Modeling of drilling process of GFRP composite using a hybrid random vector functional link network/parasitism-predation algorithm

Non-laminated glass fiber-reinforced epoxy composites (GFREC) have shown promising applications in various engineering fields. In this study, an experimental investigation followed by artificial intelligent modeling is carried out on the drilling of GFREC under two cooling conditions, namely internal and external. The damage factor of the drilled holes and the temperature of the drill tip were considered as main process responses. The ex-periments were conducted under different combinations of feed, spindle speed and coolant pressure. All holes were drilled using fresh tungsten carbide twist drills coated by TiN/TiAlN layer. A fine-tuned random vector functional link network (RVFL) model incor-porated with a new optimizer called parasitism-predation algorithm (PPA) was developed to model the drilling process. PPA acts as an advanced metaheuristic optimizer to obtain the best model variables of RVFL. The developed model was compared with the stand-alone RVFL as well as the fined-tuned RVFL using particle swarm optimizer (PSO). RVFL-PPA outperformed other models in terms of coefficient of determination, root mean square error and other measures. The coefficient of determination of RVFL-PPA for different responses and cooling conditions ranges between 0.995 and 1. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study conducted experimental investigations and artificial intelligent modeling on the drilling process of non-laminated glass fiber-reinforced epoxy composites (GFREC), developing an advanced model with optimized variables that outperformed other models in accuracy and precision. The developed model successfully captured the drilling process under different cooling conditions, showcasing high performance in terms of determination coefficient and error measurement.