TY - GEN
T1 - Prediction of cutting force in end milling of glass fiber reinforced polymer (GFRP) composites using adaptive neuro fuzzy inference system (ANFIS)
AU - Effendi, Mohammad Khoirul
AU - Soepangkat, Bobby Oedy Pramoedyo
AU - Suhardjono,
AU - Norcahyo, Rachmadi
AU - Sutikno,
AU - Sampurno,
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/12/10
Y1 - 2019/12/10
N2 - The anisotropic and heterogeneous properties of glass fiber-reinforced plastic (GFRP) composites lead to a challenging machining process. The end milling process of these materials generates excessive cutting force that leads to several undesirable damages such as high surface roughness and delamination. Therefore, it is necessary to model the cutting force during the end milling process of GFRP composites materials to obtain an accurate prediction of cutting force. End milling process parameters, i.e., depth of cut (Aa), feeding speed (Vf), and spindle speed (n) are used as an input parameter and each has three levels. Hence, a randomized full factorial 3 × 3 × 3 is applied as the design of experiments. On the other hand, the cutting force (Fc) was used as an output parameter. In this study, an adaptive network-based fuzzy inference system (ANFIS) method is applied to model the cutting force during the end milling process of GFRP composites.
AB - The anisotropic and heterogeneous properties of glass fiber-reinforced plastic (GFRP) composites lead to a challenging machining process. The end milling process of these materials generates excessive cutting force that leads to several undesirable damages such as high surface roughness and delamination. Therefore, it is necessary to model the cutting force during the end milling process of GFRP composites materials to obtain an accurate prediction of cutting force. End milling process parameters, i.e., depth of cut (Aa), feeding speed (Vf), and spindle speed (n) are used as an input parameter and each has three levels. Hence, a randomized full factorial 3 × 3 × 3 is applied as the design of experiments. On the other hand, the cutting force (Fc) was used as an output parameter. In this study, an adaptive network-based fuzzy inference system (ANFIS) method is applied to model the cutting force during the end milling process of GFRP composites.
UR - http://www.scopus.com/inward/record.url?scp=85076756778&partnerID=8YFLogxK
U2 - 10.1063/1.5138311
DO - 10.1063/1.5138311
M3 - Conference contribution
AN - SCOPUS:85076756778
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 -