An Analytic Approach to the Cox Proportional Hazards Model for Estimating the Lifespan of Cutting Tools

The machining industry raises an ever-growing concern for the significant cost of cutting tools in the production process of mechanical parts, with a focus on the replacement policy of these inserts. While an early maintenance induces lower tool return on investment, scraps and inherent costs stem from late replacement. The framework of this paper is the attempt to predict the tool inserts Mean Up Time, based solely on the value of a cutting parameter (the cutting speed in this particular turning application). More specifically, the use of the Cox Proportional Hazards (PH) Model for this prediction is demonstrated. The main contribution of this paper is the analytic approach that was conducted about the relevance on data transformation prior to using the Cox PH Model. It is shown that the logarithm of the cutting speed is analytically much more relevant in the prediction of the Mean Up Time through the Cox PH model than the raw cutting speed value. The paper also covers a numerical validation designed to show and discuss the benefits of this data transformation and the overall interest of the Cox PH model for the lifetime prognosis. This methodology, however, necessitates the knowledge of an analytical law linking the covariate to the Mean Up Time. It also shows how the necessary data for the numerical experiment was obtained through a gamma process simulating the degradation of cutting inserts. The results of this paper are expected to help manufacturers in the assessment of tool lifespan.

[1]  Wafaa Rmili,et al.  An automatic system based on vibratory analysis for cutting tool wear monitoring , 2016 .

[2]  Jose Vicente Abellan-Nebot,et al.  A review of machining monitoring systems based on artificial intelligence process models , 2010 .

[3]  Chee Khiang Pang,et al.  Gamma process with recursive MLE for wear PDF prediction in precognitive maintenance under aperiodic monitoring , 2015 .

[4]  A. M. M. Sharif Ullah,et al.  Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing , 2015, Journal of Intelligent Manufacturing.

[5]  Huamin Liu,et al.  Cutting-tool reliability assessment in variable machining conditions , 1996, IEEE Trans. Reliab..

[6]  Hongyue WANG,et al.  Log-transformation and its implications for data analysis , 2014, Shanghai archives of psychiatry.

[7]  João Paulo Davim,et al.  Tribology in manufacturing technology , 2013 .

[8]  Marek Balazinski,et al.  Estimating the remaining useful tool life of worn tools under different cutting parameters: A survival life analysis during turning of titanium metal matrix composites (Ti-MMCs) , 2016 .

[9]  Soumaya Yacout,et al.  Optimal replacement times for machining tool during turning titanium metal matrix composites under variable machining conditions , 2017 .

[10]  P. J. Vlok,et al.  Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions , 2004 .

[11]  Christophe Letot,et al.  Estimate of Cutting Tool Lifespan through Cox Proportional Hazards Model , 2016 .

[12]  Ján Duplák,et al.  Analytical Expression of T-vC Dependence in Standard ISO 3685 for Cutting Ceramic , 2011 .

[13]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[14]  Woon Kiow Lee,et al.  Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision , 2019, The International Journal of Advanced Manufacturing Technology.

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

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

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

[18]  Walter Eversheim,et al.  Tool Management: The Present and the Future , 1991 .

[19]  New stochastic wear law to predict the abrasive flank wear and tool life in machining process , 2014 .

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

[21]  Roger Serra,et al.  Cutting tools reliability and residual life prediction from degradation indicators in turning process , 2016 .

[22]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[23]  Hoang Pham,et al.  Handbook of reliability engineering , 2013 .

[24]  Svetan Ratchev,et al.  In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis , 2018 .

[25]  M. S Nikulin,et al.  Cox Model and Its Applications , 2016 .

[26]  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..

[27]  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.