Issue |
Manufacturing Rev.
Volume 9, 2022
|
|
---|---|---|
Article Number | 3 | |
Number of page(s) | 13 | |
DOI | https://doi.org/10.1051/mfreview/2022003 | |
Published online | 01 February 2022 |
Research Article
Multi-criteria decision making under the MARCOS method and the weighting methods: applied to milling, grinding and turning processes
Faculty of Mechanical Engineering, Hanoi University of Industry, Vietnam
* e-mail: doductrung@haui.edu.vn
Received:
14
October
2021
Accepted:
15
January
2022
The efficiency of cutting machining methods is generally evaluated through many parameters such as surface roughness, material removal rate, cutting force, etc. A machining process is considered highly efficient when it meets the requirements for these parameters, such as ensuring small surface roughness, high material removal rate, or small cutting force, etc. However, for each specific machining condition, sometimes the objective functions give contradictory requirements. In this case, it is necessary to implement multi-criteria decision making, i.e., make a decision to ensure harmonization of all required objectives. In this paper, a multi-criteria decision-making study is presented for three common machining methods: milling, grinding, and turning. In each machining method, the weights of the criteria were determined by four different methods, including Equal weight, ROC weight, RS weight and Entropy weight. The MARCOS method was applied for multi-criteria decision making. The best alternative was found to be the same as the weights were determined using the Equal weight and Entropy weight methods. In the remaining two weighting methods, the best alternative found depends on the order where the criteria were arranged, not these methods themselves. Direction for further research has been suggested in this study as well.
Key words: Multi-criteria decision making / MARCOS method / multi-criteria / weight
© D. Duc Trung, Published by EDP Sciences 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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