The complexity of drilling carbon fiber reinforced polymers (CFRP) requires accurate predictive models. This study addresses the challenge using an ensemble machine learning (ML) approach with stacked generalization. The model captures the relationships between key input variables—such as graphene nanoplatelet (GNP) content, ultrasonic assistance, tool type, stacking sequence, and feed rate—and output parameters, specifically thrust force and delamination. A nested feature scoring (NFS) method was employed for importance analysis, revealing tooling type and feed rate as key features for minimizing delamination and reducing thrust force, respectively. The machinability results revealed that ultrasonic drilling lowered thrust force by improving chip evacuation and reducing fiber breakage. HSS tools with cobalt content, alongside symmetrical stacking sequence, helped to further minimize both thrust force and delamination. However, the inclusion of GNPs led to an increase in thrust force and delamination, attributed to the increased strength of the CFRP/GNP composite. The process involved meticulous training, resulting in four optimal-fit models serving as inputs for the stacked meta-model. Iterative enhancements fortified the ensemble robustness, with fine-tuning of hyperparameters through Bayesian optimization. The ensemble superiority over individual models manifested in a remarkable reduction of mean absolute error (MAE) and root mean squared error (RMSE) by up to 97% and 124% for delamination, and 205% and 154% for thrust force, compared to the best base learner. Visual and statistical assessments effectively illuminated the intricate interactions between variables in the drilling process. The methodology resulted in a highly adaptable predictive model with applications across diverse manufacturing contexts. ... Read more

The perturbed transitional area between the nanoparticle and matrix shapes the properties of polymer nanocomposites. Due to the stochastic nature of these interphase regions, their size and physical properties are intricately linked. For instance, a higher interphase modulus, E_int, might result from a thinner interphase, and vice versa. The inherent randomness can introduce variability in the interphase modulus with respect to interphase thickness, t_int. This challenges the practicality of conventional micromechanical approaches, which assume the interphase modulus to be either a constant or a function of filler and matrix properties when predicting the elastic modulus of polymer nanocomposites. Unlike conventional approaches, which simply used interphase quantification to predict global stiffness and treated the interphase modulus independently of its thickness, this study aims, for the first time, to consider the stochastic nature of the interphase, seeking to exclusively explore the interdependencies within the E_int−t_int relationship in polystyrene/SiO₂ nanocomposites. Simulations were conducted using finite element analysis, FEA, providing high accuracy and flexibility. To manage the large number of simulations, FEA was streamlined with a customized Python scripting, generating a spectrum of (E_int,t_int) solutions for varying SiO₂ contents based on experimental measurements and a rigorous methodology. Subsequently, empirical equations were formulated, unveiling the relationship between E_int and t_int per composition. The FEA-driven interphase intercorrelation scheme was compared to the results obtained from a modified three-phase Halpin-Tsai model. Additionally, the FEA scheme was utilized to modulate the HT model by adjusting its relevant interphase terms. ... Read more