One of the major challenges in grinding is to conciliate the material removal rate with components free from damages. Due to the poor thermal conductivity of conventional grinding wheels, most of the heat generated during grinding is transferred to the workpiece surface and subsurface, which can cause thermal damages and impair the performance in service of machined components. Hence, it is very important to monitor the grinding process to ensure the quality of the machined parts. Thus, this work presents an innovative study comparing two indirect monitoring methods in monitoring surface integrity of steel during grinding: the acoustic emission technique and the electromechanical impedance technique. Worktable speed and radial depth of cut were used as input parameters. Visual inspection and scanning electron microscopy images of ground surfaces as well as microhardness were the output variables used to assess surface integrity and to establish a relationship with the acoustic emission and electromechanical impedance techniques. Since the acoustic emission signals are non-stationary in nature, these signals were analyzed in the time–frequency domain by applying the short-time Fourier transform and the continuous wavelet transform. The root mean square deviation index was extracted as feature from the acoustic emission and electromechanical impedance signals. Results showed that both techniques presented similar results. The root mean square deviation index showed a good correlation with alterations in surface integrity under the conditions investigated.
MarinescuIDHitchinerMUhlmannE, et al. Handbook of machining with grinding wheels. Boca Raton, FL: CRC Press, 2006.
2.
NeslušanMČížekJKolaříkK, et al. Monitoring of grinding burn via Barkhausen noise emission in case-hardened steel in large-bearing production. J Mater Process Tech2017; 240: 104–117.
3.
MalkinSGuoC.Thermal analysis of grinding. CIRP Ann: Manuf Techn2007; 56: 760–782.
4.
YangZWuHYuZ, et al. A non-destructive surface burn detection method for ferrous metals based on acoustic emission and ensemble empirical mode decomposition: from laser simulation to grinding process. Meas Sci Technol2014; 25: 035602.
5.
XiangQHeYHouT.An exploration of surface integrity remanufacturing for aeroengine components. Front Eng Manag2016; 3: 107–114.
6.
NeslušanMMičietováAČillikováM.Monitoring of surface burn after grinding larger bearing rings. AIP Conf Proc2014; 1581: 1264–1271.
7.
ThanedarADongreGGSinghR, et al. Surface integrity investigation including grinding burns using Barkhausen noise (BNA). J Manuf Process2017; 30: 226–240.
8.
RibeiroDMSAguiarPRFabianoLFG, et al. Spectra measurements using piezoelectric diaphragms to detect burn in grinding process. IEEE T Instrum Meas2017; 66: 3052–3063.
9.
TetiRJemielniakKO’DonnellG, et al. Advanced monitoring of machining operations. CIRP Ann: Manuf Techn2010; 59: 717–739.
10.
YünlüLÇolakOKurbanoǧluC.Taguchi DOE analysis of surface integrity for high pressure jet assisted machining of Inconel 718. Proc CIRP2014; 13: 333–338.
11.
YangZYuZ.Experimental study of burn classification and prediction using indirect method in surface grinding of AISI 1045 steel. Int J Adv Manuf Tech2013; 68: 2439–2449.
12.
YangZYuZWuH, et al. Laser-induced thermal damage detection in metallic materials via acoustic emission and ensemble empirical mode decomposition. J Mater Process Tech2014; 214: 1617–1626.
13.
DiasEAPereiraFBRibeiro FilhoSLM, et al. Monitoring of through-feed centreless grinding processes with acoustic emission signals. Measurement2016; 94: 71–79.
14.
HeBWeiCDingS, et al. A survey of methods for detecting metallic grinding burn. Measurement2019; 134: 426–439.
15.
GuB-PYangZ-SPanL, et al. Evolution of the microstructure, mechanical properties, and high-order modal characteristics of AISI 1045 steel subjected to a simulative environment of surface grinding burn. Int J Adv Manuf Tech2016; 82: 253–263.
16.
KwakJSHaMK.Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. J Mater Process Tech2004; 147: 65–71.
17.
AguiarPRSerniPJADottoFRL, et al. In-process grinding monitoring through acoustic emission. J Braz Soc Mech Sci2006; 28: 118–124.
18.
WangZWillettPde AguiarPR, et al. Neural network detection of grinding burn from acoustic emission. Int J Mach Tool Manu2001; 41: 283–309.
19.
LiuQChenXGindyN.Investigation of acoustic emission signals under a simulative environment of grinding burn. Int J Mach Tool Manu2006; 46: 284–292.
20.
LiuQChenXGindyN.Fuzzy pattern recognition of AE signals for grinding burn. Int J Mach Tool Manu2005; 45: 811–818.
21.
GaoZLinJWangX, et al. Grinding burn detection based on cross wavelet and wavelet coherence analysis by acoustic emission signal. Chin J Mech Eng2019; 32: 68.
22.
GriffinJMChenX.Multiple classification of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming. Int J Adv Manuf Tech2009; 45: 1152–1168.
23.
De Souza RabeloDSteffenVFinzi NetoRM, et al. Impedance-based structural health monitoring and statistical method for threshold-level determination applied to 2024-T3 aluminum panels under varying temperature. Struct Health Monit2017; 16: 365–381.
24.
Finzi NetoRMSteffenVRadeDA, et al. A low-cost electromechanical impedance-based SHM architecture for multiplexed piezoceramic actuators. Struct Health Monit2011; 10: 391–402.
25.
MarchiMBaptistaFGde AguiarPR, et al. Grinding process monitoring based on electromechanical impedance measurements. Meas Sci Technol2015; 26: 045601.
26.
Da SilvaRBFerreiraFIBaptistaFG, et al. Electromechanical impedance (EMI) technique as alternative to monitor workpiece surface damages after the grinding operation. Int J Adv Manuf Tech2018; 98: 2429–2438.
27.
JianmingSYongxiangLGongW, et al. Milling tool wear monitoring through time-frequency analysis of sensory signals. In: Proceedings of the 2016 IEEE international conference on prognostics and health management (ICPHM), Ottawa, ON, Canada, 20–22 June 2016.
28.
WangSYAzizTZSteinJF, et al. Time–frequency analysis of transient neuromuscular events: dynamic changes in activity of the subthalamic nucleus and forearm muscles related to the intermittent resting tremor. J Neurosci Meth2005; 145: 151–158.
29.
RotherAJelaliMSöffkerD.A brief review and a first application of time-frequency-based analysis methods for monitoring of strip rolling mills. J Process Contr2015; 35: 65–79.
30.
LeeBYTarngYS.Milling cutter breakage detection by the discrete wavelet transform. Mechatronics1999; 9: 225–234.
31.
YangZYuZ.Grinding wheel wear monitoring based on wavelet analysis and support vector machine. Int J Adv Manuf Tech2012; 62: 107–121.
32.
MeyerY.Wavelets and operators. In: BerksonERPeckNTUhlJ (eds) Analysis at Urbana, vol. 1. Cambridge: Cambridge University Press, 1989, pp. 256–365.
33.
WuYDuR.Feature extraction and assessment using wavelet packets for monitoring of machining processes. Mech Syst Signal Pr1996; 10: 29–53.
34.
MenegazGLTsurutaKMFinzi NetoRM, et al. Use of the electromechanical impedance method in the detection of inclusions: application to mammary tumors. Struct Health Monit. Epub ahead of print 22January2019. DOI: 10.1177/1475921718825138.
35.
RoweWB. Principles of modern grinding technology. 2nd ed.Amsterdam: Elsevier, 2013.
36.
SoepangkatBOPAgustinHCKSubiyantoH.An investigation of force, surface roughness and chip in surface grinding of SKD 11 tool steel using minimum quantity lubrication-MQL technique. AIP Conf Proc2017; 1855: 020014.
37.
ZhaoBDingW-FXuJ-H, et al. Comparative study on cutting behavior of vitrified cubic boron nitride wheel and electroplated cubic boron nitride wheel in high-speed grinding of (TiCp+ TiBw)/Ti-6Al-4V composites. Proc IMechE, Part B: J Engineering Manufacture2016; 230: 428–438.
38.
BaptistaFGFilhoJV.A new impedance measurement system for PZT-based structural health monitoring. IEEE T Instrum Meas2009; 58: 3602–3608.
39.
SettiDKirschBAurichJC.An analytical method for prediction of material deformation behavior in grinding using single grit analogy. Proc CIRP2017; 58: 263–268.
40.
MalkinSGuoC. Grinding technology: theory and application of machining with abrasives. 2nd ed.New York: Industrial Press, 2008.
41.
LiSWuYYamamuraK, et al. Improving the grindability of titanium alloy Ti–6Al–4V with the assistance of ultrasonic vibration and plasma electrolytic oxidation. CIRP Ann: Manuf Techn2017; 66: 345–348.
42.
GuoCMalkinS.Energy partition and cooling during grinding. J Manuf Process2000; 2: 151–157.
43.
BabelRKoshyPWeissM.Acoustic emission spikes at workpiece edges in grinding: origin and applications. Int J Mach Tool Manu2013; 64: 96–101.
44.
KiymikMKGülerIDizibüyükA, et al. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Med2005; 35: 603–616.
45.
BaptistaFGBudoyaDEde AlmeidaVAD, et al. An experimental study on the effect of temperature on piezoelectric sensors for impedance-based structural health monitoring. Sensors2014; 14: 1208–1227.
46.
BaptistaFGFilhoJVInmanDJ.Real-time multi-sensors measurement system with temperature effects compensation for impedance-based structural health monitoring. Struct Health Monit2012; 11: 173–186.
