The lack of an assembly error control method in the hull rib plate assembly process with the pull-in method results in time-consuming assembly, low precision, and high rework rates. This paper proposes an error analysis and control method based on inverse modeling to address these challenges. A multi-site fusion scheme for acquiring and pre-processing solid shape data of large-scale hull components is designed, and based on the cross-sectional geometrical features of these components, their accurate inverse models are reconstructed. The error analysis and repair guidance for the fit clearance between the longitudinal member and penetration holes of rib plates is achieved by integrating digital error detection and virtual pre-assembly methods. To verify the effectiveness of the proposed method, we applied it to a double-bottom section assembly and analyzed the results. This study provides operational guidance for the hull rib plate assembly site and significantly improves assembly efficiency and accuracy.
ZhouHYanZJingX, et al. Research on the three-dimensional visual management and control for marine intelligent mould bed based on digital twin. Ships Offshore Struct2024; 19(6): 756–768.
2.
HeYChenYZouX, et al. A clearance control and interference repair method for ship rib plate assembly based on reverse engineering. Ocean Eng2024; 309(1): 118402.
3.
WangCZhouDMaoB, et al. Application research of 3D laser scanning technology in hull rib assembly. In: 2022 International Conference on Manufacturing, Industrial Automation and Electronics (ICMIAE), 2022, pp.174–178. New York: IEEE.
4.
YangC.Optimization of section construction process for 300000 series oil tanker. Liaoning: Dalian University of Technology, 2018.
5.
WangJYuZLiL.Technical research of floor pulled-in method. Ship Stand Eng2015; 48(5): 27–29.
6.
LiuE.Construction method and procedure research of large vessel in modern shipbuilding mode. Heilongjiang: Harbin Engineering University, 2011.
7.
WangKHuQLiuJ.Digital twin-driven approach for process management and traceability towards ship industry. Processes2022; 10(6): 1083.
8.
IwańkowiczR.A multi-case-based assembly management method for the shipbuilding industry. Pol Marit Res2021; 28(2): 27–35.
9.
WangY.Application of three-dimensional solid modeling of virtual reality technology in ship assembling. Int J Mechatron Appl Mech2017; 1(1): 33–38.
10.
YoonSWangQSohnH.Optimal placement of precast bridge deck slabs with respect to precast girders using 3D laser scanning. Autom Constr2018; 86: 81–98.
11.
TranTTHaC.Non-contact gap and flush measurement using monocular structured multi-line light vision for vehicle assembly. Int J Control Autom Syst2018; 16(5): 2432–2445.
12.
PengM.Research on pre-correction of ship curvy component. Hubei: Wuhan University of Technology, 2016.
13.
FelskiA.Measuring the speed of docking ship with total station. Commun Sci Lett Univ Zilina2022; 24(1): E1–E10.
14.
KimYKimJKangS.A study on welding deformation prediction for ship blocks using the equivalent strain method based on inherent strain. Appl Sci2019; 9(22): 4906.
15.
LiJ.Research on improved frame-curvature unfolding method of the complex curved hull plate. Liaoning: Dalian University of Technology, 2015.
16.
FengYLiuYWangJ, et al. Research on an SVM prediction of welding deformation rectification for high-strength steel fillet-welded joints after traveling induction heating. J Ship Prod Des2023; 39(1): 43–53.
17.
HuGYaoLJianH, et al. Simulation of welding and line heating process chain for a T-shape part of ship hull stiffened palate. China Shipbuilding2017; 58(1): 115–124.
18.
de Campos PorathMSimoniRde Araujo NunesR, et al. Feasibility of measurement-assisted assembly of ship hull blocks. Mar Syst Ocean Technol2019; 14: 23–33.
19.
KunkeraZOpetukTHadžićN, et al. Using digital twin in a shipbuilding project. Appl Sci2022; 12(24): 12721.
20.
DevAKFungZK. Simulation of hull panel logistics improvement in a shipyard. In: Proceedings of the international conference on computer applications in shipbuilding, 2019, pp.2–6, Amsterdam.
21.
ZhuYYangJZhangH, et al. Intelligent assembly assistance for hull structure construction based on optical projection. Assem Autom2022; 42(2): 258–267.
22.
HeYYangJHouX, et al. ICP registration with DCA descriptor for 3D point clouds. Opt Express2021; 29(13): 20423–20439.
23.
ShiXLiuTHanX.Improved iterative closest point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registrationInt J Remote Sens2020; 41(8): 3197–3220.
24.
LiKZhangYTongX, et al. Research on de-noising and filtering algorithm of single photon lidar data. Navig Control2020; 19(1): 67–76.
25.
AsokanAAnithaJ.Adaptive cuckoo search based optimal bilateral filtering for denoising of satellite images. ISA Trans2020; 100: 308–321.
26.
BlundellHLWeirTKerrB, et al. Predictability of overbite control with the invisalign appliance. Am J Orthod Dentofacial Orthop2021; 160(5): 725–731.
27.
ZhangXJiaZZhangX.Template based interference detection technology of large components assembly. J Mech Eng2010; 46(1): 147–153.
