This study aims at the parametric investigation of Fused Deposition Modelling (FDM) and developed Polylactic acid (PLA) for the optimization of surface roughness. A hybrid Response Surface Optimization with Artificial Neural Network i.e., RSO-ANN methodology was adopted for the optimization of different input parameters namely number of layers, infill ratio, infill orientation and printing temperature which affect the surface roughness of PLA. The ANN model predicted with the regression values 0.9225 which represent the adequacy of the predicted model. The predicted results closely match the experimental data with an error percentage ranging from 0.83% to 1.04%. Thus, it resulted into the effectiveness of the ANN model. Under these parametric configuration, the surface roughness is significantly reduced from 1.2 to 0.8 µm.
FidanIHuseynovOAliMA, et al.Recent inventions in additive manufacturing: holistic review. Inventions2023; 8: 103.
2.
SilvaLRGLopesCECTanakaAA, et al.Electrochemical biosensors 3D printed by fused deposition modeling: actualities, trends, and challenges. Biosensors2025; 15: 57.
SeghierMEA BenKeshtegarBTaleb-berrouaneM, et al.Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines. Process Saf Environ Prot[Internet]2021; 147: 818–833. Available from:
5.
ErricoVPosaPAngelastroA, et al.International journal of refractory metals and hard materials enhancing coatings mechanical performance by advanced laser deposition of WCCoCr-colmonoy composites. Int J Refract Met Hard Mater2024; 125: 106898.
6.
JiRLiuYZhangY, et al.Influence of dielectric and machining parameters on the process performance for electric discharge milling of SiC ceramic. Int J Adv Manuf Technol[Internet]2012; 59: 127–136. Available from:
ZhangYChouYK. Three-dimensional finite element analysis simulations of the fused deposition modelling process. Proc Inst Mech Eng Part B J Eng Manuf2006; 220: 1663–1671.
JiRZhaoQZhaoL, et al.Study on high wear resistance surface texture of electrical discharge machining based on a new water-in-oil working fluid. Tribol Int2023; 180: 108218.
11.
HuYMouritzAPLadaniRB, et al.3D Printing of curved continuous fibre filaments using fused deposition modelling. Mater Des2025; 252: 113762.
12.
SukindarNAYasirASHMAzharMD, et al.Evaluation of the surface roughness and dimensional accuracy of low-cost 3D-printed parts made of PLA–aluminum. Heliyon2024; 10: e25508.
13.
GaoSWangHHuangH, et al.Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding. Int J Extrem Manuf[Internet]2025; 7: 35103. Available from:
14.
ChakrabortyPZhaoGZhouC, et al.Unprecedented sensing of interlayer defects in three-dimensionally printed polymer by capacitance measurement. Smart Mater Struct2018; 27: 115012.
15.
SkibickiSSzewczykPMajewskaJ, et al.The effect of interlayer adhesion on stress distribution in 3D printed beam elements. J Build Eng2024; 87: 109093.
16.
PerezDBCelikEKarkkainenRL.Investigation of interlayer interface strength and print morphology effects in fused deposition modeling 3D-printed PLA. 3D Print Addit Manuf2021; 8: 23–32.
17.
LongJZhouYLinJ, et al.Printing dense and low-resistance copper microstructures via highly directional laser-induced forward transfer. Addit Manuf2025; 103: 104755.
18.
AtakokGKamMKocHB. Tensile, three-point bending and impact strength of 3D printed parts using PLA and recycled PLA filaments: a statistical investigation. J Mater Res Technol2022; 18: 1542–1554.
19.
Lalegani DezakiMAriffinMKAMSerjoueiA, et al.Influence of infill patterns generated by CAD and FDM 3D printer on surface roughness and tensile strength properties. Appl Sci2021; 11: 7272.
20.
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.
21.
FayazfarHSharifiJKeshavarzMK, et al.An overview of surface roughness enhancement of additively manufactured metal parts: a path towards removing the post-print bottleneck for complex geometries. Int J Adv Manuf Technol2023; 125: 1061–1113.
22.
SunQRizviGMBellehumeurCT, et al.Effect of processing conditions on the bonding quality of FDM polymer filaments. Rapid Prototyp J2008; 14: 72–80.
23.
ZhaoDGuoW. Shape and performance controlled advanced design for additive manufacturing: a review of slicing and path planning. J Manuf Sci Eng2020; 142: 10801.
24.
KumaresanRKadirgamaKSamykanoM, et al.Optimization of inter-layer printing parameters for enhanced mechanical performance of PETG in fused deposition modeling (FDM). Results Eng2025; 25: 104564.
25.
ZhengYTianZYuZ, et al.A thermal history-based approach to predict mechanical properties of plasma arc additively manufactured IN625 thin-wall. J Manuf Process[Internet]2025; 140: 91–107. https://www.sciencedirect.com/science/article/pii/S1526612525001926. Available from.
26.
JakupiKDukovskiVHodolliG. Surface roughness modeling of material extrusion PLA flat surfaces. Int J Polym Sci2023; 2023: 8844626.
ChandRSharmaVSTrehanR, et al.Investigating the dimensional accuracy and surface roughness for 3D printed parts using a multi-jet printer. J Mater Eng Perform2023; 32: 1145–1159.
31.
MoradiMMeiabadiMSSSiddiqueU, et al.Circular economy-driven repair of 3D printed polylactic acid (PLA) by fused deposition modelling (FDM) through statistical approach. Mater Today Commun2025; 42: 111264.
32.
ZhaoKYeZSuZ, et al.A diffusion-controlled kinetic model for binder burnout in a green part fabricated by binder jetting based on the thermal decomposition kinetics of TEG-DMA. Addit Manuf2025; 105: 104793.
33.
PeiXPeiJHouH, et al.Optimizing casting process using a combination of small data machine learning and phase-field simulations. npj Comput Mater[Internet]2025; 11: 27. Available from:
SharmaVSinghJPHussanK, et al.Machine learning approach for predicting the machining characteristics of additive manufactured ABS material. Proc Inst Mech Eng Part C J Mech Eng Sci2025; 239: 129–144.
36.
FerreiraSLCBrunsREFerreiraHS, et al.Box-Behnken design: an alternative for the optimization of analytical methods. Anal Chim Acta2007; 597: 179–186.
37.
Szpisják-GulyásNAl-TayawiANHorváthZet al.Methods for experimental design, central composite design and the box–behnken design, to optimise operational parameters: a review. Acta Aliment2023; 52: 521–537.
38.
PengLGaoXWangL, et al.Design of experiment techniques for the optimization of chromatographic analysis conditions: a review. Electrophoresis2022; 43: 1882–1898.
39.
LeeBCYMahtabMSNeoTH, et al.A comprehensive review of design of experiment (DOE) for water and wastewater treatment application-key concepts, methodology and contextualized application. J Water Process Eng2022; 47: 102673.
40.
LatifAMaqboolAZhouR, et al.Optimized degradation of bisphenol A by immobilized laccase from trametes versicolor using box-behnken design (BBD) and artificial neural network (ANN). J Environ Chem Eng2022; 10: 107331.
41.
SinghJSinghS.Neural network supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating. J King Saud Univ–Eng Sci[Internet]2024; 36: 662–676. Available from:
42.
WangPZouBDingS. Modeling of surface roughness based on heat transfer considering diffusion among deposition filaments for FDM 3D printing heat-resistant resin. Appl Therm Eng2019; 161: 114064.
43.
AndreuAKimSDittusJ, et al.Hybrid material extrusion 3D printing to strengthen interlayer adhesion through hot rolling. Addit Manuf2022; 55: 102773.
44.
BakhtiariHNikzadMTolouei-RadM. Influence of three-dimensional printing parameters on compressive properties and surface smoothness of polylactic acid specimens. Polymers (Basel)2023; 15: 3827.
45.
RastogiPGhardeSKandasubramanianB. Thermal effects in 3D printed parts. In: Singh S, Prakash C and Singh R (eds) 3D printing in biomedical engineering. Materials horizons: from nature to nanomaterials. Singapore: Springer, 2020, pp. 43–68.
46.
MoradiMRezayatMRozhbianyFAR, et al.Correlation between infill percentages, layer width, and mechanical properties in fused deposition modelling of poly-lactic acid 3D printing. Machines2023; 11: 950.
47.
ÖzkülMKuncanFUlkirO. Predicting mechanical properties of FDM-produced parts using machine learning approaches. J Appl Polym Sci2025; 142: e56899.
48.
DjurovićSLazarevićDĆirkovićB, et al.Modeling and prediction of surface roughness in hybrid manufacturing–milling after FDM using artificial neural networks. Appl Sci2024; 14: 5980.
49.
TsaiY-HChenJCLouS-J. An in-process surface recognition system based on neural networks in end milling cutting operations. Int J Mach Tools Manuf1999; 39: 583–605.
50.
YilmazSAriciAAFeyzullahogluE. Surface roughness prediction in machining of cast polyamide using neural network. Neural Comput Appl2011; 20: 1249–1254.
51.
ShieJ-R. Optimization of dry machining parameters for high-purity graphite in end-milling process by artificial neural networks: a case study. Mater Manuf Process2006; 21: 838–845.
