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Engineering >> 2023, Volume 22, Issue 3 doi: 10.1016/j.eng.2022.08.019

Digital-Twin-Enhanced Quality Prediction for the Composite Materials

a School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
b Research Institute for Frontier Science, Beihang University, Beijing 100191, China
c Department of Automation, Tsinghua University, Beijing 100084, China
d School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China

Received: 2022-03-29 Revised: 2022-07-22 Accepted: 2022-08-24 Available online: 2023-01-11

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Abstract

Composite materials are widely used in many fields due to their excellent properties. Quality defects in composite materials can lead to lower quality components, creating potential risk of accidents. Experimental and simulation methods are commonly used to predict the quality of composite materials. However, it is difficult to predict the quality of composite materials accurately due to the uncertain curing environment and incomplete feature space. To address this problem, a digital twin (DT) visual model of a composite material is first constructed. Then, a static autoclave DT virtual model is coupled with a variable composite material DT virtual model to construct a model of the curing process. Features are added to the proposed model by generating simulated data to enhance the quality prediction. An extreme learning machine (ELM) for quality prediction is trained with the generated data. Finally, the effectiveness of the proposed method is verified through result analysis.

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References

[ 1 ] Singh AK, Bedi R, Kaith BS. Composite materials based on recycled polyethylene terephthalate and their properties—a comprehensive review. Compos B Eng 2021;219(15):108928.

[ 2 ] Guo P, Wang Z, Han X, Heng L. Nepenthes pitcher inspired isotropic/ anisotropic polymer solidliquid composite interface: preparation, function, and application. Mater Chem Front 2021;5(4):1716–42.

[ 3 ] Omran T, Garoushi S, Lassila L, Shinya A, Vallittu PK. Bonding interface affects the load-bearing capacity of bilayered composites. Dent Mater J 2019;38 (6):1002–11.

[ 4 ] Kosmatopoulos EB, Papageorgiou M, Vakouli A, Kouvelas A. Adaptive finetuning of nonlinear control systems with application to the urban traffic control strategy TUC. IEEE Trans Control Syst Technol 2007;15(6):991–1002.

[ 5 ] Abdelal GF, Robotham A, Cantwell W. Autoclave cure simulation of composite structures applying implicit and explicit FE techniques. Int J Mech Mater Des 2012;9:55–63.

[ 6 ] Fei L, Deng A, Zhao Q, Duan J. Research on influence mechanism of composite interlaminar shear strength under normal stress. Sci Eng Compos Mater 2020;27(1):119–28.

[ 7 ] Kim Y, White S. Stress relaxation behavior of 3501–6 epoxy resin during cure. Polym Eng Sci 2010;36(23):2852–62.

[ 8 ] Ren L, Meng Z, Wang X, Zhang L, Yang L. A data-driven approach of product quality prediction for complex production systems. IEEE T Ind Inform 2021;17 (9):6457–65.

[ 9 ] Tao F, Qi Q. Make more digital twins. Nat Commun 2019:573490–1.

[10] Tao F, ChengY ZL, Nee AYC. Advanced manufacturing systems: socialization features and trends. J Intell Manuf 2015;28:1079–94.

[11] Hao L, Bian L, Gebraeel N, Shi J. Residual life prediction of multistage manufacturing process with interation between tool wear and product quality degradation. IEEE Trans Autom 2015;14(2):1211–24.

[12] Zhang X, Manabu K, Masahiro K, Junichi M, Junji I, Kohhei H. Prediction and causal analysis of defects in steel products: handling nonnegative and highly over dispersed count data. Control Eng Pract 2020;95:104258.

[13] Kim J, Kim H, Lee D. Compaction of thick carbon/phenolic fabric composites with autoclave method. Compos Struct 2004;66(1–4):467–77.

[14] Koutawa Y, Biscani F, Belouettar S, Nasser H, Carrera E. Toward micromechanics of coupled fields materials containing functionally graded inhomogeneities: multi-coating approach. Mech Adv Mater Struct 2011;18(7):524–30.

[15] Tao F, Zhang M. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 2017;5:20418–27.

[16] Tao F, Zhang M, Liu Y, Nee AYC. Digital twin driven prognostics and health management for complex equipment. CIRP Ann 2018;67(1):169–72.

[17] Guivarch D, Mermoz E, Marino Y, Sartor M. Creation of helicopter dynamic systems digital twin using multibody simulations. CIRP Ann Manuf Technol 2019;68(1):133–6.

[18] Bilberg A, Malik AA. Digital twin driven human–robot collaborative assembly. CIRP Ann Manuf Technol 2019;68(1):499–502.

[19] Wang T, Tao F, Zhang M, Wang L, Zuo Y. Digital twin enhanced fault prediction for the autoclave with insufficient data. J Manuf Syst 2021;60(1): 350–9.

[20] Tifkitsis KI, Mesogitis TS, Struzziero G, Skordos AA. Stochastic multi-objective optimisation of the cure process of thick laminates. Compos Part A Appl Sci Manuf 2018:112383–94.

[21] Huang G, Zhu Q, Siew CK. Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks; 2004 July 25–29; Budapest, Hungary. New York City: IEEE; 2004. p. 985–90.

[22] Weber TA, Arent J, Munch L, Duhovic M, Balvers JM. A fast method for the generation of boundary conditions for thermal autoclave simulation. Compos Part A Appl Sci Manuf 2016;88:216–25.

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