Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.260093

Keywords:

CBN grinding, multi-criteria decision making, MCDM, TOPSIS, MAIRCA, EARM

Abstract

Determining the best cutting mode is a common problem for machining processes as well as for CBN (Cubic Boron Nitride) grinding on Computer Numerical Control (CNC) machines. It is even more important when it is necessary to choose a solution that meets many goals, which are in conflict. This paper presents the results of a multi-criteria decision-making (MCDM) study on CBN grinding of cylindrical-shaped parts on CNC milling machines. Three MCDM methods,  including TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), and EAMR (Evaluation by an Area-based Method of Ranking) were applied in this work. Besides, MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were used to determine the weights of the criteria. In addition, the Taguchi method with L18 orthogonal array (6^1+3^3) design was used for the design of an experiment, which has four input factors including the depth of dressing cut, the spindle speed, the feed rate, and the wheel diameter. Two criteria, including the surface roughness (SR) and the material removal speed (MRS) were selected as the response outputs. The reason for choosing these two criteria is because SR and MRS are two very important output factors of a mechanical machining process as well as of the CBN grinding process on a CNC milling machine. In particular, these two criteria are always in conflict with each other. Small SR requirements will require small values of the feed speed and the depth of cut. This will lead to the reduction of MRS. From the results of this study, the use of different methods for MCDM was evaluated. In addition, rankings of alternatives have been given according to MCDM methods. Furthermore, the best alternative to guarantee both the minimum SR and the maximum MRS has been found

Author Biographies

Trieu Quy Huy, University of Economics-Technology for Industries

PhD

Department of Mechanical Engineering

Bui Thanh Hien, Thai Nguyen University of Technology

Master of Science

Department of Mechanical Engineering

Tran Huu Danh, Vinh Long University of Technology Education

PhD

Department of Mechanical Engineering

Pham Duc Lam, Nguyen Tat Thanh University

Master of Science

Department of Mechanical Engineering

Nguyen Hong Linh, Electric Power University

PhD

Department of Mechanical Engineering

Vu Van Khoa, National Research Institute of Mechanical Engineering

PhD, Vice Director

Le Xuan Hung, Thai Nguyen University of Technology

PhD

Department of Mechanical Engineering

Vu Ngoc Pi, Thai Nguyen University of Technology

PhD, Associate Professor

Department of Mechanical Engineering

References

  1. Mühlbacher, A. C., Kaczynski, A. (2015). Making Good Decisions in Healthcare with Multi-Criteria Decision Analysis: The Use, Current Research and Future Development of MCDA. Applied Health Economics and Health Policy, 14 (1), 29–40. doi: http://doi.org/10.1007/s40258-015-0203-4
  2. Wu, H., Xu, Z., Ren, P., Liao, H. (2018). Hesitant fuzzy linguistic projection model to multi-criteria decision making for hospital decision support systems. Computers & Industrial Engineering, 115, 449–458. doi: http://doi.org/10.1016/j.cie.2017.11.023
  3. Shaikh, S. A., Memon, M., Kim, K.-S. (2021). A Multi-Criteria Decision-Making Approach for Ideal Business Location Identification. Applied Sciences, 11 (11), 4983. doi: http://doi.org/10.3390/app11114983
  4. Rostamzadeh, R., Ismail, K., Zavadskas, E. K. (2014). Multi criteria decision making for assisting business angels in investments. Technological and Economic Development of Economy, 20 (4), 696–720. doi: http://doi.org/10.3846/20294913.2014.984364
  5. Basilico, N., Amigoni, F. (2011). Exploration strategies based on multi-criteria decision making for searching environments in rescue operations. Autonomous Robots, 31 (4), 401–417. doi: http://doi.org/10.1007/s10514-011-9249-9
  6. Caruzzo, A., Belderrain, M. C. N., Fisch, G., Young, G. S., Hanlon, C. J., Verlinde, J. (2018). Modelling weather risk preferences with multi-criteria decision analysis for an aerospace vehicle launch. Meteorological Applications, 25 (3), 456–465. doi: http://doi.org/10.1002/met.1713
  7. Yahyai, S. A., Charabi, Y., Badi, A. A., Gastli, A. (2013). Wind resource assessment using numerical weather prediction models and multi-criteria decision making technique: case study (Masirah Island, Oman). International Journal of Renewable Energy Technology, 4 (1), 17–33. doi: http://doi.org/10.1504/ijret.2013.051070
  8. Çalışkan, H., Kurşuncu, B., Kurbanoğlu, C., Güven, Ş. Y. (2013). Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods. Materials & Design, 45, 473–479. doi: http://doi.org/10.1016/j.matdes.2012.09.042
  9. Do, D. T. (2021). A combination method for multi-criteria decision making problem in turning process. Manufacturing Review, 8, 26. doi: http://doi.org/10.1051/mfreview/2021024
  10. Duc, 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: http://doi.org/10.36897/jme/142599
  11. Varatharajulu, M., Duraiselvam, M., Kumar, M. B., Jayaprakash, G., Baskar, N. (2021). Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91. Journal of Magnesium and Alloys. doi: http://doi.org/10.1016/j.jma.2021.05.006
  12. 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: http://doi.org/10.36897/jme/144260
  13. Sahu, S. N., Nayak, N. C. (2018). Multi-criteria decision making with PCA in EDM of A2 tool steel. Materials Today: Proceedings, 5 (9), 18641–18648. doi: http://doi.org/10.1016/j.matpr.2018.06.209
  14. Vu, N.-P., Nguyen, Q.-T., Tran, T.-H., Le, H.-K., Nguyen, A.-T., Luu, A.-T., Nguyen, V.-T., Le, X.-H. (2019). Optimization of grinding parameters for minimum grinding time when grinding tablet punches by CBN wheel on CNC milling machine. Applied sciences, 9 (5), 957. doi: http://doi.org/10.3390/app9050957
  15. Hwang, C.-L., Lai, Y.-J., Liu, T.-Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20 (8), 889–899. doi: http://doi.org/10.1016/0305-0548(93)90109-v
  16. Nguyen, H.-Q., Le, X.-H., Nguyen, T.-T., Tran, Q.-H., Vu, N.-P. (2022). A Comparative Study on Multi-Criteria Decision-Making in Dressing Process for Internal Grinding. Machines, 10 (5), 303. doi: http://doi.org/10.3390/machines10050303
  17. Pamučar, D.V. L., Lukovac, V. (2014). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICА model. Proceedings of the XVI International Scientific-expert Conference on Railways. Niš: Railcon, 89–92.
  18. Amiri, M., Antucheviciene, J. (2016). Evaluation by an area-based method of ranking interval type-2 fuzzy sets (EAMRIT-2F) for multi-criteria group decision-making. Transform Bus Econ, 15 (3), 39.
  19. Hieu, T. T., Thao, N. X., Thuy, L. (2019). Application of MOORA and COPRAS Models to Select Materials for Mushroom Cultivation. Vietnam Journal of Agricultural Sciences, 17 (4), 322–331.
  20. Keshavarz-Ghorabaee, M. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Scientific Reports, 11 (1). doi: http://doi.org/10.1038/s41598-021-98698-y
  21. Athawale, V. M., Chatterjee, P., Chakraborty, S. (2010). Selection of industrial robots using compromise ranking method. Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management.

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Published

2022-06-30

How to Cite

Huy, T. Q., Hien, B. T., Danh, T. H., Lam, P. D., Linh, N. H. ., Khoa, V. V., Hung, L. X., & Pi, V. N. (2022). Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding . Eastern-European Journal of Enterprise Technologies, 3(1 (117), 58–66. https://doi.org/10.15587/1729-4061.2022.260093

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Section

Engineering technological systems