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Sustainability-Focused Multi-objective Optimization of a Turning process

  • Iván La Fé PerdomoEmail author
  • Ramón Quiza
  • Dries Haeseldonckx
  • Marcelino Rivas
Regular Paper

Abstract

Using optimized cutting parameters can represent a key contribution for obtaining sustainable machining processes. This study presents the multi-objective optimization of the multi-passes cylindrical turning, where conflicting goals are simultaneously considered: economic, environmental and social sustainability. The first costs. The environmental impact was taking into account through the carbon dioxide emission. Finally, the key issue in the social sustainability was the operational safety. Also, the constraints resulting from the technical aspects of the turning process were also considered. From the decision-making point of view, an a posteriori approach was used, where the optimization process, which gives a Pareto front, is followed by the selection of the most convenient solution, depending on the specific workshop conditions. The non-sorting genetic algorithm (NSGA-II) was used as optimization heuristic. The main contribution of the paper is the use of a tridimensional Pareto front for selecting the best cutting conditions, by considering the three pillars of the sustainability.

Graphic Abstract

Keywords

Sustainability Turning process Optimization Pareto front Genetic algorithm 

Notes

Acknowledgements

The authors gratefully acknowledge VLIR (Flemish Interuniversity Council) for the support through the project “Cleaner Production in the city of Matanzas”.

Compliance with Ethical Standards

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  1. 1.
    Hong, M.-P., Kim, W.-S., Sung, J.-H., Kim, D.-H., Bae, K.-M., & Kim, Y.-S. (2018). High-performance eco-friendly trimming die manufacturing using heterogeneous material additive manufacturing technologies. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(1), 133–142.CrossRefGoogle Scholar
  2. 2.
    Peralta, M. E., Marcos, M., & Aguayo, F. (2016). A Review of Sustainable Machining Engineering: Optimization Process Through Triple Bottom Line. Journal of Manufacturing Science and Engineering, 138(10), 100801–100817.CrossRefGoogle Scholar
  3. 3.
    Hegab, H. A., Darras, B., & Kishawy, H. A. (2018). Towards sustainability assessment of machining processes. Journal of Cleaner Production, 170, 694–703.CrossRefGoogle Scholar
  4. 4.
    Peralta, M. E., Marcos, M., & Aguayo, F. (2017). On the sustainability of machining processes. Proposal for a unified framework through the triple bottom-line from an understanding review. Journal of Cleaner Production, 142, 3890–3904.CrossRefGoogle Scholar
  5. 5.
    Cai, W., Liu, C., Lai, K., Li, L., Cunha, J., & Hu, L. (2019). Energy performance certification in mechanical manufacturing industry: A review and analysis. Energy Conversion and Management, 186, 415–432.CrossRefGoogle Scholar
  6. 6.
    Cai, W., Lai, K., Liu, C., Wei, F., Ma, M., Jia, S., et al. (2019). Promoting sustainability of manufacturing industry through the lean energy-saving and emission-reduction strategy. Science of the Total Environment, 665, 23–32.CrossRefGoogle Scholar
  7. 7.
    Deng, Z., Zhang, H., Fu, Y., Wan, L., & Liu, W. (2017). Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption. Journal of Cleaner Production, 166, 1407–1414.CrossRefGoogle Scholar
  8. 8.
    Dambharea, S. G., Deshmukhb, S. J., & Borade, A. B. (2015). Machining parameter optimization in turning process for sustainable manufacturing. International Journal of Industrial Engineering Computations, 6, 327–338.CrossRefGoogle Scholar
  9. 9.
    Yan, J., & Li, L. (2013). Multi-objective optimization of milling parameters—the trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production, 52, 462–471.CrossRefGoogle Scholar
  10. 10.
    Li, W., Winter, M., Kara, S., & Herrmann, C. (2012). Eco-efficiency of manufacturing processes: A grinding case. CIRP Annals, 61(1), 59–62.CrossRefGoogle Scholar
  11. 11.
    Wang, C., Lin, H., Wang, X., Zheng, L., & Xiong, W. (2017). Effect of different oil-on-water cooling conditions on tool wear in turning of compacted graphite cast iron. Journal of Cleaner Production, 148, 477–489.CrossRefGoogle Scholar
  12. 12.
    Yip, W. S., & To, S. (2017). Tool life enhancement in dry diamond turning of titanium alloys using an eddy current damping and a magnetic field for sustainable manufacturing. Journal of Cleaner Production, 168, 929–939.CrossRefGoogle Scholar
  13. 13.
    Zhang, H., Deng, Z., Fu, Y., Lv, L., & Yan, C. (2017). A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions. Journal of Cleaner Production, 148, 174–184.CrossRefGoogle Scholar
  14. 14.
    Nam, J., & Lee, S. W. (2018). Machinability of titanium alloy (Ti-6Al-4 V) in environmentally-friendly micro-drilling process with nanofluid minimum quantity lubrication using nanodiamond particles. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(1), 29–35.CrossRefGoogle Scholar
  15. 15.
    Bhushan, R. K. (2013). Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production, 39, 242–254.CrossRefGoogle Scholar
  16. 16.
    Kant, G., & Sangwan, K. S. (2014). Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production, 83, 151–164.CrossRefGoogle Scholar
  17. 17.
    Camposeco-Negrete, C. (2013). Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. Journal of Cleaner Production, 53, 195–203.CrossRefGoogle Scholar
  18. 18.
    Eker, B., Ekici, B., Kurt, M., & Bakır, B. (2014). Sustainable machining of the magnesium alloy materials in the CNC lathe machine and optimization of the cutting conditions. Mechanics, 20(3), 310–316.CrossRefGoogle Scholar
  19. 19.
    Huang, S., Lv, T., Wang, M., & Xu, X. (2018). Effects of Machining and Oil Mist Parameters on Electrostatic Minimum Quantity Lubrication–EMQL Turning Process. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(2), 317–326.CrossRefGoogle Scholar
  20. 20.
    Davoodi, B., & Tazehkandi, A. H. (2014). Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid. Journal of Cleaner Production, 68, 234–242.CrossRefGoogle Scholar
  21. 21.
    Koyee, R. D., Heisel, U., Eisseler, R., & Schmauder, S. (2014). Modeling and optimization of turning duplex stainless steels. Journal of Manufacturing Processes, 16(4), 451–467.CrossRefGoogle Scholar
  22. 22.
    Wolf, K., Scheumann, R., Minkov, N., Chang, Y-J., Neugebauer, S., Finkbeiner, M. (2015). Selection criteria for suitable indicators for value creation starting with a look at the environmental dimension. In: Procedia CIRP 12th Global Conference on Sustainable Manufacturing, Vol. 26, pp. 24–29, 2015.Google Scholar
  23. 23.
    Ic, Y. T., Saraloğlu Güler, E., Cabbaroğlu, C., Dilan Yüksel, E. and Maide Sağlam, H. (2018). Optimisation of cutting parameters for minimizing carbon emission and maximising cutting quality in turning process. International Journal of Production Research, 56(11), 4035–4055.CrossRefGoogle Scholar
  24. 24.
    Quiza, R., Albelo, J. E., & Davim, J. P. (2009). Multi-objective optimisation of multipass turning by using a genetic algorithm. International Journal of Materials and Product Technology, 35(1-2), 134–144.Google Scholar
  25. 25.
    Deb, K., Pratap, A., Agarwal, A., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transections on Evolutionary Computation, 6(2), 182–197.CrossRefGoogle Scholar
  26. 26.
    SANDVIK Coromant, “Herramientas de torneado”, 2012.Google Scholar
  27. 27.
    Li, T., Sun, X., Lu, Z., & Wu, Y. (2016). A novel multiobjective optimization method based on sensitivity analysis. Mathematical Problems in Engineering, 2016, 12.Google Scholar
  28. 28.
    Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32(2), 135–154.CrossRefGoogle Scholar
  29. 29.
    Saltelli, A., & Sobol, I. M. (1995). About the use of rank transformation in sensitivity analysis of model output. Reliability Engineering & System Safety, 50(3), 225–239.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  1. 1.Study Centre on Advanced and Sustainable ManufacturingUniversity of MatanzasMatanzasCuba
  2. 2.Faculty of Engineering TechnologyUniversity of LeuvenLeuvenBelgium

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