Measurement of properties of graphene sheets subjected to drilling operation using computer simulation

• MD is conducted to study mechanical strength of graphene subjected to drilling.
• Data obtained from MD is further is used for training MGGP and ANN models.
• Out of two methods, MGGP evolves a model with better generalization ability.
• MGGP shows great potential to predict strength and drilling time of graphene.

Drilling being one of the primary machining processes find wide spread applications in manufacturing of functional components. Optimization of drilling process performance requires critical understanding of process parameters which govern the mechanism of drilling process. Machining process at nanoscale level has been studied extensively using numerical modeling approaches owing to complexity in conducting experiments at nanoscale level. In this paper, we propose a new evolutionary approach based on multi-gene genetic programming (MGGP) to numerically model the drilling process of graphene sheet, a two dimensional nanoscale material. The performance of our proposed MGGP model is compared with that of the artificial neural network (ANN) and we observe that our predictions are well in agreement with the data obtained using conventional numerical approach for modeling machining process of nanoscale materials. We anticipate that our proposed MGGP model can find applications in optimizing the machining processes of nanoscale materials.

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