Dynamic characteristics and energy consumption modelling of machine tools based on bond graph theory

Abstract Fossil fuel depletion, air pollution, and climate change are imposing great pressure on industrial sectors, especially for manufacturing sectors. Energy consumption modelling is an important measure to promote the energy efficiency in manufacturing, which offers the fundamental basis for energy efficiency-related optimization. Although dynamic characteristics have a significant effect on operation of machine tools, traditional energy consumption models hardly take dynamic characteristics into consideration. This paper takes the feed system as an example and proposes a dynamic energy consumption model of machine tools with bond graph theory. Based on the structure of feed system, the proposed model is firstly expressed to a physical model and the bond graph model are established according to the law of energy conservation. Subsequently, with the augmented bond graph model, mathematical models of dynamic characteristics and energy consumption are proposed with state variables. Finally, the simulation and analysis of the proposed model are given. Results show that the proposed dynamic characteristics model and energy consumption model based on bond graph theory are reasonable and effective. Additionally, the proposed model can be used to explore the correlation between energy-consuming components and energy consumption of machine tools for realizing the high energy efficiency design of machine tools.

[1]  Kawtar Benabdelaziz,et al.  Battery dynamic energy model for use in electric vehicle simulation , 2016, 2016 International Renewable and Sustainable Energy Conference (IRSEC).

[2]  Naoki Uchiyama,et al.  Iterative NC program modification and energy saving for a CNC machine tool feed drive system with linear motors , 2019, The International Journal of Advanced Manufacturing Technology.

[3]  Konrad Wegener,et al.  The Total Energy Efficiency Index for machine tools , 2016 .

[4]  Xuchu Jiang,et al.  Research on modal analysis method of CNC machine tool based on operational impact excitation , 2019, The International Journal of Advanced Manufacturing Technology.

[5]  Junxue Ren,et al.  A novel energy consumption model for milling process considering tool wear progression , 2018 .

[6]  Jun Xie,et al.  An integrated model for predicting the specific energy consumption of manufacturing processes , 2016 .

[7]  J. Hung,et al.  Investigation of the Dynamic Characteristics and Machining Stability of a Bi-rotary Milling Tool , 2019, Advances in Science and Technology Research Journal.

[8]  Wei Cai,et al.  Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement , 2017 .

[9]  Guoqing Zhu,et al.  Development of a bond graph based model library for turbocharged diesel engines , 2018 .

[10]  Sangkee Min,et al.  Empirical power consumption model for rotational axes in machine tools , 2018 .

[11]  Zhibing Liu,et al.  Stability prediction of five-axis ball-end finishing milling by considering multiple interaction effects between the tool and workpiece , 2019, Mechanical Systems and Signal Processing.

[12]  Liping Chen,et al.  Hybrid Multi-Domain Analytical and Data-Driven Modeling for Feed Systems in Machine Tools , 2019, Symmetry.

[13]  Guofu Yin,et al.  Research on modeling and identification of machine tool joint dynamic characteristics , 2019 .

[14]  Zhendong Shang,et al.  Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies , 2019, Energy.

[15]  Yang Yong,et al.  Dynamic Characteristic Optimization of Ball Screw Feed Drive in Machine Tool Based on Modal Extraction of State Space Model , 2019, IEEE Access.

[16]  Feiyan Han,et al.  Optimization of cutter orientation for multi-axis NC machining based on minimum energy consumption of motion axes , 2019 .

[17]  Wang Qi,et al.  A mechanics based prediction model for tool wear and power consumption in drilling operations and its applications , 2019, Journal of Cleaner Production.

[18]  Hui Chen,et al.  Effects of rolling bearing configuration on stiffness of machine tool spindle , 2018 .

[19]  Lakhdar Khochemane,et al.  Bond graph modeling approach development for fuel cell PEMFC systems , 2014 .

[20]  Li Li,et al.  Multi-objective optimization of tool path considering efficiency, energy-saving and carbon-emission for free-form surface milling , 2018 .

[22]  Li Li,et al.  Impact of surface machining complexity on energy consumption and efficiency in CNC milling , 2019, The International Journal of Advanced Manufacturing Technology.

[23]  Jun Wu,et al.  Mechatronics modeling and vibration analysis of a 2-DOF parallel manipulator in a 5-DOF hybrid machine tool , 2018 .

[24]  Klaus Brun,et al.  Measurement and Prediction of Centrifugal Compressor Axial Forces During Surge: Part 1 — Surge Force Measurements , 2017 .

[25]  Zakaria Chalh,et al.  Wind turbine: bond Graph modelling and sliding mode control , 2018, Mechatron. Syst. Control..

[26]  Abdallah Farrage,et al.  Improvement of motion accuracy and energy consumption of a mechanical feed drive system using a Fourier series-based nonlinear friction model , 2018 .

[27]  Zhang Hua,et al.  A novel energy evaluation approach of machining processes based on data analysis , 2019, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[28]  Gautam Biswas,et al.  Efficient simulation of hybrid systems: A hybrid bond graph approach , 2011, Simul..

[29]  Jing Li,et al.  Energy consumption model and energy efficiency of machine tools: a comprehensive literature review , 2016 .

[30]  B. O. Bouamama,et al.  Dynamic and multiphysic PEM electrolysis system modelling: A bond graph approach , 2017 .