Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.

[1]  Xiaoli Li,et al.  Discrete wavelet transform for tool breakage monitoring , 1999 .

[2]  K. Mori,et al.  Prediction of small drill bit breakage by wavelet transforms and linear discriminant functions , 1999 .

[3]  Y. S. Tarng,et al.  Application of the Discrete Wavelet Transform to the Monitoring of Tool Failure in End Milling Using the Spindle Motor Current , .

[4]  Ibrahim N. Tansel,et al.  Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN) , 1995 .

[5]  C. H. Chen,et al.  Multiresolution wavelet analysis based feature extraction for neural network classification , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[8]  Peter D. Welch,et al.  The Fast Fourier Transform and Its Applications , 1969 .

[9]  Ibrahim N. Tansel,et al.  Tool wear estimation in micro-machining.: Part II: neural-network-based periodic inspector for non-metals , 2000 .

[10]  Ji Zhao,et al.  Research on a fault monitoring system in free-form surface CNC machining based on wavelet analysis , 2002 .

[11]  Nagayoshi Kasashima,et al.  Online Failure Detection in Face Milling Using Discrete Wavelet Transform , 1995 .

[12]  Xiaoli Li,et al.  Tool wear detection with fuzzy classification and wavelet fuzzy neural network , 1999 .

[13]  Fritz Klocke,et al.  Application of a wavelet-based signal analysis for evaluating the tool state in cutting operations , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[14]  Toshiyuki Obikawa,et al.  Monitoring of flank wear of coated tools in high speed machining with a neural network ART2 , 2004 .

[15]  D. E. Goldberg,et al.  Optimization and Machine Learning , 2022 .

[16]  Gaoyong Luo,et al.  Real-Time Condition Monitoring by Significant and Natural Frequencies Analysis of Vibration Signal with Wavelet Filter and Autocorrelation Enhancement , 2000 .

[17]  Kazuo Mori,et al.  Detection of Tool Failure in Milling Process Using Discrete Wavelet Transform. , 1995 .

[18]  Yung C. Shin,et al.  On-Line Chatter Detection Using Wavelet-Based Parameter Estimation , 2000, Manufacturing Engineering.

[19]  Bean Yin Lee,et al.  Application of the Discrete Wavelet Transform to the Monitoring of Tool Failure in End Milling Using the Spindle Motor Current , 1999 .

[20]  Hiroshi Ota,et al.  Application of Wavelet Analysis to Monitoring of Cutting Conditions in Milling , 1994 .

[21]  Vikram M. Gadre,et al.  Accuracy improvement for CNC system using wavelet-neural networks , 2000, Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482).

[22]  Abhijit Chandra,et al.  Tool wear monitoring in ramp cuts in end milling using the wavelet transform , 2004 .

[23]  Geok Soon Hong,et al.  A new tool life criterion for tool condition monitoring using a neural network , 1995 .

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

[25]  D. Carroll.  Chemical laser modeling with genetic algorithms , 1996 .

[26]  Geok Soon Hong,et al.  Multi-Category Classification of Tool Conditions Using Wavelet Packets and ART2 Network , 1998 .

[27]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

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

[29]  Chunxin Yang,et al.  Evaluation of the wavelet transform method for machined surface topography 2: fractal characteristic analysis , 2003 .

[30]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[31]  F. S. Bai,et al.  Wavelets Analysis on the Feature Collection and the Influencing Factors of the Surface Roughness in Vibratory Machining , 2003, WAA.

[32]  Mac A. Cody.  The fast wavelet transform , 1992 .

[33]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[34]  Ibrahim N. Tansel,et al.  Tool wear estimation in micro-machining.: Part I: tool usage–cutting force relationship , 2000 .

[35]  Ratna Babu Chinnam,et al.  On-line reliability estimation of individual components using neural networks and wavelets , 1998 .

[36]  Ingrid Daubechies,et al.  Orthonormal Bases of Wavelets with Finite Support — Connection with Discrete Filters , 1989 .

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

[38]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[39]  S. Grossberg,et al.  ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.