Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 489261, 8 pages

http://dx.doi.org/10.1155/2013/489261

Research Article

Tool Wear Detection Using Lipschitz Exponent and Harmonic Wavelet

Song Wanqing, Li Qing, and Wang Yuming

College of Electronic and Electrical Engineering, Shanghai University of Engineering and Science, Shanghai 201620, China

Received 20 July 2013; Accepted 31 August 2013

Academic Editor: Carlo Cattani

Copyright © 2013 Song Wanqing et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The paper researches a novel engineering application of Lipschitz exponent function and harmonic wavelet for detecting tool condition. Tool wear affects often the quality grade of products and is gradually formed during cutting process. Meanwhile, since cutting noise is very strong, we think tool wear belongs to detecting weak singularity signals in strong noise. It is difficult to obtain a reliable worn result by raw sampled data. We propose singularity analysis with harmonic wavelet for data processing and a new concept of Lipschitz exponent function. The method can be quantitative tool condition and make maintaining decision. Test result was validated with 27 kinds of cutting conditions with the sharp tool and the worn tool; 54 group data are sampled by acoustic emission (AE).

1. Introduction

In recent years, great achievements in tool wear monitoring have been made based on the advanced mathematical models [1–8], even though none of these methods was successful online monitoring due to the complex performance of the machining processes. How to be quantitative tool condition monitoring is very important for practical engineering applications. This is a signal process problem that detects weak singularity signals in strong noise. However, the classical signal process is Fourier theory which has four defects [9–11]: suits stationary stochastic signal, this kind of signal does not almost exists in engineering; for discrete Fourier, Fourier analysis is precise with time series long from zero to infinite, otherwise truncation error is made; weak singularity signal, relatively strange signal, is omitted in FFT however, weak singularity signal often contains important fault information; when signal frequency happen a sudden change with adjacent time intervals, Fourier method only obtains two frequencies, it cannot display the moment two frequency changes or occurs (i.e., the singularities) in frequency domain. In order to overcome the above four defects to improve the measuring precision of weak singular signal in strong noise, we propose the combination between Lipschitz exponent index and complex wavelet for the detection of quantitative tool wear.

Lipschitz functions appear almost everywhere in mathematics [12]. Lipschitz functions are an important class of strong variations originating from smooth deformations of corresponding nonsmooth function [13]; the local regularity is often measured by Lipschitz exponents (LE); the singular variations can be viewed as combinations of weak increasing and decreasing variations; it means the case of discontinuity occurence on smooth function. Since the paper [14] is published in 1992 to detect signal's singularities, which proposed that the instantaneous frequencies can be approximately identified from the modulus maxima of a real wavelet based on general maxima, which are the locations of the largest modulus along maxima lines through an extension of [14], the complex-wavelet modulus maxima can detect and characterize singularities [15, 16] and has taken excellent result for measuring weak singularities of signal. Harmonic wavelet is one kind of complex wavelet; it is very sensitive to singularity feature of the signal that contains weak faults [17–20] and satisfies multiwavelets frames with arbitrary integer dilation factor [21]. The paper combines the modulus maxima of using a harmonic wavelet, and Lipschitz functions can be quantitative tool condition during cutting process. By 27 kinds of cutting conditions and turning experiments on HL-32 NC turning center, 54 group data are sampled by acoustic emission (AE). We have demonstrated that combining the modulus maxima of harmonic wavelet and Lipschitz functions is better for detecting and estimating tool wear when a worn tool generates a singular signal in a strong noise.

2. Harmonic Wavelet Transform Algorithm

Discrete harmonic wavelets are given by

where . is different levels (scale), and is different steps (time). The functions of its scale and time are given by Figure 1.

Figure 1: Real (thick line) and imaginary (thin line).

The Fourier transform of (1) is as below:

The amplitude-frequency characteristics of (2) is exactly like a box with band limited, as shown Figure 2. This characteristic is the best than any real wavelets for decomposed signal to difference frequency band.

Figure 2: Amplitude-frequency characteristics of the discrete harmonic wavelets.

3. The Definition of Lipschitz Exponent

Lipschitz is from Taylor formula [16, 22, 23] Suppose that function is times differentiable in ; then, Taylor expands in the neighborhood of :

exists one approximate error:

which satisfies:

The th order differentiability of in the neighborhood of yields an upper bound on the error when tends to . The Lipschitz regularity refines this upper bound with noninteger exponents. The definition of Lipschitz exponent is as follows: a function is of Lipschitz at , if and only if there exists a constant A such that for all in a neighborhood of

We say that is uniformly Lipschitz for all . Equation (6) is a kind of fractional order [20]. In a neighborhood of , is singular when , and is continuous when . In other words, If is discontinuous and bounded in a neighborhood of , then . If is continuously differentiable then and is not singular.

4. Modulus Maxima of Wavelet

Reference [14] depicts the modulus maxima of real wavelet to detect singular signal, and [15] extends to the modulus maxima of complex wavelet, and algorithm is deduced in detail. We say a function is a modulus maximum of wavelet at the point ; then,

or

where belongs to either the right or the left neighborhood of . A maxima line is consisted of a connected curve of the modulus maxima in the scale space . These multiscale modulus maxima are used to locate discontinuities [16], and if has no modulus maxima at fine scales, then is locally regular. Singular point can be recognized by modulus maxima.

5. Lipschitz Exponent Based on Modulus Maxima of Harmonic Wavelet

It has been shown that the Lipschitz exponent of a local singularity can be characterized by being placed on their local modulus maxima at each scale [14, 16]. References [15, 24] extend to using the modulus maxima of a complex wavelet. Here, we research that the modulus maxima of harmonic wavelet can be used to detect singularities, by means of a Lipschitz exponent of a function, and quantitative analysis on signal's singularities is measured. By examining the asymptotic decay of wavelet modulus maxima from coarser scale to finer scale, the strength of the singularity can be characterized by Lipschitz exponent.

Let , in terms of [15, 25], function , , and let th ()　derivative of and be finite deviations for each scale . If a scale and constants and exist such that for and , all the modulus maxima of belong to a cone defined by

such that at each modulus maxima in the cone

then is uniformly Lipschitz at when , and is Lipschitz at when , and is a noninteger. Equation (10) takes the logarithm on both sides:

In [15, 24], and are computed by

The problem of estimating transforms into optimization based on a nonlinear least squared, and when (12) is minimized at all scale, and are obtained.

According to [24] description of the results, we use instead of , we get the following:

is called Lipschitz exponent function that describes the change of Lipschitz exponent value along the temporal axis . The variable is sampling point by acoustic emission (AE) during cutting process. Figure 5 is a simulation example of extracting Lipschitz exponent function on a sine function with singular points. Here, we set in (13).

Figure 3 demonstrates the potential ability of singularity detection (or fault detection) with Lipschitz exponent function .

(a) Sine function with two singular points

(b) along the temporal axis

Figure 3: Extracting Lipschitz exponent function .

6. Application Platform

In turning experiments, the feasibility for tool condition monitoring is demonstrated by 27 kinds of cutting conditions (see Table 1); 54 group data are sampled by AE; they are shaper1shaper27 and wear1wear27, respectively. Machining tests were carried out on HL-32 NC turning center [26]. The work material was chosen for ease of machining, allowing for generation of surfaces of varying quality without the use of cutting fluids. The experiment equipment is shown in Figure 4. Currently, AE-based sensing technology is the area of most intense research activity for developing intelligent tool condition systems. The reason is that the sensitivity of AE to tool wear and fracture is coupled with a high response rate of the signal. Sharp1 signal and wear1 signal show Figures 5 and 6, respectively in time domain.

Table 1: Experimental cutting conditions.

No.
Speed
Depth of cut
Feed
No.
Speed
Depth of cut
Feed

r/min
mm
mm/r
r/min
mm
mm/r

1
1500
1
0.1
15
1000
0.2
0.05

2
1500
0.5
0.1
16
800
1
0.05

3
1500
0.2
0.1
17
800
0.5
0.05

4
1000
1
0.1
18
800
0.2
0.05

5
1000
0.5
0.1
19
1500
1
0.02

6
1000
0.2
0.1
20
1500
0.5
0.02

7
800
1
0.1
21
1500
0.2
0.02

8
800
0.5
0.1
22
1000
1
0.02

9
800
0.2
0.1
23
1000
0.5
0.02

10
1500
1
0.05
24
1000
0.2
0.02

11
1500
0.5
0.05
25
800
1
0.02

12
1500
0.2
0.05
26
800
0.5
0.02

13
1000
1
0.05
27
800
0.2
0.02

14
1000
0.5
0.05

Figure 4: AE measurement in metal cutting.

Figure 5: Waveform of sharp tool in first condition.

Figure 6: Waveform of wear tool in first condition.

According to (13), we set harmonic wavelet decomposed level , and Lipschitz exponent function of 54 group data is shown in Figure 7.

Figure 7: Lipschitz exponent function of 54 group data.

In Figure 7, all sharp tools are of positive Lipschitz exponent function , and all wear tools are of a negative Lipschitz exponent function . Wear tool and sharp tool can be distinguished by Lipschitz exponent function . A threshold value is obtained as a criterion of the tool condition monitoring during cutting process.

7. Conclusion

The proposed research provides a new theoretical basis and a new engineering application on the online tool monitoring which can distinguish between worn tool and sharp tool during cutting process. The results of experiment demonstrate that this method is more precise and robust.

The method which was described in this paper can be used as a valuable method for tool condition monitoring. In comparison to conventional data processing, the advantages of Lipschitz exponent and harmonic wavelet were shown. The zero threshold is determined conveniently which can distinguish between worn tool and sharp tool. For the future development of the presented techniques in laboratory, several approaches will be tested, including new broadband sensors application.

Acknowledgment

This project is supported by Shanghai Municipal Education Commission of China (Grant no. 13XKCZ07/YLJX12-7).

References

V. B. Magdum and V. R. Naik, “Tool wear monitoring when turning EN 8 Steel with HSS-M2 tool,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 2, no. 5, pp. 1706–1711, 2013.

Y. Huang and S. Y. Liang, “Modeling of cutting forces under hard turning conditions considering tool wear effect,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, vol. 127, no. 2, pp. 262–270, 2005.

C. -W. Hung and M. -C. Lu, “Model development for tool wear effect on AE signal generation in micromilling,” The International Journal of Advanced Manufacturing Technology, vol. 66, no. 9–12, pp. 845–1858, 2013.

M. A. Xavior and M. Adithan, “Determining the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel,” Journal of Materials Processing Technology, vol. 209, no. 2, pp. 900–909, 2009.

R. Quiza, L. Figueira, and J. P. Davim, “Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel,” International Journal of Advanced Manufacturing Technology, vol. 37, no. 7-8, pp. 641–648, 2008.

S. Cho, S. Binsaeid, and S. Asfour, “Design of multisensor fusion-based tool condition monitoring system in end milling,” International Journal of Advanced Manufacturing Technology, vol. 46, no. 5–8, pp. 681–694, 2010.

D. Iliescu, D. Gehin, M. E. Gutierrez, and F. Girot, “Modeling and tool wear in drilling of CFRP,” International Journal of Machine Tools and Manufacture, vol. 50, no. 2, pp. 204–213, 2010.

F. Girardin, D. Rémond, and J.-F. Rigal, “Tool wear detection in milling-An original approach with a non-dedicated sensor,” Mechanical Systems and Signal Processing, vol. 24, no. 6, pp. 1907–1920, 2010.

A. E. Zonst, Understanding FFT Applications: A Tutorial for Laymen, Students, Technicians & Working Engineers, Citrus Press, Titusville, Fla, USA, 2nd edition, 2003.

J. F. James, A Student's Guide to Fourier Transforms: With Applications in Physics and Engineering, Cambridge University Press, London, UK, 3rd edition, 2011.

A. H. Kaiser, Digital Signal Processing Using the Fast Fourier Transform (FFT), GRIN Verlag, Munich, Germany, 2013.

J. Heinonen, “Lectures on Lipschitz Analysis, Lectures at the 14th Jyvaskyla Summer School in August 2004,” Supported by NSF grant DMS 0353549 and DMS 0244421

Y. Grabovsky, V. A. Kucher, and L. Truskinovsky, “Weak variations of Lipschitz graphs and stability of phase boundaries,” Continuum Mechanics and Thermodynamics, vol. 23, no. 2, pp. 87–123, 2011.

S. Mallat and W. L. Hwang, “Singularity detection and processing with wavelets,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 617–643, 1992.

C.-L. Tu, W.-L. Hwang, and J. Ho, “Analysis of singularities from modulus maxima of complex wavelets,” IEEE Transactions on Information Theory, vol. 51, no. 3, pp. 1049–1062, 2005.

S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego, Calif, USA, 3rd edition, 2008.

C. Cattani, “Harmonic wavelet approximation of random, fractal and high frequency signals,” Telecommunication Systems, vol. 43, no. 3-4, pp. 207–217, 2010.

D. E. Newland, An Introduction to Random Vibrations, Spectral and Wavelet Analysis, Dover, London, UK, 3rd edition, 2005.

D. E. Newland, “Harmonic wavelet analysis,” Proceedings of the Royal Society of London. Series A, vol. 443, pp. 203–222, 1993.

D. E. Newland, “Wavelet analysis of vibration, part 2: wavelet maps,” Journal of Vibration and Acoustics, vol. 116, no. 4, pp. 417–425, 1994.

Y. Huang, Q. Li, and M. Li, “Minimum-energy multiwavelet frames with arbitrary integer dilation factor,” Mathematical Problems in Engineering, vol. 2012, Article ID 640789, 37 pages, 2012.

K. A. Innanen, “Local signal regularity and lipschitz exponents as a means to estimate Q,” Journal of Seismic Exploration, vol. 12, no. 1, pp. 53–74, 2003.

J. Lindenstrauss, D. Preiss, and J. Tiser, Fréchet Differentiability of Lipschitz Functions and Porous Sets in Banach Spaces (AM-179), Princeton University Press, Princeton, NJ, USA.

Q. Miao, H.-Z. Huang, and X. Fan, “Singularity detection in machinery health monitoring using Lipschitz exponent function,” Journal of Mechanical Science and Technology, vol. 21, no. 5, pp. 737–744, 2007.

P. Venkatakrishnan, S. Sangeetha, and M. Sundar, “Measurement of Lipschitz exponent (LE) using wavelet transform modulus maxima (WTMM),” International Journal of Scientific & Engineering Research, vol. 3, no. 6, 2012.

W. Song and J. Zhang, “Tool state detection by harmonic wavelet and sample entropy,” Chinese Journal of Mechanical Engineering, vol. 24, no. 6, pp. 1068–1073, 2011.