Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning

Abstract On-line monitoring of surface finish in machining processes has proven to be a substantial advancement over traditional post-process quality control techniques by reducing inspection times and costs and by avoiding the manufacture of defective products. This study applied techniques for processing cutting force signals based on the wavelet packet transform (WPT) method for the monitoring of surface finish in computer numerical control (CNC) turning operations. The behaviour of 40 mother wavelets was analysed using three techniques: global packet analysis (G-WPT), and the application of two packet reduction criteria: maximum energy (E-WPT) and maximum entropy (SE-WPT). The optimum signal decomposition level ( L j ) was determined to eliminate noise and to obtain information correlated to surface finish. The results obtained with the G-WPT method provided an in-depth analysis of cutting force signals, and frequency ranges and signal characteristics were correlated to surface finish with excellent results in the accuracy and reliability of the predictive models. The radial and tangential cutting force components at low frequency provided most of the information for the monitoring of surface finish. The E-WPT and SE-WPT packet reduction criteria substantially reduced signal processing time, but at the expense of discarding packets with relevant information, which impoverished the results. The G-WPT method was observed to be an ideal procedure for processing cutting force signals applied to the real-time monitoring of surface finish, and was estimated to be highly accurate and reliable at a low analytical-computational cost.

[1]  Dragos Axinte,et al.  A time–frequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously , 2009 .

[2]  Weiguo Gong,et al.  A method of recognizing tool-wear states based on a fast algorithm of wavelet transform , 2005 .

[3]  Roberto Teti,et al.  Wavelet Transform Feature Extraction for Chip form Recognition during Carbon Steel Turning , 2013 .

[4]  Roberto Teti,et al.  Chip form Classification in Carbon Steel Turning through Cutting Force Measurement and Principal Component Analysis , 2012 .

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

[6]  José Javier Dolado,et al.  On the problem of the software cost function , 2001, Inf. Softw. Technol..

[7]  Dong-Sik Kim,et al.  Development of a combined-type tool dynamometer with a piezo-film accelerometer for an ultra-precision lathe , 1997 .

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

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

[10]  Jin Jiang,et al.  Fault diagnosis in machine tools using selective regional correlation , 2006 .

[11]  Joseph C. Chen,et al.  Development of a fuzzy-nets-based surface roughness prediction system in turning operations , 2007, Comput. Ind. Eng..

[12]  Durul Ulutan,et al.  A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials , 2016 .

[13]  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.

[14]  Soundarr T. Kumara,et al.  Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals , 2000 .

[15]  Eustaquio García Plaza,et al.  Contribution of Surface Finish Monitoring Signals in CNC Taper Turning , 2014 .

[16]  Tarek Mabrouki,et al.  On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations , 2013 .

[17]  E. García Plaza,et al.  Surface roughness monitoring by singular spectrum analysis of vibration signals , 2017 .

[18]  Jae-Seob Kwak,et al.  Application of wavelet transform technique to detect tool failure in turning operations , 2006 .

[19]  Ryutaro Tanaka,et al.  Effect of different features to drill-wear prediction with back propagation neural network , 2014 .

[20]  E. García-Plaza,et al.  Online diagnosis and monitoring of roundness defects in CNC machining processes , 2010 .

[21]  Carlos Henrique Lauro,et al.  Monitoring and processing signal applied in machining processes – A review , 2014 .

[22]  Dragos Axinte,et al.  On monitoring chip formation, penetration depth and cutting malfunctions in bone micro-drilling via acoustic emission , 2016 .

[23]  Ruxu Du,et al.  FEATURE EXTRACTION AND ASSESSMENT USING WAVELET PACKETS FOR MONITORING OF MACHINING PROCESSES , 1996 .

[24]  Pierre Dehombreux,et al.  Tool wear monitoring by machine learning techniques and singular spectrum analysis , 2011 .

[25]  David R. Burton,et al.  Wavelet strategy for surface roughness analysis and characterisation , 2001 .

[26]  J. Paulo Davim,et al.  Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts , 2007 .

[27]  Roberto Teti,et al.  Signal processing and pattern recognition for surface roughness assessment in multiple sensor monitoring of robot-assisted polishing , 2017 .

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

[29]  Krzysztof Jemielniak,et al.  Chip form monitoring through advanced processing of cutting force sensor signals , 2006 .

[30]  Sagar Kamarthi,et al.  FOURIER AND WAVELET TRANSFORM FOR FLANK WEAR ESTIMATION — A COMPARISON , 1997 .

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

[32]  Uday S. Dixit,et al.  Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process , 2003 .

[33]  Yaguo Lei,et al.  Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform , 2013 .

[34]  M. Lalor,et al.  Frequency normalised wavelet transform for surface roughness analysis and characterisation , 2002 .

[35]  D. R. Salgado,et al.  In-process surface roughness prediction system using cutting vibrations in turning , 2009 .

[36]  Roberto Teti,et al.  Residual Stress Assessment in Inconel 718 Machining Through Wavelet Sensor Signal Analysis and Sensor Fusion Pattern Recognition , 2013 .

[37]  Pramod Kumar Jain,et al.  In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals , 2013 .

[38]  Khalil Khalili,et al.  Determination of Tool Wear in Turning Process Using Undecimated Wavelet Transform and Textural Features , 2015 .

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

[40]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[41]  D. R. Salgado,et al.  Analysis of the structure of vibration signals for tool wear detection , 2008 .

[42]  Dongfeng Shi,et al.  Development of an online machining process monitoring system : Application in hard turning , 2007 .

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

[44]  Roberto Teti,et al.  Cognitive Decision Making in Multiple Sensor Monitoring of Robot Assisted Polishing , 2015 .

[45]  Ossama B. Abouelatta,et al.  Surface roughness prediction based on cutting parameters and tool vibrations in turning operations , 2001 .