Characterising energy efficiency in maching processes: A milling case

With the development of the energy costs and government legislation for sustainability round the world, manufacturing industry has been forced to apply sustainable machining to production to reduce energy consumption. Therefore, energy efficient and optimal activities must be performed to reduce energy consumption. As we know, the energy-consumption model is the core to achieve this goal. The present paper proposes an empirical power consumption model of milling processes based on experiments. According to the experimental results, it comes to the conclusion that the new model performs more accurate and efficient than the main existing models for given cutting parameters. Additional investigations are applied to analysis the influences of cutting parameters on power consumption by ANOVA.

[1]  T. Gutowski,et al.  Electrical Energy Requirements for Manufacturing Processes , 2006 .

[2]  Sami Kara,et al.  An empirical model for predicting energy consumption of manufacturing processes: a case of turning process , 2011 .

[3]  Yan He,et al.  Characteristics of Additional Load Losses of Spindle System of Machine Tools , 2010 .

[4]  Liang Gao,et al.  Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm , 2016 .

[5]  Satish C. Sharma,et al.  Vibration-based fault diagnosis of a rotor bearing system using artificial neural network and support vector machine , 2012, Int. J. Model. Identif. Control..

[6]  MengChu Zhou,et al.  A new model for predicting power consumption of machining processes: A turning case , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[7]  Michael P Sealy,et al.  Energy consumption and process sustainability of hard milling with tool wear progression , 2016 .

[8]  M. Taisch,et al.  Sustainable manufacturing: trends and research challenges , 2012 .

[9]  Vimal Dhokia,et al.  Energy efficient process planning for CNC machining , 2012 .

[10]  Eberhard Abele,et al.  Analyzing Energy Consumption of Machine Tool Spindle Units and Identification of Potential for Improvements of Efficiency , 2011 .

[11]  Chih-Min Lin,et al.  Fuzzy sliding PDC control for some non-linear systems , 2012, Int. J. Model. Identif. Control..

[12]  Sung-Hoon Ahn,et al.  Towards greener machine tools – A review on energy saving strategies and technologies , 2015 .

[13]  U. Prakash,et al.  Tool wear prediction by Regression Analysis in turning A356 with 10% SiC , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[14]  Li Li,et al.  Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost , 2017 .

[15]  David Dornfeld,et al.  Moving towards green and sustainable manufacturing , 2014 .

[16]  Farouq Alhourani,et al.  Factors affecting the implementation rates of energy and productivity recommendations in small and medium sized companies , 2009 .

[17]  Ao San-mei,et al.  Green manufacturing — the sustainable development model of modern manufacturing industries , 2005 .

[18]  F. Klocke,et al.  Sustainabilty in Manufacturing – Energy Consumption of Cutting Processes , 2011 .

[19]  Joost Duflou,et al.  Optimization of energy consumption and surface quality in finish turning , 2012 .

[20]  Kang Tai,et al.  Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach , 2014 .

[21]  Lin Li,et al.  Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling , 2013 .

[22]  Paul Xirouchakis,et al.  A multi-criteria decision method for sustainability assessment of the use phase of machine tool systems , 2011 .

[23]  Wen Feng Lu,et al.  A hybrid approach to energy consumption modelling based on cutting power: a milling case , 2015 .