Study of energy consumption in Haas UMC-750 and Leadwell V-40iT® CNC machining centers
Article Sidebar
Main Article Content
Machining centers are complex equipments that demand high energy consumption affecting the production costs. Different studies have related some factors with the energy consumption, however it is not completely clear which variables or factors have a greater incidence in this energy consumption of these machining centers without decreasing the quality of the finishing surfaces of the manufactured parts. In the present study, an analysis of the influence of cutting depth, forward velocity and spindle speed on the energy consumption and roughness of the surface finish was carried out in the Haas UMC-750 and the Leadwell V-40iT® machining centers. An experiment was developed with an nK factorial design keeping some variables as constants such as the base material, the cutting tool and the machining path. With the results obtained, it was possible to identify that the forward velocity is the factor that depends the most on energy consumption. Besides, the interaction between the factors was evidenced, identifying that the cutting depth has a moderate influence on the roughness or surface finish of the manufactured parts. The energy consumption varies for each machining center and the experimental design developed helped to characterize the influence of some cutting variables on the energy consumption of each machining center and the experimental design developed helped to characterize the influence of some cutting variables on the energy consumption of each machining center.
María I. Ardila , Institución Universitaria Pascual Bravo, Facultad de Ingeniería, Medellín, Colombia
https://orcid.org/0000-0002-6817-0378
Juan G. Ardila, Universidad Surcolombiana, Facultad de Ingeniería, Neiva, Colombia
https://orcid.org/0000-0002-3755-2189
Johnatan Cardona Jiménez, Institución Universitaria Pascual Bravo, Facultad de Ingeniería, Medellín, Colombia
https://orcid.org/0000-0002-6370-8837
Cesar Isaza, Institución Universitaria Pascual Bravo, Facultad de Ingeniería, Medellín, Colombia
https://orcid.org/0000-0002-0995-6231
(1) W. Cai and K. hung Lai. Sustainability assessment of mechanical manufacturing systems in the industrial sector, Renew. Sustain. Energy Rev., vol. 135, no. May 2020, p. 110169, 2021. https://doi.org/10.1016/j.rser.2020.110169
(2) N. Sihag and K. S. Sangwan. A systematic literature review on machine tool energy consumption, J. Clean. Prod., vol. 275, p. 123125, 2020. http://dx.doi.org/10.1016/j.jclepro.2020.123125
(3) M. Moradnazhad and H. O. Unver. Energy efficiency of machining operations: A review, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 231, no. 11, pp. 1871–1889, 2017. http://dx.doi.org/10.1177/0954405415619345
(4) L. Li, C. Li, Y. Tang, and Q. Yi. Influence factors and operational strategies for energy efficiency improvement of CNC machining. Journal of Cleaner Production, vol. 161. Elsevier Ltd, pp. 220–238, 10-Sep-2017. http://dx.doi.org/10.1016/j.jclepro.2017.05.084
(5) J. Yan and L. Li. Multi-objective optimization of milling parameters-the trade-offs between energy, production rate and cutting quality. J. Clean. Prod., vol. 52, pp. 462–471, 2013. http://dx.doi.org/10.1016/j.jclepro.2013.02.030
(6) T. Behrendt, A. Zein, and S. Min. Development of an energy consumption monitoring procedure for machine tolos. CIRP Ann. - Manuf. Technol., vol. 61, no. 1, pp. 43–46, 2012. http://dx.doi.org/10.1016/j.cirp.2012.03.103
(7) T. V. Peng and X. Xu. A universal hybrid energy consumption model for CNC machining systems. Re-Engineering Manuf. Sustain. - Proc. 20th CIRP Int. Conf. Life Cycle Eng., pp. 251–256, 2013. http://dx.doi.org/10.1007/978-981-4451-48-2_41
(8) S. S. Pavanaskar. Improving Energy Efficiency in CNC Machining. University of California, Berkeley, 2014. http://dx.doi.org/10.1038/132817a0
(9) X. Yan and Y. P. Fang. CO2 emissions and mitigation potential of the Chinese manufacturing industry. J. Clean. Prod., vol. 103, pp. 759–773, 2015. http://dx.doi.org/10.1016/j.jclepro.2015.01.051
(10) L. Zhou, J. Li, F. Li, Q. Meng, J. Li, and X. Xu. Energy consumption model and energy efficiency of machine tools: A comprehensive literature review. J. Clean. Prod., vol. 112, pp. 3721–3734, 2016. http://dx.doi.org/10.1016/j.jclepro.2015.05.093
(11) W. Cai, C. Liu, K. hung Lai, L. Li, J. Cunha, and L. Hu. Energy performance certification in mechanical manufacturing industry: A review and análisis. Energy Convers. Manag., vol. 186, no. February, pp. 415–432, 2019. http://dx.doi.org/10.1016/j.enconman.2019.02.041
(12) L. Li, C. Li, Y. Tang, and L. Li. An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining. J. Clean. Prod., vol. 162, pp. 458–473, 2017. http://dx.doi.org/10.1016/j.jclepro.2017.06.034
(13) L. Li, C. Li, Y. Tang, and Q. Yi. Influence factors and operational strategies for energy efficiency improvement of CNC machining. J. Clean. Prod., vol. 161, pp. 220–238, 2017. http://dx.doi.org/10.1016/j.jclepro.2017.05.084
(14) J. Ma, X. Ge, S. I. Chang, and S. Lei. Assessment of cutting energy consumption and energy efficiency in machining of 4140 steel. Int. J. Adv. Manuf. Technol., vol. 74, no. 9–12, pp. 1701–1708, 2014. http://dx.doi.org/10.1007/s00170-014-6101-3
(15) N. Abishekraj, T. Gowtham, V. Krishnaraj, R. Bibeye Jahaziel, and B. Geetha Priyadarshini. Surface roughness evaluation in machining titanium alloys using non-textured and textured cutting inserts. in IOP Conference Series: Materials Science and Engineering, 2020, vol. 912, no. 3, p. 032076. http://dx.doi.org/10.1088/1757-899X/912/3/032076
(16) F. Draganescu, M. Gheorghe, and C. V. Doicin. Models of machine tool efficiency and specific consumed energy. J. Mater. Process. Technol., vol. 141, no. 1, pp. 9–15, 2003. http://dx.doi.org/10.1016/S0924-0136(02)00930-5
(17) A. N. Haq, P. Marimuthu, and R. Jeyapaul. Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method. Int. J. Adv. Manuf. Technol., vol. 37, no. 3–4, pp. 250–255, May 2008. http://dx.doi.org/10.1007/s00170-007-0981-4
(18) M. Y. Liu, C. F. Cheung, X. Feng, L. T. Ho, and S. M. Yang. Gaussian process machine learning-based surface extrapolation method for improvement of the edge effect in surface filtering. Meas. J. Int. Meas. Confed., vol. 137, pp. 214–224, Apr. 2019. http://dx.doi.org/10.1016/j.measurement.2019.01.048
(19) A. Kumar Parida and K. Maity. Modeling of machining parameters affecting flank wear and surface roughness in hot turning of Monel-400 using response surface methodology (RSM). Meas. J. Int. Meas. Confed., vol. 137, pp. 375–381, Apr. 2019. http://dx.doi.org/10.1016/j.measurement.2019.01.070
(20) L. Guedes de Oliveira, C. Henrique de Oliveira, T. Gonçalves de Brito, E. José de Paiva, A. Paulo de Paiva, and J. R. Ferreira. Nonlinear optimization strategy based on multivariate prediction capability ratios: Analytical schemes and model validation for duplex stainless steel end milling. Precis. Eng., vol. 66, pp. 229–254, 2020. http://dx.doi.org/10.1016/j.precisioneng.2020.06.005
(21) T. Misaka et al. Prediction of surface roughness in CNC turning by model-assisted response surface method. Precis. Eng., vol. 62, no. December 2019, pp. 196–203, 2020. http://dx.doi.org/10.1016/j.precisioneng.2019.12.004
(22) D. Manivel and R. Gandhinathan. Optimization of surface roughness and tool wear in hard turning of austempered ductile iron (grade 3) using Taguchi method. Meas. J. Int. Meas. Confed., vol. 93, pp. 108–116, Nov. 2016. http://dx.doi.org/10.1016/j.measurement.2016.06.055
Accepted 2021-10-02
Published 2022-05-26
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors grant the journal and Universidad del Valle the economic rights over accepted manuscripts, but may make any reuse they deem appropriate for professional, educational, academic or scientific reasons, in accordance with the terms of the license granted by the journal to all its articles.
Articles will be published under the Creative Commons 4.0 BY-NC-SA licence (Attribution-NonCommercial-ShareAlike).