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2022 | OriginalPaper | Chapter

6. Signal Processing for Machining Process Modeling and Condition Monitoring

Author : Kunpeng Zhu

Published in: Smart Machining Systems

Publisher: Springer International Publishing

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Abstract

Signal processing plays an important role in manufacturing automation and industrial control. In CNC machining, by utilizing the monitored information from the platform to direct the further actions, the signal processing bridges the gap of human instruction and full automation.

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Literature
1.
go back to reference Oppenheim AV, Schafer RW (2010) Discrete-time signal processing, 3rd edn. Prentice Hall Oppenheim AV, Schafer RW (2010) Discrete-time signal processing, 3rd edn. Prentice Hall
2.
go back to reference Bendat JS, Piersol AG (2010) Random data: analysis and measurement procedures, 4th edn. Wiley Bendat JS, Piersol AG (2010) Random data: analysis and measurement procedures, 4th edn. Wiley
3.
go back to reference Woyczynski W (2011) A first course in statistics for signal analysis, 2nd edn. Springer Woyczynski W (2011) A first course in statistics for signal analysis, 2nd edn. Springer
4.
go back to reference Cohen L (1989) Time–frequency distribution—a review. Proc IEEE 77:941–981 Cohen L (1989) Time–frequency distribution—a review. Proc IEEE 77:941–981
5.
go back to reference Candy JV (2006) Model-based signal processing. Wiley Candy JV (2006) Model-based signal processing. Wiley
6.
go back to reference Oppenheim AV, Willsky AS, Nawab SN (1996) Signals and systems, 2nd edn. Prentice Hall Oppenheim AV, Willsky AS, Nawab SN (1996) Signals and systems, 2nd edn. Prentice Hall
7.
go back to reference Duhamel P, Vetterli M (1990) Fast Fourier transforms: a tutorial review and a state of the art. Signal Process 19:259–299 Duhamel P, Vetterli M (1990) Fast Fourier transforms: a tutorial review and a state of the art. Signal Process 19:259–299
8.
go back to reference Kammler DW (2008) A first course in Fourier analysis. Cambridge University Press Kammler DW (2008) A first course in Fourier analysis. Cambridge University Press
9.
go back to reference Allen RL, Mills DW (2004) Signal analysis time, frequency, scale, and structure. Wiley Allen RL, Mills DW (2004) Signal analysis time, frequency, scale, and structure. Wiley
10.
go back to reference Kamarthi SV, Pittner S (1997) Fast Fourier and wavelet transform for flank wear estimation–a comparison. Mech Syst Signal Process 11:791–809 Kamarthi SV, Pittner S (1997) Fast Fourier and wavelet transform for flank wear estimation–a comparison. Mech Syst Signal Process 11:791–809
11.
go back to reference Dowling MJ (1993) Application of non-stationary analysis to machinery monitoring, ICASSP-93. In: IEEE international conference on acoustics, speech, and signal processing, pp 59–62 Dowling MJ (1993) Application of non-stationary analysis to machinery monitoring, ICASSP-93. In: IEEE international conference on acoustics, speech, and signal processing, pp 59–62
12.
go back to reference Dimla SDE, Lister PM (2000) On-line metal cutting tool condition monitoring I: force and vibration analyses. Int J Mach Tools Manuf 40:739–768 Dimla SDE, Lister PM (2000) On-line metal cutting tool condition monitoring I: force and vibration analyses. Int J Mach Tools Manuf 40:739–768
13.
go back to reference Elbestawi MA, Papazafiriou TA, Du RX (1991) In-process monitoring of tool wear in milling using cutting force signature. Int J Mach Tools Manuf 31(1):55–73 Elbestawi MA, Papazafiriou TA, Du RX (1991) In-process monitoring of tool wear in milling using cutting force signature. Int J Mach Tools Manuf 31(1):55–73
14.
go back to reference Gong W, Obikawa T, Shirakashi T (1997) Monitoring of tool wear states in turning based on wavelet analysis. JSME Int J (Series C) 40:447–453 Gong W, Obikawa T, Shirakashi T (1997) Monitoring of tool wear states in turning based on wavelet analysis. JSME Int J (Series C) 40:447–453
15.
go back to reference Kwak JS, Ha MK (2004) Detection of dressing time using the grinding force signal based on the discrete wavelet decomposition. Int J Adv Manuf Technol 23(1–2):87–92 Kwak JS, Ha MK (2004) Detection of dressing time using the grinding force signal based on the discrete wavelet decomposition. Int J Adv Manuf Technol 23(1–2):87–92
16.
go back to reference Lee BY, Tarng YS (1999) Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. Int J Adv Manuf Technol 15:238–243 Lee BY, Tarng YS (1999) Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. Int J Adv Manuf Technol 15:238–243
17.
go back to reference Li X, Guan XP (2004) Time-frequency-analysis-based minor cutting edge fracture detection during end milling. Mech Syst Signal Process 18:1485–1496 Li X, Guan XP (2004) Time-frequency-analysis-based minor cutting edge fracture detection during end milling. Mech Syst Signal Process 18:1485–1496
18.
go back to reference Tansel IN, Rodriguez O, Trujillo M, Paz E, Li W (1998) Micro-end-milling—I: wear and breakage. Int J Mach Tools Manuf 38:1419–1436 Tansel IN, Rodriguez O, Trujillo M, Paz E, Li W (1998) Micro-end-milling—I: wear and breakage. Int J Mach Tools Manuf 38:1419–1436
19.
go back to reference Yesilyurt I (2006) End mill breakage detection using mean frequency analysis of scalogram. Int J Mach Tools Manuf 46(3–4):450–458 Yesilyurt I (2006) End mill breakage detection using mean frequency analysis of scalogram. Int J Mach Tools Manuf 46(3–4):450–458
20.
go back to reference Zhu KP, Hong GS, Wong YS, Wang WH (2008) Cutting force denoising in micro-milling tool condition monitoring. Int J Prod Res 46(16):4391–4408 Zhu KP, Hong GS, Wong YS, Wang WH (2008) Cutting force denoising in micro-milling tool condition monitoring. Int J Prod Res 46(16):4391–4408
21.
go back to reference Franks LE (1969) Signal theory. Prentice-Hall, USA Franks LE (1969) Signal theory. Prentice-Hall, USA
22.
go back to reference Moon TK, Stirling WC (2000) Mathematical methods and algorithms for signal processing. Prentice Hall, USA Moon TK, Stirling WC (2000) Mathematical methods and algorithms for signal processing. Prentice Hall, USA
23.
go back to reference Candès E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory 52(2):489–509 Candès E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory 52(2):489–509
24.
go back to reference Donoho DL, Compressed sensing. IEEE Trans Inform Theory 52(4):1289–1306 Donoho DL, Compressed sensing. IEEE Trans Inform Theory 52(4):1289–1306
25.
go back to reference Lim JS, Oppenheim AV (eds) (1988) Advanced topics in signal processing. Prentice-Hall Lim JS, Oppenheim AV (eds) (1988) Advanced topics in signal processing. Prentice-Hall
26.
go back to reference Mallat SG (2008) A wavelet tour of signal processing: the sparse way, 3rd edn. Academic Press Mallat SG (2008) A wavelet tour of signal processing: the sparse way, 3rd edn. Academic Press
27.
go back to reference Mallat SG (1989) A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Mach Intell 11(7):674–693 Mallat SG (1989) A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Mach Intell 11(7):674–693
28.
go back to reference Wu Y, Du R (1996) Feature extraction and assessment using wavelet packets for monitoring of machining process. Mech Syst Signal Process 10(1):29–53 Wu Y, Du R (1996) Feature extraction and assessment using wavelet packets for monitoring of machining process. Mech Syst Signal Process 10(1):29–53
29.
go back to reference Mori K, Kasashima N, Fu JC, Muto K (1999) Prediction of small drill bit breakage by wavelet transforms and linear discriminant functions. Int J Mach Tools Manuf 39:1471–1484 Mori K, Kasashima N, Fu JC, Muto K (1999) Prediction of small drill bit breakage by wavelet transforms and linear discriminant functions. Int J Mach Tools Manuf 39:1471–1484
30.
go back to reference Yoon MC, Chin DH (2005) Cutting force monitoring in the end milling operation for chatter detection. IMechE 2005 Proc IMechE, vol 219. Part B J Eng Manuf 455–465 Yoon MC, Chin DH (2005) Cutting force monitoring in the end milling operation for chatter detection. IMechE 2005 Proc IMechE, vol 219. Part B J Eng Manuf 455–465
31.
go back to reference Zhu KP, Wong YS, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49(4):537–553 Zhu KP, Wong YS, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49(4):537–553
32.
go back to reference Vetterli M, Herley C (1992) Wavelets and filter banks: theory and design. IEEE Trans Signal Proc 40(9):2207–2232 Vetterli M, Herley C (1992) Wavelets and filter banks: theory and design. IEEE Trans Signal Proc 40(9):2207–2232
33.
go back to reference Wickerhauser MV, Coifman RR (1992) Entropy based methods for best basis selection. IEEE Trans Inform Theory 38(2):719–746 Wickerhauser MV, Coifman RR (1992) Entropy based methods for best basis selection. IEEE Trans Inform Theory 38(2):719–746
34.
go back to reference Abry P (1997) Ondelettes et turbulence. Multirésolutions, algorithmes de décomposition, invaria nce d’échelles. Diderot Editeur, Paris Abry P (1997) Ondelettes et turbulence. Multirésolutions, algorithmes de décomposition, invaria nce d’échelles. Diderot Editeur, Paris
37.
go back to reference Lewicki MS, Sejnowski TJ (2000) Learning overcomplete representations. Neural Comput 12(2):337–365 Lewicki MS, Sejnowski TJ (2000) Learning overcomplete representations. Neural Comput 12(2):337–365
38.
go back to reference Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609 Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609
39.
go back to reference Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer
40.
go back to reference Bruckstein AM, Donoho DL, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81 Bruckstein AM, Donoho DL, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81
41.
go back to reference Cheng H, Liu Z, Yang L, Chen X (2013) Sparse representation and learning in visual recognition: theory and applications. Signal Process 93:1408–1425 Cheng H, Liu Z, Yang L, Chen X (2013) Sparse representation and learning in visual recognition: theory and applications. Signal Process 93:1408–1425
42.
go back to reference Mallat S, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415 Mallat S, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415
43.
go back to reference Pati YC, Rezaifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 27th Asilomar conference on signals, systems and computers, vol 1, pp 40–44 Pati YC, Rezaifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 27th Asilomar conference on signals, systems and computers, vol 1, pp 40–44
44.
go back to reference Chen S, Donoho D (1995) Atomic decomposition by basis pursuit. In: SPIE international conference on wavelets. San Diego Chen S, Donoho D (1995) Atomic decomposition by basis pursuit. In: SPIE international conference on wavelets. San Diego
45.
go back to reference Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20:33–61 Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20:33–61
46.
go back to reference Gorodnitsky IF, Rao BD (1997) Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm. IEEE Trans Signal Process 600–616 Gorodnitsky IF, Rao BD (1997) Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm. IEEE Trans Signal Process 600–616
47.
go back to reference Elad M, Bruckstein AM (2002) A generalized uncertainty principle and sparse representation in pairs of bases. IEEE Trans Inform Theory 48(9):2558–2567 Elad M, Bruckstein AM (2002) A generalized uncertainty principle and sparse representation in pairs of bases. IEEE Trans Inform Theory 48(9):2558–2567
48.
go back to reference Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J Royal Stat Soc Series B 58(1):267–288 Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J Royal Stat Soc Series B 58(1):267–288
49.
go back to reference Efron B, Johnstone I, Hastie T, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407–499 Efron B, Johnstone I, Hastie T, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407–499
50.
go back to reference Engan K, Aase SO, Husoy JH (2000) Multi-frame compression: theory and design. EURASIP Signal Process 80(10):2121–2140 Engan K, Aase SO, Husoy JH (2000) Multi-frame compression: theory and design. EURASIP Signal Process 80(10):2121–2140
51.
go back to reference Aharon M, Elad M, Bruckstein AM (2006) The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans Signal Process 54(11):4311–4322 Aharon M, Elad M, Bruckstein AM (2006) The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans Signal Process 54(11):4311–4322
52.
go back to reference Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the international conference on machine learning (ICML) Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the international conference on machine learning (ICML)
Metadata
Title
Signal Processing for Machining Process Modeling and Condition Monitoring
Author
Kunpeng Zhu
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-030-87878-8_6

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