Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is t...

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

[2]  Sami Kara,et al.  Unit process energy consumption models for material removal processes , 2011 .

[3]  Sezan Orak,et al.  An ANN-Based Method to Predict Surface Roughness in Turning Operations , 2017 .

[4]  Vallabh D. Patel,et al.  Analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel using CBN tool , 2019, Measurement.

[5]  Tao Wu,et al.  Analysis and estimation of energy consumption for numerical control machining , 2012 .

[6]  Mehmet Alper Sofuoglu,et al.  Optimization of different non-traditional turning processes using soft computing methods , 2018, Soft Computing.

[7]  Sami Kara,et al.  Carbon emissions and CES™ in manufacturing , 2008 .

[8]  G. Rutelli,et al.  Tool wear monitoring based on cutting power measurement , 1990 .

[9]  Kalipada Maity,et al.  Modeling of machining parameters affecting flank wear and surface roughness in hot turning of Monel-400 using response surface methodology (RSM) , 2019, Measurement.

[10]  Behnam Davoodi,et al.  Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA , 2020 .

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  Yuebin B. Guo,et al.  Energy Consumption Characteristics and Influence on Surface Quality in Milling , 2018 .

[13]  Balkrishna C. Rao,et al.  Methodology for adapting metal cutting to a green economy , 2010 .

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

[15]  Sung-Hoon Ahn,et al.  Empirical power-consumption model for material removal in three-axis milling , 2014 .

[16]  S. Debnath,et al.  Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method , 2016 .

[17]  Yuebin Guo,et al.  Energy consumption in machining: Classification, prediction, and reduction strategy , 2017 .

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

[19]  Satyanarayana Kosaraju,et al.  Optimal machining conditions for turning Ti-6Al-4V using response surface methodology , 2013 .

[20]  Song Yu,et al.  Support vector regression and genetic-algorithm-based multiobjective optimization of mesoscopic geometric characteristic parameters of ball-end milling tool , 2020 .

[21]  M. Gheorghe,et al.  Models of machine tool efficiency and specific consumed energy , 2003 .

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

[23]  Burak Öztürk,et al.  Optimization of surface roughness via the Taguchi method and investigation of energy consumption when milling spheroidal graphite cast iron materials , 2018 .

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

[25]  Yan He,et al.  An on-line approach for energy efficiency monitoring of machine tools , 2012 .

[26]  Nabil Gindy,et al.  Assessment of the effectiveness of a spindle power signal for tool condition monitoring in machining processes , 2004 .

[27]  Fei Liu,et al.  Content Architecture and Future Trends of Energy Efficiency Research on Machining Systems , 2013 .

[28]  Liming Wang,et al.  An improved cutting power model of machine tools in milling process , 2017 .