A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance

Determining the optimum input parameter settings (temperature, rotational velocity and feed rate) in optimizing the properties (strength and time) of the nano-drilling process can result in an improvement in its environmental performance. This is because the rotational velocity is an essential component of power consumption during drilling and therefore by determining its appropriate value required in optimization of properties, the trial-and-error approach that normally results in loss of power and waste of resources can be avoided. However, an effective optimization of properties requires the formulation of the generalized and an explicit mathematical model. In the present work, the nano-drilling process of Boron Nitride Nanosheet (BNN) panels is studied using an explicit model formulated by a molecular dynamics (MD) based computational intelligence (CI) approach. The approach consists of nano scale modeling using MD simulation which is further fed into the paradigm of a Cl cluster comprising genetic programming, which was specifically designed to formulate the explicit relationship of nano-machining properties of BNN panel with respect to process temperature, feed and rotational velocity of drill bit. Performance of the proposed model is evaluated against the actual results. We find that our proposed integrated Cl model is able to model the nano-drilling process of BNN panel very well, which can be used to complement the analytical solution developed by MD simulation. Additionally, we also conducted sensitivity and parametric analysis and found that the temperature has the least influence, whereas the velocity has the highest influence on the properties of nano-drilling process of BNN panel. (C) 2015 Elsevier B.V. All rights reserved.