Abstract
Cutting parameters and machining configurations affect the energy consumption and production time in the machining process significantly. Previous cutting parameters optimization methods are proposed for a specific machining configuration that limits its generalization ability. However, the machining configuration varies constantly with actual machining tasks, which results in the predetermined optimization method is impractical. We propose a data-driven optimization method for the multiple machining configurations, aimed at reducing energy consumption and production time. Firstly, the analysis of the relationship between energy consumption and meta-actions under different machining states is carried out, and the Gaussian process regression (GPR)-based energy consumption model is proposed. Then, a multi-objective optimization model is proposed for energy consumption and production time reduction, which is solved via a multi-objective grey wolf optimization. Finally, the experiments are conducted to verify the validity of the proposed method and the influence of meta-actions on energy consumption and production time are explicitly analyzed. The case study indicates the proposed energy consumption model has better prediction accuracy for multiple machining configurations. Optimizing cutting parameters achieves a trade-off between energy consumption and production time. Moreover, the parametric influence indicates cutting speed is the most influential cutting parameter for energy consumption and production time.
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References
International Energy Agency (IEA). (2019). International energy outlook 2019. Retrieved March 15, 2021, from https://www.eia.gov/outlooks/ieo/pdf/ieo2019.pdf
Gutowski, T., Dahmus, J., & Thiriez, A. (2006). Electrical energy requirements for manufacturing processes. Processings of 13th CIRP international conference on life cycle engineering (pp. 5–11). Leuven: Belgium.
ISO Technical Committee. (2017). Machine tools-Environmental evaluation of machine tools - Part 1: Design methodology for energy-efficient machine tools. Retrieved July 2, 2021 from https://www.iso.org/standard/70035.html
Yoon, H., Kim, E., Kim, M., Lee, J., Lee, G., & Ahn, S. (2015). Towards greener machine tools—a review on energy saving strategies and technologies. Renewable and Sustainable Energy Reviews, 48, 870–891.
Newman, S. T., Nassehi, A., Imani-Asrai, R., & Dhokia, V. (2012). Energy efficient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology, 5(2), 127–136.
Denkena, B., Abele, E., Brecher, C., Dittrich, M., Kara, S., & Mori, M. (2020). Energy efficient machine tools. CIRP Annals, 69, 646–667.
Liu, P., Liu, F., & Liu, G. (2017). A new approach for calculating the input power of machine tool main transmission systems. Advances in Mechanical Engineering, 9(9), 1–10.
Lv, J., Tang, R., Tang, W., Liu, Y., Zhang, Y., & Jia, S. (2017). An investigation into reducing the spindle acceleration energy consumption of machine tools. Journal of Cleaner Production, 143, 794–803.
Li, B., Cao, H., Bernard, H., & Gao, X. (2021). Exergy-based energy efficiency evaluation model for machine tools considering thermal stability. International Journal of Precision Engineering and Manufacturing-Green Technology, 8, 423–434.
Li, B., Tian, X., Zhang, M. (2021). Modeling and multi-objective optimization method of machine tool energy consumption considering tool wear. International Journal of Precision Engineering and Manufacturing-Green Technology. https://doi.org/10.1007/s40684-021-00320-z.
Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2018). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8), 1683–1693.
Camposeco-Negrete, C., de Dios Calderón Nájera, J., Miranda-Valenzuela, J. C. (2016). Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design. International Journal of Advanced Manufacturing Technology, 83, 1341–1347.
Bilga, P. S., Singh, S., & Kumar, R. (2016). Optimization of energy consumption response parameters for turning operation using Taguchi method. Journal of Cleaner Production, 137, 1406–1417.
Arriaza, O. V., Kim, D.-W., Lee, D. Y., & Suhaimi, M. A. (2017). Trade-off analysis between machining time and energy consumption in impeller NC machining. Robotics and Computer-Integrated Manufacturing, 43, 164–170.
Bagaber, S. A., & Yusoff, A. R. (2017). Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316. Journal of Cleaner Production, 157, 30–46.
Bhushan, R. K. (2013). Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production, 39, 242–254.
Xiao, Q., Li, C., Tang, Y., Li, L., & Li, L. (2019). A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning. Energy, 166, 142–156.
Bhinge, R., Park, J., Law, K. H., Dornfeld, D. A., Helu, M., Rachuri, S. (2017). Toward a generalized energy prediction model for machine tools. Journal of Manufacturing Science and Engineering, 139(4), 041013.
Xiao, Q., Li, C., Tang, Y., & Chen, X. (2021). Energy efficiency modeling for configuration-dependent machining via machine learning: a comparative study. IEEE Transactions on Automation Science and Engineering, 18(2), 717–730.
Li, C., Li, L., Tang, Y., Zhu, Y., & Li, L. (2019). A comprehensive approach to parameters optimization of energy-aware CNC milling. Journal of Intelligent Manufacturing, 30(1), 123–138.
Li, W., Zein, A., Kara, S., Herrmann, C. (2011). An investigation into fixed energy consumption of machine tools. In J. Hesselbach, C. Herrmann (Eds.), Glocalized Solutions for Sustainability in Manufacturing (Springer, Berlin, Heidelberg).
Jia, S., Tang, R., & Lv, J. (2014). Therblig-based energy demand modeling methodology of machining process to support intelligent manufacturing. Journal of Intelligent Manufacturing, 25(5), 913–931.
Lv, J., Tang, R., & Jia, S. (2014). Therblig-based energy supply modeling of computer numerical control machine tools. Journal of Cleaner Production, 65, 168–177.
Lee, J., Shin, Y., Kim, M., Kim, E., Yoon, H., Kim, S., Yoon, Y., Ahn, S., Min, S. (2016). A simplified machine-tool power-consumption measurement procedure and methodology for estimating total energy consumption. Journal of Manufacturing Science and Engineering, 138, 051004.
Chen, X., Li, C., Tang, Y., Li, L., Du, Y., & Li, L. (2019). Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time. Energy, 175, 1021–1137.
Bengtsson, N., Michaloski, J., Proctor, F., Shao, G., Venkatesh, S. (2010) Towards data-driven sustainable machining: combining MTConnect production data and discrete event simulation. In Proceedings of the ASME 2010 International Manufacturing Science and Engineering Conference. ASME 2010 International Manufacturing Science and Engineering Conference, vol. 1 (Erie, pp. 379–387). https://doi.org/10.1115/MSEC2010-34178.
Atziori, L., Iera, A., & Morabito, G. (2010). The internet of things: a survey. Computer Networks, 54(15), 2787–2805.
Kumar, R., Bilga, P. S., & Singh, S. (2017). Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. Journal of Cleaner Production, 164, 45–57.
Lv, L. S., Deng, Z. H., Yan, C., Liu, T., Wan, L. L., & Gu, Q. W. (2020). Modelling and analysis for processing energy consumption of mechanism and data integrated machine tool. International Journal of Production Research, 58(23), 7078–7093.
Park, J., Law, K. H., Bhinge, R., Biswas, N., Srinivasan, A., Dornfeld, D. A., Helu, M., Rachuri, S. (2015). A generalized data-driven energy prediction model with uncertainty for a milling machine tool using Gaussian Process. In Proceedings of the ASME 2015 International Manufacturing Science and Engineering Conference (Charlotte). https://doi.org/10.1115/MSEC2015-9354.
Seeger, M. (2004). Gaussian processes for machine learning. International Journal of Neural Systems, 14(02), 69–106.
Kong, D., Chen, Y., & Li, N. (2018). Gaussian process regression for tool wear prediction. Mechanical Systems and Signal Processing, 104, 556–574.
Zhang, C., Wei, H., Zhao, X., Liu, T., & Zhang, K. (2016). A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy Conversion and Management, 126, 1084–1092.
Chen, X., Li, C., Tang, Y., & Xiao, Q. (2018). An internet of things based energy efficiency monitoring and management system for machining workshop. Journal of Cleaner Production, 199, 957–968.
Hacksteiner, M., Duer, F., Ayatollahi, I., & Bleicher, F. (2017). Automatic assessment of machine tool energy efficiency and productivity. Procedia CIRP, 62, 317–322.
Gittler, T., Gontarz, A., Weiss, L., & Wegener, K. (2019). A fundamental approach for data acquisition on machine tools as enabler for analytical Industry 4.0 applications. Procedia CIRP, 79, 586–591.
Li, C., Chen, X., Tang, Y., & Li, L. (2017). Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. Journal of Cleaner Production, 140, 1805–1818.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
Kang, M., Hwang, L. K., Kwon, B. (2020). Computationally efficient optimization of wavy surface roughness in cooling channels using simulated annealing. International Journal of Heat and Mass Transfer, 150, 119300.
Acknowledgements
This work was supported in part by the National Key R&D Program of China (No. 2019YFB1706103), National Natural Science Foundation of China (No.51975075), and Chongqing Technology Innovation and Application Program (No. cstc2020jscx-msxmX0221).
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Zhao, X., Li, C., Chen, X. et al. Data-Driven Cutting Parameters Optimization Method in Multiple Configurations Machining Process for Energy Consumption and Production Time Saving. Int. J. of Precis. Eng. and Manuf.-Green Tech. 9, 709–728 (2022). https://doi.org/10.1007/s40684-021-00373-0
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DOI: https://doi.org/10.1007/s40684-021-00373-0