Reducing energy costs in production with machine tools
Energy consumption is increasingly becoming the main concern for machine tool users. But energy costs are not the only factor here. Proof of climate-neutral production is increasingly becoming a competitive advantage in the production of parts with machine tools.
SINUMERIK equipment packages as well as CNC Shopfloor Management Software from Siemens make a significant contribution to increasing the energy efficiency of the machine.
Energy-efficient machining systems: A critical review
June 2014The International Journal of Advanced Manufacturing Technology 72(9-12):1389-1406
DOI:10.1007/s00170-014-5756-0
Project: Development of an energy-efficient machining system
https://www.researchgate.net/publication/272031225_Energy-efficient_machining_systems_A_critical_review
Energy efficiency of machining operations: A review
January 2016Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture 231(11)
DOI:10.1177/0954405415619345
https://www.researchgate.net/publication/292213278_Energy_efficiency_of_machining_operations_A_review
Earlier Collection
1. C.-W. Park, K.-S. Kwon, W.-B. Kim, B.-K. Min, S.-J. Park, I.-H. Sung, Y. S. Yoon, K.-S. Lee, J.-H. Lee, J. Seok, "Energy consumption reduction technology in manufacturing—A selective review of policies standards and research", Int. J. Precis. Eng. Manuf., vol. 10, pp. 151-173, Dec. 2009.
2. F. Liu, J. Xie, S. Liu, "A method for predicting the energy consumption of the main driving system of a machine tool in a machining process", J. Cleaner Prod., vol. 105, pp. 171-177, Oct. 2015.
Liu et al. divided energy consumption into three stages, start-up, idle, and cutting, and developed a predictive model considering the characteristics of cutting force, rotation speed, kinetic energy, and magnetic field energy for each stage.
3. S. H. Hu, "Energy consumption characteristics of multiple-component of modern CNC machine tools", vol. 3, 2012.
4. S. Anderberg, S. Kara, "Energy and cost efficiency in CNC machining", Proc. 7th CIRP Conf. Sustain. Manuf., pp. 1-4, 2009.
In the extreme case with the highest forecasted energy cost and full automation, the energy cost account for as much as 14% of the total machining cost. But typical rate is less than 2% of the total machining cost.
5. DMG MORI Going Green With New Energy-Saving Functions, Sep. 2014, [online] Available: https://www.dmgmori.co.jp/corporate/en/news/pdf/20140905_energy_e.pdf.
6. Energy-Efficient Machine Tool Technologies for Any Size Shop, 2016, [online] Available: https://www.okuma.com/stuff/contentmgr/files/0/143c354d61f2562efb3e2a8bd72166a0/files/okuma_energyefficientmachinetooltechnologies_whitepaper_final_high_ res.pdf.
7. Utilization Monitoring and Analysis, [online] Available: https://english.mazak.jp/smooth-technology/monit_analysis/.
In response to these needs for energy reduction and management, leading makers of machine tools (e.g., DMG MORI, Okuma, and Mazak) are developing and commercializing human-machine interface (HMI) systems with energy monitoring functions, which support the energy consumption monitoring of representative machine tool components, such as spindles and servo motors. The purpose of this type of energy monitoring is to achieve the efficient consumption and management of energy while supporting more accurate control and decision-making.
8. G. May, B. Stahl, M. Taisch, D. Kiritsis, "Energy management in manufacturing: From literature review to a conceptual framework", J. Cleaner Prod., vol. 167, pp. 1464-1489, Nov. 2017.
Vikhorev et al. proposed a framework for monitoring and managing the energy consumption of factories. The framework collects energy data from energy-consuming objects in factories such as machine tools, generates events, avoids peak loads through an event-streaming engine called complex event processing (CEP), and assists decision-making by calculating energy consumption-related key performance indicators (KPIs).
9. V. A. Balogun, P. T. Mativenga, "Modelling of direct energy requirements in mechanical machining processes", J. Cleaner Prod., vol. 41, pp. 179-186, Feb. 2013.
Balogun and Mativenga developed a machine tool energy consumption model for the electrical energy requirements of machining toolpaths. This model considers the idle state as well as the cutting state and further refines the power characteristics of coolant and tool changes
10. T. Peng, X. Xu, L. Wang, "A novel energy demand modelling approach for CNC machining based on function blocks", J. Manuf. Syst., vol. 33, no. 1, pp. 196-208, Jan. 2014.
Peng et al. [13] developed an approach to modeling the energy demands of computer numerical control (CNC) machines through function block (FB) modeling based on the International Electrotechnical Commission (IEC) international standard IEC 61499. The FB specifies the in/out data and processing process by subdividing the hardware components. The approach is implemented to support the monitoring of energy consumption by subdividing down to fundamental levels, such as spindles and feed axes.
11. N. Xie, M. Duan, R. B. Chinnam, A. Li, W. Xue, "An energy modeling and evaluation approach for machine tools using generalized stochastic Petri nets", J. Cleaner Prod., vol. 113, pp. 523-531, Feb. 2016.
Xie et al. proposed an energy consumption model based on stochastic Petri nets. Through the proposed model, an environment for evaluating productivity-related indicators such as cycle time in connection with energy consumption was established. In addition, research has been conducted on developing a framework for more efficient energy data monitoring.
12. X. Chen, C. Li, Y. Tang, Q. Xiao, "An Internet of Things based energy efficiency monitoring and management system for machining workshop", J. Cleaner Prod., vol. 199, pp. 957-968, Oct. 2018.
Chen et al. developed a system for processing and monitoring energy data collected from various sensors and machine controllers via the internet of things (IoT). In addition, there are several researches about energy consumption efficiency.
13. T. Schudeleit, S. Züst, L. Weiss, K. Wegener, "The total energy efficiency index for machine tools", Energy, vol. 102, pp. 682-693, May 2016.
Schudeleit et al. proposed indexes for analyzing the energy efficiency of machine tools. They distinguished between indexes for sufficiency, efficiency, and consistency to quantify energy efficiency.
14. J. Lenz, J. Kotschenreuther, E. Westkaemper, "Energy efficiency in machine tool operation by online energy monitoring capturing and analysis", Procedia CIRP, vol. 61, pp. 365-369, 2017.
Lenz et al. developed similar energy efficiency measures and implemented an online-based monitoring system for capturing energy efficiency.
15. K. Schischke, E. Hohwieler, R. Feitscher, J. König, S. Kreuschner, P. Wilpert, N. F. Nissen, "Energy-using product group analysis-lot 5 machine tools and related machinery executive summary-final version", Aug. 2012.
The Fraunhofer institute defined and classified components for monitoring of a machine tool’s energy consumption and the details are available in the paper.
16. Q. Xiao, C. Li, Y. Tang, Y. Du, Y. Kou, "Deep learning based modeling for cutting energy consumed in CNC turning process", Proc. IEEE Int. Conf. Syst. Man Cybern. (SMC), pp. 1398-1403, Oct. 2018. Full Text: PDF (362KB)
Xiao et al. applied deep learning models, such as convolutional neural networks (CNNs), sparse auto encoders (SAEs), and deep belief networks (DBNs), and compared the results to predict energy consumption during processing. The power of the processing stage was subdivided into standby power, unload power, material removal power, additional load loss, and cutting-related auxiliary system power, and an SAE was found to be the most efficient method.
170. G. Y. Zhao, Z. Y. Liu, Y. He, H. J. Cao, Y. B. Guo, "Energy consumption in machining: Classification prediction and reduction strategy", Energy, vol. 133, pp. 142-157, Aug. 2017.
Zhao et al. [20] studied energy modeling and prediction methodologies from various perspectives, such as tool wear, tool intrinsic energy, and artificial neural networks.
18. P. Liu, F. Liu, H. Qiu, "A novel approach for acquiring the real-time energy efficiency of machine tools", Energy, vol. 121, pp. 524-532, Feb. 2017.
Liu et al. developed a methodology for obtaining the real-time energy efficiency (REE) of machine tools. A model was developed to derive REE from the input power of the spindle as well as actual consumption data and related processing variables without measuring the cutting force of the machine, thereby laying the foundation for more efficient energy consumption.
192. T. Peng, X. Xu, "An interoperable energy consumption analysis system for CNC machining", J. Cleaner Prod., vol. 140, pp. 1828-1841, Jan. 2017.
Peng and Xu developed a process that can perform hybrid modeling considering both the 3-axis and the 5-axis for an interoperability-based energy consumption analysis. In addition, an interoperable data model has been developed for monitoring and optimization of energy consumption based on the STEP-NC standard for exchange of product data.
20. X. Zhou, F. Liu, W. Cai, "An energy-consumption model for establishing energy-consumption allowance of a workpiece in a machining system", J. Cleaner Prod., vol. 135, pp. 1580-1590, Nov. 2016.
Zhou et al. developed a model to establish energy consumption allowance in a machining system. They defined the energy consumption step of the machining system and the input power profile and model of each step in detail. In addition, various studies have been conducted regarding the milling process and energy consumption
21. C. Zhang, Z. Zhou, G. Tian, Y. Xie, W. Lin, Z. Huang, "Energy consumption modeling and prediction of the milling process: A multistage perspective", Proc. Inst. Mech. Eng. B J. Eng. Manuf., vol. 232, no. 11, pp. 1973-1985, Sep. 2018.
Zhang et al. developed energy consumption modeling and a prediction model of milling processes. They used multiple linear regressions, a sliding filter, and variable neighborhood search–based gene expression programming to model energy consumption.
22. Z. Shang, D. Gao, Z. Jiang, Y. Lu, "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies", Energy, vol. 178, pp. 263-276, Jul. 2019.
Shang et al. strategy includes relations of power consumption between cutting and air-cutting states and assists in designing and using energy within machining process.
23. X. Luan, S. Zhang, J. Chen, G. Li, "Energy modelling and energy saving strategy analysis of a machine tool during non-cutting status", Int. J. Prod. Res., vol. 57, no. 14, pp. 4451-4467, Jul. 2019.
Luan et al. proposed an energy modeling and saving strategy analysis of machine tools during the non-cutting status. They developed models of energy consumption in the idle status of machine tools.
24. C. Li, L. Li, Y. Tang, Y. Zhu, L. Li, "A comprehensive approach to parameters optimization of energy-aware CNC milling", J. Intell. Manuf., vol. 30, no. 1, pp. 123-138, Jan. 2019.
Li et al. proposed a comprehensive approach to the parameter optimization of energy-aware CNC milling. They developed an energy consumption model for the main status and elements of machine tools using a non-linear regression. In addition, an optimization model of energy consumption was developed using the tabu search method.
25. A. Aramcharoen, P. T. Mativenga, "Critical factors in energy demand modelling for CNC milling and impact of toolpath strategy", J. Cleaner Prod., vol. 78, pp. 63-74, Sep. 2014.
26. Optimizing Material Removal Rates, [online] Available: https://www.harveyperformance.com/in-the-loupe/material-removal-rate-efficiency/.
27 TS B, 0024-1:2010, "Machine Tools-Test Methods for Electric Power Consumption—Part 1: Machining Centres", 2010.
28. Y. C. Liang, X. Lu, W. D. Li, S. Wang, "Cyber physical system and big data enabled energy efficient machining optimisation", J. Cleaner Prod., vol. 187, pp. 46-62, Jun. 2018.
29. S. Tian, T. Wang, L. Zhang, X. Wu, "An energy-efficient scheduling approach for flexible job shop problem in an Internet of manufacturing Things environment", IEEE Access, vol. 7, pp. 62695-62704, 2019. Full Text: PDF (15919KB)
Above 29 references are from:
H. S. Kang, J. Y. Lee and D. Y. Lee, "An Integrated Energy Data Analytics Approach for Machine Tools," in IEEE Access, vol. 8, pp. 56124-56140, 2020.
https://ieeexplore.ieee.org/document/9040402
https://ieeexplore.ieee.org/document/9040402
30. Improving Energy Efficiency in CNC Machining - PhD Thesis
by SS Pavanaskar · 2014
87 pages - PDF
In this century, we must put forth the objective of energy efficiency as well as productivity when researching new and existing manufacturing processes. In this dissertation, we study CNC milling with this combined objective.
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