Selection of welding robot by multi-criteria decision-making method
Keywords:welding robot selection, multi-criteria decision-making, MCDM, MARCOS, PSI
Welding is a method to join parts together by a stable connection. Welding is used in many different fields. The limitations of manual welding methods are gradually eliminated when using welding robots. The selection of a welding robot has a great influence on the efficiency of the welding process. Choosers (customers) often encounter problems when choosing among many products that are available in the market. During this study, seven types of robots were given to make a choice including AR700, AR900, AR1440, AR1730, AR2010, MA3120, VA1400 II. These seven options are commonly used in welding processes. There are a variety of different parameters (criteria) used to evaluate each of these robots. However, the value of the criteria in the robots is very different. The selection of a robot that is considered the best should be based on all those criteria. At this point, the selection of robots is called MCDM (Multi-Criteria Decision-Making). In this research, two MCDM methods were used to rank the types of robots: MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) and PSI (Preference Selection Index). The determination of important quantities for the criteria has been carried out by various methods, including the MEREC (MEthod based on the Removal Effects of Criteria), EQUAL, ROC (Rank Order Centroid) and RS (Rank Sum) methods. The MARCOS method was used four times corresponding to four different sets of weights. Meanwhile, when using the PSI method, we do not need to calculate the weights for the criteria. All five ranking results indicate the same best alternative. The results indicate that MA3120 is the best one. The two methods MARCOS and PSI are reliable enough to be used when multi-criteria decision-making is required, firstly, in the selection of welding robots.
- Chodha, V., Dubey, R., Kumar, R., Singh, S., Kaur, S. (2022). Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Materials Today: Proceedings, 50, 709–715. doi: https://doi.org/10.1016/j.matpr.2021.04.487
- Do, D. T. (2022). Expanding Data Normalization Method to CODAS Method for Multi-Criteria Decision Making. Applied Engineering Letters: Journal of Engineering and Applied Sciences, 7 (2), 54–66. doi: https://doi.org/10.18485/aeletters.2022.7.2.2
- Yalcın, N., Uncu, N. (2020). Applying EDAS as an applicable MCDM method for industrial robot selection. Sigma Journal of Engineering and Natural Sciences, 37 (3), 779–796. Available at: https://dergipark.org.tr/en/pub/sigma/issue/65390/1008391
- Rashid, T., Ali, A., Chu, Y.-M. (2021). Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection. PLOS ONE, 16 (2), e0246738. doi: https://doi.org/10.1371/journal.pone.0246738
- Trung, D. D. (2022). Comparison r and curli methods for multi-criteria decision making. Advanced Engineering Letters, 1 (2), 46–56. doi: https://doi.org/10.46793/adeletters.2022.1.2.3
- Kumar, V., Kalita, K., Chatterjee, P., Zavadskas, E. K., Chakraborty, S. (2021). A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection. Informatica, 33 (1), 35–54. doi: https://doi.org/10.15388/21-infor466
- Goswami, S. S., Behera, D. K., Afzal, A., Razak Kaladgi, A., Khan, S. A., Rajendran, P. et al. (2021). Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS. Symmetry, 13 (8), 1331. doi: https://doi.org/10.3390/sym13081331
- Athawale, V. M., Chakraborty, S. (2011). A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection. International Journal of Industrial Engineering Computations, 2 (4), 831–850. doi: https://doi.org/10.5267/j.ijiec.2011.05.002
- Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. doi: https://doi.org/10.1016/j.cie.2019.106231
- Duc Trung, D. (2022). Multi-criteria decision making under the MARCOS method and the weighting methods: applied to milling, grinding and turning processes. Manufacturing Review, 9, 3. doi: https://doi.org/10.1051/mfreview/2022003
- Maniya, K., Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design, 31 (4), 1785–1789. doi: https://doi.org/10.1016/j.matdes.2009.11.020
- Dung, H. T., Do, D. T., Nguyen, V. T. (2022). Comparison of Multi-Criteria Decision Making Methods Using The Same Data Standardization Method. Strojnícky Časopis - Journal of Mechanical Engineering, 72 (2), 57–72. doi: https://doi.org/10.2478/scjme-2022-0016
- Do, T. (2021). Application of TOPSIS an PIV Methods for Multi - Criteria Decision Making in Hard Turning Process. Journal of Machine Engineering, 21 (4), 57–71. doi: https://doi.org/10.36897/jme/142599
- Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13 (4), 525. doi: https://doi.org/10.3390/sym13040525
- Trung, D. D., Thinh, H. X. (2021). A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study. Advances in Production Engineering & Management, 16 (4), 443–456. doi: https://doi.org/10.14743/apem2021.4.412
- Robot hàn Yaskawa. Available at: https://songnguyen.vn/product/robot-han-yaskawa/
- Do, D. T., Nguyen, N.-T. (2022). Applying Cocoso, Mabac, Mairca, Eamr, Topsis and Weight Determination Methods for Multi-Criteria Decision Making in Hole Turning Process. Strojnícky Časopis - Journal of Mechanical Engineering, 72 (2), 15–40. doi: https://doi.org/10.2478/scjme-2022-0014
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