Online Condition Monitoring in Micromilling: A Force Waveform Shape Analysis Approach

The cutting force variations have long been studied for machining condition monitoring, due to their sensitivity to the tool conditions. These variations could be the statistics of the amplitude, features extracted from frequency components, wavelet coefficients, etc. Different from most current approaches, this paper introduces a novel online approach that directly correlates tool conditions with the force waveform variations and estimates the tool condition based on their corresponding singularity degree density functions. The singularities of waveforms are measured with Holder exponents (HEs), which are extracted from their wavelet transform modulus maxima. Experimental studies have shown that the HEs reflect the changes of tool conditions. The HE features are found robust to working condition variations, and their probability density functions are discriminate for tool state estimation in micromilling.

[1]  Stéphane Jaffard,et al.  Multifractal formalism for functions part I: results valid for all functions , 1997 .

[2]  Ming-Chyuan Lu,et al.  Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling , 2011, The International Journal of Advanced Manufacturing Technology.

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

[4]  Keinosuke Fukunaga,et al.  Statistical Pattern Recognition , 1993, Handbook of Pattern Recognition and Computer Vision.

[5]  Sergios Theodoridis,et al.  Pattern Recognition , 1998, IEEE Trans. Neural Networks.

[6]  Weidong Li,et al.  The milling tool wear monitoring using the acoustic spectrum , 2012 .

[7]  Allan P. Donsig,et al.  Real Analysis with Real Applications , 2001 .

[8]  K. Abou-El-Hossein,et al.  Tool Wear Monitoring Tool Wear Monitoring Using Acoustic Emission , 2011 .

[9]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[10]  E. Bacry,et al.  Singularity spectrum of fractal signals from wavelet analysis: Exact results , 1993 .

[11]  Ratna Babu Chinnam,et al.  Health-State Estimation and Prognostics in Machining Processes , 2010, IEEE Transactions on Automation Science and Engineering.

[12]  Miron Zapciu,et al.  Envelope dynamic analysis: a new approach for milling process monitoring , 2012 .

[13]  A. Trochidis,et al.  Classification of gear faults using Hoelder exponents , 2004 .

[14]  Stéphane Jaffard,et al.  Multifractal formalism for functions part II: self-similar functions , 1997 .

[15]  Ibrahim N. Tansel,et al.  Micro-end-milling—III. Wear estimation and tool breakage detection using acoustic emission signals , 1998 .

[16]  Martin B.G. Jun,et al.  Investigation of the Dynamics of Microend Milling—Part II: Model Validation and Interpretation , 2006 .

[17]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[18]  Martin B.G. Jun,et al.  Tool wear monitoring of micro-milling operations , 2009 .

[19]  Xiang Li,et al.  Multimodal Hidden Markov Model-Based Approach for Tool Wear Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[20]  Linxia Liao,et al.  Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction , 2014, IEEE Transactions on Industrial Electronics.

[21]  Chi-Man Vong,et al.  A New Framework of Simultaneous-Fault Diagnosis Using Pairwise Probabilistic Multi-Label Classification for Time-Dependent Patterns , 2013, IEEE Transactions on Industrial Electronics.

[22]  P. Tse,et al.  Singularity analysis of the vibration signals by means of wavelet modulus maximal method , 2007 .

[23]  Dongfeng Shi,et al.  Industrial Applications of Online Machining Process Monitoring System , 2007, IEEE/ASME Transactions on Mechatronics.

[24]  Hoon Sohn,et al.  Singularity detection for structural health monitoring using Holder exponents , 2003, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[25]  Laine Mears,et al.  Quality and inspection of machining operations: Tool condition monitoring , 2010 .

[26]  W. Wang,et al.  A Smart Sensing Unit for Vibration Measurement and Monitoring , 2010, IEEE/ASME Transactions on Mechatronics.

[27]  Krzysztof Jemielniak,et al.  Application of AE and cutting force signals in tool condition monitoring in micro-milling , 2008 .

[28]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[29]  A. Kumar,et al.  Micro milling of pure copper , 2001 .

[30]  Berend Denkena,et al.  Tool breakage monitoring using motor current signals for machine tools with linear motors , 2005, IEEE Transactions on Industrial Electronics.

[31]  Tony L. Schmitz,et al.  Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method , 2014 .

[32]  Sohyung Cho,et al.  Design of multisensor fusion-based tool condition monitoring system in end milling , 2010 .

[33]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[34]  Hubert Razik,et al.  Prognosis of Bearing Failures Using Hidden Markov Models and the Adaptive Neuro-Fuzzy Inference System , 2014, IEEE Transactions on Industrial Electronics.

[35]  Geok Soon Hong,et al.  Multi-category micro-milling tool wear monitoring with continuous hidden Markov models , 2009 .

[36]  Geok Soon Hong,et al.  Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results , 2009 .

[37]  Tommy W. S. Chow,et al.  Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis , 2014, IEEE Transactions on Industrial Electronics.

[38]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[39]  David Dornfeld,et al.  Sensors for precision manufacturing , 2008 .