Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study - Bagherzadeh, Faramarz; Shafighfard, Torkan; Khan, Raja Muhammad Awais; Szczuko, Piotr; Mieloszyk, Magdalena (Mechanical systems and signal processing, 2023)
Plain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning (ML) tools were integrated to obtain the model with the highest accuracy considering the maximum tensile stress of composite plates with two interacting notches while comparing the effectiveness of each technique. Finite Element (FE) simulations were carried out inside the ABAQUS software by employing python macro code to provide a data-rich framework (8960 data). The predictions given by ML methods were compared with the data given by the numerical simulations. An evolutionary algorithm (TPOT) and automatic neural network search (AuoKeras) were utilized for that purpose. An automatic grid search technique was employed to select the best method which could predict the material attribute target values (maximum stress) for different tests. 1% of the data was given as training while 99% was for testing to ensure the robustness of the model. It was concluded that the model containing the Gradient Boosting Regression (GBR), PolyFeatures, and LassoLarsCV algorithms outperformed other ML combinations and Artificial Neural Networks (ANN) for predicting the target value. The coefficient of determination (R2) and root mean square error (RMSE) of the proposed model were 0.97 and 253 respectively. Hence, this model could be utilized for prospective predictions in this type of materials and geometry by providing further reduction of the computational time and labor cost with high accuracy. •A large data set of the simulation was carried out to test the model.•A stacked ML model with high accuracy was developed.•The top/Bottom layer has the highest positive correlation with maximum stress.•A deep neural network with full grid search (AutoKeras) was developed and compared.