Behaviour of cutting tool vibrations with the progress of tool wear in turning hardened AISI 52100 steel: An approach to tool condition monitoring system

[1]  P. Sam Paul,et al.  Study on the influence of fluid application parameters on tool vibration and cutting performance during turning of hardened steel , 2016 .

[2]  Tadeusz Leppert,et al.  Effect of cooling and lubrication conditions on surface topography and turning process of C45 steel , 2011 .

[3]  D. E. Dimla,et al.  Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods , 2000 .

[4]  Fritz Klocke,et al.  Development of a tool wear-monitoring system for hard turning , 2003 .

[5]  V. K. Jain,et al.  Development of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis , 2008 .

[6]  I. Choudhury,et al.  Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning , 2014 .

[7]  R. Suresh,et al.  Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool , 2012 .

[8]  S. K. Choudhury,et al.  State of the art in hard turning , 2012 .

[9]  Tarek Mabrouki,et al.  On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations , 2013 .

[10]  Gaigai Cai,et al.  Reliability estimation for cutting tools based on logistic regression model using vibration signals , 2011 .

[11]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[12]  Ossama B. Abouelatta,et al.  Surface roughness prediction based on cutting parameters and tool vibrations in turning operations , 2001 .

[13]  Vimal Dhokia,et al.  Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids , 2012 .

[14]  Víctor González-Castro,et al.  Design of a TCM System Based on Vibration Signal for Metal Turning Processes , 2015 .

[15]  Pramod Kumar Jain,et al.  In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals , 2013 .

[16]  Masoud Monjezi,et al.  An optimized ANN model based on genetic algorithm for predicting ripping production , 2017, Neural Computing and Applications.

[17]  D. R. Salgado,et al.  Analysis of the structure of vibration signals for tool wear detection , 2008 .

[18]  S. K. Choudhury,et al.  Cutting force modeling considering tool wear effect during turning of hardened AISI 4340 alloy steel using multi-layer TiCN/Al2O3/TiN-coated carbide tools , 2016 .

[19]  M. Elbah,et al.  Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology , 2014 .

[20]  Ty G. Dawson,et al.  Tool crater wear depth modeling in CBN hard turning , 2005 .