Size sensitivity of residual stress and deformation in co-curing process of T-stiffened composite skin assembly

Zhongli, Chen; Wang, Qinglin; Wang, Xiaoxia; Gao, Lei; Guo, Yuan; Dong, Qi; Zhou, Yong; Zhang, Chen; Jia, Yuxi

doi:10.1177/0731684419858187

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Extensive efforts have been invested to address PID problems using empirical science, 5‐8 theoretical science, and computational science methods 9‐17 . The most commonly used theoretical models of the finite element method (FEM) proposed previously include the elastic model, 10,11 cure‐hardening instantaneously linear elastic (CHILE) model, 12 path‐dependent model, 13 and viscoelastic model 14,15 . Furthermore, Galinska 16 reported a comparative study for PID simulation; the results for which indicated that the difference between the elastic and theoretically more precise CHILE models is very small.…”

Section: Introductionmentioning

confidence: 99%

Rapid prediction and inverse design of distortion behaviors of composite materials using artificial neural networks

Luo

Zhang

et al. 2020

Polymers for Advanced Techs

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If the process‐induced distortions (PIDs) of asymmetrical laminates can be predicted accurately and tailored at the early design stage, the production of curved panels from flat molds could be an attractive technique in a cost‐driven production environment. A data‐driven computational methodology which integrates the finite element method (FEM) and artificial neural network (ANN) is presented to rapidly predict the maximum PID and to perform high‐throughput screening of thermosetting‐matrix composites of an asymmetrical laminate for a targeted maximum PID. We performed a grid search on ANN architectures and hyper‐parameters using cross‐validation and obtained a well‐trained ANN model with high generalization performance. For the forward problem, the ANN model was adopted to predict the maximum PIDs of CYCOM X850 and CYCOM 977‐2 prepregs, which were subsequently verified experimentally. For inverse design, a large‐scale screening method based on the ANN model was utilized to determine the candidates for a targeted maximum PID, with an experimental demonstration using one of these candidates. The well‐trained ANN model provides an alternative approach to faster computation with high accuracy for the maximum PID prediction and further guides the discovery of materials with desired distortion behaviors.

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Section: Introductionmentioning

confidence: 99%

Rapid prediction and inverse design of distortion behaviors of composite materials using artificial neural networks

Luo

Zhang

et al. 2020

Polymers for Advanced Techs

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“…In order to accurately describe the change of composite mechanical properties during curing, different constitutive models were proposed in numerical models. These constitutive models can be simply divided into three types: elastic [ 6 , 7 ], viscoelastic [ 8 , 9 , 10 , 11 , 12 , 13 ] and simplified viscoelastic models [ 14 , 15 , 16 , 17 , 18 ]. The cure hardening instantaneously elastic constitutive model proposed by Bogetti et al [ 6 ] is a typical linear elastic model, which assumes that the resin modulus increases linearly with the cure degree.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Process-Induced Deformation for Composite Parts Considering Tool-Part Interaction

Qiao

Yao

2020

Materials

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Residual stresses are generated by tool-part interaction due to the large difference in the coefficients of thermal expansion (CTE) between the tool and the composite part, resulting in more process-induced part deformation. In this paper, a 3-D numerical model considering the influence of tool-part interaction is proposed to predict the deformation in complex-shape composite parts. In this numerical model, the existing path-dependent model is improved to consider the effect of tool-part interaction by adding the residual stress generated by tool-part interaction, and a simplified self-consistent micromechanics model is selected to predict the composite mechanical properties in the viscous and rubbery stages. The predicted and experimental spring-in angles of L- and U-shaped parts are compared. A good agreement shows the validity of the proposed numerical model. A parametric study is performed and the influence of part structural parameters on the spring-in angle is analyzed quantitatively. The results show that the spring-in angles caused by chemical shrinkage and tool-part interaction decrease with the increase of part thickness, but that caused by thermal contraction is almost constant.

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Review of curing deformation control methods for carbon fiber reinforced resin composites

Zhang

et al. 2022

Polymer Composites

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Under the influence of extrinsic curing conditions and intrinsic properties of carbon fiber reinforced resin composites, the formation of curing deformation is inevitable, which makes the shape of the manufactured components differ from the designed structure, and even exceeds the assembly tolerance, resulting in the scrap of parts. Therefore, it is very urgent and important to optimize the curing deformation of carbon fiber reinforced resin composites. In this paper, the mechanism of stress and curing deformation is reviewed firstly, and then the factors affecting the curing deformation are summarized. In particular, from the point of view of materials in curing process, three curing deformation control strategies, including (i) reinforcement structure optimization, (ii) resin modification and process optimization, (iii) die surface compensation and tool‐part contact optimization, were proposed. And with the development of curing deformation simulation technology, the (iv) inverse design strategy before curing and (v) hot sizing method from the view of after cured were further put forward, respectively. By analyzing the link between processing details and curing deformation, it is found that the dominant factor affecting deformation may change under different conditions, so the interaction between factors needs to be further discussed. Subsequently, the effect of these distortion control methods on curing deformation was analyzed and evaluated, and the shortcomings were pointed out. Finally, the challenges existed in curing deformation research were identified, in order to contribute to the development of curing deformation research.

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Size sensitivity of residual stress and deformation in co-curing process of T-stiffened composite skin assembly

Cited by 5 publications

References 22 publications

Rapid prediction and inverse design of distortion behaviors of composite materials using artificial neural networks

Rapid prediction and inverse design of distortion behaviors of composite materials using artificial neural networks

Modelling of Process-Induced Deformation for Composite Parts Considering Tool-Part Interaction

Review of curing deformation control methods for carbon fiber reinforced resin composites

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