Multi-Optimization in Turning Process
DOI:
https://doi.org/10.32628/IJSRST25121203Keywords:
Turning, Optimization, FUCA method, Weight methodAbstract
This paper presents a study on the multi-objective optimization of the turning process. Twenty-five experiments, designed using the Taguchi method, were conducted. In each experiment, the values of five parameters were varied: tool nose radius, tool overhang length, spindle speed, feed rate, and depth of cut. Three output parameters were measured for each experiment: surface roughness, roundness deviation, and material removal rate. Four different methods were employed to determine the weights of the output parameters. The FUCA method was then used to solve the multi-objective optimization problem. This process was repeated four times, corresponding to the four sets of criteria weights. The objective of the multi-objective optimization was to identify the input parameter values that simultaneously minimize surface roughness and roundness deviation, while maximizing material removal rate. Surprisingly, the optimal values for the input parameters were identical across all four weighting methods. The optimal values for tool nose radius, tool overhang length, spindle speed, feed rate, and depth of cut were found to be 0.8 (mm), 40 (mm), 587 (rev/min), 0.316 (mm/rev), and 0.6 (mm), respectively.
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M. H. El-Axir, M. M. Elkhabeery, M. M. Okasha, Modeling and Parameter Optimization for Surface Roughness and Residual Stress in Dry Turning Process, Engineering, Technology & Applied Science Research, 7(5), 2017, 2047-2055, https://doi.org/10.48084/etasr.1560
N. M. M. Reddy, P. K. Chaganti, Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS, Technology & Applied Science Research, 9(1), 2019, 3822-3825, https://doi.org/10.48084/etasr.2537
D. D. Trung, Development of data normalization methods for multi-criteria decision making: applying for MARCOS method, Manufacturing review, 9(22), 2022, https://doi.org/10.1051/mfreview/2022019
D. D .Trung, Influence of cutting parameters on surface roughness during milling AISI 1045 steel, Tribology in Industry, 42(4), 2020, 658-665, https://doi.org/10.24874/ti.969.09.20.11
C. Divya, L. Suvarna Raju, B. Singaravel, Application of MCDM Methods for Process Parameter Optimization in Turning Process—A Review, Recent Trends in Mechanical Engineering, 2020, 199–207, https://doi.org/10.1007/978-981-15-7557-0_18
S. Chakraborty, T.K. Jana, S. Paul, On the application of multi criteria decision making technique for multi-response optimization of metal cutting process, Intelligent Decision Technologies, 13(1), 2019, 101–115, https://doi.org/10.3233/IDT-190356
D. D. Trung, 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), 2021, 90–105, https://doi.org/10.36897/jme/144260
D. D. Trung, Application of TOPSIS and 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
D. D. Trung, Multi-objective optimization of SKD11 steel milling process by reference ideal method, International journal of geology, 15, 2021, 1-16, https://doi.org/10.46300/9105.2021.15.1
D. D. Trung, Comparison R and CURLI methods for multi-criteria decision making, Advanced Engineering Letters, 1(2), 2022, 46-56, https://doi.org/10.46793/adeletters.2022.1.2.3
D. D. Trung, A combination method for multi-criteria decision making problem in turning process, Manufacturing review, 8(26), 2021, https://doi.org/10.1051/mfreview/2021024
D. D. Trung, M. T. Diep, D. V. Duc, N. C. Bao, N. H. Son, Application of probability theory in machine selection, Applied Engineering Letters, 9(4), 2024, 203-214, https://doi.org/10.46793/aeletters.2024.9.4.3
D. D. Trung, Influence of Cutting Parameters on Surface Roughness in Grinding of 65G Steel, Tribology in Industry, 43(1), 2021, 167-176.
M. M. L. Fernando, J. L. P. Escobedo, C. Azzaro-Pantel, L. Pibouleau, S. Domenech, A. Aguilar-Lasserre, Selecting the best alternative based on a hybrid multiobjective GA-MCDM approach for new product development in the pharmaceutical industry, IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), 2021, https://ieeexplore.ieee.org/document/5949271
D. D. Trung, Application of FUCA method for multi-criteria decision making in mechanical machining processes, Operational Research in Engineering Sciences: Theory and Applications, 5(3), 2022, 131-152, https://doi.org/10.31181/oresta051022061d
M. Baydas, 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), 2022, 245-260.
M. Baydas, Comparison of the Performances of MCDM Methods under Uncertainty: An Analysis on Bist SME Industry Index, OPUS – Journal of Society Research, 19(46), 2022, 308-326.
A. Ouattara, L. Pibouleau, C. Azzaro-Pantel, S. Domenech, P. Baudet, Y. Benjamin, Economic and environmental strategies for process design, Computers & Chemical Engineering, 36(10), 2012,174-188.
E. K. Zavadskas, J. Antucheviciene, P. Chatterjee, Multiple-Criteria Decision-Making (MCDM) Techniques for Business Processes Information Management, Information, 2019, https://doi.org/10.3390/books978-3-03897-643-1
D. D. Trung, H. X. Thinh, 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), 2021, 443-456, https://doi.org/10.14743/apem2021.4.412
R. M. Dawes, B. Coorigan, Linear Models in Decision Malking, Psychological Bulletin, Vol. 81, pp. 95–106, 1974.
H. J. Einhorn, W. Mccoach, A Symble Multiattribute Utility Procedure for Evaluation, Behavioral Scicence, 22(4), 1997, 270–282.
M. Keshavarz-Ghorabaee, M. Amiri, E. K. Zavadskas, Z. Turskis, J. Antucheviciene, Determination of objective weights using a new method based on the removal effects of criteria (MEREC), Symmetry, 13(4), 2021, 1-20.
D. D. Trung, H. X. Thinh, L. D. Ha, Comparison of the RAFSI and PIV method in multi-criteria decision making: application to turning processes, International Journal of Metrology and Quality Engineering, 13(14), 2022, https://doi.org/10.1051/ijmqe/2022014
D. T. Do, Assessing the Impact of Criterion Weights on the Ranking of the Top Ten Universities in Vietnam, Engineering, Technology & Applied Science Research, 14(4), 2024, 14899-14903, 2024, https://doi.org/10.48084/etasr.7607
D. D. Trung, A. Ašonja, D. T. T. Thuy, D. V. Duc, N. C. Bao, Applying the DEAR Method to Optimize Multi-objective Process on a Conventional Lathe
with New Cutting Tools, OTO 2024, LNNS 1242, 2025, 249–259, https://doi.org/10.1007/978-3-031-80597-4_20
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
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