TY - GEN
T1 - Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm
AU - Effendi, M. Khoirul
AU - Soepangkat, Bobby O.P.
AU - Harnany, Dinny
AU - Norcahyo, Rachmadi
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - An integrated approach has been applied to predict and optimize multi-performance characteristics, namely, cutting force (CF) and surface roughness (SR), in the end-milling process of glass fiber-reinforced polymer (GFRP) composites. The experiments were performed by varying spindle speed, feeding speed, and depth of cut. The quality characteristics of cutting force and surface roughness were the smaller, the better. Full factorial design 3 × 3 × 3 was used as the design of experiments. Backpropagation neural network (BPNN) was used to model the end-milling experiment and also to determine the objective function. This objective function will be modified into a fitness function optimized by using a differential evolution algorithm (DEA) to find the combination of drilling parameters’ levels that yield minimum cutting force and surface roughness simultaneously. As a result, the minimum cutting force can reduce the energy consumption, and the end-milling process can be performed with higher energy efficiency. Based on BPNN-DEA, the depth of cut of 2 mm, the spindle speed of 4797.5 rpm, and the feeding speed of 579.7 mm/min can simultaneously minimize the cutting force and surface roughness in the end milling of GFRP.
AB - An integrated approach has been applied to predict and optimize multi-performance characteristics, namely, cutting force (CF) and surface roughness (SR), in the end-milling process of glass fiber-reinforced polymer (GFRP) composites. The experiments were performed by varying spindle speed, feeding speed, and depth of cut. The quality characteristics of cutting force and surface roughness were the smaller, the better. Full factorial design 3 × 3 × 3 was used as the design of experiments. Backpropagation neural network (BPNN) was used to model the end-milling experiment and also to determine the objective function. This objective function will be modified into a fitness function optimized by using a differential evolution algorithm (DEA) to find the combination of drilling parameters’ levels that yield minimum cutting force and surface roughness simultaneously. As a result, the minimum cutting force can reduce the energy consumption, and the end-milling process can be performed with higher energy efficiency. Based on BPNN-DEA, the depth of cut of 2 mm, the spindle speed of 4797.5 rpm, and the feeding speed of 579.7 mm/min can simultaneously minimize the cutting force and surface roughness in the end milling of GFRP.
KW - Backpropagation neural network
KW - Differential evolution algorithm
KW - End milling
KW - Energy consumption
KW - Glass fiber-reinforced polymer
UR - http://www.scopus.com/inward/record.url?scp=85137092136&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-0867-5_39
DO - 10.1007/978-981-19-0867-5_39
M3 - Conference contribution
AN - SCOPUS:85137092136
SN - 9789811908668
T3 - Lecture Notes in Mechanical Engineering
SP - 325
EP - 333
BT - Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021
A2 - Tolj, Ivan
A2 - Reddy, M.V.
A2 - Syaifudin, Achmad
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Mechanical Engineering, ICOME 2021
Y2 - 25 August 2021 through 26 August 2021
ER -
