Feature learning method of acoustic emission signal during single-grit scratching on BK7 in brittle regime

Abstract

Acoustic emission (AE) signal is released from a material as it undergoes deformation and fracture processes. AE signal provides enough information for monitoring of the interaction between tool and material. AE RMS, AE count rates, and frequency characteristics are always employed in AE-based monitoring techniques. Monitoring sensitivity is not satisfied because information implies in AE signal is lost or weaken to some extent for the present monitoring parameters. When a hard-brittle material is removed in brittle regime, burst-type AE (b-AE) associated with crack and chip is predominant compared with low-amplitude continuous-type AE. It will be more objective and physically meaningful if b-AE event is taken as a unit to be monitored. This paper is dedicated to the method of monitoring b-AE event in AE signal. Ignoring background noise, an AE signal can be looked as a convolution of b-AE events and corresponding coefficients. b-AE events correspond to brittle transient behaviors and include information of fracture mechanism. Coefficients represent intensity and location of crack and chip during removal processes. The two elements of the convolution contain different aspects of signal feature. Considering randomness of AE signals, the convolution form will be realized by self-learning from training data set of AE signals. Dictionary-learning algorithm for decaying oscillation modes of b-AE and sparse representation procedure of an AE signal are detailed in this paper based on the convex optimization theory. AE signals in brittle regime are acquired from tests of single diamond grit scratching on BK7. Simulation and experimental results verify the correctness of the feature learning method. According to the method proposed in this paper, b-AE can be objectively monitored without the interference of varying machining parameters.

Keywords

Feature learning burst-type acoustic emission brittle regime single-grit scratching BK7

Get full access to this article

View all access options for this article.

References

Abellan-Nebot

Subirón

. A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 2010; 47: 237–257.

Snr

DED

. Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tool Manuf 2000; 40: 1073–1098.

Haber

Jiménez

Peres

et al.

An investigation of tool-wear monitoring in a high-speed machining process. Sens Actuat A Phys 2004; 116: 539–545.

Benkedjouh

Medjaher

Zerhouni

et al.

Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 2015; 26: 213–223.

Prakash

Kanthababu

. In-process tool condition monitoring using acoustic emission sensor in microendmilling. Mach Sci Technol 2013; 17: 209–227.

Liang

Hecker

Landers

. Machining process monitoring and control: the state-of-the-art. J Manuf Sci E 2004; 126: 599–610.

Song

Gao

. Review of recent research on data-based process monitoring. Ind Eng Chem Res 2013; 52: 3543–3562.

Kano

Nakagawa

. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry. Comput Chem Eng 2008; 32: 12–24.

Zhang

. Process monitoring, fault diagnosis and quality prediction methods based on the multivariate statistical techniques. IETE Tech Rev 2010; 27: 406–420.

10.

Luo

Osypiw

Irle

. Surface quality monitoring for process control by on-line vibration analysis using an adaptive spline wavelet algorithm. J Sound Vib 2003; 263: 85–111.

11.

Dornfeld

Lee

. Precision manufacturing, New York: Springer, 2008.

12.

Jayakumar

Mukhopadhyay

Venugopal

et al.

A review of the application of acoustic emission techniques for monitoring forming and grinding processes. J Mater Process Technol 2005; 159: 48–61.

13.

Hwang

Whitenton

Hsu

et al.

Acoustic emission monitoring of high speed grinding of silicon nitride. Ultrasonics 2000; 38: 614–619.

14.

Stephenson

Sun

Zervos

. A study on ELID ultra precision grinding of optical glass with acoustic emission. J Mach Tool Manuf 2006; 46: 1053–1063.

15.

Griffin

Chen

. Characteristics of the acoustic emission during horizontal single grit scratch tests, part 1: Characteristics and identification. Int J Abrasive Technol 2009; 2: 25–42.

16.

Griffin

Chen

. Characteristics of the acoustic emission during horizontal single grit scratch tests, part 2: Classification and grinding tests. Int J Abrasive Technol 2009; 2: 43–59.

17.

Lee

Hwang

Valente

CMO

et al.

Precision manufacturing process monitoring with acoustic emission. J Mach Tool Manuf 2006; 46: 176–188.

18.

Coelho

Sanchez

LEA

Oliveira

JFG

. Detection of cracks in scratching tests in ceramic materials through acoustic emission. Proc IMechE, Part B: J Engineering Manufacture 2005; 219: 685–693.

19.

Ahn

Lee

. Characterization and acoustic emission monitoring of AFM nanomachining. J Micromech Microeng 2009; 19: 45028–45033(6).

20.

Opoz

Chen

. Acoustic emission features of material removal during single grit scratching. ICAC 2013, pp. 1–6.

21.

Liao

. Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring. Eng Appl Artif Intel 2010; 23: 74–84.

22.

Sun

Jin

et al.

Acoustic emission signal processing method based on wavelet package and HHT transform. Chin J Scient Instrum 2008; 29: 67952W–1–67952W-6.

23.

Iturrospe

Dornfeld

. Bicepstrum based blind identification of the acoustic emission (AE) signal in precision turning. Mech Syst Signal Process 2005; 19: 447–466.

24.

Liang

Dornfeld

. Tool wear detection using time series analysis of acoustic emission. J Eng Ind Trans ASME 1989; 111: 199–205.

25.

. A brief review: Acoustic emission method for tool wear monitoring during turning. J Mach Tool Manuf 2002; 42: 157–165.

26.

Lewicki MS and Sejnowski TJ. Coding time-varying signals using sparse, shift-invariant representations. In: Conference on advances in neural information processing systems II. Boston, MA: MIT Press, 1999, pp.295–322.

27.

Grosse

Raina

Kwong

et al.

Shift-invariance sparse coding for audio classification, USA: Stanford University, 2012.

28.

Engan K, Aase SO and Husoy JH. Frame based signal compression using method of optimal directions (MOD). In: Proceedings of the IEEE international symposium on circuits and systems, Orlando, FL, USA, 30 May to 2 June 1999, pp.1–4.

29.

Aharon

Elad

Bruckstein

. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 2006; 54: 4311–4322.

30.

Rubinstein R, Zibulevsky M and Elad M. Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. Technical Report, CS Technion 2008-08, 2008.

31.

Elad

. Sparse and redundant representations: From theory to applications in signal and image processing, New York: Springer Publishing Company, 2010.