Key Technology and Application of Digital Twin Modeling for Deformation Control of Investment Casting
GUAN Bang1, WANG Donghong1,2(), MA Hongbo3, SHU Da1,2(), DING Zhengyi1, CUI Jiayu1, SUN Baode1,2
1 Shanghai Key Lab of Advanced High-Temperature Materials and Precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2 State Key Lab of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China 3 School of Electro-Mechanical Engineering, Xidian University, Xi'an 710126, China
Cite this article:
GUAN Bang, WANG Donghong, MA Hongbo, SHU Da, DING Zhengyi, CUI Jiayu, SUN Baode. Key Technology and Application of Digital Twin Modeling for Deformation Control of Investment Casting. Acta Metall Sin, 2024, 60(4): 548-558.
Investment casting processes are controlled separately for its complex casting system, resulting in the dimensional out of tolerance. A calculation method for node displacement transfer is proposed based on the node normal vector and the nearest neighbor points in investment casting; the relationship between injection parameters, dimensional deviation in injected wax pattern, and casting solidification is studied, which provides a data model for digital twin. The digital twin is applied to the antideformation design and process optimization under a multiprocess for ring-to-ring casting. In the design stage, the geometric model of the mold cavity is designed via reverse deformation, and the error between the simulation results of the casting after the antideformation design and the target geometric is < 0.04 mm. In the casting, the optimal control of the subsequent process is performed according to the dimensional deviation of the previous process.
Fund: National Key Research and Development Program of China(2022YFB3706800);National Key Research and Development Program of China(2020YFB1710100);National Science and Technology Major Project of China(2017-Ⅶ-0008-0102);National Science and Technology Major Project of China(J2019-VI-0004-0117);National Natural Science Foundation of China(51821001);National Natural Science Foundation of China(52090042);Zhejiang Provincial Key Research and Development Program of China(2020C01056);Zhejiang Provincial Key Research and Development Program of China(2021C01157);Zhejiang Provincial Key Research and Development Program of China(2022C01147)
Corresponding Authors:
WANG Donghong, associate professor, Tel:(021)54748974, E-mail: wangdh2009@sjtu.edu.cn;
SHU Da, professor, Tel: (021)54748974, E-mail: dshu@sjtu.edu.cn
Fig.1 Digital twin modeling and simulation framework for casting dimensions
Fig.2 Calculation diagram of node displacement deviation (Surface 1 is original geometry, surface 2 is wax pattern deformation, and surface 3 is casting deformation; d is the node displacement of wax mold, and d1 is the node displacement of casting)
Fig.3 Node displacement error divided by 1 mm grid
Boundary
X1
MPa
X2
cm3·s-1
X3
oC
X4
oC
X5
oC
X6
W·m-2·oC-1
Upper
0.5
30
62
1450
900
50
Lower
5
300
70
1600
1100
200
Table 1 Design variables and its range
Fig.4 Casting geometric model (a) schematic view of a typical casting (unit: mm) (b) model of STL format
Fig.5 Deformation simulation results of wax pattern (a) and casting (b)
Fig.6 Displacement fields (a1-c1) and statistics histograms (a2-c2) of wax pattern (a1, a2), casting (b1, b2), and total deformation between CAD and casting (c1, c2) showing displacement transfer process
Fig.7 All nodes of inner ring
Fig.8 Dimensional deviations of wax pattern (a) and casting (b) at different heights
Fig.9 Node displacements of casting after iterative design of initial geometric (a) first iteration (b) second iteration (c) third iteration (d) fourth iteration
Fig.10 Dimensional deviations of different heights between casting and wax pattern
Fig.11 Point clouds of CAD model (a), wax pattern (b), and casting (c)
Fig.12 Displacement fields (a1-c1) and statistical histograms (a2-c2) between different geometric models (a1, a2) wax pattern point cloud with CAD model (b1, b2) wax pattern point cloud with casting point cloud (c1, c2) casting point cloud with CAD model
Fig.13 Curves of average displacement vector component with blade height (a) Y direction (b) Z direction
1
Sun B D, Wang J, Shu D. Precision Forming Technology of Large Superalloy Castings for Aircraft Engines[M]. Singapore: Springer, 2021: 1
2
Galles D, Beckermann C. Simulation of distortions and pattern allowances for a production steel casting[A]. Proceedings of the 69th SFSA Technical and Operating Conference[C]. Steel Founders' Society of America, Chicago, IL, 2015
3
Motoyama Y, Takahashi H, Inoue Y, et al. Development of a device for dynamical measurement of the load on casting and the contraction of the casting in a sand mold during cooling[J]. J. Mater. Process. Technol., 2012, 212: 1399
doi: 10.1016/j.jmatprotec.2012.02.007
4
Galles D, Beckermann C. In situ measurement and prediction of stresses and strains during casting of steel[J]. Metall. Mater. Trans., 2016, 47A: 811
5
Pradyumna R, Sridhar S, Satyanarayana A, et al. Wax patterns for integrally cast rotors/stators of aeroengine gas turbines[J]. Mater. Today: Proc., 2015, 2: 1714
6
Duan C P, Tong J, Lu L, et al. Improving the performance of 3D shape measurement of moving objects by fringe projection and data fusion[J]. IEEE Access, 2021, 9: 34682
doi: 10.1109/Access.6287639
7
Wang R X, Law A C, Garcia D, et al. Development of structured light 3D-scanner with high spatial resolution and its applications for additive manufacturing quality assurance[J]. Int. J. Adv. Manuf. Technol., 2021, 117: 845
doi: 10.1007/s00170-021-07780-2
8
Haleem A, Javaid M, Goyal A, et al. Redesign of car body by reverse engineering technique using steinbichler 3D scanner and projet 3D printer[J]. J. Ind. Integr. Manag., 2022, 7: 171
9
He W T, Zhong K, Li Z W, et al. Accurate calibration method for blade 3D shape metrology system integrated by fringe projection profilometry and conoscopic holography[J]. Opt. Lasers Eng., 2018, 110: 253
doi: 10.1016/j.optlaseng.2018.06.012
10
Zhao D Y, Yang Z H, Yang G X, et al. Dimension control of X-type heavy duty gas turbine solid blade in investment casting process[J]. Spec. Cast. Nonferrous Alloys, 2020, 40: 1265
Galles D, Lu J, Beckermann C. Determination of pattern allowances for a steel casting using an inverse elastoplastic deformation analysis[J]. IOP Conf. Ser.: Mater. Sci. Eng., 2019, 529: 012077
12
Galles D, Lu J, Beckermann C. Determination of pattern allowances for steel castings using the finite element inverse deformation analysis[J]. Int. J. Cast Met. Res., 2019, 32: 123
doi: 10.1080/13640461.2018.1558562
13
Dong Y W, Zhang D H, Bu K, et al. Geometric parameter-based optimization of the die profile for the investment casting of aerofoil-shaped turbine blades[J]. Int. J. Adv. Manuf. Technol., 2011, 57: 1245
doi: 10.1007/s00170-011-3681-z
14
Zhang D H, Jiang R S, Li J L, et al. Cavity optimization for investment casting die of turbine blade based on reverse engineering[J]. Int. J. Adv. Manuf. Technol., 2010, 48: 839
doi: 10.1007/s00170-009-2343-x
15
Bu K, Wang H, Zhou T, et al. Method of virtual mold-repair for hollow turbine blades[J]. Acta Aeronaut. Astronaut. Sin., 2011, 32: 538
Bu K, Zhang Y L, Yu Q, et al. Research development on shrinkage fraction distribution of investment castings[J]. Aeronaut. Manuf. Technol., 2019, 62(20): 37
Li L Z, Wang J C, Han Y Z, et al. Displacement transfer with the mesh deformation method in multidisciplinary optimization of turbine blades[J]. J. Aerosp. Power, 2007, 22: 2101
Zhang M, Tao F, Huang B Q, et al. Digital twin data: Methods and key technologies[J]. Digital Twin, 2022, 1: 2
doi: 10.12688/digitaltwin
19
Yu J P, Wang D H, Li D Y, et al. Engineering computing and data-driven for gating system design in investment casting[J]. Int. J. Adv. Manuf. Technol., 2020, 111: 829
doi: 10.1007/s00170-020-06143-7
20
Ebrahimi A, Fritsching U, Heuser M, et al. A digital twin approach to predict and compensate distortion in a high pressure die casting (HPDC) process chain[J]. Procedia Manuf., 2020, 52: 144
21
Chen G Q, Zhu J L, Zhao Y H, et al. Digital twin modeling for temperature field during friction stir welding[J]. J. Manuf. Process., 2021, 64: 898
doi: 10.1016/j.jmapro.2021.01.042
22
Zhang M, Tao F, Huang B Q, et al. A physical model and data-driven hybrid prediction method towards quality assurance for composite components[J]. CIRP Ann., 2021, 70: 115
doi: 10.1016/j.cirp.2021.04.062
23
Tao F, Liu W R, Zhang M, et al. Five-dimension digital twin model and its ten applications[J]. Comput. Integr. Manuf. Syst., 2019, 25: 1
doi: 10.13196/j.cims.2019.01.001
Wang D H, Dong A P, Zhu G L, et al. The propagation and accumulation of dimensional shrinkage for ring-to-ring structure in investment casting[J]. Int. J. Adv. Manuf. Technol., 2018, 96: 623
doi: 10.1007/s00170-018-1631-8
25
Yu J P, Wang D H, Li D Y, et al. Process parameter effects on solidification behavior of the superalloy during investment casting[J]. Mater. Manuf. Process., 2019, 34: 1726
doi: 10.1080/10426914.2019.1666989
26
Wang D H, Sun F, Shu D, et al. Data-driven design of cast nickel-based superalloy and precision forming of complex castings[J]. Acta Metall. Sin., 2022, 58: 89
doi: 10.11900/0412.1961.2021.00355
Rajendra Y D, Mehrotra S C, Kale K V, et al. Evaluation of partially overlapping 3D point cloud's registration by using ICP variant and CloudCompare[J]. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 2014, XL-8: 891
28
Chen G D, Han X, Liu G P. A multi-objective optimization method based on the approximate model manaement[J]. Eng. Mech., 2010, 27(5): 205
