Minimization of thrust force and surface roughness during MQL coolant drilling on tool steel using BPNN-ACO

Abstract

The excessive thrust force that generated during the minimum quantity lubrication (MQL) drilling process of tool steel can lower the hole surface quality. Hence, it is necessary to properly choose the combination of machining variables to minimize thrust force (TF) and hole surface roughness (HSR) simultaneously. This study underlines the modelling and minimizing the thrust force and hole surface roughness developed during MQL drilling process by integrating a backpropagation neural network (BPNN) method and ant colony optimization (ACO). The varied drilling parameters were type of drill bit, drill point angle, feeding speed, and cutting speed. The optimum BPNN architecture could be obtained by using 4-20-2 network architecture with tansig activation function. The optimum MQL drilling parameters that can minimize TF and HSR simultaneously were HSS M2 drill bit, 107° of drill point angle, 0.045 mm/rev of feeding speed and 36 m/min of cutting speed.