
数字孪生增强的复合材料质量预测
Yucheng Wang, Fei Tao, Ying Zuo, Meng Zhang, Qinglin Qi
工程(英文) ›› 2023, Vol. 22 ›› Issue (3) : 23-33.
数字孪生增强的复合材料质量预测
Digital-Twin-Enhanced Quality Prediction for the Composite Materials
复合材料以其优异的性能被广泛应用于许多领域。复合材料的质量缺陷会导致其构件的性能下降,成为潜在的事故隐患。当前国内外研究者通常采用实验或仿真的方法对复合材料的质量进行预测。然而,由于固化环境的不确定性和对动态、静态特征考虑不全面,因此难以准确预测复合材料的质量。为了解决这一问题,本文首先建立了复合材料的数字孪生(DT)模型,然后通过实现静态热压罐DT虚拟模型与可变复合材料DT虚拟模型的耦合,完成复合材料固化过程数字孪生模型的构建。基于该固化过程模型,生成模拟数据来增加动态特征,从而提高质量预测的准确性。最后基于获取的数据,使用极限学习机(ELM)构建复合材料质量预测模型,并通过结果分析验证了所提方法的有效性。
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.
Digital twin / Quality prediction / Composites / Coupling models
[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.
|
/
〈 |
|
〉 |