Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates - Manzar Masud; Aamir Mubashar; Shahid Iqbal; Hassan Ejaz; Saad Abdul Raheem (Engineering Proceedings)
In this research, the effect of change in stacking sequences on the impact performance of bio-hybrid fiber-reinforced polymer (bio-HFRP) composite materials was analyzed and evaluated. The methodology was developed, based on the mechanical testing and utilization of tree-based machine learning regression models. Low-velocity impact (LVI) testing was performed on five distinct stacking sequences of carbon/flax bio-HFRP at energies ranging from 15 J to 90 J. For all tests, peak impact force was recorded and compared. Symmetric configurations with a uniform distribution of flax layers across the composite laminate exhibited better impact performance. Additionally, two tree-based machine learning (ML) algorithms were used: random forest (RF) and decision tree (DT). The performance metrics used to assess and compare the efficiency were the coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). The most accurate model for the prediction of peak impact force was DT with the R2 training and test dataset values of 0.9920 and 0.9045, respectively. Furthermore, lower MSE and MAE values were attained using the DT model as compared to the RF model. The developed methodology and the model serve as powerful tools to predict the damage-induced properties of bio-HFRP composite laminates utilizing minimal resources and saving time as well.