Multi-objective optimization model of cutting parameters for a sustainable multi-pass turning process

  • Wahyu Widhiarso Universitas Setia Budi Surakarta https://orcid.org/0000-0002-7668-2453
  • Ibnu Abdul Rosid Universitas Jenderal Achmad Yani Yogyakarta
  • Rieska Ernawati Universitas Islam Sultan Agung Semarang
https://doi.org/10.30656/jsmi.v7i1.5747
Abstract views: 357 , PDF downloads: 980
Keywords: Cutting parameter, MOORA, Multi-objective optimization, Multi-pass turning, Sustainable

Abstract

The turning process involves the linear removal of material from the work-piece and requires a relatively high amount of energy. The high energy consumption of the machining process increases carbon emissions, which affects the environment. Moreover, production costs will rise as the cost of energy rises. Energy savings during the machining process are crucial for achieving sustainable manufacturing. In order to determine and optimize the cutting parameters, this study creates a multi-pass turning processes optimi¬zation model. It considers cutting speeds, feed rates, and depth of cut. In this study, the model uses multi-objective optimization by incorporating three objective functions: processing time, energy consumption and product¬ion costs. OptQuest completed the proposed model in Oracle Crystal Ball software, then normalized and weighted the sum. Ordering preferences, the Multi-Objective Optimization based on Ratio Analysis (MOORA) approach is utilized. It ranks items based on their higher priority values. This paper provides a numerical example to demonstrate the application of an optimi¬zation model. Based on the preference order ranking results, the optimal values for three objective functions are as follows: total processing time of 4.953 min, the total energy consumption of 5.434 MJ, and total production cost of 395.21$.

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References

R. K. Bhushan, ‘Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites’, J. Clean. Prod., vol. 39, pp. 242–254, 2013, doi: https://doi.org/10.1016/j.jclepro.2012.08.008.

A. M. Khan et al., ‘Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling’, Energies, vol. 12, no. 4. 2019, doi: https://doi.org/10.3390/en12040710.

U.S. Energy Information Administration, ‘Industrial Sector Energy Consumption’, 2016. [Online]. Available: http://www.eia.gov/outlooks/ieo/pdf/industrial.pdf.

N. Sihag, A. Leiden, V. Bhakar, S. Thiede, K. S. Sangwan, and C. Herrmann, ‘The Influence of Manufacturing Plant Site Selection on Environmental Impact of Machining Processes’, Procedia CIRP, vol. 80, pp. 186–191, 2019, doi: https://doi.org/10.1016/j.procir.2019.01.023.

M. P. Groover, Fundamentals of Modern Manufacturing. Wiley, 2015. [Online]. Available: https://books.google.co.id/books?id=QRWRCgAAQBAJ

Q. Yi, C. Li, Y. Tang, and X. Chen, ‘Multi-objective parameter optimization of CNC machining for low carbon manufacturing’, J. Clean. Prod., vol. 95, pp. 256–264, 2015, doi: https://doi.org/10.1016/j.jclepro.2015.02.076.

L. Hu, R. Tang, W. Cai, Y. Feng, and X. Ma, ‘Optimisation of cutting parameters for improving energy efficiency in machining process’, Robot. Comput. Integr. Manuf., vol. 59, pp. 406–416, 2019, doi: https://doi.org/10.1016/j.rcim.2019.04.015.

S. Pawanr, G. Kant Garg, and S. Routroy, ‘Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS’, Procedia CIRP, vol. 86, pp. 116–120, 2019, doi: https://doi.org/10.1016/j.procir.2020.01.036.

S. Qun and Z. Weimin, ‘Carbon Footprint Analysis in Metal Cutting Process BT - Proceedings of the 1st International Conference on Mechanical Engineering and Material Science (MEMS 2012)’, Dec. 2012, pp. 717–720, doi: https://doi.org/10.2991/mems.2012.188.

W. Lin et al., ‘Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations’, Eng. Optim., vol. 47, no. 7, pp. 994–1007, Jul. 2015, doi: https://doi.org/10.1080/0305215X.2014.928818.

S. Velchev, I. Kolev, K. Ivanov, and S. Gechevski, ‘Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning’, J. Clean. Prod., vol. 80, pp. 139–149, 2014, doi: https://doi.org/10.1016/j.jclepro.2014.05.099.

Y. Xiao, Z. Jiang, Q. Gu, W. Yan, and R. Wang, ‘A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost’, J. Manuf. Syst., vol. 59, pp. 535–548, 2021, doi: https://doi.org/10.1016/j.jmsy.2021.03.023.

S. Jia et al., ‘Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption’, Sustainability, vol. 13, no. 24. 2021, doi: https://doi.org/10.3390/su132413803.

S. A. Bagaber and A. R. Yusoff, ‘Energy and cost integration for multi-objective optimisation in a sustainable turning process’, Measurement, vol. 136, pp. 795–810, 2019, doi: https://doi.org/10.1016/j.measurement.2018.12.096.

M. F. Rajemi, P. T. Mativenga, and A. Aramcharoen, ‘Sustainable machining: selection of optimum turning conditions based on minimum energy considerations’, J. Clean. Prod., vol. 18, no. 10, pp. 1059–1065, 2010, doi: https://doi.org/10.1016/j.jclepro.2010.01.025.

Y. Liu, H. Dong, N. Lohse, and S. Petrovic, ‘A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance’, Int. J. Prod. Econ., vol. 179, pp. 259–272, 2016, doi: https://doi.org/10.1016/j.ijpe.2016.06.019.

L. C. Moreira, W. D. Li, X. Lu, and M. E. Fitzpatrick, ‘Energy-Efficient machining process analysis and optimisation based on BS EN24T alloy steel as case studies’, Robot. Comput. Integr. Manuf., vol. 58, pp. 1–12, 2019, doi: https://doi.org/10.1016/j.rcim.2019.01.011.

A. Aryanfar and M. Solimanpur, ‘Optimization of multi-pass turning operations using genetic algorithms’, 2012, [Online]. Available: http://ieomsociety.org/ieom2012/pdfs/373.pdf .

A. Jabri, A. El Barkany, and A. El Khalfi, ‘Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process’, Engineering, vol. 05, no. 07, pp. 601–610, 2013, doi: https://doi.org/10.4236/eng.2013.57072.

Z. Liu, D. Sun, C. Lin, X. Zhao, and Y. Yang, ‘Multi-objective optimization of the operating conditions in a cutting process based on low carbon emission costs’, J. Clean. Prod., vol. 124, pp. 266–275, 2016, doi: https://doi.org/10.1016/j.jclepro.2016.02.087.

C. Lu, L. Gao, X. Li, and P. Chen, ‘Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm’, J. Clean. Prod., vol. 137, pp. 1516–1531, 2016, doi: https://doi.org/10.1016/j.jclepro.2016.07.029.

A. Jabri, A. El Barkany, and A. El Khalfi, ‘Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm’, Model. Simul. Eng., vol. 2017, p. 1940635, 2017, doi: https://doi.org/10.1155/2017/1940635.

W. Widhiarso and C. N. Rosyidi, ‘Multi objective optimization model for minimizing production cost and environmental impact in CNC turning process’, in AIP Conference Proceedings, 2018, vol. 1931, no. 1, p. 030024, doi: https://doi.org/10.1063/1.5024083.

C. N. Rosyidi, W. Widhiarso, and E. Pujiyanto, ‘Multi objective optimization model of CNC turning for minimizing processing time and carbon emission with real machining application’, J. Ind. Eng. Manag., vol. 14, no. 2, pp. 376–390, Mar. 2021, doi: https://doi.org/10.3926/jiem.3269.

A. Dityarini, E. Pujiyanto, and I. W. Suletra, ‘Multi-Objective Optimization Model of Multi-Pass Turning Operations to Minimize Energy, Carbon Emissions, and Production Costs’, J. Tek. Ind., vol. 21, no. 2, pp. 213–224, Aug. 2020, doi: https://doi.org/10.22219/JTIUMM.Vol21.No2.213-224.

P. Pangestu, E. Pujiyanto, and C. N. Rosyidi, ‘Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing’, Heliyon, vol. 7, no. 2, p. e06043, 2021, doi: https://doi.org/10.1016/j.heliyon.2021.e06043.

B. M. Fittamami, E. Pujiyanto, and Y. Priyandari, ‘Multi-Objective Optimization of Machining Parameters for Multi-Pass CNC Turning to Minimize Carbon Emissions, Energy, Noise and Cost’, J. Tek. Ind., vol. 23, no. 1, pp. 25–34, May 2021, doi: https://doi.org/10.9744/jti.23.1.25-34.

E. Pujiyanto, C. N. Rosyidi, M. Hisjam, and E. Liquddanu, ‘Sustainable multi-objective optimization of a machining parameter model for multi-pass turning processes’, Cogent Eng., vol. 9, no. 1, p. 2108154, Dec. 2022, doi: https://doi.org/10.1080/23311916.2022.2108154.

V. S. Gadakh, V. B. Shinde, and N. S. Khemnar, ‘Optimization of welding process parameters using MOORA method’, Int. J. Adv. Manuf. Technol., vol. 69, no. 9, pp. 2031–2039, 2013, doi: https://doi.org/10.1007/s00170-013-5188-2.

A. P. U. Siahaan, ‘Multi-objective optimization method by ratio analysis in determining results in decision support systems’, Int. J. Innov. Res. Multidiscip. F., vol. 4, no. 10, pp. 50–54, 2018, [Online]. Available: https://www.ijirmf.com/wp-content/uploads/201810008.pdf.

P. Chauhan, M. Pant, and K. Deep, ‘Parameter optimization of multi-pass turning using chaotic PSO’, Int. J. Mach. Learn. Cybern., vol. 6, no. 2, pp. 319–337, 2015, doi: https://doi.org/10.1007/s13042-013-0221-1.

Q. Wang, F. Liu, and X. Wang, ‘Multi-objective optimization of machining parameters considering energy consumption’, Int. J. Adv. Manuf. Technol., vol. 71, no. 5, pp. 1133–1142, 2014, doi: https://doi.org/10.1007/s00170-013-5547-z.

R. T. Marler and J. S. Arora, ‘Survey of multi-objective optimization methods for engineering’, Struct. Multidiscip. Optim., vol. 26, no. 6, pp. 369–395, 2004, doi: https://doi.org/10.1007/s00158-003-0368-6.

M. T. M. Emmerich and A. H. Deutz, ‘A tutorial on multiobjective optimization: fundamentals and evolutionary methods’, Nat. Comput., vol. 17, no. 3, pp. 585–609, 2018, doi: https://doi.org/10.1007/s11047-018-9685-y.

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’, Appl. Eng. Lett. J. Eng. Appl. Sci., vol. 7, no. 1, pp. 1–9, 2022, doi: https://doi.org/10.18485/aeletters.2022.7.1.1.

J. Achebo and W. E. Odinikuku, ‘Optimization of Gas Metal Arc Welding Process Parameters Using Standard Deviation (SDV) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)’, J. Miner. Mater. Charact. Eng., vol. 03, no. 04, pp. 298–308, 2015, doi: https://doi.org/10.4236/jmmce.2015.34032.

D. D. Trung, N.-T. Nguyen, and D. Van Duc, ‘Study on Multi-Objective Optimization of The Turning Process of EN 10503 Steel by Combination of Taguchi Method and MOORA Technique’, EUREKA Phys. Eng., no. 2, pp. 52–65, Mar. 2021, doi: https://doi.org/10.21303/2461-4262.2020.001414.

M. Krishna et al., ‘Application of MOORA & COPRAS integrated with entropy method for multi-criteria decision making in dry turning process of Nimonic C263’, Manuf. Rev., vol. 9, no. 20, pp. 1–10, Aug. 2022, doi: https://doi.org/10.1051/mfreview/2022014.

D. Duc Trung, ‘A combination method for multi-criteria decision making problem in turning process’, Manuf. Rev., vol. 8, no. 26, pp. 1–17, Oct. 2021, doi: https://doi.org/10.1051/mfreview/2021024.

Sihag, N., Sangwan, K.S., “Development of a sustainability assessment index for machine tools”, Procedia CIRP 80, pp. 156-161, 2019, doi: https://doi.org/10.1016/j.procir.2019.01.018.

D. D. Trung, ‘Comparison R and Curli Methods for Multi-Criteria Decision Making’, Adv. Eng. Lett., vol. 1, no. 2, pp. 46–56, Jul. 2022, doi: https://doi.org/10.46793/adeletters.2022.1.2.3.

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Published
2023-06-14
How to Cite
[1]
W. Widhiarso, I. A. Rosid, and R. Ernawati, “Multi-objective optimization model of cutting parameters for a sustainable multi-pass turning process”, j. sist. manaj. ind., vol. 7, no. 1, pp. 1-14, Jun. 2023.
Issue
Vol. 7 No. 1 (2023): June
Section
Research Article

Copyright (c) 2023 Wahyu Widhiarso, Ibnu Abdul Rosid, Rieska Ernawati

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