Published March 22, 2023 | Version v1
Journal article Open

Choosing the best machine tool in mechanical manufacturing

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Machine tools are indispensable components and play an important role in mechanical manufacturing. The equipment of machine tools has a huge effect on the operational efficiency of businesses. Each machine tool type is described by many different criteria, such as cost, technological capabilities, accuracy, energy consumption, convenience in operation, safety for workers, working noise, etc. If the selection of machine is only based on one or several criteria, it will be really easy to make mistakes, which means it is not possible to choose the real best machine. A machine is considered to be the best only when it is chosen based on all of its criteria. This work is called multi-criteria decision-making (MCDM). In this study, the selection of machine tools has been done using two different multi-criteria decision-making methods, including the FUCA method (Faire Un Choix Adéquat) and the CURLI method (Collaborative Unbiased Rank List Intergration). These are two methods with very different characteristics. When using the FUCA method, it is necessary to normalize the data and determine the weights for the criteria. Meanwhile, if using the CURLI method, these two things are not necessary. The selection of these two distinct methods is intended to produce the most generalizable conclusions. Three types of machine tool, which are considered in this study, include grinding machine, drilling machine and milling machine. The number of grinders that were offered for selection was twelve, the number of drills that were surveyed in this study was thirteen, while nine were the number of milling machines that were given for selection. The objective of this study is to determine the best solution in each type of machine. The results of ranking the machines are very similar when using the two mentioned methods. Specially, in all the surveyed cases, the two methods FUCA and CURLI always find the same best alternative. Accordingly, it is possible to firmly come to a conclusion that the FUCA method and the CURLI method are equally effective in machine tool selection. In addition, this study has determined the best three machines corresponding to the three different machine types

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References

  • Hagag, A. M., Yousef, L. S., Abdelmaguid, T. F. (2023). Multi-Criteria Decision-Making for Machine Selection in Manufacturing and Construction: Recent Trends. Mathematics, 11 (3), 631. doi: https://doi.org/10.3390/math11030631
  • Trung, D. D. (2022). Development of data normalization methods for multi-criteria decision making: applying for MARCOS method. Manufacturing Review, 9, 22. doi: https://doi.org/10.1051/mfreview/2022019
  • Li, H., Wang, W., Fan, L., Li, Q., Chen, X. (2020). A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Applied Soft Computing, 91, 106207. doi: https://doi.org/10.1016/j.asoc.2020.106207
  • Do, T. (2021). The Combination of Taguchi – Entropy – WASPAS - PIV Methods for Multi-Criteria Decision Making when External Cylindrical Grinding of 65G Steel. Journal of Machine Engineering, 21 (4), 90–105. doi: https://doi.org/10.36897/jme/144260
  • Nguyen, N.-T., Trung, D. (2021). Combination of Taguchi method, MOORA and COPRAS techniques in multi-objective optimization of surface grinding process. Journal of Applied Engineering Science, 19 (2), 390–398. doi: https://doi.org/10.5937/jaes0-28702
  • 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
  • Athawale, V. M., Chakraborty, S. (2010). A TOPSIS Method-based Approach to Machine Tool Selection, Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management. Dhaka. Available at: http://www.iieom.org/paper/Final%20Paper%20for%20PDF/124%20Chakraborty.pdf
  • Lata, S., Sachdeva, A. K., Paswan, M. K. (2021). Selection of machine tool by using FUZZY TOPSIS method. AIP Conference Proceedings. doi: https://doi.org/10.1063/5.0053536
  • Kentli, A., Akbaş, S. (2016). Lathe selection using analytic hierarchy process and information axiom. CBU International Conference Proceedings, 4, 852–856. doi: https://doi.org/10.12955/cbup.v4.864
  • Trung, D., Truong, N., Thinh, H. (2022). Combined PIPRECIA method and modified FUCA method for selection of lathe. Journal of Applied Engineering Science, 20 (4), 1355–1365. doi: https://doi.org/10.5937/jaes0-39335
  • Gupta, V., Kumar, B., Mandal, U. K. (2016). CNC Machine tool selection using MCDM techniques and application of software SANNA. International Journal of Engineering Trends and Technology, 35 (7), 323–334. doi: https://doi.org/10.14445/22315381/ijett-v35p267
  • Khosravi, J., Asoodar, M. A., Alizadeh, M. R., Peyman, M. H. (2011). Application of Multiple Criteria Decision Making System Compensatory (TOPSIS) in Selecting of Rice Milling System. World Applied Sciences Journal, 13 (11), 2306–2311. Available at: https://www.idosi.org/wasj/wasj13(11)/7.pdf
  • Tsai, J.-P., Cheng, H.-Y., Wang, S.-Y., Kao, Y.-C. (2010). Multi-criteria decision making method for selection of machine tool. 2010 International Symposium on Computer, Communication, Control and Automation (3CA). doi: https://doi.org/10.1109/3ca.2010.5533376
  • Štirbanović, Z., Stanujkić, D., Miljanović, I., Milanović, D. (2019). Application of MCDM methods for flotation machine selection. Minerals Engineering, 137, 140–146. doi: https://doi.org/10.1016/j.mineng.2019.04.014
  • Temiz, I., Calis, G. (2017). Selection of Construction Equipment by using Multi-criteria Decision Making Methods. Procedia Engineering, 196, 286–293. doi: https://doi.org/10.1016/j.proeng.2017.07.201
  • Dağdeviren, M. (2008). Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing, 19 (4), 397–406. doi: https://doi.org/10.1007/s10845-008-0091-7
  • Dominguez, L. A. P., Borroel, E. Z., Quezada, O. E. I., Ortiz-Muñoz, D., Najera-Acosta, A. (2023). CODAS, TOPSIS & AHP Methods Application for Machine Selection. Journal of Computational and Cognitive Engineering. doi: https://doi.org/10.47852/bonviewjcce3202428
  • Mendoza Luis Fernando, M., Perez Escobedo, J. L., Azzaro-Pantel, C., Pibouleau, L., Domenech, S., Aguilar-Lasserre, A. (2011). Selecting the best portfolio alternative from a hybrid multiobjective GA-MCDM approach for New Product Development in the pharmaceutical industry. 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM). doi: https://doi.org/10.1109/smdcm.2011.5949271
  • Do, D. T. (2022). Application of FUCA Method for Multi-Criteria Decision Making in Mechanical Machining Processes. Operational Research in Engineering Sciences: Theory and Applications, 5 (3), 131–152. doi: https://doi.org/10.31181/oresta051022061d
  • Baydaş, M. (2022). The effect of pandemic conditions on financial success rankings of BIST SME industrial companies: a different evaluation with the help of comparison of special capabilities of MOORA, MABAC and FUCA methods. Business & Management Studies: An International Journal, 10 (1), 245–260. doi: https://doi.org/10.15295/bmij.v10i1.1997
  • BAYDAŞ, M. (2022). Comparison of the Performances of MCDM Methods under Uncertainty: An Analysis on Bist SME Industry Index. OPUS Journal of Society Research, 19 (46). doi: https://doi.org/10.26466/opusjsr.1064280
  • Baydaş, M., Pamučar, D. (2022). Determining Objective Characteristics of MCDM Methods under Uncertainty: An Exploration Study with Financial Data. Mathematics, 10 (7), 1115. doi: https://doi.org/10.3390/math10071115
  • Baydaş, M., Elma, O. E., Pamučar, D. (2022). Exploring the specific capacity of different multi criteria decision making approaches under uncertainty using data from financial markets. Expert Systems with Applications, 197, 116755. doi: https://doi.org/10.1016/j.eswa.2022.116755
  • Kiger, J. R., Annibale, D. J. (2016). A new method for group decision making and its application in medical trainee selection. Medical Education, 50 (10), 1045–1053. doi: https://doi.org/10.1111/medu.13112
  • 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
  • Tran, D. V. (2022). Application of the Collaborative Unbiased Rank List Integration Method to Select the Materials. Applied Engineering Letters : Journal of Engineering and Applied Sciences, 7 (4), 133–142. doi: https://doi.org/10.18485/aeletters.2022.7.4.1
  • May Gia Cong Cat Got. Available at: https://maythanhloi.vn/danh-muc-san-pham/may-gia-cong-cat-got/
  • May Khoan Can. Available at: http://cokhi24h.com/may-khoan-can
  • May Khoan Phay. Available at: http://cokhi24h.com/may-khoan-ban-dung/may-khoan-phay