Open Access
Issue
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
Article Number 06015
Number of page(s) 12
Section Power Converters for Various Applications
DOI https://doi.org/10.1051/e3sconf/202454006015
Published online 21 June 2024
  1. Kant, G., & Sangwan, K. S. (2015). Predictive modeling for power consumption in machining using artificial intelligence techniques. Procedia CIRP, 26, 403–407. https://doi.org/10.1016/j.procir.2014.07.072 [CrossRef] [Google Scholar]
  2. G. Kant, V. Rao Vaibhav, K.S. Sangwan, Predictive modelling of turning operations using response surface methodology, Applied Mechanics and Materials, 307 (2013), pp. 170–173. https://doi.org/10.4028/www.scientific.net/AMM.307.170 [CrossRef] [Google Scholar]
  3. A.M. Zain, H. Haron, S. Sharif, Perdiction of surface roughness in the end milling machining using artificial neural network, Expert Systems with Applications, 37 (2010), pp. 1755–1768. https://doi.org/10.1016/j.eswa.2009.07.033 [CrossRef] [Google Scholar]
  4. Herrmann, C., Thiede, S., Zein, A., Ihlenfeldt, S., & Blau, P. (2009, June). Energy efficiency of machine tools: extending the perspective. In Proceedings of the 42nd CIRP international conference on manufacturing systems (pp. 3–5). [Google Scholar]
  5. Camposeco-Negrete, C., de Dio. Calderón-Nájera, J. Sustainable machining as a mean of reducing the environmental impacts related to the energy consumption of the machine tool: a case study of AISI 1045 steel machining. Int J Adv Manuf Technol 102, 27–41 (2019). https://doi.org/10.1007/s00170-018-3178-0 [CrossRef] [Google Scholar]
  6. Zhou, L., Li, J., Li, F., Meng, Q., Li, J., & Xu, X. (2016). Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. Journal of Cleaner Production, 112, 3721–3734. https://doi.org/10.1016/j.jclepro.2015.05.093 [CrossRef] [Google Scholar]
  7. Zhu, D., Zhang, X., & Ding, H. (2013). Tool wear characteristics in machining of nickel-based superalloys. International Journal of Machine Tools and Manufacture, 64, 60–77. https://doi.org/10.1016/j.ijmachtools.2012.08.001 [CrossRef] [Google Scholar]
  8. Sihag, N., & Sangwan, K. S. (2020). A systematic literature review on machine tool energy consumption. Journal of Cleaner Production, 275, 123125 https://doi.org/10.1016/j.jclepro.2020.123125 [CrossRef] [Google Scholar]
  9. Zheng, J., Zheng, W., Chen, A., Yao, J., Ren, Y., Zhou, C.,... & Zhang, Z. (2020). Sustainability of unconventional machining industry considering impact factors and reduction methods of energy consumption: A review and analysis. Science of the Total Environment, 722, 137897 https://doi.org/10.1016/j.scitotenv.2020.137897 [CrossRef] [Google Scholar]
  10. Cai, W., Liu, F., Xie, J., Liu, P., & Tuo, J. (2017). A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking. Energy, 138, 332–347. https://doi.org/10.1016/j.energy.2017.07.039 [CrossRef] [Google Scholar]
  11. Moreira, L. C., Li, W., Fitzpatrick, M. E., Lu, X., & Li, X. (2018). Research on energy consumption and energy efficiency of machine tools: a comprehensive survey. International Journal of Nanomanufacturing, 14(2), 140–164. https://doi.org/10.1504/IJNM.2018.091579 [CrossRef] [Google Scholar]
  12. Shin, S. J., Woo, J., & Rachuri, S. (2017). Energy efficiency of milling machining: Component modeling and online optimization of cutting parameters. Journal of Cleaner Production, 161, 12–29. https://doi.org/10.1016/j.jclepro.2017.05.013 [CrossRef] [Google Scholar]
  13. Suresh, P. V. S., Rao, P. V., & Deshmukh, S. G. (2002). A genetic algorithmic approach for optimization of surface roughness prediction model. International Journal of Machine Tools and Manufacture, 42(6), 675–680. https://doi.org/10.1016/S0890-6955(02)00005-6 [CrossRef] [Google Scholar]
  14. Wang, X., Da, Z. J., Balaji, A. K., & Jawahir, I. S. (2007). Performance-based predictive models and optimization methods for turning operations and applications: Part 3—optimum cutting conditions and selection of cutting tools. Journal of Manufacturing Processes, 9(1), 61–74. https://doi.org/10.1016/S1526-6125(07)70108-1 [CrossRef] [Google Scholar]
  15. Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent maintenance systems and predictive manufacturing. Journal of Manufacturing Science and Engineering, 142(11), 110805 https://doi.org/10.1115/1.4047856 [CrossRef] [Google Scholar]
  16. Liang, Y. C., Lu, X., Li, W. D., & Wang, S. (2018). Cyber Physical System and Big Data enabled energy efficient machining optimisation. Journal of cleaner Production, 187, 46–62. https://doi.org/10.1016/j.jclepro.2018.03.149 [CrossRef] [Google Scholar]
  17. Kim, YM., Shin, SJ. & Cho, HW. Predictive Modeling for Machining Power Based on Multi-source Transfer Learning in Metal Cutting. Int. J. of Precis. Eng. and Manuf.-Green Tech. 9, 107–125 (2022). https://doi.org/10.1007/s40684-021-00327-6 [CrossRef] [Google Scholar]
  18. 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. https://doi.org/10.1016/j.jclepro.2014.07.073 [CrossRef] [Google Scholar]
  19. Jayal, A. D., Badurdeen, F., Dillon Jr, O. W., & 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 [CrossRef] [Google Scholar]
  20. Shin, S. J., Woo, J., Rachuri, S., & Meilanitasari, P. (2018). Standard data-based predictive modeling for power consumption in turning machining. Sustainability, 10(3), 598 https://doi.org/10.3390/su10030598 [CrossRef] [Google Scholar]
  21. Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2021). A review of datadriven decision-making methods for industry 4.0 maintenance applications. Electronics, 10(7), 828 https://doi.org/10.3390/electronics10070828 [CrossRef] [Google Scholar]
  22. Wang, J., Tian, Y., Hu, X. et al. Development of grinding intelligent monitoring and big data-driven decisionmaking expert system towards high efficiency and low energy consumption: experimental approach. J IntellManuf (2023). https://doi.org/10.1007/s10845-023-02089-1 [Google Scholar]
  23. Papazoglou, M.P., Andreou, A.S. (2019). Smart Connected Digital Factories: Unleashing the Power of Industry 4.0. In: Muñoz, V., Ferguson, D., Helfert, M., Pahl, C. (eds) Cloud Computing and Services Science. CLOSER 2018. Communications in Computer and Information Science, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-29193-8_5 [Google Scholar]
  24. Feng, C., Chen, X., Zhang, J. et al. Minimizing the energy consumption of hole machining integrating the optimization of tool path and cutting parameters on CNC machines. Int J Adv ManufTechnol 121, 215–228 (2022). https://doi.org/10.1007/s00170-022-09343-5 [CrossRef] [Google Scholar]
  25. Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B., & Sutherland, J. W. (2019). Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia Cirp, 80, 506–511. https://doi.org/10.1016/j.procir.2018.12.019 [CrossRef] [Google Scholar]
  26. Traini, E., Bruno, G., D’antonio, G., & Lombardi, F. (2019). Machine learning framework for predictive maintenance in milling. IFAC-PapersOnLine, 52(13), 177–182. https://doi.org/10.1016/j.ifacol.2019.11.172 [CrossRef] [Google Scholar]
  27. Nouiri, M., Trentesaux, D., & Bekrar, A. (2019). Towards energy efficient scheduling of manufacturing systems through collaboration between cyber physical production and energy systems. Energies, 12(23), 4448. https://doi.org/10.3390/en12234448 [CrossRef] [Google Scholar]
  28. Bakhtiyari, A. N., Wang, Z., Wang, L., & Zheng, H. (2021). A review on applications of artificial intelligence in modeling and optimization of laser beam machining. Optics & Laser Technology, 135, 106721 https://doi.org/10.3390/en12234448 [CrossRef] [Google Scholar]
  29. Bi, Z. M., & Wang, L. (2012). Optimization of machining processes from the perspective of energy consumption: A case study. Journal of manufacturing systems, 31(4), 420–428. https://doi.org/10.1016/j.jmsy.2012.07.002 [CrossRef] [Google Scholar]
  30. Xu, LH., Huang, CZ., Niu, JH. et al. Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process. Adv. Manuf. 9, 388–402 (2021). https://doi.org/10.1007/s40436-020-00339-6 [CrossRef] [Google Scholar]
  31. Sealy, M. P., Liu, Z. Y., Zhang, D., Guo, Y. B., & Liu, Z. Q. (2016). Energy consumption and modeling in precision hard milling. Journal of Cleaner Production, 135, 1591–1601. https://doi.org/10.1016/j.jclepro.2015.10.094 [CrossRef] [Google Scholar]
  32. Şahinoğlu, A., & Ulas, E. (2020). An investigation of cutting parameters effect on sound level, surface roughness, and power consumption during machining of hardened AISI 4140. Mechanics & Industry, 21(5), 523 https://doi.org/10.1051/meca/2020068 [CrossRef] [EDP Sciences] [Google Scholar]
  33. Girish Kant Garg, et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 346 012078DOI 10.1088/1757-899X/346/1/012078 [Google Scholar]
  34. Abellan-Nebot, J.V., Romero Subirón, F. A review of machining monitoring systems based on artificial intelligence process models. Int J Adv ManufTechnol 47, 237–257 (2010). https://doi.org/10.1007/s00170-009-2191-8 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.