Cutting tools reliability and residual life prediction from degradation indicators in turning process

In the field of cutting tool reliability, an investigation based on four complementary approaches for tool wear assessment is proposed: the approach 1 is a general failure times approach (statistical reliability based on flank wear threshold hitting times), the approach 2 is based on power consumption monitoring (statistical reliability based on power threshold hitting times), the approach 3 is based on vibration signal analysis (statistical reliability based on vibration threshold hitting times), and the approach 4 is based on the evolution of flank wear for each insert (statistical reliability based on predicted failure times). For this study, 30 identical inserts from a same batch were studied with a CNC lathe in identical turning conditions. As the remaining useful life assessment is the final goal, this study highlight four approaches in order to find out the safest one. The results obtained showed that the approach 1 has too many uncertainties, whereas the approach 2 provides more specific and safer replacement times. The approach 3, in the first hand, highlights the fact that cutting conditions are not exactly identical and, in the second hand, leads to a safe conditional replacement of an insert. Finally, it appeared that the approach 4 allows applying a predictive maintenance strategy.

[1]  Gary Wasserman.  Reliability Verification, Testing, and Analysis in Engineering Design , 2002 .

[2]  J. Schmidt,et al.  2D FEM estimate of tool wear in turning operation , 2005 .

[3]  Jean-François Rigal,et al.  The effects of cutting conditions on mixed ceramic and cubic boron nitride tool wear and on surface roughness during machining of X200Cr12 steel (60 HRC) , 2005 .

[4]  Mohamed Athmane Yallese,et al.  INVESTIGATION EXPERIMENTALE SUR L’USURE DES OUTILS DE COUPE EN CBN LORS DU TOURNAGE DES PIECES DURES , 2007 .

[5]  Wafaa Rmili-Mofid.  Analyse vibratoire pour l'étude de l'usure des outils de coupe en tournage , 2007 .

[6]  Malika Boudraa,et al.  Influence of machining cycle of horizontal milling on the quality of cutting force measurement for the cutting tool wear monitoring , 2008, Prod. Eng..

[7]  Gautier List,et al.  Investigation of tool wear in High Speed Machining by using a ballistic set-up , 2009 .

[8]  Roger Serra,et al.  Tool Wear Monitoring in Turning Processes Using Vibratory Analysis , 2009 .

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

[10]  Feng Ding,et al.  Cutting tool wear monitoring for reliability analysis using proportional hazards model , 2011 .

[11]  Jie Wang,et al.  CHMM for tool condition monitoring and remaining useful life prediction , 2012 .

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

[13]  Konstantinos Salonitis,et al.  Reliability assessment of cutting tools life based on advanced approximation methods , 2013 .

[14]  Roshun Paurobally,et al.  A review of flank wear prediction methods for tool condition monitoring in a turning process , 2012, The International Journal of Advanced Manufacturing Technology.

[15]  Elisabetta Ceretti,et al.  Analytical Models for Tool Wear Prediction During AISI 1045 Turning Operations , 2013 .

[16]  Che Hassan Che Haron,et al.  Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system , 2013, Appl. Soft Comput..

[17]  Surjya K. Pal,et al.  Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method , 2013 .

[18]  Roberto Teti,et al.  Image data processing via neural networks for tool wear prediction , 2013 .

[19]  Roberto Teti,et al.  Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion , 2013 .

[20]  Tony L. Schmitz,et al.  Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach , 2014 .

[21]  Suk Joo Bae,et al.  Optimal replacement strategy for stochastic deteriorating system with random wear limit under periodic inspections , 2014 .

[22]  Konstantinos Salonitis,et al.  Reliability assessment of cutting tool life based on surrogate approximation methods , 2014 .

[23]  Berend Denkena,et al.  Condition-based tool management for small batch production , 2014 .

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

[25]  Soumaya Yacout,et al.  Survival Life Analysis of the Cutting Tools During Turning Titanium Metal Matrix Composites (Ti-MMCs)☆ , 2014 .

[26]  K. Mohandas,et al.  Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools , 2015, J. Intell. Manuf..

[27]  Sounak Kumar Choudhury,et al.  Predictive modeling for flank wear progression of coated carbide tool in turning hardened steel under practical machining conditions , 2015 .

[28]  Mohd. Zaki Nuawi,et al.  Development of Tool Wear Machining Monitoring Using Novel Statistical Analysis Method, I-kaz™☆ , 2015 .

[29]  Luc Baron,et al.  Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission , 2015, Appl. Soft Comput..

[30]  Noureddine Zerhouni,et al.  Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..

[31]  Sam Turner,et al.  Tool wear monitoring using naïve Bayes classifiers , 2014, The International Journal of Advanced Manufacturing Technology.