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MULTI-OBJECTIVE OPTIMIZATION IN TURNING PROCESS USING RIM METHOD
Authors:
Nguyen Hong Son1
, Vo Thi Nhu Uyen1
1Hanoi University of Industry, Hanoi City, 100000, Vietnam
Received: 10.09.2022.
Accepted: 30.11.2022.
Available: 31.12.2022.
Abstract:
In all machining methods, surface roughness greatly influences the working ability and the life of parts. Besides, material removal rate (MRR) is the parameter that reflects machining productivity. Low surface roughness and high MRR values are ideal for most of the methods. This article presents a research on multi-objective optimization of turning process. The material used in the experiments is SCM440steel. And Taguchi method is applied to design an orthogonal array (L27), in which five parameters are selected as the input of testing process including cutting tool material, tool nose radius, spindle speed, feed rate and depth of cut. In addition, Reference Ideal Method (RIM) is applied to identify the value of the input parameters to achieve the minimum surface roughness and the maximum MRR. Accordingly, in order to obtain the maximum MRR and the minimum surface roughness at the same time, it is necessary to use TiN coated cutting tool, with the tool nose radius of 0.6 mm, the cutting speed of 94.25 m/min, the feed rate of 0.16 mm/rev, and the depth of cut of 0.5 mm. Impact of input parameters on output parameters is also analyzed in this study.
Keywords:
Turning, surface roughness, MRR, multi-objective optimization, RIM, Taguchi
References:
[1] D.D. Trung, Effect of cutting parameters on the surface roughness and roundness error when turning the interrupted surface of 40X steel using HSS-TiN insert. Applied Engineering Letters, 7(1), 2022: 1-9. https://doi.org/10.18485/aeletters.2022.7.1.1
[2] T.V. Dich, N.T. Binh, N.T. Dat, N.V. Tiep, T.X. Viet, Manufacturing technology. Science and Technics Publishing House, Ha Noi, 2003.
[3] H. Yanda, J.A. Ghani, M.N.A.M. Rodzi, K. Othman, C.H.C. Haron, Optimization of material removal rate, surface roughness and tool life on conventional dry turning of FCD700. International Journal of Mechanical and Materials Engineering, 5(2), 2010: 182-190.
[4] M.S. Ranganath, Vipin, R.S. Mishra, Optimization of surface roughness and material removal rate on conventional dry turning of aluminium (6061). International Journal of Advance Research and Innovation, 1, 2014: 62-71.
[5] A. Aswal, A. Jha, A. Tiwari, Y. Kumar Modi, CNC turning parameter optimization for surface roughness of aluminium-2014 alloy using Taguchi methodology. Journal Européen des Systèmes Automatisés, 52(4), 2019: 387-390. https://doi.org/10.18280/jesa.520408
[6] A. Pridhvijit, B.C. Yeldose, Experimental Study and Parameter Optimization of Turning Operation of Aluminium Alloy-2014. International Journal of Engineering Research and General Science, 3(5), 2015: 525-530.
[7] R. Aryan, F. John, S. Kumar, A. Kumar, Optimization of turning parameters of AL-Alloy 6082 using Taguchi method. International Journal of Advance Research and Innovation, 5(2), 2017: 268-275.
[8] V.V.D. Sahithi, T. Malayadrib, N. Srilatha, Optimization of turning parameters on surface roughness based on Taguchi technique. Materials Today: Proceedings, 18, 2019: 3657-3666. https://doi.org/10.1016/j.matpr.2019.07.299
[9] M. Gupta, S. Kumar, Investigation of surface roughness and MRR for turning of UD-GFRP using PCA and Taguchi method. Engineering Science and Technology, an International Journal, 18(1), 2015: 70-81.
https://doi.org/10.1016/j.jestch.2014.09.006
[10] S.G. Dambhare, S.J. Deshmukh, A.B. Borade, Machining parameter optimization in turning process for sustainable manufacturing. International Journal of Industrial Engineering Computations, 6, 2015: 327- 338.
https://doi.org/10.5267/j.ijiec.2015.3.002
[11] Y. Touggui, S. Belhad, S. Mechraou, A. Uysa, M.A. Yallese, M. Temmar, Multiobjective optimization of turning parameters for targeting surface roughness and maximizing material removal rate in dry turning of AISI 316L with PVDcoated cermet insert. SN Applied Sciences, 2(1360), 2020: 1-14. https://doi.org/10.1007/s42452-020-3167-4
[12] S. Dhanalakshmi, T. Rameshbabu, Multi-Aspects optimization of process parameters in CNC Turning of LM 25 alloy using the Taguchi-Grey approach. Metals, 10(4), 2020: 453. https://doi.org/10.3390/met10040453
[13] F. Puh, Z. Jurkovic, M. Perinic, M. Brezocnik, S. Buljan, Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysis. Tehnički vjesnik, 23(2), 2016: 377-382.
https://doi.org/10.17559/TV-20150526131717
[14] N.H. Alharthi, S. Bingol, A.T. Abbas, A.E. Ragab, M.F. Aly, H.F. Alharbi, Prediction of cutting conditions in turning AZ61 and parameters optimization using regression analysis and artificial neural network. Advances in Materials Science and Engineering, 2018, 2018: 1-11. https://doi.org/10.1155/2018/1825291
[15] D.D. Trung, N.V. Thien, N.T. Nguyen, Application of TOPSIS method in multi-objective optimization of the grinding process using segmented grinding wheel. Tribology in Industry, 43(1), 2021: 12-22.
https://doi.org/10.24874/ti.998.11.20.12
[16] N.T. Nguyen, D.D. Trung, Combination of Taguchi method, Moora and Copras techniques in multi-objective optimization of surface grinding process. Journal of Applied Engineering Science, 19(2), 2021: 390-398.
https://doi.org/10.5937/jaes0-28702
[17] N.T. Nguyen, D.D. Trung, A study on the surface grinding process of the SUJ2 steel using CBN slotted grinding wheel. AIMS Materials Science, 7(6), 2020: 871-886. https://doi.org/10.3934/matersci.2020.6.87
[18] Khan, K. Maity, D. Jhodkar, An Integrated Fuzzy-MOORA Method for the Selection of Optimal Parametric Combination in Turing of Commercially Pure Titanium. Optimization of Manufacturing Processes, 2020(719), 2020: 163-184. https://doi.org/10.1007/978-3-030-19638-7_7
[19] M.A. Sofuoglu, R.A. Arpoglu, S. Orak, Multi objective optimization of turning operation using hybrid decision making analysis. Anadolu University Journal of Science and Technology A – Applied Sciences and Engineering, 18(3), 2017: 595-610.
[20] M.A. Sofuoglu, S. Orak, A Novel Hybrid Multi Criteria Decision Making Model: Application to Turning Operations. International Journal of Intelligent Systems and Applications in Engineering, 5(3), 2017: 124-131.
[21] E.C. Perez, M.T. Lamata, J.L. Verdegay, RIM-Reference ideal method in multicriteria decision making. Information Sciences, 337-338, 2016: 1-10. https://doi.org/10.1016/j.ins.2015.12.011
[22] J.M. Sánchez-Lozano, O.N. Rodríguez, Application of fuzzy reference ideal method (FRIM) to the military advanced training aircraft selection. Applied Soft Computing Journal, 88, 2020: 106061. https://doi.org/10.1016/j.asoc.2020.106061
[23] M.A. Sofuoğlu, A new hybrid decision making model to optimize machining operations. The Online Journal of Science and Technology, 8(2), 2018: 5-8.
[24] S. Gurgen, F.H. Cakır, M.A. Sofuoglu, S. Orak, M.C. Kushan, H. Li, Multi-criteria decision-making analysis of different non-traditional machining operations of Ti6Al4V. Soft Computing, 23, 2019: 5259-5272. https://doi.org/10.1007/s00500-019-03959-8
[25] D.D. Trung, Multi-objective optimization of SKD11 steel milling process by Reference Ideal Method. International journal of geology, 15, 2021: 1016. https://doi.org/10.46300/9105.2021.15.1
[26] V.R. Ravipudi, L. Jaya, R-method: A simple ranking method for multi-attribute decision-making in the industrial environment. Journal of Project Management, 6, 2021: 223-230. https://doi.org/10.5267/j.jpm.2021.5.001
[27] S. Nguyen Hong, U. Vo Thi Nhu, Multi-objective Optimization in Turning Operation of AISI 1055 Steel Using DEAR Method. Tribology in Industry, 41(1), 2021: 57-65. https://doi.org/10.24874/ti.1006.11.20.01
[28] U. Vo Thi Nhu, S. Nguyen Hong, Improving accuracy of surface roughness model while turning 9XC steel using a Titanium Nitride-coated cutting tool with Johnson and Box-Cox transformation. AIMS Materials Science, 8(1), 2021: 1-17. https://doi.org/10.3934/matersci.2021001
[29] G.K. Kumar, Ch.M. Rao, V.V.S. Kesava Rao, Investigation of effects of speed and depth of cut on multiple responses using Vikor analysis. International Journal of Modern Trends in Engineering and Research, 5, 2018: 164-168.
[30] D.D. Trung, Application of TOPSIS an PIV Methods for Multi – Criteria Decision Making in Hard Turning Process. Journal of Machine Engineering, 21(4), 2021: 57-71. https://doi.org/10.36897/jme/142599
[31] R. Kumar, R. Dubey, S. Singh, S. Singh, C. Prakash, Y. Nirsanametla, G. Krolczy, R. Chudy, Multiple – Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component. Materials, 14(8), 2021: 2084. https://doi.org/10.3390/ma14082084
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024
How to Cite
N.H. Son, V.T.N. Uyen, Multi-Objective Optimization in Turning Process Using RIM Method. Applied Engineering Letters, 7(4), 2022: 143–153. https://doi.org/10.18485/aeletters.2022.7.4.2
More Citation Formats
Son, N. H., & Uyen, V. T. N, (2022). Multi-Objective Optimization in Turning Process Using RIM Method. Applied Engineering Letters, 7(4), 143–153. https://doi.org/10.18485/aeletters.2022.7.4.2
Son, Nguyen Hong , and Uyen, Vo Thi Nhu. “Multi-Objective Optimization in Turning Process Using RIM Method.” Applied Engineering Letters, vol. 7, no. 4, 2022, pp. 143–53, https://doi.org/10.18485/aeletters.2022.7.4.2.
Son, Nguyen Hong and Vo Thi Nhu Uyen. 2022. “Multi-Objective Optimization in Turning Process Using RIM Method.” Applied Engineering Letters 7 (4): 143–53. https://doi.org/10.18485/aeletters.2022.7.4.2.
Son, H.S. and Uyen, V.T.N, (2022). Multi-Objective Optimization in Turning Process Using RIM Method. Applied Engineering Letters, 7(4), pp.143–153.
doi: 10.18485/aeletters.2022.7.4.2.
MULTI-OBJECTIVE OPTIMIZATION IN TURNING PROCESS USING RIM METHOD
Authors:
Nguyen Hong Son1
, Vo Thi Nhu Uyen1
1Hanoi University of Industry, Hanoi City, 100000, Vietnam
Received: 10.09.2022.
Accepted: 30.11.2022.
Available: 31.12.2022.
Abstract:
In all machining methods, surface roughness greatly influences the working ability and the life of parts. Besides, material removal rate (MRR) is the parameter that reflects machining productivity. Low surface roughness and high MRR values are ideal for most of the methods. This article presents a research on multi-objective optimization of turning process. The material used in the experiments is SCM440steel. And Taguchi method is applied to design an orthogonal array (L27), in which five parameters are selected as the input of testing process including cutting tool material, tool nose radius, spindle speed, feed rate and depth of cut. In addition, Reference Ideal Method (RIM) is applied to identify the value of the input parameters to achieve the minimum surface roughness and the maximum MRR. Accordingly, in order to obtain the maximum MRR and the minimum surface roughness at the same time, it is necessary to use TiN coated cutting tool, with the tool nose radius of 0.6 mm, the cutting speed of 94.25 m/min, the feed rate of 0.16 mm/rev, and the depth of cut of 0.5 mm. Impact of input parameters on output parameters is also analyzed in this study.
Keywords:
Turning, surface roughness, MRR, multi-objective optimization, RIM, Taguchi
References:
[1] D.D. Trung, Effect of cutting parameters on the surface roughness and roundness error when turning the interrupted surface of 40X steel using HSS-TiN insert. Applied Engineering Letters, 7(1), 2022: 1-9. https://doi.org/10.18485/aeletters.2022.7.1.1
[2] T.V. Dich, N.T. Binh, N.T. Dat, N.V. Tiep, T.X. Viet, Manufacturing technology. Science and Technics Publishing House, Ha Noi, 2003.
[3] H. Yanda, J.A. Ghani, M.N.A.M. Rodzi, K. Othman, C.H.C. Haron, Optimization of material removal rate, surface roughness and tool life on conventional dry turning of FCD700. International Journal of Mechanical and Materials Engineering, 5(2), 2010: 182-190.
[4] M.S. Ranganath, Vipin, R.S. Mishra, Optimization of surface roughness and material removal rate on conventional dry turning of aluminium (6061). International Journal of Advance Research and Innovation, 1, 2014: 62-71.
[5] A. Aswal, A. Jha, A. Tiwari, Y. Kumar Modi, CNC turning parameter optimization for surface roughness of aluminium-2014 alloy using Taguchi methodology. Journal Européen des Systèmes Automatisés, 52(4), 2019: 387-390. https://doi.org/10.18280/jesa.520408
[6] A. Pridhvijit, B.C. Yeldose, Experimental Study and Parameter Optimization of Turning Operation of Aluminium Alloy-2014. International Journal of Engineering Research and General Science, 3(5), 2015: 525-530.
[7] R. Aryan, F. John, S. Kumar, A. Kumar, Optimization of turning parameters of AL-Alloy 6082 using Taguchi method. International Journal of Advance Research and Innovation, 5(2), 2017: 268-275.
[8] V.V.D. Sahithi, T. Malayadrib, N. Srilatha, Optimization of turning parameters on surface roughness based on Taguchi technique. Materials Today: Proceedings, 18, 2019: 3657-3666. https://doi.org/10.1016/j.matpr.2019.07.299
[9] M. Gupta, S. Kumar, Investigation of surface roughness and MRR for turning of UD-GFRP using PCA and Taguchi method. Engineering Science and Technology, an International Journal, 18(1), 2015: 70-81.
https://doi.org/10.1016/j.jestch.2014.09.006
[10] S.G. Dambhare, S.J. Deshmukh, A.B. Borade, Machining parameter optimization in turning process for sustainable manufacturing. International Journal of Industrial Engineering Computations, 6, 2015: 327- 338. https://doi.org/10.5267/j.ijiec.2015.3.002
[11] Y. Touggui, S. Belhad, S. Mechraou, A. Uysa, M.A. Yallese, M. Temmar, Multiobjective optimization of turning parameters for targeting surface roughness and maximizing material removal rate in dry turning of AISI 316L with PVDcoated cermet insert. SN Applied Sciences, 2(1360), 2020: 1-14. https://doi.org/10.1007/s42452-020-3167-4
[12] S. Dhanalakshmi, T. Rameshbabu, Multi-Aspects optimization of process parameters in CNC Turning of LM 25 alloy using the Taguchi-Grey approach. Metals, 10(4), 2020: 453. https://doi.org/10.3390/met10040453
[13] F. Puh, Z. Jurkovic, M. Perinic, M. Brezocnik, S. Buljan, Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysis. Tehnički vjesnik, 23(2), 2016: 377-382. https://doi.org/10.17559/TV-20150526131717
[14] N.H. Alharthi, S. Bingol, A.T. Abbas, A.E. Ragab, M.F. Aly, H.F. Alharbi, Prediction of cutting conditions in turning AZ61 and parameters optimization using regression analysis and artificial neural network. Advances in Materials Science and Engineering, 2018, 2018: 1-11. https://doi.org/10.1155/2018/1825291
[15] D.D. Trung, N.V. Thien, N.T. Nguyen, Application of TOPSIS method in multi-objective optimization of the grinding process using segmented grinding wheel. Tribology in Industry, 43(1), 2021: 12-22.
https://doi.org/10.24874/ti.998.11.20.12
[16] N.T. Nguyen, D.D. Trung, Combination of Taguchi method, Moora and Copras techniques in multi-objective optimization of surface grinding process. Journal of Applied Engineering Science, 19(2), 2021: 390-398. https://doi.org/10.5937/jaes0-28702
[17] N.T. Nguyen, D.D. Trung, A study on the surface grinding process of the SUJ2 steel using CBN slotted grinding wheel. AIMS Materials Science, 7(6), 2020: 871-886. https://doi.org/10.3934/matersci.2020.6.87
[18] Khan, K. Maity, D. Jhodkar, An Integrated Fuzzy-MOORA Method for the Selection of Optimal Parametric Combination in Turing of Commercially Pure Titanium. Optimization of Manufacturing Processes, 2020(719), 2020: 163-184. https://doi.org/10.1007/978-3-030-19638-7_7
[19] M.A. Sofuoglu, R.A. Arpoglu, S. Orak, Multi objective optimization of turning operation using hybrid decision making analysis. Anadolu University Journal of Science and Technology A – Applied Sciences and Engineering, 18(3), 2017: 595-610.
[20] M.A. Sofuoglu, S. Orak, A Novel Hybrid Multi Criteria Decision Making Model: Application to Turning Operations. International Journal of Intelligent Systems and Applications in Engineering, 5(3), 2017: 124-131.
[21] E.C. Perez, M.T. Lamata, J.L. Verdegay, RIM-Reference ideal method in multicriteria decision making. Information Sciences, 337-338, 2016: 1-10. https://doi.org/10.1016/j.ins.2015.12.011
[22] J.M. Sánchez-Lozano, O.N. Rodríguez, Application of fuzzy reference ideal method (FRIM) to the military advanced training aircraft selection. Applied Soft Computing Journal, 88, 2020: 106061. https://doi.org/10.1016/j.asoc.2020.106061
[23] M.A. Sofuoğlu, A new hybrid decision making model to optimize machining operations. The Online Journal of Science and Technology, 8(2), 2018: 5-8.
[24] S. Gurgen, F.H. Cakır, M.A. Sofuoglu, S. Orak, M.C. Kushan, H. Li, Multi-criteria decision-making analysis of different non-traditional machining operations of Ti6Al4V. Soft Computing, 23, 2019: 5259-5272. https://doi.org/10.1007/s00500-019-03959-8
[25] D.D. Trung, Multi-objective optimization of SKD11 steel milling process by Reference Ideal Method. International journal of geology, 15, 2021: 1016. https://doi.org/10.46300/9105.2021.15.1
[26] V.R. Ravipudi, L. Jaya, R-method: A simple ranking method for multi-attribute decision-making in the industrial environment. Journal of Project Management, 6, 2021: 223-230. https://doi.org/10.5267/j.jpm.2021.5.001
[27] S. Nguyen Hong, U. Vo Thi Nhu, Multi-objective Optimization in Turning Operation of AISI 1055 Steel Using DEAR Method. Tribology in Industry, 41(1), 2021: 57-65. https://doi.org/10.24874/ti.1006.11.20.01
[28] U. Vo Thi Nhu, S. Nguyen Hong, Improving accuracy of surface roughness model while turning 9XC steel using a Titanium Nitride-coated cutting tool with Johnson and Box-Cox transformation. AIMS Materials Science, 8(1), 2021: 1-17. https://doi.org/10.3934/matersci.2021001
[29] G.K. Kumar, Ch.M. Rao, V.V.S. Kesava Rao, Investigation of effects of speed and depth of cut on multiple responses using Vikor analysis. International Journal of Modern Trends in Engineering and Research, 5, 2018: 164-168.
[30] D.D. Trung, Application of TOPSIS an PIV Methods for Multi – Criteria Decision Making in Hard Turning Process. Journal of Machine Engineering, 21(4), 2021: 57-71. https://doi.org/10.36897/jme/142599
[31] R. Kumar, R. Dubey, S. Singh, S. Singh, C. Prakash, Y. Nirsanametla, G. Krolczy, R. Chudy, Multiple – Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component. Materials, 14(8), 2021: 2084. https://doi.org/10.3390/ma14082084
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024