TY - GEN
T1 - The combined methodology of backpropagation neural network with genetic algorithm to optimize delamination factor and surface roughness in end-milling of carbon fiber reinforced polymer composites
AU - Effendi, Mohammad Khoirul
AU - Soepangkat, Bobby Oedy Pramoedyo
AU - Pramujati, Bambang
AU - Norcahyo, Rachmadi
AU - Nurullah, Fajar Perdana
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/12/10
Y1 - 2019/12/10
N2 - Two of the critical-to-quality characteristics (CTQs) in the end milling of carbon fiber reinforced polymer (CFRP) composites are delamination factor (FD) and surface roughness (SR). The target of these two CTQs is smaller is better. The minimum FD and SR can be achieved by selecting the correct end milling parameters such as the speed of spindle and feeding speed, and also depth of cut. In this study, the minimization of FD and SR is conducted by applying a combination of backpropagation neural network (BPNN) and genetic algorithm (GA). BPNN is utilized to develop the model of FD and SR, while GA has two purposes. The first purpose of GA is to determine the BPNN network architecture that can produce a minimal mean square error (MSE) to precisely predict FD and SR. The second purpose is to find the best setting of end milling parameters (spindle speed, feeding speed, and depth of cut) that can minimize FD and SR simultaneously during the end milling process of CFRP composite. This study uses a randomized full-factorial 2 × 3 × 3 experimental design. The depth of cut of end milling parameters has two levels, while spindle speed and feeding speed each has three levels. The best BPNN network structure consists of three neurons in input layers, three neurons in one hidden layer, and two neurons in output layer; with tangent sigmoidal as the activation function. The setting of the end milling parameters that can minimize FD and SR simultaneously in end milling process of CFRP composites by using BPNN-GA is spindle speed of 3425 rpm, feeding speed of 29.4 mm/min, and depth of cut 1 mm.
AB - Two of the critical-to-quality characteristics (CTQs) in the end milling of carbon fiber reinforced polymer (CFRP) composites are delamination factor (FD) and surface roughness (SR). The target of these two CTQs is smaller is better. The minimum FD and SR can be achieved by selecting the correct end milling parameters such as the speed of spindle and feeding speed, and also depth of cut. In this study, the minimization of FD and SR is conducted by applying a combination of backpropagation neural network (BPNN) and genetic algorithm (GA). BPNN is utilized to develop the model of FD and SR, while GA has two purposes. The first purpose of GA is to determine the BPNN network architecture that can produce a minimal mean square error (MSE) to precisely predict FD and SR. The second purpose is to find the best setting of end milling parameters (spindle speed, feeding speed, and depth of cut) that can minimize FD and SR simultaneously during the end milling process of CFRP composite. This study uses a randomized full-factorial 2 × 3 × 3 experimental design. The depth of cut of end milling parameters has two levels, while spindle speed and feeding speed each has three levels. The best BPNN network structure consists of three neurons in input layers, three neurons in one hidden layer, and two neurons in output layer; with tangent sigmoidal as the activation function. The setting of the end milling parameters that can minimize FD and SR simultaneously in end milling process of CFRP composites by using BPNN-GA is spindle speed of 3425 rpm, feeding speed of 29.4 mm/min, and depth of cut 1 mm.
UR - http://www.scopus.com/inward/record.url?scp=85076740036&partnerID=8YFLogxK
U2 - 10.1063/1.5138310
DO - 10.1063/1.5138310
M3 - Conference contribution
AN - SCOPUS:85076740036
T3 - AIP Conference Proceedings
BT - Innovative Science and Technology in Mechanical Engineering for Industry 4.0
A2 - Djanali, Vivien
A2 - Mubarok, Fahmi
A2 - Pramujati, Bambang
A2 - Suwarno, null
PB - American Institute of Physics Inc.
T2 - 4th International Conference on Mechanical Engineering: Innovative Science and Technology in Mechanical Engineering for Industry 4.0, ICOME 2019
Y2 - 28 August 2019 through 29 August 2019
ER -