This is an open access article distributed under the CC BY 4.0
Volume 20 article 914 pages: 145-149
Energy consumption in machining processes has become a problem for today's manufacturing industry. The use of neural networks and optimization algorithms for modeling and prediction of consumption as a function of the cut-off parameters in processes of this type has aroused the interest of research groups. The present work used artificial neural networks (ANN) to predict the energy consumption of a Leadwell V-40iT® five-axis CNC machining center, based on experimental data obtained through a factorial experimental design 53. ANN was programed in Matlab®. From the study was concluded that the depth per pass (Ap) is the variable that has the most influence on the prediction model of energy consumption with a 77% of relative importance, while the feed rate is the least relevant with 9% of importance.
This work was supported by the Instituto Tecnológico Metropolitano de Medellín (Colombia), under the research line of Advanced Computing and Digital Design (CADD) and Mathematical Modelling, and Programming and Optimization Applied to Engineering, which belongs to the research group of Advanced Materials and Energy (MATyER). It was also supported by Universidad Surcolombiana, and Institución Universitaria Pascual Bravo.
1. Hu, L., Peng, C., Evans, S., Peng, T. (2017) Minimising the machining energy consumption of a machine tool by sequencing the features of a part, Energy, vol. 121, pp. 292–305, doi: 10.1016/j.energy.2017.01.039.
2. Li, L., Li, C., Tang, Y., Li, L. (2018) Integration of process planning and cutting parameter optimization for energy-aware CNC machining, IEEE Int. Conf. Autom. Sci. Eng., vol. 2017-Augus, pp. 263–268, doi: 10.1109/COASE.2017.8256112.
3. Ma, F., Zhang, H., Cao, H., Hon, K. K. B. (2017) An energy consumption optimization strategy for CNC milling, Int. J. Adv. Manuf. Technol., vol. 90, no. 5–8, pp. 1715–1726, doi: 10.1007/s00170-016-9497-0.
4. Li, J., Salim, R. D., Aldlemy, M. S., Abdullah, J. M., Yaseen, Z. M. (2019) Fiberglass-Reinforced Polyester Composites Fatigue Prediction Using Novel Data-Intelligence Model, Arab. J. Sci. Eng., vol. 44, no. 4, pp. 3343–3356, Apr., doi: 10.1007/s13369-018-3508-4.
5. Xiangxue, W., Lunhui, X., Kaixun, C. (2019) Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN, Arab. J. Sci. Eng., vol. 44, no. 4, pp. 3043–3060, Apr., doi: 10.1007/s13369-018-3390-0.
6. Mori, M., Fujishima, M., Inamasu, Y., Oda, Y. (2011) A study on energy efficiency improvement for machine tools, CIRP Ann. - Manuf. Technol., vol. 60, no. 1, pp. 145–148, doi: 10.1016/j.cirp.2011.03.099.
7. Yan, J., Li, L. (2013) Multi-objective optimization of milling parameters-the trade-offs between energy, production rate and cutting quality, J. Clean. Prod., vol. 52, pp. 462–471, doi: 10.1016/j.jclepro.2013.02.030.
8. Jiang, Z., Zhou, F., Zhang, H., Wang, Y., Sutherland, J. W. (2015) Optimization of machining parameters considering minimum cutting fluid consumption,” J. Clean. Prod., vol. 108, pp. 183–191, doi: 10.1016/j.jclepro.2015.06.007.
9. Calvanese, M. L., Albertelli, P., Matta, A., Taisch, M. (2013) Analysis of energy consumption in CNC machining centers and determination of optimal cutting conditions, Re-Engineering Manufacturing for Sustainability - Proceedings of the 20th CIRP International Conference on Life Cycle Engineering, pp. 227–232.
10. Hanafi, I., Cabrera, F. M., Dimane, F., Manzanares, J. T. (2015) Application of Particle Swarm Optimization for Optimizing the Process Parameters in Turning of PEEK CF30 Composites, Procedia Technol., vol. 22, no. October, pp. 195–202, 2016, doi: 10.1016/j.protcy.2016.01.044.
11. Harsha, N., Kumar, I. A., Raju, K. S. R., Rajesh, S. (2018) Prediction of Machinability characteristics of Ti6Al4V alloy using Neural Networks and Neuro-Fuzzy techniques, Mater. Today Proc., vol. 5, no. 2, pp. 8454–8463, doi: 10.1016/j.matpr.2017.11.541.
12. Karayel, D. (2009) Prediction and control of surface roughness in CNC lathe using artificial neural network, J. Mater. Process. Technol., vol. 209, no. 7, pp. 3125–3137, doi: 10.1016/j.jmatprotec.2008.07.023.
13. El-Mounayri, H., Kishawy, H., Briceno, J. (2005) Optimization of CNC ball end milling: A neural network-based model, J. Mater. Process. Technol., vol. 166, no. 1, pp. 50–62, doi: 10.1016/j.jmatprotec.2004.07.097.
14. Hernández, J. A., Rivera, W., Colorado, D., Moreno-Quintanar, G. (2012) Optimal COP prediction of a solar intermittent refrigeration system for ice production by means of direct and inverse artificial neural networks, Sol. Energy, vol. 86, no. 4, pp. 1108–1117, doi: 10.1016/j.solener.2011.12.021.
15. Khataee, A. R., Mirzajani (2010) O., UV/peroxydisulfate oxidation of C. I. Basic Blue 3: Modeling of key factors by artificial neural network, Desalination, vol. 251, no. 1–3, pp. 64–69, doi: 10.1016/j.desal.2009.09.142.