The State of the Art in Energy Consumption Model – The Key to Sustainable Machining


Article Preview

Development of reliable energy consumption models is the essential step toward sustainable machining. Due to the complex relationship between energy consumption and a large number of contributing factors, obtaining an accurate model is very difficult, if not impossible. This paper aims to review the literature in this field over the last two decades. The research work on energy models is grouped into three categories, i.e. theoretical, experimental and discrete event-based models. It is found that machine tool structure, set-up conditions, and machining parameters have major impacts on energy consumption, suggesting that energy consumption models need to be constructed not only at the process level but also at the system level. Hybrid models that combine more than one approach are considered an option.



Edited by:

Amanda Wu




P. Tao and X. Xun, "The State of the Art in Energy Consumption Model – The Key to Sustainable Machining", Applied Mechanics and Materials, Vol. 232, pp. 592-599, 2012

Online since:

November 2012





[1] T. Peng and X. Xu, A framework of an energy-informed machining system, in Proceedings of the 7th International Conference on Digital Enterprise Technology, Athens, Greece, 2011, pp.160-169.

[2] C. W. Park, et al., Energy consumption reduction technology in manufacturing - A selective review of policies, standards, and research, International Journal of Precision Engineering and Manufacturing, vol. 10 (2009), pp.151-173.

[3] R. Neugebauer, et al., Modelling of energy and resource-efficient machining, in 4th CIRP International Conference on High Performance Cutting (2010).

[4] B. Kuhrke, et al., Methodology to assess the energy consumption of cutting machine tools, in 17th CIRP International Conference on Life Cycle Engineering, Hefei, China (2010), pp.76-82.

[5] A. A. Munoz and P. Sheng, An analytical approach for determining the environmental impact of machining processes, Journal of Materials Processing Tech., vol. 53 (1995), pp.736-758.


[6] A. E. Bayoumi, et al., On the closed form mechanistic modeling of milling: Specific cutting energy, torque, and power, Journal of Materials Engineering and Performance, vol. 3 (1994), pp.151-158.


[7] H. Shao, et al., A cutting power model for tool wear monitoring in milling, International Journal of Machine Tools and Manufacture, vol. 44 (2004), pp.1503-1509.


[8] H. A. Kishawy, et al., An energy based analytical force model for orthogonal cutting of metal matrix composites, CIRP Annals - Manufacturing Technology, vol. 53 (2004), pp.91-94.


[9] P. Palanisamy, et al., Prediction of cutting force and temperature rise in the end-milling operation, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 220 (2006), pp.1577-1587.


[10] R. B. Jerard, et al., Cutting power model-sensor integration for tool condition monitoring, in Proceedings of the 34th North American Manufacturing Research Conference, Milwaukee, (2006), pp.13-15.

[11] M. Xu, et al., Energy based cutting force model calibration for milling, Computer-Aided Design and Applications, vol. 4 (2007), pp.341-351.


[12] F. Draganescu, et al., Models of machine tool efficiency and specific consumed energy, Journal of Materials Processing Technology, vol. 141 (2003), pp.9-15.


[13] T. Radhakrishnan and U. Nandan, Milling force prediction using regression and neural networks, Journal of Intelligent Manufacturing, vol. 16 (2005), pp.93-102.


[14] K. Kadirgama and K. Abou-El-Hossein, Power Prediction Model for Milling 618 Stainless Steel Using Response Surface Methodology, American Journal of Applied Sciences, vol. 2 (2005), pp.1182-1187.


[15] K. Kadirgama and K. A. Abou-El-Hossein, Torque, power and cutting force prediction model by using response surface method and factorial design, European Journal of Scientific Research, vol. 18 (2007), pp.20-44.

[16] A. Aggarwal, et al., Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique-A comparative analysis, Journal of Materials Processing Technology, vol. 200 (2008), pp.373-384.


[17] G. Yun-qi, et al., Knowledge-based calculation and prediction of energy consumption in product manufacturing process, Journal of South China University and Technology, vol. 37 (2009), pp.14-19.

[18] G. Quintana, et al., Modelling power consumption in ball-end milling operations, Materials and Manufacturing Processes, vol. 26 (2011), pp.746-756.


[19] M. H. F. Al-Hazza, et al., Energy cost modeling for high speed hard turning, Journal of Applied Sciences, vol. 11 (2011), pp.2578-2584.

[20] T. Gutowski, et al., Environmentally benign manufacturing: Observations from Japan, Europe and the United States, Journal of Cleaner Production, vol. 13 (2005), pp.1-17.

[21] A. Dietmair and A. Verl, Energy Consumption Modeling and Optimization for Production Machines, in Proceedings of the IEEE International Conference on Sustainable Energy Technologies (2008), pp.574-579.


[22] A. Dietmair and A. Verl, A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing, International Journal of Sustainable Engineering, vol. 2 (2009), pp.123-133.


[23] R. Larek, et al., A discrete-event simulation approach to predict power consumption in machining processes, Production Engineering, (2011), in press.

[24] Y. He, et al., A modeling method of task-oriented energy consumption for machining manufacturing system, Journal of Cleaner Production, vol. 23 (2012), pp.167-174.


[25] S. T. Newman, et al., Energy efficient process planning for CNC machining, (2010) Draft, pp.1-22.

[26] O. Avram, et al., A multi-criteria decision method for sustainability assessment of the use phase of machine tool systems, International Journal of Advanced Manufacturing Technology, vol. 53 (2010), pp.811-828.


[27] A. Dietmair and A. Verl, Energy consumption assessment and optimisation in the design and use phase of machine tools, in 17th CIRP International Conference on Life Cycle Engineering, Hefei, China (2010), pp.116-121.

[28] D. P. Gupta, Energy Sensitive Machining Parameter Optimization Model, Master of Science Master thesis, Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia (2005).