基于热-流-固多场耦合的厚截面复合材料凝胶前后固化均匀性优化

doi:10.19936/j.cnki.2096-8000.20250928.007

复合材料科学与工程 ›› 2025, Vol. 0 ›› Issue (9): 47-54.DOI: 10.19936/j.cnki.2096-8000.20250928.007

基于热-流-固多场耦合的厚截面复合材料凝胶前后固化均匀性优化

黄顺枫¹, 刘文博², 王培培³, 杨帆³, 王荣国³, 胡可军^1*

1.江苏理工学院材料工程学院,常州213001;
2.常州融信复合材料有限公司,常州213033;
3.哈尔滨工业大学复合材料与结构研究所,哈尔滨150080

收稿日期:2024-08-08 发布日期:2025-10-23
通讯作者: 胡可军(1988—),男,博士,副教授,研究方向为复合材料强度预测与损伤分析,kejun@just.edu.cn。
作者简介:黄顺枫(1998—),男,硕士,研究方向为复合材料固化成型研究。
基金资助:
国家重点研发计划(2020YFB1506100);常州市科技成果转化及产业化计划(CE20230039)

Optimization of cure uniformity for thick-section composite materials before and after gelation based on thermo-fluid-solid multi-physics coupling

HUANG Shunfeng¹, LIU Wenbo², WANG Peipei³, YANG Fan³, WANG Rongguo³, HU Kejun^1*

1. School of Materials Engineering, Jiangsu University of Technology, Changzhou 213001, China;
2. Changzhou Rongxin Composite Materials Co., Ltd., Changzhou 213033, China;
3. Center for Composite Materials and Structure, Harbin Institute of Technology, Harbin 150080, China

Received:2024-08-08 Published:2025-10-23

摘要/Abstract

摘要： 为解决厚截面复合材料固化过程中由于凝胶点前后温度场和固化度场不均匀而产生的质量缺陷问题,基于热-流-固多物理场耦合有限元模型,结合优化拉丁超立方抽样(OLHS),建立径向基神经网络(RBF)代理模型的固化均匀性多目标优化方法,实现对凝胶前后温度梯度和固化度梯度均匀性优化。优化结果与原设计相比,在总固化时长仅增加6.7%的情况下,凝胶点前后的温度梯度分别下降了51.7%和66.5%,固化度梯度分别下降了33.3%和63.6%,整个固化过程最大温度峰值下降了8.8%。结果表明本文提出的方法能够显著提升凝胶前后的固化均匀性。

关键词: 厚截面复合材料, 多场耦合, 固化均匀性, 凝胶点, 多目标优化

Abstract: To address quality defects arising from non-uniform temperature and cure degree fields before and after gelation in the curing process of thick-section composite materials, a multi-objective optimization approach for cure uniformity is developed. This approach utilizes a thermo-fluid-solid multi-physics coupled finite element model in conjunction with optimized Latin hypercube sampling (OLHS) to construct a radial basis function (RBF) surrogate model. The goal is to optimize the uniformity of temperature and cure degree gradients before and after gelation. Compared to the original design, the optimization results demonstrate a decrease in temperature gradients of 51.7% and 66.5% before and after gelation, respectively, and a reduction in cure degree gradients of 33.3% and 63.6%, respectively, with only a 6.7% increase in total curing time. The maximum temperature peak during the entire curing process was reduced by 8.8%. These results indicate that the proposed method can significantly improve the uniformity of curing before and after gelation.

Key words: thick-section composite materials, multi-physics coupling, cure uniformity, gelation point, multi-objective optimization

中图分类号:

TB332

黄顺枫, 刘文博, 王培培, 杨帆, 王荣国, 胡可军. 基于热-流-固多场耦合的厚截面复合材料凝胶前后固化均匀性优化[J]. 复合材料科学与工程, 2025, 0(9): 47-54.

HUANG Shunfeng, LIU Wenbo, WANG Peipei, YANG Fan, WANG Rongguo, HU Kejun. Optimization of cure uniformity for thick-section composite materials before and after gelation based on thermo-fluid-solid multi-physics coupling[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(9): 47-54.

参考文献

[1] FAN S, ZHANG J, WANG B, et al. A deep learning method for fast predicting curing process-induced deformation of aeronautical composite structures[J]. Composites Science and Technology, 2023, 232: 109844.
[2] YUAN Z, WANG Y, YANG G, et al. Evolution of curing residual stresses in composite using multi-scale method[J]. Composites Part B: Engineering, 2018, 155: 49-61.
[3] CHEN J, WANG J, LI X, et al. Monitoring of temperature and cure-induced strain gradient in laminated composite plate with FBG sensors[J]. Composite Structures, 2020, 242: 112168.
[4] HANNA E G, YOUNES K, AMINE S, et al. Exploring gel-point identification in epoxy resin using rheology and unsupervised learning[J]. Gels, 2023, 9(10): 828.
[5] 李树健, 湛利华, 白海明, 等. 基于树脂流动的变截面复合材料结构固化过程热-流-固多场强耦合数值仿真[J]. 复合材料学报, 2018, 35(8): 2095-2102.
[6] SREEKANTAMURTHY T, HUDSON T B, HOU T-H, et al. Composite cure process modeling and simulations using COMPRO (registered trademark) and validation of residual strains using fiber optics sensors[C]//Technical Conference of the American Society for Composites. Williamsburg, VA: American Society for Composites, 2016.
[7] ZHANG G, LUO L, LIN T, et al. Multi-objective optimisation of curing cycle of thick aramid fibre/epoxy composite laminates[J]. Polymers, 2021, 13(23): 4070.
[8] GONCALVES P T, ARTEIRO A, ROCHA N. Micro-mechanical analysis of the effect of ply thickness on curing micro-residual stresses in a carbon/epoxy composite laminate[J]. Composite Structures, 2023, 319: 117158.
[9] ESPOSITO L, SORRENTINO L, PENTA F, et al. Effect of curing overheating on interlaminar shear strength and its modelling in thick FRP laminates[J]. The International Journal of Advanced Manufacturing Technology, 2016, 87: 2213-2220.
[10] ZHANG W, XU Y, HUI X, et al. A multi-dwell temperature profile design for the cure of thick CFRP composite laminates[J]. The International Journal of Advanced Manufacturing Technology, 2021, 117: 1133-1146.
[11] GAO Y, LIN Z, ZHOU Y, et al. Size effect in curing optimization for thick composite laminates[J]. Materials Today Communications, 2023, 34: 105276.
[12] 乔巍, 姚卫星, 黄杰. 基于RBF神经网络的复合材料固化均匀性优化[J]. 机械设计与制造工程, 2020, 49(12): 91-95.
[13] 唐良军, 朱永国, 龚晓, 等. 基于温度曲线优化的复合材料固化温度和固化度协同控制[J]. 工业技术创新, 2022, 9(3): 23-31.
[14] YANG W, LIU W, JIA Y, et al. Coupled filling-curing simulation and optimized design of cure cycle in liquid composite molding[J]. The International Journal of Advanced Manufacturing Technology, 2024, 132(5): 2489-2501.
[15] GAO Y, YE J, YUAN Z, et al. Optimization strategy for curing ultra-thick composite laminates based on multi-objective genetic algorithm[J]. Composites Communications, 2022, 31: 101115.
[16] YUAN Z, KONG L, GAO D, et al. Multi-objective approach to optimize cure process for thick composite based on multi-field coupled model with RBF surrogate model[J]. Composites Communications, 2021, 24: 100671.
[17] TIFKITSIS K, MESOGITIS T, STRUZZIERO G, et al. Stochastic multi-objective optimisation of the cure process of thick laminates[J]. Composites Part A: Applied Science and Manufacturing, 2018, 112: 383-394.
[18] LU B, MOYA C, LIN G. NSGA-PINN: a multi-objective optimization method for physics-informed neural network training[J]. Algorithms, 2023, 16(4): 194.
[19] ALEKSENDRIC′ D, BELLINI C, CARLONE P, et al. Neural-fuzzy optimization of thick composites curing process[J]. Materials and Manufacturing Processes, 2019, 34(3): 262-273.
[20] XU Y, ZHAO Z, SHRESTHA K, et al. A coupled data-physics computational framework for temperature, residual stress, and distortion modeling in autoclave process of composite materials[J]. Composites Part A: Applied Science and Manufacturing, 2024, 183: 108218.
[21] NIAKI S A, HAGHIGHAT E, CAMPBELL T, et al. Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture[J]. Computer Methods in Applied Mechanics and Engineering, 2021, 384: 113959.
[22] WHITE S R, KIM Y K. Process-induced residual stress analysis of AS4/3501-6 composite material[J]. Mechanics of Composite Materials and Structures an International Journal, 1998, 5(2): 153-186.
[23] BEHZAD T, SAIN M. Finite element modeling of polymer curing in natural fiber reinforced composites[J]. Composites Science and Technology, 2007, 67(7-8): 1666-1673.
[24] SPRINGER G S, TSAI S W. Thermal conductivities of unidirectional materials[J]. Journal of Composite Materials, 1967, 1(2): 166-173.
[25] GUTOWSKI T, DILLON G. The elastic deformation of lubricated carbon fiber bundles: comparison of theory and experiments[J]. Journal of Composite Materials, 1992, 26(16): 2330-2347.
[26] DAVÉ R. A unified approach to modeling resin flow during composite processing[J]. Journal of Composite Materials, 1990, 24(1): 22-41.
[27] LEE W I, LOOS A C, SPRINGER G S. Heat of reaction, degree of cure, and viscosity of Hercules 3501-6 resin[J]. Journal of Composite Materials, 1982, 16(6): 510-520.
[28] SHIN D D, HAHN H T. Compaction of thick composites: simulation and experiment[J]. Polymer Composites, 2004, 25(1): 49-59.
[29] YUAN Z, TONG X, YANG G, et al. Curing cycle optimization for thick composite laminates using the multi-physics coupling model[J]. Applied Composite Materials, 2020, 27: 839-860.
[30] LU L, HUAN S, LU M, et al. Three-dimensional thermo-chemo-mechanical coupled curing analysis for the filament wound composite shell[J]. Polymers, 2024, 16(12): 1643.
[31] BRAUNER C, FRERICH T, HERRMANN A S. Cure-dependent thermomechanical modelling of the stress relaxation behaviour of composite materials during manufacturing[J]. Journal of Composite Materials, 2017, 51(7): 877-898.
[32] WANG Q, WANG L, ZHU W, et al. Numerical investigation of the effect of thermal gradients on curing performance of autoclaved laminates[J]. Journal of Composite Materials, 2020, 54(1): 127-138.
[33] SHAH P, HALLS V, ZHENG J, et al. Optimal cure cycle parameters for minimizing residual stresses in fiber-reinforced polymer composite laminates[J]. Journal of Composite Materials, 2018, 52(6): 773-792.

基于热-流-固多场耦合的厚截面复合材料凝胶前后固化均匀性优化

Optimization of cure uniformity for thick-section composite materials before and after gelation based on thermo-fluid-solid multi-physics coupling

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