Energy modeling method of machine-operator system for sustainable machining

Abstract Characterizing and modeling the energy consumption of machining processes has been recognized as an effective measure to improve the energy efficiency of the manufacturing process and to reduce carbon emissions. Activities related to machine tools and operators in machining processes result in energy consumption and carbon emissions. However, energy evaluation for the activities related to operators has often been ignored in the energy modeling of machining processes, due to its complexity and variability. Consequently, energy-saving strategies developed without considering the energy consumption of operators are not optimal. To bridge this gap, this paper develops an energy consumption evaluation method for the activities related to machine tools and operators, and establishes a novel energy model of machine-operator systems to assess the energy efficiency of machining processes. The research results provide a promising method to identify new energy-saving potentials of machine-operator systems. The recommended energy-saving strategy regarding this method is different to traditional strategies, but with it a better energy-saving effect can be achieved. Finally, a case study of energy modeling and analysis for a CK6153i CNC machine-operator system is examined, showing an energy-saving potential of 15.85% and illustrating the effectiveness of the proposed method.

[1]  Shun Jia,et al.  Machining activity extraction and energy attributes inheritance method to support intelligent energy estimation of machining process , 2016, J. Intell. Manuf..

[2]  Congbo Li,et al.  Multi-objective parameter optimization of CNC machining for low carbon manufacturing , 2015 .

[3]  C. Herrmann,et al.  A Global Assessment of Manufacturing: Economic Development, Energy Use, Carbon Emissions, and the Potential for Energy Efficiency and Materials Recycling , 2013 .

[4]  Athulan Vijayaraghavan,et al.  Automated energy monitoring of machine tools , 2010 .

[5]  Ying Liu,et al.  Therblig-embedded value stream mapping method for lean energy machining , 2017 .

[6]  Chen Peng,et al.  Minimising the machining energy consumption of a machine tool by sequencing the features of a part , 2017 .

[7]  Ryuta Sato,et al.  Optimal workpiece orientation to reduce the energy consumption of a milling process , 2015 .

[8]  B E Ainsworth,et al.  Compendium of physical activities: an update of activity codes and MET intensities. , 2000, Medicine and science in sports and exercise.

[9]  Nafeez Syed,et al.  Physical Activity Measurement Accuracy in Individuals With Chronic Lung Disease: A Systematic Review With Meta-Analysis of Method Comparison Studies. , 2015, Archives of physical medicine and rehabilitation.

[10]  Laine Mears,et al.  Energy, economy, and environment analysis and optimization on manufacturing plant energy supply system , 2016 .

[11]  Shun Jia,et al.  Therblig-based energy demand modeling methodology of machining process to support intelligent manufacturing , 2014, J. Intell. Manuf..

[12]  Wei Cai,et al.  An energy management approach for the mechanical manufacturing industry through developing a multi-objective energy benchmark , 2017 .

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

[14]  Konrad Wegener,et al.  Framework of a machine tool configurator for energy efficiency , 2015 .

[15]  Shun Jia,et al.  Energy consumption modeling of machining transient states based on finite state machine , 2017 .

[16]  Paul Mativenga,et al.  E-smart toolpath machining strategy for process planning , 2016 .

[17]  X Dong.  The methods to assess physical activity and energy expenditure , 2002 .

[18]  Gui-Bing Hong,et al.  Energy efficiency benchmarking of energy-intensive industries in Taiwan , 2014 .

[19]  Yanlong Cao,et al.  Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach , 2018 .

[20]  F. Tylavsky,et al.  Comparison of heart rate monitoring with indirect calorimetry for energy expenditure evaluation , 2012 .

[21]  Sung-Hoon Ahn,et al.  A comparison of energy consumption in bulk forming, subtractive, and additive processes: Review and case study , 2014 .

[22]  John Cosgrove,et al.  A methodology for determining auxiliary and value-added electricity in manufacturing machines , 2015 .

[23]  Shun Jia,et al.  Energy modeling for variable material removal rate machining process: an end face turning case , 2016 .

[24]  M. Jetté,et al.  Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity , 1990, Clinical cardiology.

[25]  Corby K. Martin,et al.  Activity related energy expenditure, appetite and energy intake. Potential implications for weight management , 2013, Appetite.

[26]  David R Bassett,et al.  2011 Compendium of Physical Activities: a second update of codes and MET values. , 2011, Medicine and science in sports and exercise.

[27]  J. B. Weir.  New methods for calculating metabolic rate with special reference to protein metabolism , 1949, The Journal of physiology.

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

[29]  Feifei Yang,et al.  A process simulation based benchmarking approach for evaluating energy consumption of a chemical process system , 2016 .

[30]  Semih Önüt,et al.  Analysis of energy use and efficiency in Turkish manufacturing sector SMEs , 2007 .

[31]  Sanja Petrovic,et al.  A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance , 2016 .

[32]  Mohsen A. Jafari,et al.  Energy-Performance as a driver for optimal production planning , 2016 .

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

[34]  Shahin Rahimifard,et al.  Simulation of energy consumption in the manufacture of a product , 2013, Int. J. Comput. Integr. Manuf..

[35]  Gavriel Salvendy,et al.  Handbook of industrial engineering , 2001 .

[36]  Shun Jia,et al.  Energy Demand Modeling Methodology of Key State Transitions of Turning Processes , 2017 .

[37]  Siaw Kiang Chou,et al.  A performance-based method for energy efficiency improvement of buildings , 2011 .

[38]  Wei Cai,et al.  A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking , 2017 .

[39]  Shun Jia,et al.  Therblig-based energy supply modeling of computer numerical control machine tools , 2014 .

[40]  Konrad Wegener,et al.  Methods for evaluation of energy efficiency of machine tools , 2015 .