Open AccessBPNN-ACO application on minimization of hole delamination during GFRP drilling process
Author(s) -
Rachmadi Norcahyo,
Iqbal Faishal Rokhmad,
Muslim Mahardika,
Gesang Nugroho,
Bobby Oedy 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/1034/1/012103
Subject(s) - delamination (geology) , fibre reinforced plastic , drilling , ant colony optimization algorithms , drill bit , drill , machining , materials science , artificial neural network , structural engineering , point (geometry) , process (computing) , computer science , composite material , mechanical engineering , engineering , geology , artificial intelligence , mathematics , paleontology , geometry , subduction , tectonics , operating system
Delamination defects during the drilling process on the glass fiber reinforced polymer (GFRP) have a great contribution to the component failure. Hence, it is necessary to properly choose the combination of machining variables to minimize hole entry delamination (EnDel) and hole exit delamination (ExDel) during drilling process simultaneously. This study underlines the modelling and minimizing the EnDel and hole ExDel during GFRP drilling process by combining a backpropagation neural network (BPNN) method and ant colony optimization (ACO). The varied drilling parameters were type of drill point angle, feeding speed, and cutting speed. The optimum BPNN architecture could be obtained by using 3-4-2-5-8-2 network architecture with tansig activation function. The optimum GFRP drilling parameters that can minimize EnDel and ExDel simultaneously were 116° of drill point angle, 51.3 mm min −1 of feeding speed and 4975 rpm of spindle speed.