Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-28702 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 19 article 805 pages: 390-398

Nhu-Tung Nguyen
Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Technology mechanical Hanoi city, Vietnam

Do Duc Trung*
Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Technology mechanical Hanoi city, Vietnam

This study presentes a combination method of several optimization techniques and Taguchi method to solve the multi-objective optimization problem for surface grinding process of SKD11 steel. The optimization techniques that were used in this study were Multi-Objective Optimization on basis of Ratio Analysis (MOORA) and Complex Proportional Assessment (COPRAS). In surface grinding process, two parameters that were chosen as the evaluation creterias were surface roughness (Ra) and material removal rate (MRR). The orthogonal Taguchi L16 matrix was chosen to design the experimental matrix with two input parameters namely workpiece velocity and depth of cut. The two optimization techniques that mentioned above were applied to solve the multi-objective optimization problem in the grinding process. Using two above techniques, the optimized results of the cutting parameters were the same. The optimal values of workpiece velocity and cutting depth were 20 m/min and 0.02 mm, respectively. Corresponding to these optimal values of the workpiece velocity and cutting depth, the surface roughness and material removal rate were 1.16 μm and 86.67 mm3/s. These proposed techniques and method can be used to improve the quality and effectiveness of grinding processes by reducing the surface roughness and increasing the material removal rate.

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The authors thank Hanoi University of Industry for the support during the implementation of this study.

1. Mahajan, T. V., Nikalje, A. M., & Supale, J. P. (2015). Optimization of surface grinding process parameters for AISI D2 steel. Int J Eng Sci Res Technol, 4(7), 944-949.

2. Rajesh Rai P, Vijaykumar H K. (2019). Optimization of Process Parameters in Surface Grinding for AISI 410 by Taguchi Technique, AIP Conference Proceedings, 2080(050001), 1-6. doi:https://doi. org/10.1063/1.5092929

3. Atish. B. Mane., Subhash Nagwase, Sunilkumar Rajaram Patil. (2020). Optimization of Surface Grinding Process Parameters using Taguchi Method, A Journal of Composition Theory, 13(5), 78-87. doi:20.18001.AJCT.2020.V13I5.20.124377

4. Aravind, M., and Periyasamy, D. S. (2014). Optimization of Surface Grinding Process Parameters By Taguchi Method And Response Surface Methodology, International Journal of Engineering Research & Technology, 3(5), 1721-2727. https://www.ijert. org/optimization-of-surface-grinding-process-parameters- by-taguchi-method-and-response-surface- methodology

5. SHAHRI, H. R. F., Akbari, A. A., Mahdavinejad, R., & Solati, A. (2018). Surface hardness improvement in surface grinding process using combined Taguchi method and regression analysis. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 12(2), 1-14, doi: jamdsm.2018jamdsm0049

6. Patil, P. J., & Patil, C. R. (2016). Analysis of process parameters in surface grinding using single objective Taguchi and multi-objective grey relational grade. Perspectives in Science, 8, 367-369. doi:http://dx.

7. Luu Anh Tung, Vu Ngoc Pi, Vu Thi Lien, Tran Thi Hong, Le Xuan Hung, Banh Tien Long. (2019). Optimization of dressing parameters of grinding wheel for 9CrSi tool steel using the taguchi method with grey relational analysis, IOP Conf. Series: Materials Science and Engineering, 635 (012030), 1-8, doi:10.1088/1757-899X/635/1/012030

8. Tran Thi Hong, Nguyen Van Cuong, Le Hong Ky, Nguyen Quoc Tuan, Banh Tien Long, Luu Anh Tung, Nguyen Thanh Tu, Vu Ngoc Pi. (2020), Multi-Criteria Optimization of Dressing Parameters for Surface Grinding 90CrSi Tool Steel Using Taguchi Method and Grey Relational Analysis, Materials Science Forum, 998, 61-68, doi:10.4028/ MSF.998.61

9. Aqib Mashood Khan, Muhammad Jamil, Mozammel Mia, Danil Yurievich Pimenov, Vadim Rashitovich Gasiyarov, Munish Kumar Gupta, Ning He. (2018), Multi-Objective Optimization for Grinding of AISI D2 Steel with Al2O3 Wheel under MQL, Materials, 11(2269), 1-20, doi:10.3390/ma11112269

10. Prashant J. Patil, Chandrakant R. Patil. (2016), Optimization of process parameters of grinding operation using Taguchi based grey relation analysis, Proceedings in Manufacturing Systems, 11(1), 9-14.

11. Hendri Jumianto, Suhardjono, Sampurn, M. Khoirul Effendi. (2019), Multi-Response Optimization on Vibration and Surface Roughness of the Process Parameter Surface Grinding of OCR12VM Using Taguchi-Grey Method, AIP Conference Proceedings, 2187, 030015, 1-8, https://doi. org/10.1063/1.5138319

12. Gadakh. V. S. (2011). Application of MOORA method for parametric optimization of milling process, International journal of applied engineering research, Dindigul, 1(4), 743-758.

13. Mesran, Rivalri Kristianto Hondro, Muhammad Syahrizal. (2017). Andysah Putera Utama Siahaan, Robbi Rahim, Suginam, Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA), 4th International seminar: Research for science, technology and culture, 1-7.

14. Nguyen Huu-Phan, Banh Tien-Long, Le Quang- Dung, Nguyen Duc-Toan, T. Muthuramalingam. (2019). Multi-Criteria Decision Making Using Preferential Selection Index in Titanium based Die-Sinking PMEDM, journal of the korean society for precision engineering, 36(9), 793-802, doi:https://doi. org/10.7736/KSPE.2019.36.9.793

15. Tran Trung Hieu, Nguyen Xuan Thao, Phan Trong Tien, Le Thi Minh Thuy. (2019). Application of MOORA and COPRAS Models to Select Materials for Mushroom Cultivation, Journal of Vietnam Agricultural Science and Technology, 17(4), 322-331.

16. Zavadskas E.K., Kaklauskas A, Sarka V. (1994). The new method of multicriteria complex proportional assessment of projects, Technological and economic development of economy, 1(3), 131-139.

17. Edmundas Kazimieras Zavadskas, Jurgita Antucheviciene, Prasenjit Chatterjee. (2020). Multiple- Criteria Decision Making (MCDM) Techniques for Business Processes Information Management, information, MPDI, doi: info10010004

18. Triantaphyllou, Evangelos. (2000). Multi-criteria Decision Making Methods: A Comparative Study, Springer – Science + Busines media. https://www.

19. Brauers W.K.M. (2004). Optimization methods for a stakeholder society. A revolution in economic thinking by multi-objective optimization, Boston: Kluwer Academic Publishers