TY - GEN
T1 - Optimization in airless tires design using backpropagation neural network (BPNN) and genetic algorithm (GA) approaches
AU - Pramono, Agus Sigit
AU - Effendi, Mohammad Khoirul
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/12/10
Y1 - 2019/12/10
N2 - Airless tires are designed and produced to overcome problems in the radial tires and solid tires. This tire provides a safe and comfortable driving experience in a vehicle during operation. Moreover, this tire still work when it hit by sharp objects (i.e., spike, nail, gun projectile, etc.). This research will be focused on designing airless tires using three parameters input, namely spoke thickness, rhombic angle, and rubber material. Each parameter uses three different levels, so the total design number is 27 designs. The thickness parameter of spoke levels was varied from 2 mm, 3 mm, and 4 mm, where the rhombic angles parameter was varied from 100°, 120°, and 135°. The last parameter (i.e., type of rubber material) was used in designing are Polyurathane L42, Polyurathane L100, and Polyurathane L135. The value of deflection and total stress every model are then calculated using finite element software. Furthermore, artificial intelligence using backpropagation of neural network (BPNN) was developed and utilized as a forecasting tool to predict the relationship between input (spoke thickness, rhombic angle, and rubber material) and output (deflection and total stress) of the airless tire models. Next, an optimization method using genetic algorithm (GA) is then employed to find the best design of the airless tire. Moreover, the best airless design will be selected to be produced as an airless-tire prototype.
AB - Airless tires are designed and produced to overcome problems in the radial tires and solid tires. This tire provides a safe and comfortable driving experience in a vehicle during operation. Moreover, this tire still work when it hit by sharp objects (i.e., spike, nail, gun projectile, etc.). This research will be focused on designing airless tires using three parameters input, namely spoke thickness, rhombic angle, and rubber material. Each parameter uses three different levels, so the total design number is 27 designs. The thickness parameter of spoke levels was varied from 2 mm, 3 mm, and 4 mm, where the rhombic angles parameter was varied from 100°, 120°, and 135°. The last parameter (i.e., type of rubber material) was used in designing are Polyurathane L42, Polyurathane L100, and Polyurathane L135. The value of deflection and total stress every model are then calculated using finite element software. Furthermore, artificial intelligence using backpropagation of neural network (BPNN) was developed and utilized as a forecasting tool to predict the relationship between input (spoke thickness, rhombic angle, and rubber material) and output (deflection and total stress) of the airless tire models. Next, an optimization method using genetic algorithm (GA) is then employed to find the best design of the airless tire. Moreover, the best airless design will be selected to be produced as an airless-tire prototype.
UR - http://www.scopus.com/inward/record.url?scp=85076793229&partnerID=8YFLogxK
U2 - 10.1063/1.5138331
DO - 10.1063/1.5138331
M3 - Conference contribution
AN - SCOPUS:85076793229
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 -