Quality and inspection of machining operations: Tool condition monitoring

Tool condition monitoring (TCM) is an important aspect of condition based maintenance (CBM) in all manufacturing processes. Recent work on TCM has generated significant successes for a variety of cutting operations. In particular, lower cost and on-board sensors in conjunction with enhanced signal processing capabilities and improved networking has permitted significant enhancements to TCM capabilities. This paper presents an overview of TCM for drilling, turning, milling, and grinding. The focus of this paper is on the hardware and algorithms that have demonstrated success in TCM for these processes. While a variety of initial successes are reported, significantly more research is possible to extend the capabilities of TCM for the reported cutting processes as well as for many other manufacturing processes. Furthermore, no single unifying approach has been identified for TCM. Such an approach will enable the rapid expansion of TCM into other processes and a tighter integration of TCM into CBM for a wide variety of manufacturing processes and production systems.

[1]  Joseph C. Chen,et al.  Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system , 2008 .

[2]  Xiaozhi Chen,et al.  Acoustic emission method for tool condition monitoring based on wavelet analysis , 2007 .

[3]  Zbigniew J. Pasek,et al.  CUTTING PROCESS DIAGNOSTICS UTILISING A SMART CUTTING TOOL , 2002 .

[4]  Jay Lee,et al.  Watchdog Agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction , 2003, Adv. Eng. Informatics.

[5]  Katsushi Furutani,et al.  In-process measurement of topography change of grinding wheel by using hydrodynamic pressure , 2002 .

[6]  Li Song,et al.  Development of in situ fan curve measurement for VAV AHU systems , 2005 .

[7]  Krzysztof Jemielniak,et al.  Tool Wear Monitoring Using Genetically-Generated Fuzzy Knowledge Bases , 2002 .

[8]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[9]  Bruno Torrésani,et al.  Time-Frequency and Time-Scale Analysis , 1999 .

[10]  Amiya R Mohanty,et al.  Estimation of tool wear during CNC milling using neural network-based sensor fusion , 2007 .

[11]  Jae-Seob Kwak,et al.  Detection of dressing time using the grinding force signal based on the discrete wavelet decomposition , 2004 .

[12]  Jun Qu,et al.  GRINDING WHEEL CONDITION MONITORING WITH HIDDEN MARKOV MODEL-BASED CLUSTERING METHODS , 2006 .

[13]  Stephen Malkin,et al.  Grinding Technology: Theory and Applications of Machining with Abrasives , 1989 .

[14]  Ching-Huan Tseng,et al.  On-line breakage monitoring of small drills with input impedance of driving motor , 2007 .

[15]  Issam Abu-Mahfouz,et al.  Drilling wear detection and classification using vibration signals and artificial neural network , 2003 .

[16]  Chong Nam Chu,et al.  Prediction of drill failure using features extraction in time and frequency domains of feed motor current , 2008 .

[17]  Yoke San Wong,et al.  Multiclassification of tool wear with support vector machine by manufacturing loss consideration , 2004 .

[18]  Xin Yao,et al.  Multi-scale statistical process monitoring in machining , 2005, IEEE Transactions on Industrial Electronics.

[19]  Ruxu Du,et al.  Fuzzy transition probability: A new method for monitoring progressive faults. Part 2: Application examples , 2006, Eng. Appl. Artif. Intell..

[20]  Elijah Kannatey-Asibu,et al.  Hidden Markov model-based tool wear monitoring in turning , 2002 .

[21]  Jin Jiang,et al.  Erratum to: State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[22]  João Fernando Gomes de Oliveira,et al.  Fast Grinding Process Control with AE Modulated Power Signals , 2004 .

[23]  Krzysztof Jemielniak Tool Wear Monitoring Based on a Non-Monotonic Signal Feature , 2006 .

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

[25]  Richard E. DeVor,et al.  A Model-Based Monitoring and Fault Diagnosis Methodology for Free-Form Surface Machining Process , 2003 .

[26]  Robert Bauer,et al.  Analysis of wheel wear using force data in surface grinding , 2003 .

[27]  Rene de Jesus Romero-Troncoso,et al.  Sensorless tool failure monitoring system for drilling machines , 2006 .

[28]  Cuneyt Oysu,et al.  Drill wear monitoring using cutting force signals , 2004 .

[29]  Jay Lee,et al.  Feature signature prediction of a boring process using neural network modeling with confidence bounds , 2006 .

[30]  Albert H. Nuttall Detection performance of power‐law processors for random signals of unknown location, structure, extent, and strength , 1996 .

[31]  D. R. Salgado,et al.  Tool wear detection in turning operations using singular spectrum analysis , 2006 .

[32]  Gary D. Bernard,et al.  Multilevel Classification of Milling Tool Wear with Confidence Estimation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Samy E. Oraby,et al.  A Diagnostic Approach for Turning Tool Based on the Dynamic Force Signals , 2005 .

[34]  Krzysztof Jemielniak,et al.  Hierarchical Strategies in Tool Wear Monitoring , 2006 .

[35]  M. A. El-Damcese,et al.  Maintenance scheduling for a parallel system subject to exponential power hazard , 2012, Int. J. Syst. Assur. Eng. Manag..

[36]  Hans Kurt Tönshoff,et al.  Process Monitoring in Grinding , 2002 .

[37]  Soumitra Paul,et al.  The efficacy of back propagation neural network with delta bar delta learning in predicting the wear of carbide inserts in face milling , 2006 .

[38]  Xiaoli Li,et al.  Detection of tool flute breakage in end milling using feed-motor current signatures , 2001 .

[39]  S. Malkin,et al.  Model-Based Monitoring and Control of Continuous Dress Creep-Feed Form Grinding , 2004 .

[40]  Akira Hosokawa,et al.  Evaluation of Grinding Wheel Surface by Means of Grinding Sound Discrimination , 2004 .

[41]  P. S. Heyns,et al.  WEAR MONITORING IN TURNING OPERATIONS USING VIBRATION AND STRAIN MEASUREMENTS , 2001 .

[42]  Rene de Jesus Romero-Troncoso,et al.  SENSORLESS DETECTION OF TOOL BREAKAGE IN MILLING , 2006 .

[43]  Yoke San Wong,et al.  Effective training data selection in tool condition monitoring system , 2006 .

[44]  Elso Kuljanić,et al.  TWEM, a method based on cutting forces—monitoring tool wear in face milling , 2005 .

[45]  P. S. Heyns,et al.  A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear , 2005, Neural Computing & Applications.

[46]  K. Subramanian,et al.  Sensing of Drill Wear and Prediction of Drill Life , 1977 .

[47]  P. S. Heyns,et al.  An industrial tool wear monitoring system for interrupted turning , 2004 .

[48]  Yonghong Peng,et al.  Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage , 2006 .

[49]  U. Prakash,et al.  Tool wear prediction by Regression Analysis in turning A356 with 10% SiC , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[50]  Jechang Jeong,et al.  Kernel design for reduced interference distributions , 1992, IEEE Trans. Signal Process..

[51]  Elijah Kannatey-Asibu,et al.  Analysis of Sound Signal Generation Due to Flank Wear in Turning , 2000, Manufacturing Engineering.

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

[53]  M. Cemal Cakir,et al.  Detecting tool breakage in turning aisi 1050 steel using coated and uncoated cutting tools , 2005 .

[54]  Mathieu Ritou,et al.  A new versatile in-process monitoring system for milling , 2006, 1309.3915.

[55]  R. Ryan Vallance,et al.  Monitoring force in precision cylindrical grinding , 2005 .

[56]  Erkki Jantunen,et al.  A summary of methods applied to tool condition monitoring in drilling , 2002 .

[57]  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 .

[58]  Gino Dini,et al.  Tool condition monitoring in end milling using a torque-based sensorized toolholder , 2007 .

[59]  F. A. Farrelly,et al.  Statistical properties of acoustic emission signals from metal cutting processes , 2004 .

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

[61]  Ibrahim N. Tansel,et al.  Genetic tool monitor (GTM) for micro-end-milling operations , 2005 .

[62]  Sung-Chong Chung,et al.  Monitoring of Micro-Drill Wear by Using the Machine Vision System , 2006 .

[63]  Ruxu Du,et al.  Monitoring machining Processes Based on Discrete Wavelet Transform and Statistical Process Control , 2004, Int. J. Wavelets Multiresolution Inf. Process..

[64]  Anwar Khalil Sheikh,et al.  Use of electrical power for online monitoring of tool condition , 2005 .

[65]  Jun Qu,et al.  Grinding wheel condition monitoring with boosted minimum distance classifiers , 2008 .

[66]  Hongli Gao,et al.  Intelligent Tool Condition Monitoring System for Turning Operations , 2005, ISNN.

[67]  Ming Liang,et al.  Mechanical fault detection using fuzzy index fusion , 2007 .

[68]  Ichiro Inasaki,et al.  Monitoring Systems for Grinding Processes , 2006 .

[69]  H. Shao,et al.  A cutting power model for tool wear monitoring in milling , 2004 .

[70]  Christopher A. Suprock,et al.  METHODS FOR ON-LINE DIRECTIONALLY INDEPENDENT FAILURE PREDICTION OF END MILLING CUTTING TOOLS , 2007 .

[71]  Surjya K. Pal,et al.  Drill wear monitoring using back propagation neural network , 2006 .

[72]  Anwar Khalil Sheikh,et al.  Empirical models of mechanical and electrical drilling power of mild steel , 2004 .

[73]  Debasis Sengupta,et al.  Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques , 2007 .

[74]  C H Srinivasa Rao,et al.  Online prediction of diffusion wear on the flank through tool tip temperature in turning using artificial neural networks , 2006 .

[75]  Romero-Troncoso René de Jesús,et al.  Driver current analysis for sensorless tool breakage monitoring of CNC milling machines , 2003 .

[76]  D. R. Salgado,et al.  An approach based on current and sound signals for in-process tool wear monitoring , 2007 .

[77]  David Newland Ridge and Phase Identification in the Frequency Analysis of Transient Signals by Harmonic Wavelets , 1999 .

[78]  Won Tae Kwon,et al.  Drilling torque control using spindle motor current and its effect on tool wear , 2004 .

[79]  Eduardo Carlos Bianchi,et al.  In-process grinding monitoring by acoustic emission , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[80]  Robert Heinemann,et al.  Use of process signals for tool wear progression sensing in drilling small deep holes , 2007 .

[81]  R. DeVor,et al.  A Mechanistic Approach to Predicting the Cutting Forces in Drilling: With Application to Fiber-Reinforced Composite Materials , 1993 .

[82]  Zimin Yang Dynamic maintenance scheduling using online information about system condition. , 2005 .

[83]  R. Krishnamurthy,et al.  Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform , 2005 .

[84]  Sang Jo Lee,et al.  Development of in situ system to monitor the machining process using a piezo load cell , 2005 .

[85]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[86]  Adam G. Rehorn,et al.  State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[87]  U. Zuperl,et al.  Tool cutting force modeling in ball-end milling using multilevel perceptron , 2004 .

[88]  Jay Lee,et al.  Similarity based method for manufacturing process performance prediction and diagnosis , 2007, Comput. Ind..

[89]  Kenneth A. Loparo,et al.  Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs) , 2001 .

[90]  T. M. Romberg,et al.  A Comparison of Traditional Fourier and Maximum Entropy Spectral Methods for Vibration Analysis , 1984 .

[91]  Roger Ivor Grosvenor,et al.  Sweeping filters and tooth rotation energy estimation (TREE) techniques for machine tool condition monitoring , 2006 .

[92]  Jun Qu,et al.  A wavelet-based methodology for grinding wheel condition monitoring , 2007 .

[94]  D. Leea,et al.  Precision manufacturing process monitoring with acoustic emission , 2005 .

[95]  Michael Thurston,et al.  Standards Developments for Condition-Based Maintenance Systems , 2001 .

[96]  Changqing Liu,et al.  Robustness improvement of tool life estimation assisted by a virtual manufacturing cell , 2006 .

[97]  Ekkard Brinksmeier,et al.  Development and Application of a Wheel Based Process Monitoring System in Grinding , 2005 .

[98]  Ruxu Du,et al.  Fuzzy transition probability: a new method for monitoring progressive faults. Part 1: the theory , 2004, Eng. Appl. Artif. Intell..

[99]  Jun Ni,et al.  Maintenance scheduling for a manufacturing system of machines with adjustable throughput , 2007 .

[100]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[101]  Surjya K. Pal,et al.  APPLICATION OF WAVELET PACKET ANALYSIS IN DRILL WEAR MONITORING , 2007 .

[102]  Krzysztof Jemielniak,et al.  Tool condition monitoring using artificial intelligence methods , 2002 .

[103]  Kevin Kelly,et al.  Ai-based condition monitoring of the drilling process , 2002 .

[104]  Yongdai Kim Averaged Boosting: A Noise-Robust Ensemble Method , 2003, PAKDD.

[105]  João Fernando Gomes de Oliveira,et al.  Precision manufacturing process monitoring with acoustic emission , 2006 .