TY - GEN
T1 - Comparison of Various Weight Allocation Methods for the Optimization of EDM Process Parameters Using TOPSIS
AU - Mintri, Sunil
AU - Sapkota, Gaurav
AU - Khan, Nameer
AU - Das, Soham
AU - Shivakoti, Ishwer
AU - Ghadai, Ranjan Kumar
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Multi-Criteria Decision Making (MCDM) techniques are widely used for optimization of process parameters in various engineering problems. The weight allotted to each criterion plays a crucial role in effectively implementing MCDM techniques. In this work, we use the TOPSIS technique with five different subjective and objective weight allocation methods to select the best operating conditions for the machining of SKD11 tool using Electro Discharge Machining (EDM) process. Our results indicate that experimental runs no. 22 and 25 are the best alternatives among all the options tested. The rank plot also suggests that an increase in peak current is better for the overall performance of the EDM process. We observe that the TOPSIS method is not very sensitive to the criteria weights for the current dataset, as evidenced by the correlation between the ranks obtained using the two methods. Moreover, we find that the weights allotted have little effect on the predicted optimum process parameters in the case of the TOPSIS method.
AB - Multi-Criteria Decision Making (MCDM) techniques are widely used for optimization of process parameters in various engineering problems. The weight allotted to each criterion plays a crucial role in effectively implementing MCDM techniques. In this work, we use the TOPSIS technique with five different subjective and objective weight allocation methods to select the best operating conditions for the machining of SKD11 tool using Electro Discharge Machining (EDM) process. Our results indicate that experimental runs no. 22 and 25 are the best alternatives among all the options tested. The rank plot also suggests that an increase in peak current is better for the overall performance of the EDM process. We observe that the TOPSIS method is not very sensitive to the criteria weights for the current dataset, as evidenced by the correlation between the ranks obtained using the two methods. Moreover, we find that the weights allotted have little effect on the predicted optimum process parameters in the case of the TOPSIS method.
UR - https://www.scopus.com/pages/publications/85180792261
UR - https://www.scopus.com/pages/publications/85180792261#tab=citedBy
U2 - 10.1007/978-3-031-50330-6_11
DO - 10.1007/978-3-031-50330-6_11
M3 - Conference contribution
AN - SCOPUS:85180792261
SN - 9783031503290
T3 - Lecture Notes in Networks and Systems
SP - 104
EP - 113
BT - Intelligent Computing and Optimization - Proceedings of the 6th International Conference on Intelligent Computing and Optimization 2023 ICO2023
A2 - Vasant, Pandian
A2 - Shamsul Arefin, Mohammad
A2 - Panchenko, Vladimir
A2 - Thomas, J. Joshua
A2 - Munapo, Elias
A2 - Weber, Gerhard-Wilhelm
A2 - Rodriguez-Aguilar, Roman
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Intelligent Computing and Optimization, ICO 2023
Y2 - 27 April 2023 through 28 April 2023
ER -