A novel energy evaluation approach of machining processes based on data analysis

References

• Balogun, V. A., and P. T. Mativenga. 2014. Impact of un-deformed chip thickness on specific energy in mechanical machining processes. Journal of Cleaner Production 69:260–68. doi:10.1016/j.jclepro.2014.01.036.
   Web of Science ®Google Scholar
• Bayoumi, A. E., G. Yucesan, and D. V. Hutton. 1994. On the closed form mechanistic modelling of milling: Specific cutting energy, torque, and power. Journal of Materials Engineering and Performance 3:151–58. doi:10.1007/BF02654511.
   Web of Science ®Google Scholar
• Cai, W., L. Li, S. Jia, C. Liu, J. Xie, and L. Hu. 2019. Task-oriented energy benchmark of machining systems for energy-efficient production. International Journal of Precision Engineering and Manufacturing-Green Technology. doi:10.1007/s40684-019-00137-x.
   Web of Science ®Google Scholar
• Cai, W., F. Liu, X. N. Zhou, and J. Xie. 2016. Fine energy consumption allowance of workpieces in the mechanical manufacturing industry. Energy 114:623–33. doi:10.1016/j.energy.2016.08.028.
   Web of Science ®Google Scholar
• Campatelli, G., L. Lorenzini, and A. Scippa. 2014. Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. Journal of Cleaner Production 66:309–16. doi:10.1016/j.jclepro.2013.10.025.
   Web of Science ®Google Scholar
• Chan, D. Y.-L., C.-F. Huang, W.-C. Lin, and G.-B. Hong. 2014. Energy efficiency benchmarking of energy intensive industries in Taiwan. Energy Conversion & Management 77:216–20. doi:10.1016/j.enconman.2013.09.027.
   Web of Science ®Google Scholar
• Diaz, N., E. Redelsheimer, and D. Dornfeld. 2011. Energy consumption characterization and reduction strategies for milling machine tool use. Glocalized Solutions for Sustainability in Manufacturing 263–67. doi:10.1007/978-3-642-19692-8_46.
   Google Scholar
• Ding, Z., Z. Jiang, H. Zhang, W. Cai, and Y. Liu. 2018. An integrated decision-making method for selecting machine tool guideways considering remanufacturability. International Journal of Computer Integrated Manufacturing 1–15. doi:10.1080/0951192X.2018.1550680.
   Web of Science ®Google Scholar
• Draganescu, F., M. Gheorghe, and C. V. Doicin. 2003. Models of machine tool efficiency and specific consumed energy. Journal of Materials Processing Technology 141 (1):9–15. doi:10.1016/s0924-0136(02)00930-5.
   Web of Science ®Google Scholar
• Gutowski, T. 2010. Energy and environmental issues for manufacturing processes. http://web.mit.edu/2.810/www/lecture2011/Environment.pdf; 2010.
   Google Scholar
• Ivakhnenko, A. G. 1971. Polynomial theory of complex systems. IEEE Transactions on Systems Man & Cybernetics SMC-1 (4):364–78. doi:10.1109/TSMC.1971.4308320.
   Web of Science ®Google Scholar
• Jiang, Z. G., Z. Y. Ding, H. Zhang, W. Cai, and Y. Liu. 2019. Data-driven ecological performance evaluation for remanufacturing process. Energy Conversion and Management. doi:10.1016/j.enconman.2019.111844.
   Web of Science ®Google Scholar
• Kara, S., and W. Li. 2011. Unit process energy consumption models for material removal processes. CIRP Annals - Manufacturing Technology 60:37–40. doi:10.1016/j.cirp.2011.03.018.
   Web of Science ®Google Scholar
• Kishawy, H. A., S. Kannan, and M. Balazinski. 2004. An energy based analytical force model for orthogonal cutting of metal matrix composites. CIRP Annals - Manufacturing Technology 53 (1):91–94. doi:10.1016/S0007-8506(07)60652-0.
   Web of Science ®Google Scholar
• Kuram, E., B. Ozcelik, M. Bayramoglu, E. Demirbas, and B. T. Simsek. 2013. Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. Journal of Cleaner Production 42:159–66. doi:10.1016/j.jclepro.2012.11.003.
   Web of Science ®Google Scholar
• Li, A. P., Z. Y. Gu, J. Zhu, X. Liu, N. Xie, and L. Yang. 2015. Optimization of cutting parameters for multi-pass hole machining based on low carbon manufacturing. Computer Integrated Manufacturing Systems 21 (6):1515–22. doi:10.13196/j.cims.2015.06.013.
   Google Scholar
• Li, L., J. Yan, and Z. Xing. 2013. Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. Journal of Cleaner Production 52:113–21. doi:10.1016/j.jclepro.2013.02.039.
   Web of Science ®Google Scholar
• Liu, P., F. Liu, and H. Qiu. 2017. A Novel Approach for Acquiring the Real-Time Energy Efficiency of Machine Tools. Energy 121:524–32. doi: 10.1016/j.energy.2017.01.047.
   Google Scholar
• Lucca, D., Y. Seo, and R. Komanduri. 1993. Effect of tool edge geometry on energy dissipation in ultraprecision machining. CIRP Annals-Manufacturing Technology 42:83–86. doi:10.1016/S0007-8506(07)62397-X.
   Google Scholar
• Luo, M., H. Luo, D. Zhang, and K. Tang. 2018. Improving tool life in multi-axis milling of Ni-based superalloy with ball-end cutter based on the active cutting edge shift strategy. Journal of Materials Processing Technology 252:105–15. doi:10.1016/j.jmatprotec.2017.09.010.
   Web of Science ®Google Scholar
• Ma, F., H. Zhang, H. J. Cao, and K. K. B. Hon. 2017. An energy consumption optimization strategy for CNC milling. The International Journal of Advanced Manufacturing Technology 90 (5–8):1715–26. doi:10.1007/s00170-016-9497-0.
   Web of Science ®Google Scholar
• Ma, F., H. Zhang, K. K. B. Hon, and Q. Gong. 2018. An optimization approach of selective laser sintering considering energy consumption and material cost. Journal of Cleaner Production 199:529–37. doi:10.1016/j.jclepro.2018.07.185.
   Web of Science ®Google Scholar
• Munoz, A. A., and P. Sheng. 2016. An analytical approach for determining the environmental impact of machining processes. Journal of Materials Processing Technology 53 (3–4):736–58. doi:10.1016/0924-0136(94)01764-R.
   Google Scholar
• Nikolaev, N. Y., and H. Iba. 2003. Polynomial harmonic GMDH learning networks for time series modelling. Neural Netw 16:1527–40. doi:10.1016/S0893-6080(03)00188-6.
   PubMed Web of Science ®Google Scholar
• Shao, H., H. L. Wang, and X. M. Zhao. 2004. A cutting power model for tool wear monitoring in milling. International Journal of Machine Tools and Manufacture 44 (14):1503–09. doi:10.1016/j.ijmachtools.2004.05.003.
   Web of Science ®Google Scholar
• Tuo, J., F. Liu, P. Liu, H. Zhang, and W. Cai. 2018. Energy efficiency evaluation for machining systems through virtual part. Energy 159:172–83. doi:10.1016/j.energy.2018.06.096.
   Web of Science ®Google Scholar
• Yang, J. 2016. Energy accounting for the Three Gorges Dam project: Three scenarios for the estimation of non-renewable sediment cost. Journal of Cleaner Production 112:3000–06. doi:10.1016/j.jclepro.2015.10.110.
   Web of Science ®Google Scholar
• Yoon, H., J. Lee, M. Kim, and S.-H. Ahn. 2014. Empirical power-consumption model for material removal in three- axis milling. Journal of Cleaner Production 78:54–62. doi:10.1016/j.jclepro.2014.03.061.
   Web of Science ®Google Scholar