• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2021, Vol. 57 ›› Issue (8): 154-165.doi: 10.3901/JME.2021.08.154

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Influence of Different Bonding Parameters on the Strength of CFRP Laminates with Single Lap Bonding Structure and Optimization

HU Chunxing, HOU Yuliang, TIE Ying, LI Cheng, MAO Zhengang   

  1. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001
  • Received:2020-04-10 Revised:2020-10-14 Online:2021-04-20 Published:2021-06-15

Abstract: To obtain the optimization of single lap joint(SLJ) of CFRP laminates, the effects of different bonding parameters on the mechanical properties of SLJ are firstly studied by using continuum damage mechanics(CDM) model, 3D Hashin damage criterion and cohesion zone model(CZM). Moreover, a surrogate model of tensile strength is constructed using the Latin hypercube sampling (LHS) method and response surface method(RSM). Finally, the optimized design of CFRP SLJ structure is obtained by combination of the surrogate model and genetic algorithm(GA). The results show that, the error is less than 10% between the numerical and experimental results, which proves the validity of the used numerical model. As the lap length increases, the tensile strength and the delamination damage between adjacent layers of the laminates are gradually increased. Additionally, the shear strength and the shear failure level of the adhesive are gradually reduced. The connection performance and failure form of the adhesively bonded structure change with different stacking sequences. The best stacking sequences, lap lengths, adhesive thickness and lap widths are [03/903]2s, 20 mm, 0.060 7 mm, and 10 mm, respectively. Compared with the conventional SLJ, the tensile and shear strengths are increased by 25.92% and 25.88%, respectively. Genetic algorithm is used to optimize the SLJ of CFRP laminates, which is of great significance to improve the mechanical properties of SLJ.

Key words: single-lap joints, adhesively bonding parameters, connection strength, surrogate model, optimization

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