A novel hybrid approach for cutting parameters optimization considering processing energy and efficiency in turning process

References

• Abdullah, F., and Z. M. S. Ahmed. 2019. Hybrid constrained permutation algorithm and genetic algorithm for process planning problem. Journal of Intelligent Manufacturing 209 (5):2186–92. doi:10.1007/s10845-019-01496-7.
   Google Scholar
• Alagumurthi, N., K. Palaniradja, and V. Soundararajan. 2006. Optimization of grinding process through design of experiment (DOE)-A comparative study. Materials and Manufacturing Processes 33:1–9. doi:10.1080/AMP-200060605.
   Google Scholar
• Asiltürk, İ., S. Neşeli, and M. A. İnce. 2016. Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the taguchi and RSM methods. Measurement 3 (3):32–49. doi:10.1016/j.measurement.2015.09.052.
   Google Scholar
• Asokanp, S., and K. Vijayakumar. 2003. Machining parameters optimisation for turning cylindrical stock into a continuous finished profile using genetic algorithm(GA) and simulated annealing(SA). International Journal of Advanced Manufacturing Technology 30 (4):1581–99. doi:10.1007/s001700300000.
   Google Scholar
• Cai, W., K. H. Lai, C., . H. Liu, F. F. Wei, M. D. Ma, S. Jia, Z. G. Jiang, and L. Lv. 2019. Promoting sustainability of manufacturing industry through the lean energy-saving and emission-reduction strategy. Sci.Total Environ 665:23–32. doi:10.1016/j.scitotenv.2019.02.069.
   PubMed Web of Science ®Google Scholar
• Chen, X. Z., C. C. Li, Y. Tang, L. Li, Y. B. Du, and L. L. Li. 2019. Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time. Energy 175:1021–37. doi:10.1016/j.energy.2019.02.157.
   Web of Science ®Google Scholar
• Chen, Y., P. Y. Qi, S. Q. Liu, and W. Zhang. 2020b. The use of improved algorithm of adaptive neuro-fuzzy inference system in optimization of machining parameters. Journal of Intelligent & Fuzzy Systems 38 (4):3755–64. doi:10.3233/JIFS-179598.
   Web of Science ®Google Scholar
• Choudhury, S. K., and R. Appa. 1999. Optimization of cutting parameters for maximizing tool life. International Journal of Machine Tools&Manufacture 22 (1):42–46. doi:10.1016/S0890-6955(98)00028-5.
   Google Scholar
• Dai, M., D. B. Tang, G. Adriana, A. S., . Miguel, and W. D. Li. 2013. Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer Integrated Manufacturing 29 (5):418–29. doi:10.1016/j.rcim.2013.04.001.
   Web of Science ®Google Scholar
• Deng, Z. H., L. S. Lv, W. L. Huang, and Y. D. Shi. 2019. A high efficiency and low carbon oriented machining process route optimization model and its application. International Journal of the Japan Society for Precision Engineering 10:598. doi:10.1007/s40684-019-00029-0.
   Google Scholar
• Enes, K., R. Y. Ali, M. S., . Sadiq, and B. Sujin. 2020. A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails. Materials Testing 62 (3):251–60. doi:10.3139/120.111478.
   Web of Science ®Google Scholar
• Feng, W., J. E. Huang, H. L. Lv, D. P. Guo, and Z. C. Huang. 2019. Determination of the economical insulation thickness of building envelopes simultaneously in energy-saving renovation of existing residential buildings. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 35 (4):4071–81. doi:10.1080/15567036.2018.1520349.
   Google Scholar
• Fountas, N. A., R. Benhadj-Djilali, C. I. Stergiou, and N. M. Vaxevanidis. 2019. An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence. Journal of Intelligent Manufacturing 29 (3):603–15. doi:10.1007/s10845-017-1338-y.
   Google Scholar
• Galetakis, R. 2015. A multi-objective response surface analysis for the determination of the optimal cut-off quality and minimum thickness for selective mining of multiple-layered lignite deposits. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 6 (7):260–66. doi:10.1080/15567036.2011.588675.
   Google Scholar
• Girish, D. G., and K. P. Arun. 2018. Teaching learning algorithm based optimization of kerf deviations in pulsed Nd: YAGlaser cutting of Kevlar-29 composite laminates. Infrared Physics and Technology 5957:293–98. doi:10.1016/j.infrared.2017.12.017.
   Google Scholar
• Hanafi, I., A. Khamlichi, F. M. Cabrera, E. Almansa, and A. Jabbouri. 2012. Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiNTools. Journal of Cleaner Production 21:1–9. doi:10.1016/j.jclepro.2012.05.005.
   Google Scholar
• Islam., M. Z., N. I. A. Wahab, V. Veerasamy, H. Hizam, N. F. Mailah, J. M. Guerrero, and M. N. Mohd. 2020. A harris hawks optimization based single and multi-objective optimal power flow considering environmental emission. Sustainability 12 (13):5248. doi:10.3390/su12135248.
   Web of Science ®Google Scholar
• Jurić, Ž., and D. Ljubas. 2020. Comparative assessment of carbon footprints of selected organizations: The application of the enhanced bilan carbone model. Sustainability 12 (2):1–32. doi:10.3390/su12229618.
   PubMedGoogle Scholar
• Li, C. B., L. L. Li, Y. Tang, Y. T. Zhu, and L. Li. 2019. A comprehensive approach to parameters optimization of energy-aware CNC milling. Journal of Intelligent Manufacturing 60:267–75. doi:10.1007/s10845-016-1233-y.
   Google Scholar
• Li, C. B., Q. G. Xiao, Y. Tang, and L. Li. 2016. A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving. Journal of Cleaner Production 135:263–75. doi:10.1016/j.jclepro.2016.06.097.
   Web of Science ®Google Scholar
• Lin, Q. G., G. H. Huang, B. Bass, Y. F. Huang, and L. Liu. 2010. The optimization of energy systems under changing policies of greenhouse-gas emission Control—A study for the province of Saskatchewan, Canada. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 140:1842–49. doi:10.1080/15567030902756121.
   Google Scholar
• Liu, Q., H. Lu, X. B. Zhang, Y. Qiao, Q. Cheng, Y. Q. Zhang, and Y. J. Wang. 2020. A Non-delay error compensation method for dual-driving gantry-type machine tool. Processes 21 (1):19–21. doi:10.3390/pr8070748.
   Google Scholar
• Lu, H., Q. Cheng, X. B. Zhang, Q. Liu, Y. Qiao, and Y. Q. Zhang. 2020. A novel geometric error compensation method for gantry-moving CNC machine regarding dominant errors. Processes 78:120–28. doi:10.3390/pr8080906.
   Google Scholar
• Ma, F., H. Zhang, Q. S. Gong, and K. K. B. Hon. 2019. A novel energy evaluation approach of machining processes based on data analysis. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 6 (1):23–41. doi:10.1080/15567036.2019.1670761.
   Google Scholar
• Ma, H. Y., W. Liu, X. H. Zhou, Q. Niu, and C. P. Kong. 2020. An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining. Journal of Intelligent Manufacturing 30 (1):123–38. doi:10.1007/s10845-019-01489-6.
   Google Scholar
• Mahmoodzadeh, M., and T. Niknam. 2017. Reducing losses in a deregulated power system with teaching learning based optimization algorithm. International Journal of Scientific Engineering and Technology 28 (8):1769–85. doi:10.5958/2277-1581.2017.00033.X.
   Google Scholar
• Maroua, N. B., J. Abdelghani, N. S. Abderezak, and C. A. Ahmed. 2018. An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. Journal of Intelligent Manufacturing 31:1–21. doi:10.1007/s10845-015-1039-3.
   Google Scholar
• Masmiati, N., and A. D. Sarhan. 2015. Optimizing cutting parameters in inclined end milling for minimum surface residual stress-Taguchi approach. Measurement 12 (5323). doi: 10.1016/j.measurement.2014.10.002.
   Google Scholar
• Mativenga, P. T., and M. F. Rajemi. 2011. Calculation of optimum cutting parameters based on minimum energy footprint. CIRP Ann-Manuf. Techn 31:967–84. doi:10.1016/j.cirp.2011.03.088.
   Google Scholar
• Mcbrien, M., A. C. Serrenho, and J. M. Allwood. 2016. Potential for energy savings by heat recovery in an integrated steel supply chain. Applied Thermal Engineering 7 (811):1–9. doi:10.1016/j.applthermaleng.2016.04.099.
   Google Scholar
• Natee, P., P. Nantiwat, B. Sujin, R. Y. Ali, M. S. Sadiq, and S. M. Sait. 2020. Seagull optimization algorithm for solving real-world design optimization problems. Materials Testing 62 (6):640–44. doi:10.1515/MT-2020-0091.
   Web of Science ®Google Scholar
• Nicolae, C., N. P. Emil, S. R. Adrian, P. Dumitru, and B. Marin. 2020a. Optimization of the process parameters in milling of aluminum alloys. Applied Mechanics and Materials 5957:293–98. d oi:1 0.4028/w w w .scientific.net/AMM.896.293.
   Google Scholar
• Nobahari, H., M. Nikusokhan, and P. Siarry. 2012. A multi-objective gravitational search algorithm based on non-dominated sorting. International Journal of Swarm Intelligence Research (IJSIR) 8 (7):1–25. doi:10.4018/jsir.2012070103.
   Google Scholar
• Raza, M. Y., M. Wasim, Sarwar, and M. S. S.S. 2020. Muhammad. development of renewable energy technologies in rural areas of Pakistan. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42 (6):740–60. doi:10.1080/15567036.2019.1588428.
   Web of Science ®Google Scholar
• Sarvanan, R., P. Asokan, and K. Vijayakuman. 2003. Machining parameters optimization for turning cylindrical stock into a continuous finished profile using genetic algorithm(GA) and simulated annealing(SA). International Journal of Advanced Manufacturing Technology 31:967–84. doi:10.1007/s001700300000.
   Google Scholar
• Seung-Jun, S., W. Jungyub, R. Sudarsan, and M. Prita. 2018. Standard data-based predictive modeling for power consumption in turning machining. Sustainability 12:9618. doi:10.3390/su10030598.
   Google Scholar
• Thepsonthi, T., and T. Zel. 2012. Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy. International Journal of Advanced Manufacturing Technology 8 (748):1–25. doi:10.1007/s00170-012-3980-z.
   Google Scholar
• Venkata, R. R., and V. D. Kalyankar. 2013. Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence 26 (1):524–31. doi:10.1016/j.engappai.2012.06.007.
   Web of Science ®Google Scholar
• Wang, L., Y. Wang, and Y. Song. 2020. Optimal operation of microgrid with multi-energy complementary based on moth flame optimization algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 226:706–19. doi:10.1080/15567036.2019.1587067.
   Google Scholar
• Wu, M. P., and W. H. Liao. 2005. Internet-based machining parameter optimization and management system for high-speed machining.Transactions. Of Nanjing University of Aeronautics&Astronautics 21 (1):1–9. doi:10.16356/j.1005-1120.2005.01.007.
   Google Scholar
• Xiao, Y. M., Q. S. Gong, and X. W. Chen. 2019. Energy saving and low-cost-oriented design processes of blank’s dimensions based on multi-objective optimization model. Processes 13 (3):1433–39. doi:10.3390/pr7110811.
   Google Scholar
• Yan, W., H. Zhang, Z. G. Jiang, and K. K. B. Hon. 2017. Multi-objective optimization of arc welding parameters: The trade-offs between energy and thermal efficiency. Journal of Cleaner Production 50:742–53. doi:10.1016/j.jclepro.2016.03.171.
   Google Scholar
• Yıldız, A. B. S., N. Pholdee, S. Bureerat, A. R. Yıldız, and S. M. Sait. 2020. Sine-cosine optimization algorithm for the conceptual design of automobile components. Materials Testing 62 (7):744–48. doi:10.1515/MT-2020-620714.
   Web of Science ®Google Scholar
• Yildiz, A. R. 2013. Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Applied Soft Computing 63(9–12): 903–14. doi:10.1016/j.asoc.2012.01.012.
   Google Scholar
• Zarei, O., F. B. FesangharyM, S. Jalili, and M. R. Razfar. 2009. Optimization of multi-pass face-milling via harmony search algorithm. Journal of Materials Processing Technology 48 (9–12):1075–90. doi:10.1016/j.jmatprotec.2008.05.029.
   Google Scholar