation skills.

Graphical abstract
Keywords
3D printing
Design optimisation
Design-led innovation
Project-based pedagogies
Digital design
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
References
  1. Dilberoglu UM, Gharehpapagh B, Yaman U, Dolen M. The Role of Additive Manufacturing in the Era of Industry 4.0. Procedia Manuf. 2017;11:545-554. doi: 10.1016/j.promfg.2017.07.148
  2. Tee YL, Tran P. On bioinspired 4d printing: materials, design and potential applications. Aust J Mech Eng. 2021;19(5):642-652. doi: 10.1080/14484846.2021.1988434
  3. Liu J, Nguyen-Van V, Panda B, Fox K, du Plessis A, Tran P. Additive Manufacturing of Sustainable Construction Materials and Form-finding Structures: A Review on Recent Progresses. 3D Print Addit Manuf. 2022;9(1):12-34. doi: 10.1089/3dp.2020.0331
  4. du Plessis A, Babafemi AJ, Paul SC, Panda B, Tran JP, Broeckhoven C. Biomimicry for 3D concrete printing: A review and perspective. Addit Manuf. 2021;38:101823. doi: 10.1016/j.addma.2020.101823
  5. Wickramasinghe S, Do T, Tran P. FDM-based 3D printing of polymer and associated composite: A review on mechanical properties, defects and treatments. Polymers. 2020;12(7):1529. doi: 10.3390/polym12071529
  6. Maconachie T, Leary M, Tran P, et al. The effect of topology on the quasi-static and dynamic behaviour of SLM AlSi10Mg lattice structures. Int J Adv Manuf Technol. 2022;118(11-12):4085-4104. doi: 10.1007/s00170-021-08203-y
  7. Despeisse M, Minshall T. Skills and Education for Additive Manufacturing: A Review of Emerging Issues. Springer International Publishing; 2017;513:289-297. doi: 10.1007/978-3-319-66923-6_34
  8. Sculpteo.com. The State of 3D Printing Report: 2021. Accessed August 12, 2026. https://www.sculpteo.com/en/ebooks/state-of-3d-printing-report-2021/
  9. Sarvankar SG, Yewale SN. Additive manufacturing in automobile industry. Int J Res Aeronaut Mech Eng. 2019;7(4):1-10.
  10. Peng C, Lan T, Wang H, Do T, Tran P. Impact responses of heat-treated functional graded 3D printed gyroid lattice structures. Prog Addit Manuf. 2026;11(6):6315-6336. doi: 10.1007/s40964-026-01706-8
  11. Lan T, Al-Ketan O, Tran P. Controlled deformation of metallic hollow octet-truss lattice structures. Thin-Walled Struct. 2026;223:114639. doi: 10.1016/j.tws.2026.114639
  12. Blakey-Milner B, Gradl P, Snedden G, et al. Metal additive manufacturing in aerospace: A review. Mater Des. 2021;209:110008. doi: 10.1016/j.matdes.2021.110008
  13. Peng C, Marzocca P, Tran P. Triply periodic minimal surfaces based honeycomb structures with tuneable mechanical responses. Virtual Phys Prototyp. 2023;18(1):e2125879. doi: 10.1080/17452759.2022.2125879
  14. Lan T, Tran P, Nguyen KT, Nguyen-Xuan H, Thai D-K. 3D printed functionally graded hollow lattice structures: Multi-dimensional design for large deformation. Compos Struct. 2025;371:119490. doi: 10.1016/j.compstruct.2025.119490
  15. Javaid M, Haleem A. Additive manufacturing applications in medical cases: A literature based review. Alex J Med. 2018;54(4):411-422. doi: 10.1016/j.ajme.2017.09.003
  16. Lozanovski B, Downing D, Tran P, et al. A Monte Carlo simulation-based approach to realistic modelling of additively manufactured lattice structures. Addit Manuf. 2020;32:101092. doi: 10.1016/j.addma.2020.101092
  17. Lozanovski B, Downing D, Tino R, et al. Image-Based Geometrical Characterization of Nodes in Additively Manufactured Lattice Structures. 3D Print Addit Manuf. 2021;8(1):51-68. doi: 10.1089/3dp.2020.0091
  18. Derakhshanfar S, Mbeleck R, Xu K, Zhang X, Zhong W, Xing M. 3D bioprinting for biomedical devices and tissue engineering: A review of recent trends and advances. Bioact Mater. 2018;3(2):144-156. doi: 10.1016/j.bioactmat.2017.11.008
  19. Murphy SV, De Coppi P, Atala A. Opportunities and challenges of translational 3D bioprinting. Nat Biomed Eng. 2020;4(4):370-380. doi: 10.1038/s41551-019-0471-7
  20. Sun B, Ma Q, Wang X, Liu J, Rejab MRM. Additive manufacturing in medical applications: A brief review. IOP Conf Ser Mater Sci Eng. 2021;1078(1):012007. doi: 10.1088/1757-899X/1078/1/012007
  21. Delgado Camacho D, Clayton P, O’Brien WJ, et al. Applications of additive manufacturing in the construction industry – A forward-looking review. Autom Constr. 2018;89:110-119. doi: 10.1016/j.autcon.2017.12.031
  22. Vanderploeg A, Lee SE, Mamp M. The application of 3D printing technology in the fashion industry. Int J Fash Des Technol Educ. 2017;10(2):170-179. doi: 10.1080/17543266.2016.1223355
  23. Inkwoodresearch.com. Global 3D printing market forecast 2019-2027. Accessed March 9, 2022. https://inkwoodresearch.com/reports/3d-printing-market/#report-summary
  24. Statista.com. Number of 3D printing & additive manufacturing devices worldwide from 2020 to 2030. Accessed March 9, 2022. https://www.statista.com/statistics/1259618/3d-printing-and-additive-manufacturing-devices-worldwide/
  25. Grandviewresearch.com. Additive Manufacturing Market (2026 - 2033). Accessed August 10, 2026. https://www.grandviewresearch.com/industry-analysis/additive-manufacturing-market
  26. SAM. Sector Skills Strategy in Additive Manufacturing. Accessed February 25, 2022. https://skills4am.eu/observatory_about.html
  27. Go J, Hart AJ. A framework for teaching the fundamentals of additive manufacturing and enabling rapid innovation. Addit Manuf. 2016;10:76-87. doi: 10.1016/j.addma.2016.03.001
  28. Lin F, Zhang L, Zhang T, Wang J, Zhang R. Innovative Education in Additive Manufacturing in China. University of Texas at Austin. Preprint posted online 2012. doi: 10.26153/TSW/15331
  29. Alabi MO, de Beer DJ, Wichers H, Kloppers CP. Framework for effective additive manufacturing education: a case study of South African universities. Rapid Prototyp J. 2020;26(5):801-826. doi: 10.1108/RPJ-02-2019-0041
  30. Minetola P, Iuliano L, Bassoli E, Gatto A. Impact of additive manufacturing on engineering education - Evidence from Italy. Rapid Prototyp J. 2015;21(5):535-555. doi: 10.1108/RPJ-09-2014-0123
  31. Greenhalgh S. The effects of 3D printing in design thinking and design education. J Eng Des Technol. 2016;14(4):752-769. doi: 10.1108/JEDT-02-2014-0005
  32. Prabhu R, Miller SR, Simpson TW, Meisel NA. Exploring the Effects of Additive Manufacturing Education on Students’ Engineering Design Process and its Outcomes. J Mech Des. 2019;142(4). doi: 10.1115/1.4044324
  33. Williams CB, Seepersad CC. Design for Additive Manufacturing Curriculum: A Problem- and Project-Based Approach. University of Texas at Austin. Preprint posted online 2012. doi: 10.26153/TSW/15334
  34. Stern A, Rosenthal Y, Dresler N, Ashkenazi D. Additive manufacturing: An education strategy for engineering students. Addit Manuf. 2019;27:503-514. doi: 10.1016/j.addma.2019.04.001
  35. Kempton WL, Killi S, Morrison A. Meeting learning challenges in Product Design education with and through Additive Manufacturing. Systemics Cybern Inform. 2017;15(6):119-129. https://www.iiisci.org/Journal/PDV/sci/pdfs/IP043LL17.pdf
  36. Mo JPT, Tang YM. Project-based learning of systems engineering V model with the support of 3D printing. Australas J Eng Educ. 2017;22(1):3-13. doi: 10.1080/22054952.2017.1338229
  37. Gatto A, Bassoli E, Denti L, Iuliano L, Minetola P. Multi-disciplinary approach in engineering education: Learning with additive manufacturing and reverse engineering. Rapid Prototyp J. 2015;21(5):598-603. doi: 10.1108/RPJ-09-2014-0134
  38. Chen T, Egan P, Stöckli F, Shea K. Studying the impact of incorporating an additive manufacturing based design exercise in a large, first year technical drawing and CAD course. In: Volume 3: 17th International Conference on Advanced Vehicle Technologies; 12th International Conference on Design Education; 8th Frontiers in Biomedical Devices. American Society of Mechanical Engineers; 2015. doi: 10.1115/detc2015-47312
  39. Alhamad IM, Ahmed WK, Ali HZ. Boosting Teaching Experience in Mechanical Engineering Courses Using Additive Manufacturing Technologies. 2019 Advances in Science and Engineering Technology International Conferences (ASET). 2019:1-6. doi: 10.1109/ICASET.2019.8714338
  40. Kwon YM, Lee YA, Kim SJ. Case study on 3D printing education in fashion design coursework. Fashion Text. 2017;4(1). doi: 10.1186/s40691-017-0111-3
  41. Colorado HA, Mendoza DE, Valencia FL. A Combined Strategy of Additive Manufacturing to Support Multidisciplinary Education in Arts, Biology, and Engineering. J Sci Educ Technol. 2021;30(1):58-73. doi: 10.1007/s10956-020-09873-1
  42. Sharma RS, Singhal I, Gupta S. Innovative training framework for additive manufacturing ecosystem to accelerate adoption of three-dimensional printing technologies. 3D Print Addit Manuf. 2018;5(2):170-179. doi: 10.1089/3dp.2017.0003
  43. Radharamanan R. Additive manufacturing in manufacturing education: a new course development and implementation. Presented at: 2017 ASEE Annual Conference & Exposition; 2017. doi: 10.18260/1-2--27540
  44. Verner I, Merksamer A. Digital Design and 3D Printing in Technology Teacher Education. Procedia CIRP. 2015;36:182-186. doi: 10.1016/j.procir.2015.08.041
  45. MITx. Additive Manufacturing for Innovative Design and Production. Accessed March 10, 2022. https://prolearn.mit.edu/additive-manufacturing-innovative-design-and-production
  46. ASTM. Additive Manufacturing General Personnel Certificate. Accessed March 10, 2022. https://amcoe.org/events/additive-manufacturing-general-personnel-certificate-program-online
  47. Bendsøe MP, Sigmund O. Topology Optimization: Theory, Methods, and Applications. Vol 2. Springer Berlin; 2004. doi: 10.1007/978-3-662-05086-6
  48. Bi M, Tran P, Xie YM. Topology optimization of 3D continuum structures under geometric self-supporting constraint. Addit Manuf. 2020;36:101422. doi: 10.1016/j.addma.2020.101422
  49. Bi M, Xia L, Tran P, et al. Continuous contour-zigzag hybrid toolpath for large format additive manufacturing. Addit Manuf. 2022;55:102822. doi: 10.1016/j.addma.2022.102822
  50. Bi M, Tran P, Xia L, Ma G, Xie YM. Topology optimization for 3D concrete printing with various manufacturing constraints. Addit Manuf. 2022;57. doi: 10.1016/j.addma.2022.102982
  51. Nguyen KC, Tran P, Nguyen HX. Multi-material topology optimization for additive manufacturing using polytree-based adaptive polygonal finite elements. Autom Constr. 2019;99:79-90. doi: 10.1016/j.autcon.2018.12.005
  52. Huynh HD, Tran P, Zhuang X, Nguyen-Xuan H. An extended polygonal finite element method for large deformation fracture analysis. Eng Fract Mech. 2019;209:344-368. doi: 10.1016/j.engfracmech.2019.01.024
  53. Wang C, Zhao Z, Zhou M, Sigmund O, Zhang XS. A comprehensive review of educational articles on structural and multidisciplinary optimization. Struct Multidisc Optim. 2021;64(5):2827-2880. doi: 10.1007/s00158-021-03050-7
  54. Wu J, Sigmund O, Groen JP. Topology optimization of multi-scale structures: a review. Struct Multidisc Optim. 2021;63(3):1455-1480. doi: 10.1007/s00158-021-02881-8
  55. Meng L, Zhang W, Quan D, et al. From Topology Optimization Design to Additive Manufacturing: Today’s Success and Tomorrow’s Roadmap. Arch Comput Methods Eng. 2020;27(3):805-830. doi: 10.1007/s11831-019-09331-1
  56. Liu J, Gaynor AT, Chen S, et al. Current and future trends in topology optimization for additive manufacturing. Struct Multidisc Optim. 2018;57(6):2457-2483. doi: 10.1007/s00158-018-1994-3
  57. Lazarov BS, Wang F, Sigmund O. Length scale and manufacturability in density-based topology optimization. Arch Appl Mech. 2016;86(1):189-218. doi: 10.1007/s00419-015-1106-4
  58. Xia L, Zhang L, Xia Q, Shi T. Stress-based topology optimization using bi-directional evolutionary structural optimization method. Comput Methods Appl Mech Eng. 2018;333:356-370. doi: 10.1016/j.cma.2018.01.035
  59. Norato JA, Bell BK, Tortorelli DA. A geometry projection method for continuum-based topology optimization with discrete elements. Comput Methods Appl Mech Eng. 2015;293:306-327. doi: 10.1016/j.cma.2015.05.005
  60. Wang MY, Wang X, Guo D. A level set method for structural topology optimization. Comput Methods Appl Mech Eng. 2003;192(1):227-246. doi: 10.1016/S0045-7825(02)00559-5
  61. Picelli R, Townsend S, Brampton C, Norato J, Kim HA. Stress-based shape and topology optimization with the level set method. Comput Methods Appl Mech Eng. 2018;329:1-23. doi: 10.1016/j.cma.2017.09.001
  62. Hagishita T, Ohsaki M. Topology optimization of trusses by growing ground structure method. Struct Multidisc Optim. 2009;37(4):377-393. doi: 10.1007/s00158-008-0237-4
  63. Ramos AS, Paulino GH. Convex topology optimization for hyperelastic trusses based on the ground-structure approach. Struct Multidisc Optim. 2015;51(2):287-304. doi: 10.1007/s00158-014-1147-2
  64. 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). doi: 10.1115/1.4027609
  65. Zhang W, Yuan J, Zhang J, Guo X. A new topology optimization approach based on Moving Morphable Components (MMC) and the ersatz material model. Struct Multidisc Optim. 2016;53(6):1243-1260. doi: 10.1007/s00158-015-1372-3
  66. Lan T, Tran P. Multiscale Topology Optimization of Lattice Structure Using 3D Moving Hollow Morphable Bars. JOM. 2021;73(12):4141-4153. doi: 10.1007/s11837-021-04917-2
  67. Sigmund O. A 99 line topology optimization code written in Matlab. Struct Multidisc Optim. 2001;21(2):120-127. doi: 10.1007/s001580050176
  68. Andreassen E, Clausen A, Schevenels M, Lazarov BS, Sigmund O. Efficient topology optimization in MATLAB using 88 lines of code. Struct Multidisc Optim. 2011;43(1):1-16. doi: 10.1007/s00158-010-0594-7
  69. Talischi C, Paulino GH, Pereira A, Menezes IFM. PolyTop: a Matlab implementation of a general topology optimization framework using unstructured polygonal finite element meshes. Struct Multidisc Optim. 2012;45(3):329-357. doi: 10.1007/s00158-011-0696-x
  70. Liu K, Tovar A. An efficient 3D topology optimization code written in Matlab. Struct Multidisc Optim. 2014;50(6):1175-1196. doi: 10.1007/s00158-014-1107-x
  71. Lagaros ND, Vasileiou N, Kazakis G. A C# code for solving 3D topology optimization problems using SAP2000. Optim Eng. 2019;20(1):1-35. doi: 10.1007/s11081-018-9384-7
  72. Ferrari F, Sigmund O. A new generation 99 line Matlab code for compliance topology optimization and its extension to 3D. Struct Multidisc Optim. 2020;62(4):2211-2228. doi: 10.1007/s00158-020-02629-w
  73. Aage N, Andreassen E, Lazarov BS. Topology optimization using PETSc: An easy-to-use, fully parallel, open source topology optimization framework. Struct Multidisc Optim. 2015;51(3):565-572. doi: 10.1007/s00158-014-1157-0
  74. Zhang Z-D, Ibhadode O, Bonakdar A, Toyserkani E. TopADD: a 2D/3D integrated topology optimization parallel-computing framework for arbitrary design domains. Struct Multidisc Optim. 2021;64(3):1701-1723. doi: 10.1007/s00158-021-02917-z
  75. Xia L, Breitkopf P. Design of materials using topology optimization and energy-based homogenization approach in Matlab. Struct Multidisc Optim. 2015;52(6):1229-1241. doi: 10.1007/s00158-015-1294-0
  76. Gao J, Luo Z, Xia L, Gao L. Concurrent topology optimization of multiscale composite structures in Matlab. Struct Multidisc Optim. 2019;60(6):2621-2651. doi: 10.1007/s00158-019-02323-6
  77. Lan T, Do T, Al-Ketan O, Fox K, Tran P. Concurrent multiscale topology optimisation towards design and additive manufacturing of bio-mimicking porous structures. Virtual Phys Prototyp. 2023;18(1):e2150867. doi: 10.1080/17452759.2022.2150867
  78. Tee YL, Tran P, Leary M, Pille P, Brandt M. 3D Printing of polymer composites with material jetting: Mechanical and fractographic analysis. Addit Manuf. 2020;36:101558. doi: 10.1016/j.addma.2020.101558
  79. Wickramasinghe S, Nguyen TK, Al-Ketan O, Peng C, Do T, Tran P. Bio-inspired suture architectures for enhanced impact resistance in 3D-printed composite structures. Smart Mater Struct. 2025;34(11). doi: 10.1088/1361-665X/ae19ae
  80. Sanders ED, Pereira A, Aguiló MA, Paulino GH. PolyMat: an efficient Matlab code for multi-material topology optimization. Struct Multidisc Optim. 2018;58(6):2727-2759. doi: 10.1007/s00158-018-2094-0
  81. Ferrari F, Sigmund O, Guest JK. Topology optimization with linearized buckling criteria in 250 lines of Matlab. Struct Multidisc Optim. 2021;63(6):3045-3066. doi: 10.1007/s00158-021-02854-x
  82. Giraldo-Londoño O, Paulino GH. PolyStress: a Matlab implementation for local stress-constrained topology optimization using the augmented Lagrangian method. Struct Multidisc Optim. 2021;63(4):2065-2097. doi: 10.1007/s00158-020-02760-8
  83. Deng H, Vulimiri PS, To AC. An efficient 146-line 3D sensitivity analysis code of stress-based topology optimization written in MATLAB. Optim Eng. 2022;23(3):1733-1757. doi: 10.1007/s11081-021-09675-3
  84. Chen Q, Zhang X, Zhu B. A 213-line topology optimization code for geometrically nonlinear structures. Struct Multidisc Optim. 2019;59(5):1863-1879. doi: 10.1007/s00158-018-2138-5
  85. Zhu B, Zhang X, Li H, et al. An 89-line code for geometrically nonlinear topology optimization written in FreeFEM. Struct Multidisc Optim. 2021;63(2):1015-1027. doi: 10.1007/s00158-020-02733-x
  86. Giraldo-Londoño O, Paulino GH. PolyDyna: a Matlab implementation for topology optimization of structures subjected to dynamic loads. Struct Multidisc Optim. 2021;64(2):957-990. doi: 10.1007/s00158-021-02859-6
  87. Liang Y, Cheng G. Further elaborations on topology optimization via sequential integer programming and Canonical relaxation algorithm and 128-line MATLAB code. Struct Multidisc Optim. 2020;61(1):411-431. doi: 10.1007/s00158-019-02396-3
  88. Picelli R, Sivapuram R, Xie YM. A 101-line MATLAB code for topology optimization using binary variables and integer programming. Struct Multidisc Optim. 2021;63(2):935-954. doi: 10.1007/s00158-020-02719-9
  89. Zuo ZH, Xie YM. A simple and compact Python code for complex 3D topology optimization. Adv Eng Softw. 2015;85:1-11. doi: 10.1016/j.advengsoft.2015.02.006
  90. Ansola Loyola R, Querin OM, Garaigordobil Jiménez A, Alonso Gordoa C. A sequential element rejection and admission (SERA) topology optimization code written in Matlab. Struct Multidisc Optim. 2018;58(3):1297-1310. doi: 10.1007/s00158-018-1939-x
  91. Lin H, Xu A, Misra A, Zhao R. An ANSYS APDL code for topology optimization of structures with multi-constraints using the BESO method with dynamic evolution rate (DER-BESO). Struct Multidisc Optim. 2020;62(4):2229-2254. doi: 10.1007/s00158-020-02588-2
  92. Han Y, Xu B, Liu Y. An efficient 137-line MATLAB code for geometrically nonlinear topology optimization using bi-directional evolutionary structural optimization method. Struct Multidisc Optim. 2021;63(5):2571-2588. doi: 10.1007/s00158-020-02816-9
  93. Challis VJ. A discrete level-set topology optimization code written in Matlab. Struct Multidisc Optim. 2010;41(3):453-464. doi: 10.1007/s00158-009-0430-0
  94. Laurain A. A level set-based structural optimization code using FEniCS. Struct Multidisc Optim. 2018;58(3):1311-1334. doi: 10.1007/s00158-018-1950-2
  95. Chung H, Hwang JT, Gray JS, Kim HA. Topology optimization in OpenMDAO. Struct Multidisc Optim. 2019;59(4):1385-1400. doi: 10.1007/s00158-019-02209-7
  96. Wei P, Li Z, Li X, Wang MY. An 88-line MATLAB code for the parameterized level set method based topology optimization using radial basis functions. Struct Multidisc Optim. 2018;58(2):831-849. doi: 10.1007/s00158-018-1904-8
  97. Otomori M, Yamada T, Izui K, Nishiwaki S. Matlab code for a level set-based topology optimization method using a reaction diffusion equation. Struct Multidisc Optim. 2015;51(5):1159-1172. doi: 10.1007/s00158-014-1190-z
  98. Yaghmaei M, Ghoddosian A, Khatibi MM. A filter-based level set topology optimization method using a 62-line MATLAB code. Struct Multidisc Optim. 2020;62(2):1001-1018. doi: 10.1007/s00158-020-02540-4
  99. Kim C, Jung M, Yamada T, Nishiwaki S, Yoo J. FreeFEM++ code for reaction-diffusion equation–based topology optimization: for high-resolution boundary representation using adaptive mesh refinement. Struct Multidisc Optim. 2020;62(1):439-455. doi: 10.1007/s00158-020-02498-3
  100. Yago D, Cante J, Lloberas-Valls O, Oliver J. Topology optimization using the unsmooth variational topology optimization (UNVARTOP) method: an educational implementation in MATLAB. Struct Multidisc Optim. 2021;63(2):955-981. doi: 10.1007/s00158-020-02722-0
  101. Suresh K. A 199-line Matlab code for Pareto-optimal tracing in topology optimization. Struct Multidisc Optim. 2010;42(5):665-679. doi: 10.1007/s00158-010-0534-6
  102. Smith H, Norato JA. A MATLAB code for topology optimization using the geometry projection method. Struct Multidisc Optim. 2020;62(3):1579-1594. doi: 10.1007/s00158-020-02552-0
  103. Zegard T, Paulino GH. Bridging topology optimization and additive manufacturing. Struct Multidisc Optim. 2016;53(1):175-192. doi: 10.1007/s00158-015-1274-4
  104. Wang C, Zhu JH, Zhang WH, Li SY, Kong J. Concurrent topology optimization design of structures and non-uniform parameterized lattice microstructures. Struct Multidisc Optim. 2018;58(1):35-50. doi: 10.1007/s00158-018-2009-0
  105. Chen Y, Wang Q, Wang C, et al. Topology Optimization Design and Experimental Research of a 3D-Printed Metal Aerospace Bracket Considering Fatigue Performance. Appl Sci. 2021;11(15):6671. doi: 10.3390/app11156671
  106. Shi G, Dai Y, Yu Z, et al. Experimental and Numerical Study on Various Liquid-Cooled Heat Sinks Including Topology Optimization. Micromachines. 2025;16(11):1213. doi: 10.3390/mi16111213
  107. Peng C, Tran P, Lalor S, Tirosh O, Rutz E. Tuning the mechanical responses of 3D-printed ankle-foot orthoses: A numerical study. Int J Bioprint. 2024;10(3). doi: 10.36922/ijb.3390
  108. Peng C, Tran P, Rutz E. Accelerating hybrid lattice structures design with machine learning. Mater Sci Addit Manuf. 2024;3(2):3430. doi: 10.36922/msam.3430

 

Share
Back to top
Engineering Science in Additive Manufacturing, Electronic ISSN: 3082-849X Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept