Skip to main content
SpringerLink
Log in

Application of acoustic emissions in machining processes: analysis and critical review

The International Journal of Advanced Manufacturing Technology volume 98, pages 1391–1407 (2018)Cite this article

Abstract

Monitoring and controlling of metal cutting processes is an essential task in any modern precision machining setup. The implementation of proper monitoring process leads to promising results in terms of cutting tool life, machining costs, and production rates. Several techniques have been used to detect, monitor, and analyze different parameters associated with the cutting processes such as cutting tool wear, chip breakage and fracture, chatter vibrations, and formation of built-up edge (BUE). In this work, a review study is presented to discuss the research activities using the acoustic emission (AE) signals to monitor and control various machining processes. The discussed work does not only present an investigation of the AE signals, measured variables, and AE sensor setup during machining processes, but also shows several methods used for analyzing and processing the AE signals. The work focuses on studies, which employed AE in monitoring, and analyzing some specific characteristics such as chip formation and morphology, surface quality, and tool wear evolution for different machining operations and materials.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

References

  1. Lauro CH, Brandão LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes—a review. Measurement 58:73–86

    Google Scholar 

  2. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739

    Google Scholar 

  3. Zhu K, San Wong Y, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49(7–8):537–553

    Google Scholar 

  4. García-Plaza E, Nunez PJ, Salgado DR, Cambero I, Olivenza JH, Sanz-Calcedo JG (2013) Surface finish monitoring in taper turning CNC using artificial neural network and multiple regression methods. Procedia Eng 63:599–607

    Google Scholar 

  5. Deng WJ, Li Q, Li BL, He YT, Xia W, Tang Y (2013) Study on the cutting force of cylindrical turning with novel restricted contact tools. Int J Adv Manuf Technol 69(5–8):1625–1638

    Google Scholar 

  6. Childs T (2000) Metal machining: theory and applications. Butterworth-Heinemann, Oxford

    Google Scholar 

  7. Devillez A, Dudzinski D (2007) Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers. Mech Syst Signal Process 21(1):441–456

    Google Scholar 

  8. Dimla DE (2002) The correlation of vibration signal features to cutting tool wear in a metal turning operation. Int J Adv Manuf Technol 19(10):705–713

    Google Scholar 

  9. Hegab H, Umer U, Soliman M, Kishawy HA (2018) Effects of nano-cutting fluids on tool performance and chip morphology during machining Inconel 718. Int J Adv Manuf Technol 96(1–10):3449–3458

    Google Scholar 

  10. Hegab H, Umer U, Deiab I, Kishawy H (2018) Performance evaluation of Ti–6Al–4V machining using nano-cutting fluids under minimum quantity lubrication. Int J Adv Manuf Technol (1–13)

  11. Hegab H, Kishawy HA, Gadallah MH, Umer U, Deiab I (2018) On machining of Ti-6Al-4V using multi-walled carbon nanotubes-based nano-fluid under minimum quantity lubrication. Int J Adv Manuf Technol:1–11

  12. Byrne G (1987) Thermoelectric signal characteristics and average interfacial temperatures in the machining of metals under geometrically defined conditions. Int J Mach Tools Manuf 27(2):215–224

    Google Scholar 

  13. Sivasakthivel PS, Sudhakaran R (2013) Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 67(9–12):2313–2323

    Google Scholar 

  14. Ueda T, Tanaka H, Torii A, Matsuo T (1993) Measurement of grinding temperature of active grains using infrared radiation pyrometer with optical fiber. CIRP Ann Manuf Technol 42(1):405–408

    Google Scholar 

  15. Mohamed AMO, Warkentin A, Bauer R (2012) Variable heat flux in numerical simulation of grinding temperatures. Int J Adv Manuf Technol 63(5–8):549–554

    Google Scholar 

  16. Lauro CH, Ribeiro Filho SLM (2013) Monitoring the temperature of the milling process using infrared camera. Sci Res Essays 7(23):1112–1120

    Google Scholar 

  17. Holford KM (2000) Acoustic emission—basic principles and future directions. Strain 36(2):51–54

    Google Scholar 

  18. Crouse NRC-B (1996) Acoustic emission applications [online]. Available from: https://www.ndeed.org/EducationResources/CommunityCollege/Other%20Methods/AE/AE_Applications.php

  19. Grosse CU, Ohtsu M (2008) Acoustic emission testing. Springer Science & Business Media, Berlin

    Google Scholar 

  20. Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65(1–4):371–393

    Google Scholar 

  21. Griffin JM, Diaz F, Geerling E, Clasing M, Ponce V, Taylor C et al (2017) Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals. Mech Syst Signal Process 85:1020–1034

    Google Scholar 

  22. Wang B, Liu Z (2017) Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy. J Manuf Process 27:114–125

    Google Scholar 

  23. Raj B, Jayakumar T (1991) Acoustic emission during tensile deformation and fracture in austenitic alloys. In: Acoustic emission: current practice and future directions. ASTM International, West Conshohocken

    Google Scholar 

  24. Inasaki I (1998) Application of acoustic emission sensor for monitoring machining processes. Ultrasonics 36(1–5):273–281

    Google Scholar 

  25. Bhuiyan MSH, Choudhury IA, Dahari M, Nukman Y, Dawal SZ (2016) Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement 92:208–217

    Google Scholar 

  26. Liao Z, Axinte DA (2016) On monitoring chip formation, penetration depth and cutting malfunctions in bone micro-drilling via acoustic emission. J Mater Process Technol 229:82–93

    Google Scholar 

  27. Karimi NZ, Heidary H, Minak G, Ahmadi M (2013) Effect of the drilling process on the compression behavior of glass/epoxy laminates. Compos Struct 98:59–68

    Google Scholar 

  28. Webster J, Dong WP, Lindsay R (1996) Raw acoustic emission signal analysis of grinding process. CIRP Ann Manuf Technol 45(1):335–340

    Google Scholar 

  29. Aguiar PR, Serni PJ, Dotto FR, Bianchi EC (2006) In-process grinding monitoring through acoustic emission. J Braz Soc Mech Sci Eng 28(1):118–124

    Google Scholar 

  30. Egan MD (1988) Architectural acoustics. McGraw-Hill Custom Publishing, New York

    Google Scholar 

  31. Kinsler LE, Frey AR, Coppens AB, Sanders JV (1999) In: Kinsler LE, Frey AR, Coppens AB, Sanders JV (eds) Fundamentals of acoustics. Fundamentals of acoustics, 4th edn. Wiley-VCH, Weinheim, p 560. ISBN 0-471-84789-5

    Google Scholar 

  32. Rogers LM (1979) The application of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings. Tribol Int 12(2):51–58

    Google Scholar 

  33. Hundt W, Leuenberger D, Rehsteiner F, Gygax P (1994) An approach to monitoring of the grinding process using acoustic emission (AE) technique. CIRP Ann Manuf Technol 43(1):295–298

    Google Scholar 

  34. Sutowski P, Święcik R (2018) The estimation of machining results and efficiency of the abrasive electro-discharge grinding process of Ti6Al4V titanium alloy using the high-frequency acoustic emission and force signals. Int J Adv Manuf Technol 94(1–4):1263–1282

    Google Scholar 

  35. Woulfe M (2000) On the relationship between acoustic emission and surface integrity in machining. Doctoral dissertation, PhD Thesis, University of College Dublin, Eire

  36. Hutton DV, Hu F (1999) Acoustic emission monitoring of tool wear in end-milling using time-domain averaging. J Manuf Sci Eng 121(1):8–12

    Google Scholar 

  37. Li X, Dong S, Yuan Z (1999) Discrete wavelet transform for tool breakage monitoring. Int J Mach Tools Manuf 39(12):1935–1944

    Google Scholar 

  38. Karpuschewski B, Wehmeier M, Inasaki I (2000) Grinding monitoring system based on power and acoustic emission sensors. CIRP Ann Manuf Technol 49(1):235–240

    Google Scholar 

  39. Jemielniak K (2001) Some aspects of acoustic emission signal pre-processing. J Mater Process Technol 109(3):242–247

    Google Scholar 

  40. Kuljanic E, Totis G, Sortino M (2009) Development of an intelligent multisensor chatter detection system in milling. Mech Syst Signal Process 23(5):1704–1718

    Google Scholar 

  41. Kalvoda T, Hwang YR (2010) Analysis of signals for monitoring of nonlinear and non-stationary machining processes. Sensors Actuators A Phys 161(1–2):39–45

    Google Scholar 

  42. Han Z, Jin H, Han D, Fu H (2017) ESPRIT-and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system. Int J Adv Manuf Technol 89(9–12):2731–2746

    Google Scholar 

  43. Kang MC, Kim JS, Kim JH (2001) A monitoring technique using a multi-sensor in high speed machining. J Mater Process Technol 113(1–3):331–336

    Google Scholar 

  44. Liao TW, Ting CF, Qu J, Blau PJ (2007) A wavelet-based methodology for grinding wheel condition monitoring. Int J Mach Tools Manuf 47(3–4):580–592

    Google Scholar 

  45. Liu X, Ahmad F, Yamazaki K, Mori M (2005) Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics. Int J Mach Tools Manuf 45(4–5):433–444

    Google Scholar 

  46. Rubio EM, Teti R, Baciu IL (2006) Advanced signal processing in acoustic emission monitoring systems for machining technology. In Intelligent production machines and systems. Intelligent production machines and systems: 2nd I*PROMS Virtual International. https://doi.org/10.1016/B978-008045157-2/50008-0

  47. Scheffer C, Heyns PS (2001) Wear monitoring in turning operations using vibration and strain measurements. Mech Syst Signal Process 15(6):1185–1202

    Google Scholar 

  48. Grzesik W, Brol S (2009) Wavelet and fractal approach to surface roughness characterization after finish turning of different workpiece materials. J Mater Process Technol 209(5):2522–2531

    Google Scholar 

  49. Kasashima N, Mori K, Ruiz GH, Taniguchi N (1995) Online failure detection in face milling using discrete wavelet transform. CIRP Ann Manuf Technol 44(1):483–487

    Google Scholar 

  50. Xu C, Liu Z, Luo W (2009) A frequency band energy analysis of vibration signals for tool condition monitoring. In Measuring technology and mechatronics automation, 2009. ICMTMA'09. International Conference on IEEE 1:385–388

  51. Feldman M (2011) Hilbert transform in vibration analysis. Mech Syst Signal Process 25(3):735–802

    Google Scholar 

  52. Cao H, Lei Y, He Z (2013) Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform. Int J Mach Tools Manuf 69:11–19

    Google Scholar 

  53. Marinescu I, Axinte D (2009) A time–frequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously. Int J Mach Tools Manuf 49(1):53–65

    Google Scholar 

  54. Simeone A, Segreto T, Teti R (2013) Residual stress condition monitoring via sensor fusion in turning of Inconel 718. Procedia CIRP 12:67–72

    Google Scholar 

  55. Lange JH, Abu-Zahra NH (2002) Tool chatter monitoring in turning operations using wavelet analysis of ultrasound waves. Int J Adv Manuf Technol 20(4):248–254

    Google Scholar 

  56. Velayudham A, Krishnamurthy R, Soundarapandian T (2005) Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform. Mater Sci Eng A 412(1–2):141–145

    Google Scholar 

  57. Yang Z, Yu Z, Xie C, Huang Y (2014) Application of Hilbert–Huang transform to acoustic emission signal for burn feature extraction in surface grinding process. Measurement 47:14–21

    Google Scholar 

  58. Tarng YS, Lee BY (1999) Amplitude demodulation of the induction motor current for the tool breakage detection in drilling operations. Robot Comput Integr Manuf 15(4):313–318

    Google Scholar 

  59. Zhong W, Zhao D, Wang X (2010) A comparative study on dry milling and little quantity lubricant milling based on vibration signals. Int J Mach Tools Manuf 50(12):1057–1064

    Google Scholar 

  60. Bhaskaran J, Murugan M, Balashanmugam N, Chellamalai M (2012) Monitoring of hard turning using acoustic emission signal. J Mech Sci Technol 26(2):609–615

    Google Scholar 

  61. Choi Y, Narayanaswami R, Chandra A (2004) Tool wear monitoring in ramp cuts in end milling using the wavelet transform. Int J Adv Manuf Technol 23(5–6):419–428

    Google Scholar 

  62. Bassiuny AM, Li X (2007) Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator. Int J Mach Tools Manuf 47(6):1011–1020

    Google Scholar 

  63. Li Z, Wang G, He G (2018) Surface quality monitoring based on time-frequency features of acoustic emission signals in end milling Inconel-718. Int J Adv Manuf Technol (1–9)

  64. Liang SY, Dornfeld DA (1989) Tool wear detection using time series analysis of acoustic emission. J Eng Ind 111(3):199–205

    Google Scholar 

  65. Mukhopadhyay CK, Jayakumar T, Raj B, Venugopal S (2012) Statistical analysis of acoustic emission signals generated during turning of a metal matrix composite. J Braz Soc Mech Sci Eng 34(2):145–154

    Google Scholar 

  66. Alexandre FA, Lopes WN, Dotto FRL, Ferreira FI, Aguiar PR, Bianchi EC, Lopes JC (2018) Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model. Int J Adv Manuf Technol:1–13

  67. Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21(1):466–479

    Google Scholar 

  68. Kwak JS, Ha MK (2004) Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. J Mater Process Technol 147(1):65–71

    Google Scholar 

  69. Uehara K, Kanda Y (1984) Identification of chip formation mechanism through acoustic emission measurements. CIRP Ann Manuf Technol 33(1):71–74

    Google Scholar 

  70. Bifano TG, Yi Y (1992) Acoustic emission as an indicator of material-removal regime in glass micro-machining. Precis Eng 14(4):219–228

    Google Scholar 

  71. Kakade S, Vijayaraghavan L, Krishnamurthy R (1994) In-process tool wear and chip-form monitoring in face milling operation using acoustic emission. J Mater Process Technol 44(3–4):207–214

    Google Scholar 

  72. Barry J, Byrne G (2001) Study on acoustic emission in machining hardened steels part 1: acoustic emission during saw-tooth chip formation. Proc Inst Mech Eng B J Eng Manuf 215(11):1549–1559

    Google Scholar 

  73. Barry J, Byrne G, Lennon D (2001) Observations on chip formation and acoustic emission in machining Ti–6Al–4V alloy. Int J Mach Tools Manuf 41(7):1055–1070

    Google Scholar 

  74. Barry J, Byrne G (2001) Study on acoustic emission in machining hardened steels part 2: acoustic emission during continuous chip formation with a non-overlapping cutting arrangement. Proc Inst Mech Eng B J Eng Manuf 215(11):1561–1570

    Google Scholar 

  75. Mian AJ, Driver N, Mativenga PT (2011) Estimation of minimum chip thickness in micro-milling using acoustic emission. Proc Inst Mech Eng B J Eng Manuf 225(9):1535–1551

    Google Scholar 

  76. Mian AJ, Driver N, Mativenga PT (2011) Chip formation in microscale milling and correlation with acoustic emission signal. Int J Adv Manuf Technol 56(1–4):63–78

    Google Scholar 

  77. Bhuiyan MSH, Choudhury IA, Nukman Y (2012) An innovative approach to monitor the chip formation effect on tool state using acoustic emission in turning. Int J Mach Tools Manuf 58:19–28

    Google Scholar 

  78. Hase A, Wada M, Koga T, Mishina H (2014) The relationship between acoustic emission signals and cutting phenomena in turning process. Int J Adv Manuf Technol 70(5–8):947–955

    Google Scholar 

  79. Dhale A (2013) Acoustic emission method for selection of optimum cutting parameters in turning using different fluids: a review. IJIRD 2(7)

  80. Prakash M, Kanthababu M, Rajurkar KP (2015) Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy. Int J Adv Manuf Technol 77(5–8):1499–1511

    Google Scholar 

  81. Filippov AV, Rubtsov VE, Tarasov SY, Podgornykh OA, Shamarin NN (2018) Detecting transition to chatter mode in peakless tool turning by monitoring vibration and acoustic emission signals. Int J Adv Manuf Technol 95(1–4):157–169

    Google Scholar 

  82. Wertheim R, Ben-Hanan U, Ihlenfeldt S, Stoll A, Treppe F, Wabner M (2012) Acoustic emission for controlling drill position in fiber-reinforced plastic and metal stacks. CIRP Ann Manuf Technol 61(1):75–78

    Google Scholar 

  83. Diniz AE, Liu JJ, Dornfeld DA (1992) Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning. Wear 152(2):395–407

    Google Scholar 

  84. Beggan C, Woulfe M, Young P, Byrne G (1999) Using acoustic emission to predict surface quality. Int J Adv Manuf Technol 15(10):737–742

    Google Scholar 

  85. Susič E, Grabec I (2000) Characterization of the grinding process by acoustic emission. Int J Mach Tools Manuf 40(2):225–238

    Google Scholar 

  86. de Oliveira JG, Dornfeld DA (2001) Application of AE contact sensing in reliable grinding monitoring. CIRP Ann Manuf Technol 50(1):217–220

    Google Scholar 

  87. Guo YB, Ammula SC (2005) Real-time acoustic emission monitoring for surface damage in hard machining. Int J Mach Tools Manuf 45(14):1622–1627

    Google Scholar 

  88. Pittner, S., Kamarthi, S. V., Wongsiripatanakul P, Wongvasu N (2000) Correlation between acoustic emission statistics and surface roughness in finish turning. Citeseerx

  89. Dornfeld DA, Oliveira JD, Lee D, Valente CMO (2003) Analysis of tool and workpiece interaction in diamond turning using graphical analysis of acoustic emission. CIRP Ann Manuf Technol 52(1):479–482

    Google Scholar 

  90. Bourne KA, Jun MB, Kapoor SG, DeVor RE (2008) An acoustic emission-based method for determining contact between a tool and workpiece at the microscale. J Manuf Sci Eng 130(3):031101

    Google Scholar 

  91. Min S, Lidde J, Raue N, Dornfeld D (2011) Acoustic emission based tool contact detection for ultra-precision machining. CIRP Ann Manuf Technol 60(1):141–144

    Google Scholar 

  92. Pawade RS, Joshi SS (2012) Analysis of acoustic emission signals and surface integrity in the high-speed turning of Inconel 718. Proc Inst Mech Eng B J Eng Manuf 226(1):3–27

    Google Scholar 

  93. Marinescu I, Axinte DA (2008) A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. Int J Mach Tools Manuf 48(10):1148–1160

    Google Scholar 

  94. Gok A, Gologlu C, Demirci HI, Kurt M (2012) Determination of surface qualities on inclined surface machining with acoustic sound pressure. Stroj Vestn-J Mech E 58(10):587–597

    Google Scholar 

  95. Gaja H, Liou F (2016) Automatic detection of depth of cut during end milling operation using acoustic emission sensor. Int J Adv Manuf Technol 86(9–12):2913–2925

    Google Scholar 

  96. Reddy TS, Reddy CE (2010) Real time monitoring of surface roughness by acoustic emissions in CNC turning. JESTR 3(1)

  97. Haglund AJ, Kishawy HA, Rogers RJ (2008) An exploration of friction models for the chip–tool interface using an arbitrary Lagrangian–Eulerian finite element model. Wear 265(3–4):452–460

    Google Scholar 

  98. Kishawy HA, Pang L, Balazinski M (2011) Modeling of tool wear during hard turning with self-propelled rotary tools. Int J Mech Sci 53(11):1015–1021

    Google Scholar 

  99. Kishawy HA, Wilcox J (2003) Tool wear and chip formation during hard turning with self-propelled rotary tools. Int J Mach Tools Manuf 43(4):433–439

    Google Scholar 

  100. Li L, Kishawy HA (2006) A model for cutting forces generated during machining with self-propelled rotary tools. Int J Mach Tools Manuf 46(12–13):1388–1394

    Google Scholar 

  101. Kishawy HA, Li L, El-Wahab AI (2006) Prediction of chip flow direction during machining with self-propelled rotary tools. Int J Mach Tools Manuf 46(12–13):1680–1688

    Google Scholar 

  102. Diei EN, Dornfeld DA (1987) A model of tool fracture generated acoustic emission during machining. J Eng Ind 109(3):227–233

    Google Scholar 

  103. Diei EN, Dornfeld DA (1987) Acoustic emission from the face milling process—the effects of process variables. J Eng Ind 109(2):92–99

    Google Scholar 

  104. Heiple CR, Carpenter SH, Armentrout DL, McManigle AP (1994) Acoustic emission from single point machining: source mechanisms and signal changes with tool wear. Mater Eval (United States) 52(5)

  105. Ravindra HV, Srinivasa YG, Krishnamurthy R (1997) Acoustic emission for tool condition monitoring in metal cutting. Wear 212(1):78–84

    Google Scholar 

  106. Cho SS, Komvopoulos K (1997) Correlation between acoustic emission and wear of multi-layer ceramic coated carbide tools. J Manuf Sci Eng 119(2):238–246

    Google Scholar 

  107. Dolinšek S, Kopač J (1999) Acoustic emission signals for tool wear identification. Wear 225:295–303

    Google Scholar 

  108. Kopač J, Šali S (2001) Tool wear monitoring during the turning process. J Mater Process Technol 113(1–3):312–316

    Google Scholar 

  109. Pai PS, Rao PR (2002) Acoustic emission analysis for tool wear monitoring in face milling. Int J Prod Res 40(5):1081–1093

    MATH  Google Scholar 

  110. Giriraj, B., Raja, V. P., Gandhinadhan R, Ganeshkumar R (2006) Prediction of tool wear in high speed machining using acoustic emission technique and neural network. Indian Journal of Engineering Materials & Materials Sciences 13(1):275–280

  111. Arul S, Vijayaraghavan L, Malhotra SK (2007) Online monitoring of acoustic emission for quality control in drilling of polymeric composites. J Mater Process Technol 185(1–3):184–190

    Google Scholar 

  112. Jemielniak K, Arrazola PJ (2008) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1(2):97–102

    Google Scholar 

  113. Sundaram S, Senthilkumar P, Kumaravel A, Manoharan N (2008) Study of flank wear in single point cutting tool using acoustic emission sensor techniques. APRN:JEAS 3(4):32–36

  114. Gómez MP, Hey AM, Ruzzante JE, D’Attellis CE (2010) Tool wear evaluation in drilling by acoustic emission. Phys Procedia 3(1):819–825

    Google Scholar 

  115. Patra K (2011) Acoustic emission based tool condition monitoring system in drilling. In: Proceedings of the world congress on engineering, vol 3, pp 6–8

    Google Scholar 

  116. Prakash M, Kanthababu M (2013) In-process tool condition monitoring using acoustic emission sensor in microendmilling. Mach Sci Technol 17(2):209–227

    Google Scholar 

  117. Ai CS, Sun YJ, He GW, Ze XB, Li W, Mao K (2012) The milling tool wear monitoring using the acoustic spectrum. Int J Adv Manuf Technol 61(5–8):457–463

    Google Scholar 

  118. Bhuiyan MSH, Choudhury IA, Nukman Y (2012) Tool condition monitoring using acoustic emission and vibration signature in turning. In Proceedings of the world congress on engineering, vol 3, pp 1–5

  119. Kosaraju S, Anne VG, Popuri BB (2013) Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling. Int J Adv Manuf Technol 67(5–8):1947–1954

    Google Scholar 

  120. Tamizharasan T, Barnabas JK, Pakkirisamy V (2012) Optimization of turning parameters by using design of experiments and simulated annealing algorithm based on audible acoustic emission signals. Proc Inst Mech Eng B J Eng Manuf 226(7):1159–1173

    Google Scholar 

  121. Moia DFG, Thomazella IH, Aguiar PR, Bianchi EC, Martins CHR, Marchi M (2015) Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks. J Braz Soc Mech Sci Eng 37(2):627–640

    Google Scholar 

  122. Olufayo O, Abou-El-Hossein K (2015) Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel. Int J Adv Manuf Technol 81(1–4):39–51

    Google Scholar 

  123. Seemuang N, McLeay T, Slatter T (2016) Using spindle noise to monitor tool wear in a turning process. Int J Adv Manuf Technol 86(9–12):2781–2790

    Google Scholar 

  124. Uekita M, Takaya Y (2017) Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals. Int J Adv Manuf Technol 89(1–4):65–75

    Google Scholar 

  125. Yiming M, Zhonghua Y, Zhensheng Y (2017) Experimental investigation of correlation between attrition wear and features of acoustic emission signals in single-grit grinding. Int J Adv Manuf Technol 93(5–8):2275–2287

    Google Scholar 

  126. Badger J, Murphy S, O'Donnell GE (2018) Acoustic emission in dressing of grinding wheels: AE intensity, dressing energy, and quantification of dressing sharpness and increase in diamond wear-flat size. Int J Mach Tools Manuf 125:11–19

    Google Scholar 

  127. Boaron A, Weingaertner WL (2018) Dynamic in-process characterization method based on acoustic emission for topographic assessment of conventional grinding wheels. Wear 406:218–229

    Google Scholar 

Download references

Funding

The authors thank the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP# 0059.

Author information

Authors and Affiliations

  1. Machining Research Laboratory, UOIT, Oshawa, Ontario, Canada

    H. A. Kishawy & H. Hegab

  2. Mechanical Design and Production Engineering Department, Cairo University, Giza, Egypt

    H. Hegab

  3. Advanced Manufacturing Institute, King Saud University, Riyadh, Saudi Arabia

    U. Umer

  4. Aeroacoustic and Noise Control Laboratory, UOIT, Oshawa, Ontario, Canada

    A. Mohany

Authors
  1. H. A. Kishawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Hegab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. U. Umer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Mohany
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. A. Kishawy.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kishawy, H.A., Hegab, H., Umer, U. et al. Application of acoustic emissions in machining processes: analysis and critical review. Int J Adv Manuf Technol 98, 1391–1407 (2018). https://doi.org/10.1007/s00170-018-2341-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-2341-y

Keywords

search

Navigation