Acoustic emission analysis for the detection of appropriate cutting operations in honing processes

Abstract

In the present paper, acoustic emission was studied in honing experiments obtained with different abrasive densities, 15, 30, 45 and 60. In addition, 2D and 3D roughness, material removal rate and tool wear were determined. In order to treat the sound signal emitted during the machining process, two methods of analysis were compared: Fast Fourier Transform (FFT) and Hilbert Huang Transform (HHT). When density 15 is used, the number of cutting grains is insufficient to provide correct cutting, while clogging appears with densities 45 and 60. The results were confirmed by means of treatment of the sound signal. In addition, a new parameter S was defined as the relationship between energy in low and high frequencies contained within the emitted sound. The selected density of 30 corresponds to S values between 0.1 and 1. Correct cutting operations in honing processes are dependent on the density of the abrasive employed. The density value to be used can be selected by means of measurement and analysis of acoustic emissions during the honing operation. Thus, honing processes can be monitored without needing to stop the process.

Effect of Grain Size and Density of Abrasive on Surface Roughness, Material Removal Rate and Acoustic Emission Signal in Rough Honing Processes

“…The results of the present paper show that, as a general trend, roughness, material removal rate, and tool wear increase with grain size and density as expected [41][42][43]. However, when clogging occurs, unusually low values for the three a.m. properties are reported [44]. Roughness values obtained for grain size 91 and 46 lie within the 0.05-1.6 µm range, determined by Petropoulos et al High grain size of 181 provided even higher Ra values up to 3.02 µm.…”

“…First, the signal is decomposed by the Empirical Mode Decomposition (EMD), in order to obtain a family of signals from different frequency ranges, the Intrinsic Mode Functions (IMF's). Second, the Hilbert transform is applied to each one in order to obtain the instantaneous frequency over time [44]. A hybrid treatment would consist of the calculation of EMD/IMF's and chirplet application to each part of the signal.…”

Application of Acoustic Emission (Ae) in Mining and Earth Sciences: A Review

“…Accordingly, the AE signal processing methods can be classified into three categories: (i) signal processing, (ii) feature extraction, and (iii) pattern recognition. The most important and widely used (Qin et al, 2018) × (Karakus & Perez, 2014) × × × × (Bastari et al, 2011) × (Parsian et al, 2017) × × × (Marinescu & Axinte, 2009) × × (Xiao, Hurich et al, 2018) × (Li et al, 2018) × × (Buj-Corral et al,, 2018) × (Shaffer et al,, 2018) × × × (Wang et al, 2017) × × (Rivero et al, 2008) × (Flegner et al, 2014) × (Feng & Yi, 2017) × (Yari et al, 2017) × (Yari & Bagherpour, 2018 (a&b)) × (Shreedharan et al, 2014) × (Kostur & Futo, 2007) × ( Kawamura et al, 2017) × (Pedrayes et al, 2018) × × (Gradl et al, 2012) × × × (Vardhan et al, 2009) × × × (Beheshtizadeh et al, 2017) × (Miklusova et al, 2006) × × × × (Goyal & Pabla, 2016) × (Liew & Wang, 1998) × × (Kong et al, 2015) × (Jain et al, 2001) The Mining-Geology-Petroleum Engineering Bulletin and the authors ©, 2019, pp. 19-32, DOI: 10.17794/rgn.2019.4.3 methods of the first category (signal processing) include: time series statistical models, Short Time Fourier Transform (STFT), Fast Fourier Transform (FFT), Wavelet Packet Decomposition (WPD), Hilbert-Huang transform (HHT), Wigner-Ville distribution, signal spectrum analysis, Adaptive Line Enhancer (ALE), wavelet transform, and the Peak-Hold-Down-Sample (PHDS) algorithm.…”

Optimization of Honing Surface Roughness of Carburized Holes Based on GRA-RSM

“…The authors of [2,7] reviewed the application of honing technology in the machining of internal combustion engine cylinders, analyzed the surface wear mechanism, and concluded that the size and the shape of the abrasive grains mainly affect the surface quality. Buj-Corral et al [8] investigated the effect of abrasive density on process parameters and concluded that the oilstone density was selected according to the acoustic emission of the grinding. The authors of [9][10][11] investigated the application of honing techniques in hardened steel, CuNiCr copper-nickel alloy, analyzed the effects of honing angle and abrasive size on roughness and residual stress, and finally obtained the optimized process parameters.…”