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REVIEW ARTICLE

Engineering science of additively manufactured patient-specific mandibular implants

Shengping Zhong1,2 ,  Jeroen Van Dessel3,4 ,  Shoufeng Yang1,2*
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1 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
2 College of Materials Sciences and Engineering, Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
3 Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
4 Department of Imaging & Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
ESAM 2026, 2(2), 026220011 https://doi.org/10.36922/ESAM026220011
Received: 29 May 2026 | Revised: 4 June 2026 | Accepted: 13 June 2026 | Published online: 26 June 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/ )
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Abstract

Mandibular reconstruction is a major biomechanical challenge because it requires simultaneous restoration of anatomical form and structural stability under dynamic masticatory loading. While the structural degradation and poor conformity inherent to manually bent systems are overcome by patient-specific mandibular reconstruction plates (PSMRPs) fabricated via laser powder bed fusion (LPBF), clinical longevity remains constrained by manufacturing-induced defects and fatigue failure. In this critical review, the foundational engineering science governing LPBF-fabricated titanium alloy PSMRPs is examined, and an integrated paradigm spanning manufacturing science, computational biomechanics, structural optimization, and experimental validation is established. The process–structure–property relationships in LPBF are systematically evaluated, with specific detail given to how porosity morphology, residual stress, microstructural anisotropy, and surface integrity are dictated by volumetric energy density and complex thermal histories, ultimately governing fatigue crack initiation under cyclic loading. Moving from material to system-level mechanics, assembly failure modes are analyzed, while the biomechanical performance of locking versus non-locking fixation is compared. The role of finite element analysis alongside advanced structural optimization strategies is critically evaluated, and the mitigation of stress concentrations and stress shielding through size, shape, and topology optimization is demonstrated. Furthermore, state-of-the-art experimental validation methodologies are scrutinized, with emphasis placed on full-field strain characterization via digital image correlation and biomimetic cyclic fatigue testing. Finally, future translational frontiers are outlined, including artificial intelligence-driven generative design and mechanobiological simulations of long-term osseous remodeling. Collectively, the evidence suggests that the mastery of the interplay between additive manufacturing parameters and multi-scale biomechanics is paramount to the engineering of predictable, optimized cranio-maxillofacial patient-specific implants.

Graphical abstract
Keywords
Additive manufacturing
Laser powder bed fusion
Patient-specific implants
Mandibular reconstruction
Biomechanical optimization
Finite element analysis
Computational mechanics
Digital image correlation
Funding
The authors gratefully acknowledge the financial support from the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB1590000), the CAS Pioneer Hundred Talents Program, the Science and Technology Innovation Key R&D Program of Chongqing, and the Young Elite Program of Brain-Gain Plan of New Chongqing.
Conflict of interest
The authors declare they have no competing interests.
References
  1. Kumar BP, Venkatesh V, Kumar KJ, Yadav BY, Mohan SR. Mandibular reconstruction: overview. J Maxillofac Oral Surg. 2016;15(4):425-441. doi: 10.1007/s12663-015-0766-5
  2. Awadalkreem F, Kamal H. Mandibular Resection: Disabilities, Challenges, Reconstruction Techniques, Advances, and Quality of Life. In: Bourzgui F, ed. Cranio- Maxillofacial Surgery-Orthognathic and Orthodontic Techniques. IntechOpen; 2025. doi: 10.5772/intechopen.1009198
  3. Paré A, Bossard A, Laure B, Weiss P, Gauthier O, Corre P. Reconstruction of segmental mandibular defects: Current procedures and perspectives. Laryngoscope Investig Otolaryngol. 2019;4(6):587-596. doi: 10.1002/lio2.325
  4. Vincent A, Hohman MH. Mandible Reconstruction. StatPearls Publishing. Updated 2023. Available from: https:// www.ncbi.nlm.nih.gov/books/NBK563241/ [Last accessed on May 29, 2026].
  5. Coletti D, Ord R, Liu X. Mandibular reconstruction and second generation locking reconstruction plates: outcome of 110 patients. Int J Oral Maxillofac Surg. 2009;38(9):960-963. doi: 10.1016/j.ijom.2009.03.721
  6. Zhong S, Zhang Y, Shi Q, et al. Biomechanics of manual-bent versus patient-specific mandibular implants. J Biomech Eng. 2025;147(12):121001. doi: 10.1115/1.4069665
  7. Gutwald R, Jaeger R, Lambers FM. Customized mandibular reconstruction plates improve mechanical performance in a mandibular reconstruction model. Comput Methods Biomech Biomed Engin. 2017;20(4):426-435. doi: 10.1080/10255842.2016.1240788
  8. Wilde F, Cornelius C-P, Schramm A. Computer-assisted mandibular reconstruction using a patient-specific reconstruction plate fabricated with computer-aided design and manufacturing techniques. Craniomaxillofac Trauma Reconstr. 2014;7(2):158-166. doi: 10.1055/s-0034-1371356
  9. Alasseri N, Alasraj A. Patient-specific implants for maxillofacial defects: challenges and solutions. Maxillofac Plast Reconstr Surg. 2020;42(1):15. doi: 10.1186/s40902-020-00262-7
  10. Memon AR, Wang E, Hu J, Egger J, Chen X. A review on computer-aided design and manufacturing of patient- specific maxillofacial implants. Expert Rev Med Devices. 2020;17(4):345-356. doi: 10.1080/17434440.2020.1736040
  11. Sing SL, Yeong WY. Laser powder bed fusion for metal additive manufacturing: perspectives on recent developments. Virtual Phys Prototyp. 2020;15(3):359-370. doi: 10.1080/17452759.2020.1779999
  12. Vaezi M, Chianrabutra S, Mellor B, Yang S. Multiple material additive manufacturing–Part 1: a review: this review paper covers a decade of research on multiple material additive manufacturing technologies which can produce complex geometry parts with different materials. Virtual Phys Prototyp. 2013;8(1):19-50. doi: 10.1080/17452759.2013.778175
  13. Zhang LC, Attar H. Selective laser melting of titanium alloys and titanium matrix composites for biomedical applications: a review. Adv Eng Mater. 2016;18(4):463-475. doi: 10.1002/adem.201500419
  14. Shi Q, Sun Y, Yang S, et al. Failure analysis of an in-vivo fractured patient-specific Ti6Al4V mandible reconstruction plate fabricated by selective laser melting. Eng Fail Anal. 2021;124:105353. doi: 10.1016/j.engfailanal.2021.105353
  15. Wei W, Zhu L, Wu W, Fan S. TiC/Ti6Al4V functionally graded composite fabricated by in-situ laser additive manufacturing via gas–liquid reaction. J Alloys Compd. 2022;900:163406. doi: 10.1016/j.jallcom.2021.163406
  16. Li L, Shi Q, Yang S. In-situ bonding of horizontal bimetallic interface by laser offset during laser powder bed fusion of copper/nickel multi-material structures and underlying thermodynamic mechanisms. J Mater Process Technol. 2025;339:118831. doi: 10.1016/j.jmatprotec.2025.118831
  17. Li X, Wang C, Fan H, Shi Q, Li K, Yang S. In-situ fabrication of compositionally gradient interfaces via programmable powder deposition in multi-material laser powder bed fusion. Virtual Phys Prototyp. 2026;21(1):e2656556. doi: 10.1080/17452759.2026.2656556
  18. Shi Q, Yang S, Sun Y, et al. In-situ formation of Ti-Mo biomaterials by selective laser melting of Ti/Mo and Ti/Mo2C powder mixtures: A comparative study on microstructure, mechanical and wear performance, and thermal mechanisms. J Mater Sci Technol. 2022;115:81-96. doi: 10.1016/j.jmst.2021.09.017
  19. Shi Q, Hu Y, Fan H, Yang S. Fundamental role of vertical building sequence and its thermodynamic mechanisms during multi-material additive manufacturing of 18Ni300- CuSn10 structures with IN718 interlayer. J Manuf Process. 2025;144:243-260. doi: 10.1016/j.jmapro.2025.04.048
  20. Wang C, Wang Y, Fan H, Sun Y, Yang S. Crack-free nickel-copper (Monel K500/GRCop-42) interfaces formed under alternating build sequences via multi-material laser powder bed fusion. Addit Manuf. 2025;114:105037. doi: 10.1016/j.addma.2025.105037
  21. Zhong S, Shi Q, Sun Y, et al. Biomechanical comparison of locking and non-locking patient-specific mandibular reconstruction plate using finite element analysis. J Mech Behav Biomed Mater. 2021;124:104849. doi: 10.1016/j.jmbbm.2021.104849
  22. van Kootwijk A, Jonker BP, Wolvius EB, et al. Biomechanical evaluation of additively manufactured patient-specific mandibular cage implants designed with a semi-automated workflow: A cadaveric and retrospective case study. J Mech Behav Biomed Mater. 2023;146:106097. doi: 10.1016/j.jmbbm.2023.106097
  23. Zhong S, Shi Q, Van Dessel J, et al. Biomechanical feasibility of non-locking system in patient-specific mandibular reconstruction using fibular free flaps. J Mech Behav Biomed Mater. 2023;148:106197. doi: 10.1016/j.jmbbm.2023.106197
  24. Zhong S, Shi Q, Van Dessel J, et al. Biomechanical validation of structural optimized patient-specific mandibular reconstruction plate orienting additive manufacturing. Comput Methods Programs Biomed. 2022;224:107023. doi: 10.1016/j.cmpb.2022.107023
  25. Shi Q, Sun Y, Yang S, et al. Preclinical study of additive manufactured plates with shortened lengths for complete mandible reconstruction: Design, biomechanics simulation, and fixation stability assessment. Comput Biol Med. 2021;139:105008. doi: 10.1016/j.compbiomed.2021.105008
  26. Zhong S, Shi Q, Van Dessel J, Politis C, Sun Y, Yang S. From virtual design to physical prototype: Topology-optimised patient-specific mandibular reconstruction plate. Virtual Phys Prototyp. 2026;21(1):e2653335. doi: 10.1080/17452759.2026.2653335
  27. Koper DC, Leung CAW, Smeets LCP, Laeven PFJ, Tuijthof GJM, Kessler P. Topology optimization of a mandibular reconstruction plate and biomechanical validation. J Mech Behav Biomed Mater. 2021;113:104157. doi: 10.1016/j.jmbbm.2020.104157
  28. Schupp W, Arzdorf M, Linke B, Gutwald R. Biomechanical testing of different osteosynthesis systems for segmental resection of the mandible. J Oral Maxillofac Surg. 2007;65(5):924-30. doi: 10.1016/j.joms.2006.06.306
  29. Zhang H, Hsu JT, Fuh LJ, Huang HL. Biomechanical evaluation of 3D-printed porous lattice versus solid mandibular implants: an in vitro study. Sci Rep. 2026. doi: 10.1038/s41598-026-50741-6
  30. Zhong S, Zhang Y, Shi Q, et al. Superior biomechanics of patient-specific mandibular reconstruction plate: A validated computational-experimental framework. Ann Biomed Eng. 2026;54(1):225-247. doi: 10.1007/s10439-025-03878-w
  31. Heller MO. Finite element analysis in orthopedic biomechanics. In: Innocenti B, Galbusera F, eds. Human orthopaedic biomechanics. Elsevier; 2022:637-658. doi: 10.1016/B978-0-12-824481-4.00026-3
  32. Taylor M, Prendergast PJ. Four decades of finite element analysis of orthopaedic devices: where are we now and what are the opportunities? J Biomech. 2015;48(5):767-78. doi: 10.1016/j.jbiomech.2014.12.019
  33. Erdemir A, Guess TM, Halloran J, Tadepalli SC, Morrison TM. Considerations for reporting finite element analysis studies in biomechanics. J Biomech. 2012;45(4):625-633. doi: 10.1016/j.jbiomech.2011.11.038
  34. Zhang Y, Yamanaka A, Cooreman S, Kuwabara T, Coppieters S. Inverse identification of plastic anisotropy through multiple non-conventional mechanical experiments. Int J Solids Struct. 2023;285:112534. doi: 10.1016/j.ijsolstr.2023.112534
  35. Wegner N, Klein M, Scholz R, Kotzem D, Macias Barrientos M, Walther F. Mechanical in vitro fatigue testing of implant materials and components using advanced characterization techniques. J Biomed Mater Res B Appl Biomater. 2022;110(4):898-909. doi: 10.1002/jbm.b.34970
  36. Sutton MA, Hild F. Recent advances and perspectives in digital image correlation. Exp Mech. 2015;55(1):1-8. doi: 10.1007/s11340-015-9991-6
  37. Wu J, Jiang L, Shao L, et al. A mandibular defect dataset for autonomous reconstruction planning in oral and maxillofacial surgery. Sci Data. 2025;12(1):1763. doi: 10.1038/s41597-025-06048-8
  38. Peng W-m, Cheng K-j, Liu Y-f, et al. Biomechanical and mechanostat analysis of a titanium layered porous implant for mandibular reconstruction: The effect of the topology optimization design. Mater Sci Eng C. 2021;124:112056. doi: 10.1016/j.msec.2021.112056
  39. Gao H, Li X, Wang C, Ji P, Wang C. Mechanobiologically optimization of a 3D titanium-mesh implant for mandibular large defect: A simulated study. Mater Sci Eng C Mater Biol Appl. 2019;104:109934. doi: 10.1016/j.msec.2019.109934
  40. Zheng L, Wang C, Hu M, et al. An innovative additively manufactured implant for mandibular injuries: Design and preparation processes based on simulation model. Front Bioeng Biotechnol. 2022;10:1065971. doi: 10.3389/fbioe.2022.1065971
  41. Van Kootwijk A, Moosabeiki V, Saldivar MC, et al. Semi-automated digital workflow to design and evaluate patient-specific mandibular reconstruction implants. J Mech Behav Biomed Mater. 2022;132:105291. doi: 10.1016/j.jmbbm.2022.105291
  42. Biswal R, Syed AK, Zhang X. Assessment of the effect of isolated porosity defects on the fatigue performance of additive manufactured titanium alloy. Addit Manuf. 2018;23:433-442. doi: 10.1016/j.addma.2018.08.024
  43. Kruth JP, Levy G, Klocke F, Childs THC. Consolidation phenomena in laser and powder-bed based layered manufacturing. CIRP Ann. 2007;56(2):730-759. doi: 10.1016/j.cirp.2007.10.004
  44. Luo Q, Yin L, Simpson TW, Beese AM. Effect of processing parameters on pore structures, grain features, and mechanical properties in Ti-6Al-4V by laser powder bed fusion. Addit Manuf. 2022;56:102915. doi: 10.1016/j.addma.2022.102915
  45. Tammas-Williams S, Withers PJ, Todd I, Prangnell P. The influence of porosity on fatigue crack initiation in additively manufactured titanium components. Sci Rep. 2017;7(1):7308. doi: 10.1038/s41598-017-06504-5
  46. Cunningham R, Zhao C, Parab N, et al. Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging. Science. 2019;363(6429):849-852. doi: 10.1126/science.aav4687
  47. Ni C, Zhu J, Zhang B, et al. Recent advance in laser powder bed fusion of Ti–6Al–4V alloys: microstructure, mechanical properties and machinability. Virtual Phys Prototyp. 2025;20(1):e2446952. doi: 10.1080/17452759.2024.2446952
  48. Wilson-Heid AE, Qin S, Beese AM. Anisotropic multiaxial plasticity model for laser powder bed fusion additively manufactured Ti-6Al-4V. Mater Sci Eng A. 2018;738:90-97. doi: 10.1016/j.msea.2018.09.077
  49. Wei W-H, Shen J. Effect of laser energy density on microstructures and mechanical properties of selective laser melted Ti-6Al-4V alloy. Int J Mater Res. 2018;109(5):437- 442. doi: 10.3139/146.111615
  50. Shi Q, Gu D, Xia M, Cao S, Rong T. Effects of laser processing parameters on thermal behavior and melting/solidification mechanism during selective laser melting of TiC/Inconel 718 composites. Opt Laser Technol. 2016;84:9-22. doi: 10.1016/j.optlastec.2016.04.009
  51. Simonelli M, Tse YY, Tuck C. The formation of α+ β microstructure in as-fabricated selective laser melting of Ti–6Al–4V. J Mater Res. 2014;29(17):2028-2035. doi: 10.1557/jmr.2014.166
  52. Leuders S, Lieneke T, Lammers S, Tröster T, Niendorf T. On the fatigue properties of metals manufactured by selective laser melting–The role of ductility. J Mater Res. 2014;29(17):1911-1919. doi: 10.1557/jmr.2014.157
  53. Levkulich N, Semiatin S, Gockel J, Middendorf J, DeWald A, Klingbeil N. The effect of process parameters on residual stress evolution and distortion in the laser powder bed fusion of Ti-6Al-4V. Addit Manuf. 2019;28:475-484. doi: 10.1016/j.addma.2019.05.015
  54. Vrancken B, Thijs L, Kruth J-P, Van Humbeeck J. Heat treatment of Ti6Al4V produced by Selective Laser Melting: Microstructure and mechanical properties. J Alloys Compd. 2012;541:177-185. doi: 10.1016/j.jallcom.2012.07.022
  55. Vrancken B, Cain V, Knutsen R, Van Humbeeck J. Residual stress via the contour method in compact tension specimens produced via selective laser melting. Scr Mater. 2014;87:29- 32. doi: 10.1016/j.scriptamat.2014.05.016
  56. Mercelis P, Kruth JP. Residual stresses in selective laser sintering and selective laser melting. Rapid Prototyp J. 2006;12(5):254-265. doi: 10.1108/13552540610707013
  57. Parry L, Ashcroft I, Wildman RD. Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation. Addit Manuf. 2016;12:1-15. doi: 10.1016/j.addma.2016.05.014
  58. Wan B, Yoda N, Zheng K, et al. On effect of residual stress on fracture behavior of mandibular reconstruction plates. Eng Fract Mech. 2024;305:110158. doi: 10.1016/j.engfracmech.2024.110158
  59. Valentinčič J, Koroth JE, Zeidler H. Advancements in surface finish for additive manufacturing of metal parts: a comprehensive review of plasma electrolytic polishing (PEP). Virtual Phys Prototyp. 2024;19(1):e2364222. doi: 10.1080/17452759.2024.2364222
  60. Xiang H, Zhou Y, Zhang X, et al. Supportfree printing in laser powder bed fusion: Formation mechanisms of discontinuity, dross and surface roughness. Opt Laser Technol. 2024;177:111201. doi: 10.1016/j.optlastec.2024.111201
  61. Pegues J, Roach M, Williamson RS, Shamsaei N. Surface roughness effects on the fatigue strength of additively manufactured Ti-6Al-4V. Int J Fatigue. 2018;116:543-552. doi: 10.1016/j.ijfatigue.2018.07.013
  62. Narra N, Valasek J, Hannula M, et al. Finite element analysis of customized reconstruction plates for mandibular continuity defect therapy. J Biomech. 2014;47(1):264-8. doi: 10.1016/j.jbiomech.2013.11.016
  63. Zeller A, Neuhaus M, Weissbach L, et al. Patient-specific mandibular reconstruction plates increase accuracy and long-term stability in immediate alloplastic reconstruction of segmental mandibular defects. J Maxillofac Oral Surg. 2020;19(4):609-615. doi: 10.1007/s12663-019-01323-9
  64. Ellis E, Schubert W. Locking plate principles. In: Gossous Z, Luqman U, Cypriano R, Aquilina P, Shah I, Thieringer FM, eds. Maxillomandibular fixation (MMF). 2020:chap Biomechanics. Accessed May 29, 2026. https:// surgeryreference.aofoundation.org/cmf/basic-technique/ locking-plate-principles.
  65. Smith WR, Ziran BH, Anglen JO, Stahel PF. Locking plates: tips and tricks. J Bone Joint Surg. 2007;89(10):2298-2307. doi: 10.2106/00004623-200710000-00028
  66. Bel J-C. Pitfalls and limits of locking plates. Orthop Traumatol Surg Res. 2019;105(1):S103-S109. doi: 10.1016/j.otsr.2018.04.031
  67. Gibson I, Rosen D, Stucker B, Khorasani M. Additive manufacturing technologies. 3rd ed. Cham: Springer; 2021:675. doi: 10.1007/978-3-030-56127-7
  68. Zhong S, Shi Q, Deng Y, Sun Y, Politis C, Yang S. High-performance zirconia ceramic additively manufactured via NanoParticle Jetting. Ceram Int. 2022;48(22):33485-33498. doi: 10.1016/j.ceramint.2022.07.294
  69. Zhong G, Kang M, Yang S. Precision obtained using an artificial neural network for predicting the material removal rate in ultrasonic machining. Appl Sci. 2017;7(12):1268. doi: 10.3390/app7121268
  70. Vaezi M, Seitz H, Yang S. A review on 3D micro-additive manufacturing technologies. Int J Adv Manuf Technol. 2013;67(5):1721-1754. doi: 10.1007/s00170-012-4605-2
  71. Vandenbroucke B, Kruth JP. Selective laser melting of biocompatible metals for rapid manufacturing of medical parts. Rapid Prototyp J. 2007;13(4):196-203. doi: 10.1108/13552540710776142
  72. Moosabeiki V, de Winter N, Cruz Saldivar M, et al. 3D printed patient-specific fixation plates for the treatment of slipped capital femoral epiphysis: Topology optimization vs. conventional design. J Mech Behav Biomed Mater. 2023;148:106173. doi: 10.1016/j.jmbbm.2023.106173
  73. Solórzano-Requejo W, Martínez Cendrero A, Altun AA, et al. Topology optimisation and lithography-based ceramic manufacturing of short-stem hip prostheses with enhanced biomechanical and mechanobiological performance. Virtual Phys Prototyp. 2024;19(1):e2387280. doi: 10.1080/17452759.2024.2387280
  74. Mehboob A, Barsoum I, Mehboob H, Abu Al-Rub RK, Ouldyerou A. Topology optimization and biomechanical evaluation of bone plates for tibial bone fractures considering bone healing. Virtual Phys Prototyp. 2024;19(1):e2391475. doi: 10.1080/17452759.2024.2391475
  75. Li CH, Wu CH, Lin CL. Design of a patient-specific mandible reconstruction implant with dental prosthesis for metal 3D printing using integrated weighted topology optimization and finite element analysis. J Mech Behav Biomed Mater. 2020;105:103700. doi: 10.1016/j.jmbbm.2020.103700
  76. Park S-M, Park S, Park J, Choi M, Kim L, Noh G. Design process of patient-specific osteosynthesis plates using topology optimization. J Comput Des Eng. 2021;8(5):1257- 1266. doi: 10.1093/jcde/qwab047
  77. Sun C, Kang J, Wang L, Jin Z, Liu C, Li D. Stress-dependent design and optimization methodology of gradient porous implant and application in femoral stem. Comput Methods Biomech Biomed Eng. 2023;26(11):1308-1319. doi: 10.1080/10255842.2022.2115291
  78. Sing SL, Yeong WY, Wiria FE, et al. Direct selective laser sintering and melting of ceramics: a review. Rapid Prototyp J. 2017;23(3):611-623. doi: 10.1108/RPJ-11-2015-0178
  79. Dhekne PP, Bönisch M, Seefeldt M, Vanmeensel K. In-situ synchrotron X-ray diffraction investigation of martensite decomposition in Laser Powder Bed Fusion (L-PBF) processed Ti–6Al–4V. Mater Sci Eng A. 2024;899:146421. doi: 10.1016/j.msea.2024.146421
  80. Chowdhury S, Yadaiah N, Prakash C, et al. Laser powder bed fusion: a state-of-the-art review of the technology, materials, properties & defects, and numerical modelling. J Mater Res Technol. 2022;20:2109-2172. doi: 10.1016/j.jmrt.2022.07.11
  81. Thijs L, Verhaeghe F, Craeghs T, Van Humbeeck J, Kruth J-P. A study of the microstructural evolution during selective laser melting of Ti–6Al–4V. Acta Mater. 2010;58(9):3303- 3312. doi: 10.1016/j.actamat.2010.02.004
  82. Yadroitsev I. Selective laser melting: direct manufacturing of 3D-objects by selective laser melting of metal powders. LAP Lambert Academic Publishing; 2009:280.
  83. Buhairi MA, Foudzi FM, Jamhari FI, et al. Review on volumetric energy density: influence on morphology and mechanical properties of Ti6Al4V manufactured via laser powder bed fusion. Prog Addit Manuf. 2023;8(2):265-283. doi: 10.1007/s40964-022-00328-0
  84. Haase F, Siemers C, Rosler J. Laser powder bed fusion (LPBF) of commercially pure titanium and alloy development for the LPBF process. Front Bioeng Biotechnol. 2023;11:1260925. doi: 10.3389/fbioe.2023.1260925
  85. Javidrad H, Koc B, Bayraktar H, Simsek U, Gunaydin K. Fatigue performance of metal additive manufacturing: a comprehensive overview. Virtual Phys Prototyp. 2024;19(1):e2302556. doi: 10.1080/17452759.2024.2302556
  86. Wan B, Entezari A, Zhang Z, et al. On fatigue failure prediction of prosthetic devices through XFEM analysis. Int J Fatigue. 2021;147:106160. doi: 10.1016/j.ijfatigue.2021.106160
  87. Wan B, Yoda N, Zheng K, et al. On interaction between fatigue of reconstruction plate and time-dependent bone remodeling. J Mech Behav Biomed Mater. 2022;136:105483. doi: 10.1016/j.jmbbm.2022.105483
  88. Do J, Han JJ, Lee JH, Kwon IJ. Clinical outcomes and mechanical evaluation of patient-specific mandibular reconstruction following tumor resection: a retrospective study with finite element analysis. World J Surg Oncol. 2026;24(1):167. doi: 10.1186/s12957-026-04283-w
  89. Li L, Shi Q, Xin M, Zhang Z, Yu H, Yang S. Achieving heterogeneous interface uniformity in micronozzle-controlled laser powder bed fusion via continuously variable transmission-inspired in-layer gradients of powder deposition. Int J Mach Tools Manuf. 2026;217:104384. doi: 10.1016/j.ijmachtools.2026.104384
  90. Yang Y, Zhang J, Wei W. Microstructure and mechanical properties of TiC/Ti6Al4V nanocomposites fabricated by gas–liquid reaction laser powder bed fusion. Mater Sci Eng A. 2023;869:144829. doi: 10.1016/j.msea.2023.144829
  91. Lu Z, Ho NSK, Pang JHL, et al. Process optimisation on fatigue performance of additive manufactured Ti-6Al-4V in high layer thickness for PBF-LB/M. Virtual Phys Prototyp. 2026;21(1):e2633869. doi: 10.1080/17452759.2026.2633869
  92. Park SJ, Yang J, Lee JH, et al. High-strength of additive-manufactured PEEK and Ti6Al4V structure via warm isostatic pressing: applications in medical implants and injection moulds. Virtual Phys Prototyp. 2025;20(1):e2449566. doi: 10.1080/17452759.2024.2449566
  93. Mao Y, Yuan J, Heng Y, Feng K, Cai D, Wei Q. Effect of hot isostatic pressing treatment on porosity reduction and mechanical properties enhancement of 316L stainless steel fabricated by binder jetting. Virtual Phys Prototyp. 2023;18(1):e2174703. doi: 10.1080/17452759.2023.2174703
  94. Longhitano GA, Chiarelli M, Prada D, de Carvalho Zavaglia CA, Maciel Filho R. Personalized lattice-structured prosthesis as a graftless solution for mandible reconstruction and prosthetic restoration: A finite element analysis. J Mech Behav Biomed Mater. 2024;152:106460. doi: 10.1016/j.jmbbm.2024.106460
  95. Heins JI, Merema BBJ, Kraeima J, Witjes MJH, Krushynska AO. Mandibular implants: A metamaterial-based approach to reducing stress shielding. Adv Healthc Mater. 2025;14(13):e2500405. doi: 10.1002/adhm.202500405
  96. Peto M, García-Ávila J, Rodriguez CA, Siller HR, da Silva JVL, Ramírez-Cedillo E. Review on structural optimization techniques for additively manufactured implantable medical devices. Front Mech Eng. 2024;10:1353108. doi: 10.3389/fmech.2024.1353108
  97. Alomar Z, Aramesh M, Thor A, Persson C, Concli F, D’Elia F. Towards improved functionality of mandibular reconstruction plates enabled by additively manufactured triply periodic minimal surface structures. J Mech Behav Biomed Mater. 2025;162:106826. doi: 10.1016/j.jmbbm.2024.106826
  98. Li K, Fang J, Zhan J, et al. A critical review of biomimetic structures via laser powder bed fusion: Toward multi-functional application. J Manuf Process. 2024;131:2443- 2472. doi: 10.1016/j.jmapro.2024.09.087
  99. Niebur GL, Keaveny TM. Computational modeling of trabecular bone mechanics. In: De S, Guilak F, Mofrad M, eds. Computational Modeling in Biomechanics. Dordrecht: Springer; 2010:581. doi: 10.1007/978-90-481-3575-2
  100. Daqiq O, Roossien CC, Wubs FW, van Minnen B. Biomechanical assessment of mandibular fracture fixation using finite element analysis validated by polymeric mandible mechanical testing. Sci Rep. 2024;14(1):11795. doi: 10.1038/s41598-024-62011-4
  101. Ben Achour A, Apfeld F, Lauer G, et al. Development of a test bench for biomechanical simulation—a preliminary study of mandibular forces. Front Bioeng Biotechnol. 2024;12:1335159. doi: 10.3389/fbioe.2024.1335159
  102. Merema BBJ, Kraeima J, Glas HH, Spijkervet FKL, Witjes MJH. Patient-specific finite element models of the human mandible: Lack of consensus on current set-ups. Oral Dis. 2021;27(1):42-51. doi: 10.1111/odi.13381
  103. Santanna E, Anflor C, Nascimento F. Integrated nonlinear biomechanical modeling, topology optimization, and LPBF process simulation for customized mandibular fracture fixation plates. Int J Solids Struct. 2025;326:113776. doi: 10.1016/j.ijsolstr.2025.113776
  104. Ingawale SM, Krishnan DG, Goswami T. Validation of experimental and finite element biomechanical evaluation of human cadaveric mandibles. Lubricants. 2022;10(8):169. doi: 10.3390/lubricants10080169
  105. Oliveira-Santos N, Jacobs R, Picoli FF, Lahoud P, Niclaes L, Groppo FC. Automated segmentation of the mandibular canal and its anterior loop by deep learning. Sci Rep. 2023;13(1):10819. doi: 10.1038/s41598-023-37798-3
  106. Demir E, Yalçın G, Kalaycı A. Finite element analysis of biplanar customized reconstruction plates for lateral and central segmental defects of mandible. Comput Methods Biomech Biomed Engin. 2024;27(4):489-497. doi: 10.1080/10255842.2023.2188105
  107. 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
  108. Lang JJ, Bastian M, Foehr P, et al. Improving mandibular reconstruction by using topology optimization, patient specific design and additive manufacturing?-A biomechanical comparison against miniplates on human specimen. PLoS ONE. 2021;16(6):e0253002. doi: 10.1371/journal.pone.0253002
  109. Wu N, Li S, Zhang B, et al. The advances of topology optimization techniques in orthopedic implants: A review. Med Biol Eng Comput. 2021;59(9):1673-1689. doi: 10.1007/s11517-021-02361-7
  110. Haug RH, Street CC, Goltz M. Does plate adaptation affect stability? A biomechanical comparison of locking and nonlocking plates. J Oral Maxillofac Surg. 2002;60(11):1319- 1326. doi: 10.1053/joms.2002.35732
  111. Szypryt P, Forward D. The use and abuse of locking plates. Orthop Trauma. 2009;23(4):281-290.

doi: 10.1016/j.mporth.2009.07.002

  1. Fischer RD, Klasen J, Shmatok A, Prorok BC. An additively manufactured locking fixation system for potential application in patient-specific implants. J Mech Behav Biomed Mater. 2021;124:104867. doi: 10.1016/j.jmbbm.2021.104867
  2. Dong X, Shi Q, Yang S. Machine learning-assisted thickness prediction and 3D surface reconstruction for multi-material powder beds in micronozzle-controlled laser powder bed fusion. Powder Technol. 2026;480:122659. doi: 10.1016/j.powtec.2026.122659
  3. Kasper R, Winter K, Pietzka S, Schramm A, Wilde F. Biomechanical in vitro study on the stability of patient-specific CAD/CAM mandibular reconstruction plates: a comparison between selective laser melted, milled, and hand-bent plates. Craniomaxillofac Trauma Reconstr. 2021;14(2):135-143. doi: 10.1177/1943387520952684
  4. Lin CL, Wang YT, Chang CM, Wu CH, Tsai WH. Design criteria for patient-specific mandibular continuity defect reconstructed implant with lightweight structure using weighted topology optimization and validated with biomechanical fatigue testing. Int J Bioprint. 2022;8(1):437. doi: 10.18063/ijb.v8i1.437
  5. Malefane LB, du Preez W, Maringa M. High cycle fatigue properties of as-built Ti6Al4V (Eli) produced by direct metal laser sintering. S Afr J Ind Eng. 2017;28(3):188-199. doi: 10.7166/28-3-1861
  6. Polak S, Beever L, Wade A, Fukuoka M, Worth A. Biomechanical comparison of titanium alloy additively manufactured and conventionally manufactured plate-screw constructs. N Z Vet J. 2024;72(1):17-27. doi: 10.1080/00480169.2023.2264805
  7. Merema BBJ, Spijkervet FKL, Kraeima J, Witjes MJH. A non-metallic PEEK topology optimization reconstruction implant for large mandibular continuity defects, validated using the MANDYBILATOR apparatus. Sci Rep. 2025;15(1):644. doi: 10.1038/s41598-024-82964-w
  8. Khoo S-W, Karuppanan S, Tan C-S. A review of surface deformation and strain measurement using two-dimensional digital image correlation. Metrol Meas Syst. 2016;23(3):461- 480. doi: 10.1515/mms-2016-0028
  9. Sutradhar A, Park J, Carrau D, Miller MJ. Experimental validation of 3D printed patient-specific implants using digital image correlation and finite element analysis. Comput Biol Med. 2014;52:8-17. doi: 10.1016/j.compbiomed.2014.06.002
  10. Nasajpour-Esfahani N, Karimi S, Nasseri S, et al. Advancements and applications of digital image correlation to characterize residual stress: A review. Mater Charact. 2025;228:115416. doi: 10.1016/j.matchar.2025.115416
  11. Grassi L, Väänänen SP, Ristinmaa M, Jurvelin JS, Isaksson H. How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements. J Biomech. 2016;49(5):802- 806. doi: 10.1016/j.jbiomech.2016.02.032
  12. Dall’Ara E, Tozzi G. Digital volume correlation for the characterization of musculoskeletal tissues: Current challenges and future developments. Front Bioeng Biotechnol. 2022;10:1010056. doi: 10.3389/fbioe.2022.1010056
  13. Beberniss TJ, Ehrhardt DA. High-speed 3D digital image correlation vibration measurement: Recent advancements and noted limitations. Mech Syst Signal Process. 2017;86:35- 48. doi: 10.1016/j.ymssp.2016.04.014
  14. Seidt J, Kuokkala V, Smith J, Gilat A. Synchronous full-field strain and temperature measurement in tensile tests at low, intermediate and high strain rates. Exp Mech. 2017;57(2):219-229. doi: 10.1007/s11340-016-0237-z
  15. Qiao Y, Salviato M. Micro-computed tomography analysis of damage in notched composite laminates under multi-axial fatigue. Compos B Eng. 2020;187:107789. doi: 10.1016/j.compositesb.2020.107789
  16. Lava P, Jones EM, Wittevrongel L, Pierron F. Validation of finite‐element models using full‐field experimental data: Levelling finite‐element analysis data through a digital image correlation engine. Strain. 2020;56(4):e12350. doi: 10.1111/str.12350
  17. Zdero R, Bougherara H. Orthopaedic biomechanics: a practical approach to combining mechanical testing and finite element analysis. In: Moratal D, ed. Finite Element Analysis. IntechOpen; 2010:698. doi: 10.5772/10077
  18. Zhang Y, Evans JRG, Yang S. Exploring correlations between properties using artificial neural networks. Metall Mater Trans A. 2019;51(1):58-75. doi: 10.1007/s11661-019-05502-8
  19. Lin Z, Wu H, Zhang Z, et al. Inter-layer multi-material powder bed fusion additive manufacturing of a chainmail-like 3D interlocking bonding structure. Virtual Phys Prototyp. 2025;20(1):e2525985. doi: 10.1080/17452759.2025.2525985
  20. Ten Brink RSA, Merema BJ, den Otter ME, Jensma ML, Witjes MJH, Kraeima J. Automatic MRI segmentation of masticatory muscles using deep learning enables large-scale muscle parameter analysis. Int J Oral Maxillofac Surg. 2025;54(10):956-962. doi: 10.1016/j.ijom.2025.05.008
  21. Lawand G, Gonzaga L, Issa J, et al. Artificial intelligence segmentation errors in implant planning software programs: An overview. Clin Implant Dent Relat Res. 2025;27(5):e70095. doi: 10.1111/cid.70095
  22. Huang J, Jie J, Ma H, et al. Deep learning for automated mandibular canal segmentation in CBCT scans. BMC Oral Health. 2025;25(1):1699. doi: 10.1186/s12903-025-07098-5
  23. Zhou X, Zhang G, Huang A, Zhou H. The topology optimization design and processing of 3D-printed bio-fixed bone plate applied to the mandible. BMC Oral Health. 2025;25(1):1-14. doi: 10.1186/s12903-025-06923-1
  24. Beisekenov N, Azamatov B, Sadenova M, et al. Data-driven design and additive manufacturing of patient-specific lattice titanium scaffolds for mandibular bone reconstruction. J Funct Biomater. 2025;16(9):350. doi: 10.3390/jfb16090350
  25. Rodriguez-Molinero J, Prados-Privado M. Time-resolved prediction of dental implant biomechanics through integration of finite element analysis, osseointegration dynamics, and deep learning. J Mech Behav Biomed Mater. 2026;175:107316. doi: 10.1016/j.jmbbm.2025.107316
  26. Tel A, Kornfellner E, Moscato F, et al. Optimizing efficiency in the creation of patient-specific plates through field-driven generative design in maxillofacial surgery. Sci Rep. 2023;13(1):12082. doi: 10.1038/s41598-023-39327-8
  27. Peng B, Wei Y, Qin Y, et al. Machine learning-enabled constrained multi-objective design of architected materials. Nat Commun. 2023;14(1):6630. doi: 10.1038/s41467-023-42415-y
  28. Zhong C, Xiong Y, Tang W, Guo J. A stage-wise residual attention generation adversarial network for mandibular defect repairing and reconstruction. Int J Neural Syst. 2024;34(7):2450033. doi: 10.1142/S0129065724500333
  29. Elgarba BM, Fontenele RC, Du X, et al. Artificial intelligence versus human intelligence in presurgical implant planning: A preclinical validation. Clin Oral Implants Res. 2025;36(7):835-845. doi: 10.1111/clr.14429
  30. Lian ZQ, Guan H, Loo YC, Ivanovski S, Johnson NW. Finite element simulation of bone remodelling in human mandible around osseointegrated dental implant. IOP Conf Ser Mater Sci Eng. 2010;10:012125. doi: 10.1088/1757-899x/10/1/012125
  31. Su K, Gao C, Qiu G, Yuan L, Yang J, Du J. Numerical simulation of mechanically adaptive bone remodeling around teeth and implants: A comparison with clinical images. JOM. 2022;74(12):4640-4651. doi: 10.1007/s11837-022-05533-4
  32. Yoda N, Zheng K, Chen J, et al. Biomechanical analysis of bone remodeling following mandibular reconstruction using fibula free flap. Med Eng Phys. 2018;56:1-8. doi: 10.1016/j.medengphy.2018.03.008
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Engineering Science in Additive Manufacturing, Electronic ISSN: 3082-849X Published by AccScience Publishing
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