Light-curing additive manufacturing is extensively employed in high-precision industries due to its capability to generate products with exceptional surface quality. Nonetheless, given the susceptibility of the surface of light-curing additive manufacturing models to various factors, the current model is constructed based on design parameters, which may not precisely replicate the actual model surface. This constraint hampers the analytical work across various stages. Moreover, distinct analysis stages may necessitate varied physical models. While it is feasible to produce corresponding models for each stage, this approach may result in time wastage and reduced efficiency. To address this issue, this paper introduces a digital twin system for light-curing additive manufacturing, incorporating an integrated algorithm specifically designed for constructing rough surfaces. The algorithm employs the Fast Fourier Transform (FFT), Johnson Transformation System, and autocorrelation Function to generate the surface topography of the model. Additionally, the system can monitor the printer’s stability throughout the printing process. To validate the feasibility of this system, a DT system was implemented to monitor the printer’s stability throughout the printing process and construct the surface topography of the printed model. The surface of the physical model was measured using a 3D surface profiler and perform statistical analysis of the model surface data. Finally compared with the rough surface constructed by the DT system. The results indicate that the characteristic parameter errors of the model surface are all below 5%, providing evidence that the rough surface constructed by the system fulfills the specified requirements.
HuangYLeuMCMazumderJ, et al. Additive manufacturing: current state, future potential, gaps and needs, and recommendations. J Manuf Sci Eng2015; 137(1): 014001. DOI: 10.1115/1.4028725
2.
AbdulhameedOAl-AhmariAAmeenW, et al. Additive manufacturing: challenges, trends, and applications. Adv Mech Eng2019; 11(2): 168781401882288.
3.
HuangSHLiuPMokasdarA, et al. Additive manufacturing and its societal impact: a literature review. Int J Adv Manuf Technol2013; 67(5-8): 1191–1203.
4.
Mercado RiveraFJRojas ArciniegasAJ. Additive manufacturing methods: techniques, materials, and closed-loop control applications. Int J Adv Manuf Technol2020; 109(1-2): 17–31.
5.
RajaguruKKarthikeyanTVijayanV.Additive manufacturing – state of art. Mater Today Proc2020; 21: 628–633.
6.
GalantucciLMGuerraMGDassistiM, et al. Additive manufacturing: new trends in the 4th industrial revolution. In: MonostoriLMajstorovicVDHuSJ, et al. (eds) The industry 4.0 model for advanced manufacturing AMP 2019. Cham: Springer International Publishing, 2019, pp.153–169.
7.
PagacMHajnysJMaQP, et al. A review of vat photopolymerization technology: materials, applications, challenges, and future trends of 3d printing. Polymers2021; 13(4): 598.
8.
ScheffelRMFröhlichAASilvestriM.Automated fault detection for additive manufacturing using vibration sensors. Int J Comput Integr Manuf2021; 34(5): 500–514.
9.
ChhetriSRFaeziSCanedoA, et al. Quilt: quality inference from living digital twins in iot-enabled manufacturing systems. In: Proceedings of the international conference on internet of things design and implementation, Montreal, QC, 2019, pp.237–248Association for Computing Machinery.
10.
HensonCMDeckerNIHuangQ.A digital twin strategy for major failure detection in fused deposition modeling processes. Procedia Manuf2021; 53: 359–367.
11.
ReischRTHauserTLutzB, et al. Context awareness in process monitoring of additive manufacturing using a digital twin. Int J Adv Manuf Technol2022; 119(5-6): 3483–3500.
12.
LiuXKanCYeZ.Real-time multiscale prediction of structural performance in material extrusion additive manufacturing. Addit Manuf2022; 49: 102503.
13.
SieberIThelenRGengenbachU.Enhancement of high-resolution 3d inkjet-printing of optical freeform surfaces using digital twins. Micromachines2021; 12(1): 35.
14.
WeiYHuTZhouT, et al. Consistency retention method for cnc machine tool digital twin model. J Manuf Syst2021; 58: 313–322.
15.
TaoFXiaoBQiQ, et al. Digital twin modeling. J Manuf Syst2022; 64: 372–389.
16.
LiuXJiangDTaoB, et al. A systematic review of digital twin about physical entities, virtual models, twin data, and applications. Adv Eng Inform2023; 55: 101876.
17.
SemeraroCLezocheMPanettoH, et al. Digital twin paradigm: a systematic literature review. Comput Ind2021; 130: 103469.
18.
LiuMFangSDongH, et al. Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst2021; 58: 346–361.
19.
ZhuangCMiaoTLiuJ, et al. The connotation of digital twin, and the construction and application method of shop-floor digital twin. Robot Comput Integr Manuf2021; 68: 102075.
20.
FangXWangHLiuG, et al. Industry application of digital twin: from concept to implementation. Int J Adv Manuf Technol2022; 121(7-8): 4289–4312.
21.
LiuQZhangHLengJ, et al. Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. Int J Prod Res2019; 57(12): 3903–3919.
22.
QiQTaoFHuT, et al. Enabling technologies and tools for digital twin. J Manuf Syst2021; 58: 3–21.
23.
KarkariaVGoecknerAZhaR, et al. Towards a digital twin framework in additive manufacturing: machine learning and bayesian optimization for time series process optimization. J Manuf Syst2024; 75: 322–332.
24.
ZhengZWangY.Design of digital twin monitoring system for light-curing 3D printers for rapid prototyping of shaped parts. Packag Food Mach2024; 42: 82–87.
25.
Castelló-PedreroPGarcía-GascónCGarcía-ManriqueJA.Multiscale numerical modeling of large-format additive manufacturing processes using carbon fiber reinforced polymer for digital twin applications. Int J Mater Forming2024; 17(2): 15.
26.
ZhengZWangYLiJ, et al. Development of a digital twin system for acquiring surface features of solid models in light-curing additive manufacturing. Int J Adv Manuf Technol2024; 135(1-2): 663–675.
27.
IngrassiaTNigrelliVRicottaV, et al. Process parameters influence in additive manufacturing. In: EynardBNigrelliVOliveriSM, et al. (eds) Advances on mechanics, design engineering and manufacturing: proceedings of the international joint conference on mechanics, design engineering & advanced manufacturing (JCM 2016), Catania, Italy, 14–16 September 2016, 2017, pp.261–270. Cham: Springer International Publishing.
28.
BarariAKishawyHAKajiF, et al. On the surface quality of additive manufactured parts. Int J Adv Manuf Technol2017; 89(5-8): 1969–1974.
29.
AnithaRArunachalamSRadhakrishnanP.Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol2001; 118(1–3): 385–388.
30.
JamiolahmadiSBarariA.Surface topography of additive manufacturing parts using a finite difference approach. J Manuf Sci Eng2014; 136(6): 061009. DOI: 10.1115/1.4028585
31.
ReevesPECobbRC.Reducing the surface deviation of stereolithography using in-process techniques. Rapid Prototyp J1997; 3(1): 20–31.
32.
GreenwoodJAWilliamsonJP.Contact of nominally flat surfaces. Proc R Soc Lond A Math Phys Sci1966; 295(1442): 300–319.
33.
KasarekarATBolanderNWSadeghiF, et al. Modeling of fretting wear evolution in rough circular contacts in partial slip. Int J Mech Sci2007; 49(6): 690–703.
34.
JacksonRLGreenI.On the modeling of elastic contact between rough surfaces. Tribol Trans2011; 54(2): 300–314.
35.
GalanovBA.Models of adhesive contact between rough elastic solids. Int J Mech Sci2011; 53(11): 968–977.
36.
PutignanoCAfferranteLCarboneG, et al. A new efficient numerical method for contact mechanics of rough surfaces. Int J Solids Struct2012; 49(2): 338–343.
PatirN.A numerical procedure for random generation of rough surfaces. Wear1978; 47(2): 263–277.
39.
WuJJ.Simulation of rough surfaces with fft. Tribol Int2000; 33(1): 47–58.
40.
ManeshKKRamamoorthyBSingaperumalM.Numerical generation of anisotropic 3D non-Gaussian engineering surfaces with specified 3d surface roughness parameters. Wear2010; 268(11–12): 1371–1379.
41.
ReizerR.Simulation of 3D Gaussian surface topography. Wear2011; 271(3-4): 539–543.
42.
TianALSunZYuX, et al. Numerical simulation of non-Gaussian random roughness surface. J Syst Simul2009; 21(10): 2840–2842.
43.
GoedeckeAJacksonRLMockR.A fractal expansion of a three dimensional elastic–plastic multi-scale rough surface contact model. Tribol Int2013; 59: 230–239.
44.
JacksonRL.An analytical solution to an Archard-type fractal rough surface contact model. Tribol Trans2010; 53(4): 543–553.
45.
KuleszaSBramowiczM.A comparative study of correlation methods for determination of fractal parameters in surface characterization. Appl Surf Sci2014; 293: 196–201.
46.
WangYLiuYZhangG, et al. A simulation method for non-Gaussian rough surfaces using fast Fourier transform and translation process theory. J Tribol2018; 140(2): 021403. DOI: 10.1115/1.4037793
47.
RosuIElias-BirembauxHLebonF, et al. Experimental and numerical simulation of the dynamic frictional contact between an aircraft tire rubber and a rough surface. Lubricants2016; 4(3): 29.
48.
PolgeRJHollidayEMBhagavanBK.Generation of a pseudo-random set with desired correlation and probability distribution. Simulation1973; 20(5): 153–158.
49.
WatsonWSpeddingTA.The time series modelling of non-Gaussian engineering processes. Wear1982; 83(2): 215–231.
50.
XinjianGYiyunH.A simulation method for non-normal random processes. J Time Ser Anal1989; 10(4): 373–374.
