Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone ar...

[1]  Hossam A. Kishawy,et al.  Cutting Tool Materials and Tool Wear , 2014 .

[2]  Junjie Li,et al.  Prediction of Machine Tool Condition Using Support Vector Machine , 2011 .

[3]  Ming-Chyuan Lu,et al.  Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting , 2013 .

[4]  David Dornfeld,et al.  Monitoring of Ultraprecision Machining Processes , 2003 .

[5]  P. Srinivasa Pai,et al.  Acoustic emission analysis for tool wear monitoring in face milling , 2002 .

[6]  Roberto Teti,et al.  Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning , 2014 .

[7]  Y. G. Srinivasa,et al.  Acoustic emission for tool condition monitoring in metal cutting , 1997 .

[8]  Biju Issac,et al.  Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning , 2018, Int. J. Comput. Intell. Appl..

[9]  Huiqun Yu,et al.  A method for tool condition monitoring based on sensor fusion , 2015, J. Intell. Manuf..

[10]  Yuan Zhejun,et al.  Tool wear monitoring with wavelet packet transform—fuzzy clustering method , 1998 .

[11]  Rodolfo E. Haber,et al.  An investigation of tool-wear monitoring in a high-speed machining process , 2004 .

[12]  Youcef Chibani,et al.  SVM-Based Segmentation-Verification of Handwritten Connected Digits Using the Oriented Sliding Window , 2015, Int. J. Comput. Intell. Appl..

[13]  Y. Shin,et al.  Machinability improvement of titanium alloy (Ti–6Al–4V) via LAM and hybrid machining , 2010 .

[14]  R. Krishnamurthy,et al.  The performance of CBN tools in the machining of titanium alloys , 2000 .

[15]  K. I. Ramachandran,et al.  Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features , 2018, Intell. Decis. Technol..

[16]  V. Sugumaran,et al.  Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features , 2010, Expert Syst. Appl..

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

[18]  J. Hodowany,et al.  MODELING THE PHYSICS OF METAL CUTTING IN HIGH-SPEED MACHINING , 1998 .

[19]  M. Field,et al.  Machining of Titanium Alloys , 1985 .

[20]  Xiaoli Li,et al.  A brief review: acoustic emission method for tool wear monitoring during turning , 2002 .

[21]  A. Geddam,et al.  A multi-sensor approach to the monitoring of end milling operations , 2003 .

[22]  Guo F Wang,et al.  Tool condition monitoring system based on support vector machine and differential evolution optimization , 2017 .

[23]  James K. Beard Introduction to the Radix 2 FFT , 2004 .

[24]  D. E. Dimla,et al.  On-line metal cutting tool condition monitoring.: II: tool-state classification using multi-layer perceptron neural networks , 2000 .

[25]  J. R. Landis,et al.  An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. , 1977, Biometrics.

[26]  Álisson Rocha Machado,et al.  A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission , 2015 .

[27]  V. Makis,et al.  Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models , 2007 .

[28]  Sohyung Cho,et al.  Tool breakage detection using support vector machine learning in a milling process , 2005 .

[29]  N. R. Sakthivel,et al.  Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm , 2011, Expert Syst. Appl..