Elsevier

Measurement

Volume 50, April 2014, Pages 50-62
Measurement

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

https://doi.org/10.1016/j.measurement.2013.12.028Get rights and content

Highlights

  • 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.

Abstract

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.

Introduction

Graphene is a two dimensional carbon based nanomaterial which has recently attracted significant interest in nanotechnology due to its remarkable mechanical and physical properties [1], [2]. The thickness of a single layer of graphene sheet is only about the diameter of carbon atom which makes it the thinnest material with a large specific surface area [3], [4]. This feature of graphene has resulted in materials scientists in exploring the diverse possible applications of graphene in real world. These include applications in electric circuits such as graphene-based integrated circuits (ICs), field effect transistors (FET) and solar cells [2], [5]. In addition, it is an ideal candidate for potential applications in biomedical, chemical and industrial processes enhanced or enabled by the use of new graphene materials [6], [7]. These applications of graphene requires it to be machined at nanoscale, leading to production of graphene based nanocomponents of complex geometry and functionality. The increasing demand to manufacture nanocomponents for applications in aerospace, defense and electronics is one of the major incentives to study the drilling process of graphene.

Numerous studies have been undertaken to predict the performance of graphene subjected to drilling process. Freedman et al. [8] characterized the drilling kinetics of graphene sheet using a thermionic electron source and various electron beam fluxes. In this study, the drilling process of graphene resulted in the formation of nanopores which is used for translocation of DNA analytes for bio-medical application. Liu et al. [9] and Zhao et al. [10] proposed a fast and controllable method for drilling nanopores in graphene sheet using a suspended SiN substrate. They proposed that individual graphene sheets be transferred precisely on an SiN substrate after which nanopores with different diameters from 3 to 20 nm were drilled using a transmission electron microscope. Theoretical studies on drilling process of graphene are a popular mode of research employing ab initio calculations or molecular dynamics (MD) simulation technique. Zhao et al. [11] employed MD simulations to study the mechanical properties of graphene sheet at elevated temperature. They found that graphene sheet exhibits excellent Young’s modulus even at high temperatures. Hence, theoretical models can be used as a viable alternative compared to time consuming and expensive experiments for monitoring machining process at nanoscale. However, the formulation of these models requires a thorough knowledge on the functionality and the configuration of the nanoscale system.

Application of soft computing methods such as evolutionary approach of multi-gene genetic programming (MGGP) and artificial neural networks (ANN) is on the rise [12], [13], [14], [15], [16], [17]. Several novel approaches of soft computing methods have been proposed such as hybridizing differential evolution algorithm with receptor editing property of immune system [18], [19], [20], artificial bee colony algorithm with Taguchi’s method [21], [22], differential algorithm with Taguchi’s method [23], cuckoo search algorithm (CS) [24] and immune algorithm with hill climbing local search algorithm [25], [26] to optimize the unit production cost of the machining operations of materials. These methods require input training data which can be obtained from the analytical tools such as MD simulations which is based on a specific geometry and temperature. Considering input data, the soft computing methods can then be able to generate meaningful solutions for the complicated problems [27], [28], [29]. Additionally, among the various soft computing methods described above, evolutionary approach method, namely, GP offers the advantage of a fast and cost-effective explicit formulation of a mathematical model based on multiple variables with no existing analytical models [30], [31]. It is to the best of author’s knowledge that limited or no work exists on the application of soft computing models on the performance prediction of the nanoscale system. Hence, soft computing techniques can be used as an alternative method for modeling the machining process of nanoscale materials such as graphene. Additionally, the potential future applications of graphene in electronics industry requires a thorough understanding and investigation of modeling of machining process (for e.g. drilling) of graphene.

Therefore, the main purpose of the present study is to investigate the mechanical response of graphene during a nanodrilling process. Standard MD simulation approach is employed to investigate the effect of temperature, drill bit velocity, and the feed rate of drill bit on the mechanical strength and drilling time of graphene sheet. Data generated from the MD simulations is fed into the paradigm of GP and ANN for the formulation of mathematical models. The performance of these models is evaluated against the data generated from the MD simulations.

Section snippets

MD simulation methodology

In this section, nanoscale drilling of single layer graphene sheet (hereafter referred to as ‘graphene work piece’) using MD simulation is briefly outlined. The data generated from the MD simulation is used to provide the input data to the soft computing cluster (Fig. 1) for training and generation of results. The Brenner’s second generation bond order function (REBO) [32] is used to describe the covalent bonding of the carbon atoms in graphene work piece which is described mathematically as,E

Multi-gene genetic programming (MGGP)

For understanding the functioning of evolutionary algorithm MGGP, the basics about the GP is first outlined. GP uses principle of genetic algorithms (GAs) to evolve computer programs/models of varying sizes based on Darwinian Theory of “Survival of the fittest” [44]. Although both GP and GA share the same working principle but there exists difference between them. GP algorithm starts by generating the models randomly. The numbers of models generated is represented by the population size. The

Evaluation and comparison of models

The results obtained from the two prediction modeling methods MGGP and ANN are illustrated in Fig. 11, Fig. 12, Fig. 13, Fig. 14, Fig. 15, Fig. 16, Fig. 17, Fig. 18 on training and testing data of mechanical strength and drilling time respectively. The best prediction method that gives highest accuracy is determined by comparing these two modelling methods. Square of the correlation coefficient (R2) and relative error (%) between the predicted values and the actual values of the mechanical

Parametric and sensitivity analysis of the best model

In this section, the ‘‘Parametric and Sensitivity analysis about the mean’’ was conducted for the best prediction method. Results discussed in Section 4 reveals that MGGP outperforms the ANN method in prediction of the mechanical strength and drilling time. This analysis provides a measure of the relative importance among the inputs of the MGGP model and illustrates how the two model outputs vary in response to variation of an input. For this purpose, on the MGGP trained models, the first input

Conclusion

In this paper, an evolutionary approach, MGGP, is proposed for the prediction of mechanical strength and drilling time of graphene subjected to drilling process. The performance of the proposed MGGP model is compared to that of the ANN model. The statistical comparison in Section 4 concludes that the performance of the MGGP model is better than that of the ANN model. From the present work, several advantages of MGGP over ANN can be found. High performance of the MGGP model on the testing data

Acknowledgement

The authors extend their heartfelt gratitude to the Editor-in-Chief and anonymous reviewers for suggesting constructive guidelines to improve the literal and technical content of the paper.

References (51)

  • V. Vijayaraghavan et al.

    Temperature, defect and size effect on the elastic properties of imperfectly straight carbon nanotubes by using molecular dynamics simulation

    Comput. Mater. Sci.

    (2013)
  • C.H. Wong et al.

    Nanomechanics of free form and water submerged single layer graphene sheet under axial tension by using molecular dynamics simulation

    Mater. Sci. Eng., A

    (2012)
  • V. Vijayaraghavan et al.

    Shear deformation characteristics of single walled carbon nanotube with water interactions by using molecular dynamics simulation

    Physica E

    (2013)
  • K.S. Park et al.

    Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review

    Artif. Intell. Eng.

    (1998)
  • A. Garg et al.

    Performance evaluation of microbial fuel cell by artificial intelligence methods

    Exp. Syst. Appl.

    (2014)
  • J.R. Williams et al.

    Quantum hall effect in a gate-controlled p–n junction of graphene

    Science

    (2007)
  • A.A. Balandin et al.

    Superior thermal conductivity of single-layer graphene

    Nano Lett.

    (2008)
  • K.S. Novoselov et al.

    Electric field effect in atomically thin carbon films

    Science

    (2004)
  • A.K. Geim

    Graphene: status and prospects

    Science

    (2009)
  • B. Standley et al.

    Graphene-based atomic-scale switches

    Nano Lett.

    (2008)
  • X.H. Hu et al.

    Preparation of water-soluble and biocompatible graphene

    Micro Nano Lett.

    (2013)
  • K.J. Freedman et al.

    Detection of long and short DNA using nanopores with graphitic polyhedral edges

    ACS Nano

    (2013)
  • S. Liu et al.

    Fast and controllable fabrication of suspended graphene nanopore devices

    Nanotechnology

    (2012)
  • S.J. Zhao et al.

    Drilling nanopores in graphene with clusters: a molecular dynamics study

    J. Phys. Chem. C

    (2012)
  • H. Zhao et al.

    Size and chirality dependent elastic properties of graphene nanoribbons under uniaxial tension

    Nano Lett.

    (2009)
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    The first two authors made equal contribution in this work and are both equally considered as first author.

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