TY - GEN
T1 - Multi-objective Optimization Using Backpropagation Neural Network and Teaching–Learning-Based-Optimization Method in Surface Grinding Under Dry and Minimum Quantity Lubrication Conditions (MQL)
AU - Harnany, Dinny
AU - Effendi, M. Khoirul
AU - Kis Agustin, H. C.
AU - Soepangkat, Bobby O.P.
AU - Sampurno,
AU - Norcahyo, Rachmadi
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - The present work focuses on the performance modeling of surface grinding to attain an optimum parameter setting for the minimum coefficient of friction and surface roughness. The experimental data were collected during the surface grinding process using dry conditions and minimum quantity lubrication (MQL) as a clean technology lubricant. The usage material was SKD 61 tool steel. The varied surface grinding parameters were the depth of cut and table speed, wherein each had three levels. The surface grinding operation was performed by using a full factorial design 2 × 3 × 3. Backpropagation neural network (BPNN) was first applied to obtain the modeling of the surface grinding experiment, the objective function, the predictions of coefficient of friction, and surface roughness. The objective function is then modified into a fitness function. Finally, this fitness function is utilized in multi-objective optimization using the teaching–learning-based optimization (TLBO) method to attain the surface grinding parameters’ levels that simultaneously produce a minimum coefficient of friction and surface roughness. Based on our experimental results, the combination of BPNN-TLBO can be applied to simultaneously minimize the coefficient of friction and surface roughness in the grinding of SKD 61 by implementing MQL and setting the feeding speed at 150 mm/s and the depth of cut at 0.01 mm. As the result, the minimum surface roughness is 0.376 μm, and the coefficient of friction is 0.333.
AB - The present work focuses on the performance modeling of surface grinding to attain an optimum parameter setting for the minimum coefficient of friction and surface roughness. The experimental data were collected during the surface grinding process using dry conditions and minimum quantity lubrication (MQL) as a clean technology lubricant. The usage material was SKD 61 tool steel. The varied surface grinding parameters were the depth of cut and table speed, wherein each had three levels. The surface grinding operation was performed by using a full factorial design 2 × 3 × 3. Backpropagation neural network (BPNN) was first applied to obtain the modeling of the surface grinding experiment, the objective function, the predictions of coefficient of friction, and surface roughness. The objective function is then modified into a fitness function. Finally, this fitness function is utilized in multi-objective optimization using the teaching–learning-based optimization (TLBO) method to attain the surface grinding parameters’ levels that simultaneously produce a minimum coefficient of friction and surface roughness. Based on our experimental results, the combination of BPNN-TLBO can be applied to simultaneously minimize the coefficient of friction and surface roughness in the grinding of SKD 61 by implementing MQL and setting the feeding speed at 150 mm/s and the depth of cut at 0.01 mm. As the result, the minimum surface roughness is 0.376 μm, and the coefficient of friction is 0.333.
KW - Backpropagation neural network
KW - Clean technology
KW - Minimum quantity lubrication
KW - Surface grinding
KW - Teaching–learning-based optimization
UR - http://www.scopus.com/inward/record.url?scp=85137075116&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-0867-5_40
DO - 10.1007/978-981-19-0867-5_40
M3 - Conference contribution
AN - SCOPUS:85137075116
SN - 9789811908668
T3 - Lecture Notes in Mechanical Engineering
SP - 334
EP - 342
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 -