Open AccessMinimization of thrust force and surface roughness during MQL coolant drilling on tool steel using BPNN-ACO
Author(s) -
Rachmadi Norcahyo,
Iqbal Faishal Rokhmad,
Muslim Mahardika,
Bobby Oeddy Pramoedyo Soepangkat,
Fathi Robbany
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1034/1/012102
Subject(s) - thrust , drill , drilling , surface roughness , drill bit , machining , lubrication , surface finish , mechanical engineering , tool wear , point (geometry) , engineering , materials science , composite material , mathematics , geometry
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.