Prediction of surface roughness and material removal rate in laser assisted turning of aluminium oxide using fuzzy logic

[1]  Y. Shin,et al.  Laser-assisted machining of Inconel 718 with an economic analysis , 2006 .

[2]  K. I. Ramachandran,et al.  Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing , 2007 .

[3]  Chih-Wei Chang,et al.  Evaluation of surface roughness in laser-assisted machining of aluminum oxide ceramics with Taguchi method , 2007 .

[4]  James G. Harris,et al.  Parametric Investigation of Laser‐Assisted Machining of Commercially Pure Titanium , 2008 .

[5]  K. I. Ramachandran,et al.  Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique , 2009, Expert Syst. Appl..

[6]  M. Dargusch,et al.  The Effect of a Laser Beam on Chip Formation during Machining of Ti6Al4V Alloy , 2010 .

[7]  K. Mohandas,et al.  Application of Regression and Fuzzy Logic Method for Prediction of Tool Life , 2012 .

[8]  Shreyes N. Melkote,et al.  Process capability study of laser assisted micro milling of a hard-to-machine material , 2012 .

[9]  Bin Li,et al.  A review of tool wear estimation using theoretical analysis and numerical simulation technologies , 2012 .

[10]  Hongtao Ding,et al.  Thermo-mechanical coupled analysis of laser-assisted mechanical micromilling of difficult-to-machine metal alloys used for bio-implant , 2013 .

[11]  K. Mohandas,et al.  Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques , 2015 .

[12]  K. I. Ramachandran,et al.  Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy , 2015 .