This study investigates the influence of key fused deposition modeling (FDM) parameters—namely infill density. printing speed. and nozzle temperature—on the tensile behavior of 3D-printed PLA–wood composite anti-trichiral structures. Tensile experiments were conducted to evaluate the UTS and elongation at break. highlighting the strong dependence of mechanical response on both processing conditions and architectured geometry. A Taguchi-based experimental design was employed to generate the dataset used for training an artificial neural network (ANN) model. The ANN was applied as a predictive tool to estimate tensile properties and in combination with multi-criteria decision-making (MCDM) methods. to perform a multi-objective optimization balancing ultimate tensile strength (UTS) and elongation at break within the investigated parameter range. The results show that the highest measured UTS (6.12 MPa) was achieved at an infill density of 25% while the maximum elongation at break (19.3%) occurred at a lower infill density of 5%. The ANN predictions exhibited good agreement with experimental results. indicating promising predictive capability rather than definitive accuracy. and demonstrating the potential of data-driven modeling for guiding process optimization. Overall. this work introduces a novel hybrid ANN–MCDM framework applied to bio-based PLA–wood composite anti-trichiral lattices. providing new insights into processing–structure–property relationships and highlighting the relevance of artificial intelligence tools for the design and optimization of architectured materials in additive manufacturing.
MilićevićIPopovićMDučićN, et al.Improving the mechanical characteristics of the 3d printing objects using hybrid machine learning approach. FU Mech Eng2026; 24: 157. https://doi.org/10.22190/FUME220429036M
2.
TaşcıoğluEKıtayÖKeskinAÖet al.Effect of printing parameters and postprocess on surface roughness and dimensional deviation of PLA parts fabricated by extrusionbased 3D printing. J Braz Soc Mech Sci Eng2022; 44: 139. https://doi.org/10.1007/s40430-022-03429-7
3.
RosentrittMPreisVBehrM, et al.Shear bond strength between veneering composite and PEEK after different surface modifications. Clin Oral Invest2015; 19: 739–744. https://doi.org/10.1007/s00784-014-1294-2
4.
NajeebSZafarMSKhurshidZ, et al.Applications of polyether ether ketone (PEEK) in oral implantology and prosthodontics. J Prosthodont Res2016; 60: 12–19. https://doi.org/10.1016/j.jpor.2015.10.001
5.
JyotismanBChandrasekaranM. Experimental investigation and development of artificial neural network modeling of 3D printed PEEK bio implants and its optimization. https://doi.org/10.21203/rs.3.rs-3204960/v1
6.
UlkirOKuncanFAlayFD. Experimental study and ANN development for modeling tensile and surface quality of fiber-reinforced nylon composites. Polymers2025; 17: 1528. https://doi.org/10.3390/polym17111528
7.
AlgarniMGhazaliS. Comparative study of the sensitivity of PLA. ABS. PEEK. and PETG’s mechanical properties to FDM printing process parameters. Crystals2021; 11(8). 995. https://doi.org/10.3390/cryst11080995
8.
NabipourMAkhoundiB. An experimental study of FDM parameters effects on tensile strength. Density. and production time of ABS/Cu composites. J Elastomers Plast2021; 53(2): 146–164. https://doi.org/10.1177/0095244320916838
9.
WickramasingheSDoTTranP. FDM-based 3D printing of polymer and associated composite - a review on mechanical properties. Defects and treatments. Polymers2020; 12: 1529. https://doi.org/10.3390/polym12071529
10.
Garzon-HernandezSGarcia-GonzalezaDJérusalemA, et al.Design of FDM 3D printed polymers - an experimental-modelling methodology for the prediction of mechanical properties. Mater Des2020; 188: 108414. https://doi.org/10.1016/j.matdes.2019.108414
11.
YadavDChhabraDGuptaRK, et al.Modeling and analysis of significant process parameters of FDM 3D printer using ANFIS. Mater Today Proc2019; 21(3): 1592–1604. https://doi.org/10.1016/j.matpr.2019.11.227
KamaalMAnasMRastogiH, et al.Effect of FDM process parameters on mechanical properties of 3D-Printed carbon Fibre–PLA composite. Progr Addit Manuf2021; 6: 63–69. https://doi.org/10.1007/s40964-020-00145-3
14.
VicenteCMSMartinsTSLeiteM, et al.Influence of fused deposition modeling parameters on the mechanical properties of ABS parts. Polym Adv Technol2020; 31: 501–507. https://doi.org/10.1002/pat.4787
15.
PrayitnoGImaduddinFUbaidillahAZ, et al.Recent progress of fused deposition modeling (FDM) 3D printing: constructions. Parameters and processings. IOP Conf. Series: Materials Science and Engineering2021; 1096: 012045. https://doi.org/10.1088/1757-899X/1096/1/012045
16.
SamykanoMSelvamaniSKKadirgamaK, et al.Mechanical property of FDM printed ABS: influence of printing parameters. Int J Adv Manuf Technol2019; 102: 2779–2796. https://doi.org/10.1007/s00170-019-03313-0
17.
GuptaASharmaSMadkeRR, et al.In-plane mechanical behavior of tri-chiral and anti-trichiral auxetic cellular structures. Int J Mech Sci2025; 289: 110054, ISSN 0020-7403. https://doi.org/10.1016/j.ijmecsci.2025.110054
18.
HuLLLuoZRZhangZY, et al.Mechanical property of re-entrant anti-trichiral honeycombs under large deformation. Compos B Eng2019; 163: 107–120, ISSN 1359-8368. https://doi.org/10.1016/j.compositesb.2018.11.010
19.
AliDAzadiM. Optimization of 3D printing parameters in polylactic acid bio-metamaterial under cyclic bending loading considering fracture features. Heliyon2024; 10: e26357. https://doi.org/10.1016/j.heliyon.2024.e26357
20.
ZhaobingLLeiQXingS. Mechanical characteristics of wood. ceramic. metal and carbon fiber-based PLA composites fabricated by FDM. J Mater Res Technol2019; 8(5): 3741–3751. https://doi.org/10.1016/j.jmrt.2019.06.034
21.
RajaPDurai MuruganRAravindT, et al.Mechanical properties assesment of PLA mixed wood by using additive manufacturing. IJCRT2024; 12(1): 2320–2882. January 2024 | ISSN. https://www.ijcrt.org/papers/IJCRT2401779.pdf
22.
KumarPGuptaPSinghI. Parametric optimization of FDM using the ANN-based whale optimization algorithm. Artificial intelligence for engineering design. Analysis and Manufacturing2022; 36: e27. https://doi.org/10.1017/S0890060422000142
23.
ObaidatSTamimiMFMumaniA, et al.An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling. Rapid Prototyp J2024; 30(10): 2056–2070. https://doi.org/10.1108/RPJ-04-2024-0168
24.
YadavDChhabraDGargRK, et al.Optimization of FDM 3D printing process parameters for multi-material using artificial neural network. Mater Today Proc2020; 21(Part 3): 1583–1591, ISSN 2214-7853. https://doi.org/10.1016/j.matpr.2019.11.225
25.
ShirmohammadiMGoushchiSJKeshtibanPM. Optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm. Prog Addit Manuf2021; 6: 199–215. https://doi.org/10.1007/s40964-021-00166-6
26.
AlhaddadWHeMHalabiY, et al.Optimizing the material and printing parameters of the additively manufactured fiber-reinforced polymer composites using an artificial neural network model and artificial bee colony algorithm. Structures2022; 46: 1781–1795, ISSN 2352-0124. https://doi.org/10.1016/j.istruc.2022.10.134
27.
HamrouniARebiereJ-LEl MahiA, et al.Caractérisation Mécanique d’un Meta-Matériau de Forme Anti- Trichirale Fabriqué par la Technique d’Impression 3D avec un Composite Biosourcé et Biodégradable. In: 16ème Congrès Français d’Acoustique, Marseille. France, Apr 2022. CFA2022. Société Française d’Acoustique; Laboratoire de Mécanique et d’Acoustique. ffhal-03848312.
28.
ASTM D638-14. Standard test method for tensile properties of plastics. ASTM International, 2014.
29.
BaharAHamamiABenmahiddineF, et al.The thermal and mechanical behaviour of wood-PLA composites processed by additive manufacturing for building insulation. Polymers2023; 15(14): 3056. https://doi.org/10.3390/polym15143056
30.
OzkulMKuncanFUlkirO. Predictive modeling of additively manufactured carbon fiber-PLA mechanical components via ML. Multidiscip Model Mater Struct2025; 21(5): 1092–1110. https://doi.org/10.1108/MMMS-12-2024-0387
31.
HonigmannPSharmaNOkoloB, et al.Patient-specific surgical implants made of 3D printed PEEK: material. Technology. and scope of surgical application. BioMed research international2018; 2018: 4520636. https://doi.org/10.1155/2018/4520636
32.
JózwikJOstrowskiDMilczarczykR, et al.Analysis of relation between the 3D printer laser beam power and the surface morphology properties in Ti-6Al-4V titanium alloy parts. J Braz Soc Mech Sci Eng2018; 40: 215. https://doi.org/10.1007/s40430-018-1144-2
33.
RisboodKADixitUSSahasrabudheAD. Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol2023; 13: 203–214. https://doi.org/10.1016/S0924-0136(02)00920-2
34.
HambliRKaterchiKBenhamouC-L. Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation. Biomech Model Mechanobiol2011; 10(1): 133–145. https://doi.org/10.1007/s10237-010-0222-x
35.
KhaterchiHChamekhABelHadjSalahH. Artificial neural network analysis for modeling fibril structure in bone. Int J Precis Eng Manuf2015; 16(3): 581–587. https://doi.org/10.1007/s12541-015-0078-1
36.
AzouziRGuillotM. On-line optimization of turning process using an inverse process neuron controller. J Manuf Sci Eng1998; 120: 101–108. https://doi.org/10.1115/1.2830085
37.
MuzliMFIzwanIsmailKYapTC. Effects of infill density and printing speed on the tensile behaviour of fused deposition modelling 3D printed PLA specimens. Journal of Engineering Technology and Applied Physics2024; 6. 2. 1:1–8. https://doi.org/10.33093/jetap.2024.6.2
38.
LeTDoanQTMaiDS, et al.« Prediction and optimization of processing parameters in wire and arc-based additively manufacturing of 316L stainless steel. J Braz Soc Mech Sci Eng2022; 44: 394. https://doi.org/10.1007/s40430-022-03698-2
