Advanced search
Publication Cover
Cogent Engineering Volume 9, 2022 - Issue 1
Submit an article Journal homepage
828
Views
1
CrossRef citations to date
0
Altmetric
PRODUCTION & MANUFACTURING

Sustainable multi-objective optimization of a machining parameter model for multi-pass turning processes

Eko PujiyantoIndustrial Engineering Department, Sebelas Maret University, Surakarta, IndonesiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-5986-7424View further author information
ORCID Icon,
Cucuk Nur RosyidiIndustrial Engineering Department, Sebelas Maret University, Surakarta, IndonesiaView further author information
,
Muh. HisjamIndustrial Engineering Department, Sebelas Maret University, Surakarta, IndonesiaView further author information
&
Eko LiquddanuIndustrial Engineering Department, Sebelas Maret University, Surakarta, IndonesiaView further author information
Article: 2108154 | Received 04 Feb 2021, Accepted 27 Jul 2022, Published online: 28 Aug 2022

References

  • Allwood, J. M., Cullen, J. M., & Milford, R. L. (2010). Options for achieving a 50% cut in industrial carbon emissions by 2050. Environmental Science & Technology, 44(6), 1888–27. https://doi.org/10.1021/es902909k
  • Ameur, T. (2021). Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning. Journal of King Saud University - Engineering Sciences, 33(4), 259–265. https://doi.org/10.1016/j.jksues.2020.05.001
  • Arif, M., Stroud, I. A., & Akten, O. (2014). A model to determine the optimal parameters for sustainable-energy machining in a multi-pass turning operation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 228(6), 866–877. https://doi.org/10.1177/0954405413508945
  • Aungkulanon, P., & Luangpaiboon, P. (2016). Vertical transportation systems embedded on shuffled frog leaping algorithm for manufacturing optimisation problems in industries. SpringerPlus, 5(1), 1–25. https://doi.org/10.1186/s40064-016-2449-1
  • Bagaber, S. A., & Yusoff, A. R. (2019). Energy and cost integration for multi-objective optimisation in a sustainable turning process. Measurement: Journal of the International Measurement Confederation, 136, 795–810. https://doi.org/10.1016/j.measurement.2018.12.096
  • Banister, D. (2019). The climate crisis and transport. Transport Reviews, 39(5), 565–568. https://doi.org/10.1080/01441647.2019.1637113
  • Bouzid, W. (2005). Cutting parameter optimization to minimize production time in high speed turning. Journal of Materials Processing Technology, 161(3), 388–395. https://doi.org/10.1016/j.jmatprotec.2004.07.062
  • Chen, L. G., Li, P. L., Xia, S. J., Kong, R., & Ge, Y. L. (2022). Multi-objective optimization for membrane reactor for steam methane reforming heated by molten salt. Science China Technological Sciences, 65(6), 1396–1414. https://doi.org/10.1007/s11431-021-2003-0
  • Chen, M. C., & Tsai, D. M. (1996). A simulated annealing approach for optimization of multi-pass turning operations. International Journal of Production Research, 34(10), 2803–2825. https://doi.org/10.1080/00207549608905060
  • Cirtu, N. D., Dimitriu, R., Hartley, D., & Golder, A. A (1976). Survey of the noise levels of metal-cutting machine tools. Proceedings of the sixteenth international machine tool design and research Conference, 363–369. https://doi.org/10.1007/978-1-349-81544-9_50
  • Costa, N., & Lourenço, J. (2017). Reproducibility of nondominated solutions. Chemometrics and Intelligent Laboratory Systems, 168, 1–9. https://doi.org/10.1016/j.chemolab.2017.07.002
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017
  • Du, F., He, L., Huang, H., Zhou, T., & Wu, J. (2020). Analysis and multi-objective optimization for reducing energy consumption and improving surface quality during dry machining of 304 stainless steel. Materials, 13(21), 1–26. https://doi.org/10.3390/ma13214693
  • Eslami, Y., Dassisti, M., Lezoche, M., & Panetto, H. (2019). A survey on sustainability in manufacturing organisations: Dimensions and future insights. International Journal of Production Research, 57(15–16), 5194–5214. https://doi.org/10.1080/00207543.2018.1544723
  • Faghiri, S., Aria, H. P., & Shafii, M. (2022). Multi-objective optimization of acetone droplet impingement on phase change material in direct-contact discharge method. Journal of Energy Storage, 46, 103862. https://doi.org/10.1016/j.est.2021.103862
  • Hu, L., Liu, W., Xu, K., Peng, T., Yang, H., & Tang, R. (2019). Turning part design for joint optimisation of machining and transportation energy consumption. Journal of Cleaner Production, 232, 67–78. https://doi.org/10.1016/j.jclepro.2019.05.297
  • Hwang, C.-L., Lai, Y.-J., & Liu, T.-Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889–899. https://doi.org/10.1016/0305-05489390109-V
  • Jayal, A. D., Badurdeen, F., Dillon, O. W.,sJr., & Jawahir, I. S. (2010). Sustainable manufacturing: modeling and optimization challenges at the product, process and system levels. CIRP Journal of Manufacturing Science and Technology, 2(3), 144–152. https://doi.org/10.1016/j.cirpj.2010.03.006
  • Jiang, Z., Gao, D., Lu, Y., & Liu, X. (2019). Optimization of cutting parameters for trade-off among carbon emissions, surface roughness, and processing time. Chinese Journal of Mechanical Engineering, 32(1), 1–18. (English Edition). https://doi.org/10.1186/s10033-019-0408-9
  • Kallio, S., & Siroux, M. (2021). Exergy optimization of a multi-stage solar micro-cogeneration system. IET Renewable Power Generation, 1–12. https://doi.org/10.1049/rpg2.12372
  • Khalilpourazari, S., Khalilpourazary, S., Çiftçioğlu, A. Ö., & Weber, G. (2020). Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence. Journal of Intelligent Manufacturing, 321, 1621–1647. https://doi.org/10.1007/s10845-020-01648-0
  • Li, C., Tang, Y., Cui, L., & Yi, Q. (2013). Quantitative analysis of carbon emissions of CNC-based machining systems. 10th IEEE international conference on networking, sensing and control, 869–874. https://doi.org/10.1109/ICNSC.2013.6548852
  • Liu, Z. J., Sun, D. P., Lin, C. X., Zhao, X. H., & Yang, Y. (2016). Multi-objective optimization of the operating conditions in a cutting process based on low carbon emission costs. Journal of Cleaner Production, 124, 266–275. https://doi.org/10.1016/j.jclepro.2016.02.087
  • Lu, L., & Anderson-Cook, C. M. (2013). Adapting the Hypervolume Quality Indicator to Quantify Trade-offs and Search Efficiency for Multiple Criteria Decision Making Using Pareto Fronts. Qual. Reliab. Engng. Int, 29, 1117–1133. https://doi.org/10.1002/qre.1464
  • Lu, C., Gao, L., Li, X., & Chen, P. (2016). Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm. Journal of Cleaner Production, 137, 1516–1531. https://doi.org/10.1016/j.jclepro.2016.07.029
  • MATLAB. (2020). Version 9.8 (R2020a). Natick, Massachusetts: The MathWorks Inc.
  • Nur, R., Kurniawan, D., Noordin, M. Y., & Izman, S. (2015). Optimizing power consumption for sustainable dry turning of treated aluminum alloy. Procedia Manufacturing, 2, 558–562. https://doi.org/10.1016/j.promfg.2015.07.096
  • Olaru, S. C., Slǎtineanu, L., Silitrǎ, M., Mihalache, A. N., & Coteatǎ, M (2017). Investigation of the sound intensity level in the case of a universal lathe. MATEC Web of Conferences, 112, 1–6, https://doi.org/10.1051/matecconf/201711201025
  • Pangestu, P., Pujiyanto, E., & Rosyidi, C. N. (2021). Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing. Heliyon, 7(2), e06043. https://doi.org/10.1016/j.heliyon.2021.e06043
  • Pawanr, S., Garg, G. K., & Routroy, S. (2019). multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS. 7th CIRP Global Web Conference, Procedia CIRP 86, 116–120. https://doi.org/10.1016/j.procir.2020.01.03
  • Pawanr, S., Garg, G. K., & Routroy, S. (2021). Modelling of variable energy consumption for CNC machine tools. Paper Presented at the Procedia CIRP, 98, 247–251. https://doi.org/10.1016/j.procir.2021.01.038
  • Perdomo, I. L. F., Quiza, R., Haeseldonckx, D., & Rivas, M. (2020). Sustainability-focused multi-objective optimization of a turning process. International Journal of Precision Engineering and Manufacturing - Green Technology, 7(5), 1009–1018. https://doi.org/10.1007/s40684-019-00122-4
  • Phadke, M. S. (1989). Quality engineering using robust design (pp. 40–50). Prentice Hall.
  • Rao, R. V., & Kalyankar, V. D. (2013). Multi-pass turning process parameter optimization using teaching-learning-based optimization algorithm. Scientia Iranica, 20(3), 967–974. https://doi.org/10.1016/j.scient.2013.01.002
  • Schultheiss, F., Johansson, D., Bushlya, V., Zhou, J., Nilsson, K., & Ståhl, J. (2017). Comparative study on the machinability of lead-free brass. Journal of Cleaner Production, 149, 366–377. https://doi.org/10.1016/j.jclepro.2017.02.098
  • Sellami, A., Ali, M. B., & Kairouani, L. (2020). Pareto-optimization of msf-ot/tvc desalination plant using surface response methodology and genetic algorithm. Desalination and Water Treatment, 204, 10–21. https://doi.org/10.5004/dwt.2020.26229
  • Shin, S. M., Kim, Y. M., & Meilanitasari, P. (2019). A holonic-based self-learning mechanism for energy-predictive planning in machining processes. Processes, 7(10), 739. https://doi.org/10.3390/pr7100739
  • Ståhl, J. E., Schultheiss, F., & Hägglund, S. (2011). Analytical and experimental determination of the ra surface roughness during turning. Procedia Engineering, 19, 349–356. https://doi.org/10.1016/j.proeng.2011.11.124
  • Tian, C., Zhou, G., Zhang, J, & Zhang, C. (2019). Optimization of cutting parameters considering tool wear conditions in low-carbon manufacturing environment. Journal of Cleaner Production, 226, 706–719. https://doi.org/10.1016/j.jclepro.2019.04.113
  • Yi, Q., Li, C., Tang, Y, & Chen, X. (2015). Multi-objective parameter optimization of CNC machining for low carbon manufacturing. Journal of Cleaner Production, 95, 256–264. https://doi.org/10.1016/j.jclepro.2015.02.076
  • 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. https://doi.org/10.1016/j.jclepro.2017.01.077
  • Zhang, Z., Tang, R., Peng, T., Tao, L., & Jia, S. (2016). A method for minimizing the energy consumption of machining system: integration of process planning and scheduling. Journal of Cleaner Production, 137, 1647–1662. https://doi.org/10.1016/j.jclepro.2016.03.101
  • Zhong, Q., Tang, R., & Peng, T. (2017). Decision rules for energy consumption minimization during material removal process in turning. Journal of Cleaner Production, 140, 1819–1827. https://doi.org/10.1016/j.jclepro.2016.07.084

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.