Tool Wear Monitoring and Alarm System Based on Pattern Recognition With Logical Analysis of Data

This paper presents a new tool wear monitoring and alarm system that is based on logical analysis of data (LAD). LAD is a data-driven combinatorial optimization technique for knowledge discovery and pattern recognition. The system is a nonintrusive online device that measures the cutting forces and relates them to tool wear through learned patterns. It is developed during turning titanium metal matrix composites (TiMMCs). These are a new generation of materials which have proven to be viable in various industrial fields such as biomedical and aerospace. Since they are quite expensive, our objective is to increase the tool life by giving an alarm at the right moment. The proposed monitoring system is tested by using the experimental results obtained under sequential different machining conditions. External and internal factors that affect the turning process are taken into consideration. The system's alarm limit is validated and is compared to the limit obtained when the statistical proportional hazards model (PHM) is used. The results show that the proposed system that is based on using LAD detects the worn patterns and gives a more accurate alarm for cutting tool replacement.

[1]  Shivakumar Raman,et al.  An inexpensive system for classifying tool wear states using pattern recognition , 1993 .

[2]  Wei Wang,et al.  Proportional Hazards Regression Models , 2006 .

[3]  Viliam Makis,et al.  A Control-Limit Policy And Software For Condition-Based Maintenance Optimization , 2001 .

[4]  Y. G. Srinivasa,et al.  A back-propagation algorithm applied to tool wear monitoring , 1994 .

[5]  Viliam Makis,et al.  Optimal Replacement In The Proportional Hazards Model , 1992 .

[6]  S. S. Rangwala,et al.  Machining process characterization and intelligent tool condition monitoring using acoustic emission signal analysis , 1994 .

[7]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[8]  Huamin Liu.  Modeling and optimal control of deteriorating production processes , 1997 .

[9]  Hong Seo Ryoo,et al.  MILP approach to pattern generation in logical analysis of data , 2009, Discret. Appl. Math..

[10]  Hong-Ying Hu,et al.  Research on tool failure prediction and wear monitoring based hmm pattern recognition theory , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[11]  T. Aven,et al.  Optimal replacement times — a general set-up , 1986, Journal of Applied Probability.

[12]  Soumaya Yacout.  Fault detection and diagnosis for condition based maintenance using the Logical Analysis of data , 2010, The 40th International Conference on Computers & Indutrial Engineering.

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

[14]  M. Guillot,et al.  On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion , 1997 .

[15]  Krzysztof Jemielniak,et al.  Commercial Tool Condition Monitoring Systems , 1999 .

[16]  T. Bieler,et al.  Phase Dependent Tool Wear in Turning Ti-6Al-4V Using Polycrystalline Diamond and Carbide Inserts , 2014 .

[17]  Aouni A. Lakis,et al.  Diagnosis of rotor bearings using logical analysis of data , 2011 .

[18]  Steven Y. Liang,et al.  Modeling of Cutting Forces Under Hard Turning Conditions Considering Tool Wear Effect , 2005 .

[19]  Dongfeng Shi,et al.  Tool wear predictive model based on least squares support vector machines , 2007 .

[20]  Xin Wang,et al.  WC/Co Tool Wear in Dry Turning of Commercially Pure Aluminium , 2014 .

[21]  Sarah M. Ryan,et al.  Optimal Replacement in the Proportional Hazards Model With Semi-Markovian Covariate Process and Continuous Monitoring , 2011, IEEE Transactions on Reliability.

[22]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[23]  F. O. Rasch,et al.  Machinability of Particulate Aluminium Matrix Composites , 1992 .

[24]  Soumaya Yacout,et al.  Replacement time of a cutting tool subject to variable speed , 2010 .

[25]  Peter L. Hammer,et al.  Logical analysis of data—An overview: From combinatorial optimization to medical applications , 2006, Ann. Oper. Res..

[26]  Marek Balazinski,et al.  Flank Wear Progression During Machining Metal Matrix Composites , 2006 .

[27]  Thomas A. Mazzuchi,et al.  Assessment of machine tool reliability using a proportional hazards model , 1989 .

[28]  Kathryn E. Stecke,et al.  A synthesis of decision models for tool management in automated manufacturing , 1993 .

[29]  Toshihide Ibaraki,et al.  An Implementation of Logical Analysis of Data , 2000, IEEE Trans. Knowl. Data Eng..

[30]  Viliam Makis,et al.  Optimal replacement of a tool subject to random failure , 1995 .

[31]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .