A rapid and precise approach to predicting the cure‐induced warping of T‐shaped composite parts with inconsistent layup

Abstract This work involved the precise prediction and in‐depth analysis of cure‐induced warping of T‐shaped parts with inconsistent layup. First, the warping mechanism of this kind of part was studied. Then, a finite element analysis (FEA) that considered the parts' structural characteristics and inconsistent layup using the curing mechanical constitutive model was developed to predict the warping. Following verification of the model's accuracy, the effects of material and structural characteristics on warping were investigated. When designing T‐shaped parts, selecting materials with lower thermal expansion coefficients can help minimize warping. Additionally, increasing the radius of the chamfer at the rib and flange connections is also effective. By taking into account the effects of structural parameters and inconsistent layup by using both classical laminated plate theory and artificial neural networks (ANN), a rapid and accurate surrogate model for warping prediction was finally developed. It was discovered that the two‐layer ANN model performed better than the other two models and could predict warping with accuracy and speed. For the parts analyzed in this work, this model is able to identify the fluctuations of warping captured by FEA. Moreover, by defining deviation as the difference between the predicted values of the surrogate model and the FEA, it can be observed that the majority of deviations for the validation set are within ±1.5 mm. This demonstrates that the two‐layer ANN possesses strong generalization ability and high accuracy in predicting warping. Highlights The inconsistent layup could significantly affect the T‐shaped part's warping. Cutting off a part's rib may significantly reduce its warping. A rapid and accurate model for warping prediction was developed. The layup and structure's influence could be identified by the two‐layer ANN.