Predicting remaining useful life of cutting tools with regression and ANN analysis

In manufacturing industry, cutting tools are often discarded when much of their potential life still remains. Predicting the remaining useful life of the partially degraded components and putting them to use will help to save natural resources to a great extent. This saves manufacturing cost and protects environment. The main objective of this research is to develop a comprehensive methodology to assess the reuse potential of carbide-tipped tools. In this work, based on Taguchi approach, experiments are conducted and tool life values are obtained. The analysis is carried out in two stages. In the first stage, a regression model is proposed for the prediction of remaining life of carbide-tipped tools. In the second stage, an artificial neural network model is developed for predicting tool life. The results of both models are compared.

[1]  Huairui Guo,et al.  Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model , 2006, RAMS '06. Annual Reliability and Maintainability Symposium, 2006..

[2]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[3]  S. M. Kannan,et al.  Overall line effectiveness – a performance evaluation index of a manufacturing system , 2010 .

[4]  Erry Yulian Triblas Adesta,et al.  Tool life estimation model based on simulated flank wear during high speed hard turning , 2010 .

[5]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

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

[7]  P. Baruah,et al.  HMMs for diagnostics and prognostics in machining processes , 2005 .

[8]  K. P. Maity,et al.  An experimental investigation of hot-machining to predict tool life , 2008 .

[9]  Jaharah A Ghani,et al.  THE RELIABILITY OF TOOL LIFE PREDICTION MODEL IN END MILLING , 2006 .

[10]  C.S. Byington,et al.  Data-driven neural network methodology to remaining life predictions for aircraft actuator components , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[11]  Daniel J. Inman,et al.  Damage Prognosis For Aerospace, Civil and Mechanical Systems Preface , 2005 .

[12]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[13]  Yinhui Ao,et al.  Prognostics for drilling process with wavelet packet decomposition , 2010 .

[14]  Sami Kara,et al.  Vibration-based approach to lifetime prediction of electric motors for reuse , 2010 .

[15]  Xing Wang,et al.  Sustainable Manufacturing Oriented Prognosis for Facility Reuse , 2010 .

[16]  Enrico Zio,et al.  A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..

[17]  C. James Li,et al.  Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics , 2005 .

[18]  Wen-Yuh Jywe,et al.  Life prediction system using a tool’s geometric shape for high-speed milling , 2006 .

[19]  R. P. Mohanty,et al.  Service quality modelling for life insurance business using neural networks , 2011 .

[20]  H. Kaebernick,et al.  Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks , 2007 .

[21]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[22]  Guang Meng,et al.  Updated proportional hazards model for equipment residual life prediction , 2011 .

[23]  Sami Kara,et al.  An integrated methodology for assessing physical and technological life of products for reuse , 2009 .

[24]  Zhigang Tian,et al.  A neural network approach for remaining useful life prediction utilizing both failure and suspension histories , 2010 .

[25]  John E. Freund,et al.  Probability and statistics for engineers , 1965 .

[26]  H. Kaebernick,et al.  A concept of reliability evaluation for reuse and remanufacturing , 2008 .

[27]  Gregory Levitin,et al.  The Universal Generating Function in Reliability Analysis and Optimization , 2005 .

[28]  Ling-Yau Chan,et al.  Maintenance of continuously monitored degrading systems , 2006, Eur. J. Oper. Res..