Grinding wheel wear monitoring based on wavelet ... - Springer Link

Int J Adv Manuf Technol (2012) 62:107–121 DOI 10.1007/s00170-011-3797-1

ORIGINAL ARTICLE

Grinding wheel wear monitoring based on wavelet analysis and support vector machine Zhensheng Yang & Zhonghua Yu

Received: 20 September 2011 / Accepted: 17 November 2011 / Published online: 4 December 2011 # Springer-Verlag London Limited 2011

Abstract A novel grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine is proposed. The grinding signals are collected by an acoustic emission (AE) sensor. A preprocessing method is presented to identify the grinding period signals from raw AE signals. Root mean square and variance of each decomposition level are designated as the feature vector using discrete wavelet decomposition. Various grinding experiments were performed on a surface grinder to validate the proposed classification system. The results indicate that the proposed monitoring system could achieve a classification accuracy of 99.39% with a cut depth of 10 μm, and 100% with a cut depth of 20 μm. Finally, several factors that may affect the classification results were discussed as well. Keywords Grinding wheel wear . Acoustic emission (AE) . Process monitoring . Contact detection . Discrete wavelet decomposition . Support vector machine (SVM) Nomenclature AERMS Root mean square value of AE signal a Approximation level of DWT d Detail level of DWT SFRMS RMS value of each decomposition coefficient SFVAR Variance value of each decomposition coefficient x(k) Original time-series signal g(k) Low-pass filter h(k) High-pass filter fn Sampling frequency

Z. Yang : Z. Yu (*) Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, People’s Republic of China e-mail: [email protected]

coef μ

Discrete wavelet decomposition coefficient Mean value of discrete wavelet decomposition coefficient

1 Introduction Grinding wheel topography changes during grinding. As a result, the efficiency of grinding process and the quality of the workpiece are affected negatively most of the time. Wheel wear may induce grinding burn and bad surface quality, even serious accidents. Unfortunately, current approaches dealing with wheel wear are based on human experience and dressing interval is roughly determined, usually by skilled operators. This induces two adverse impacts. Firstly, grinding wheel wear might already happen before dressing process, which usually causes grinding quality problems. On the contrary, if dressing process is carried out ahead of wheel wear, the grinding efficiency is definitely reduced and the abrasive materials are wasted at the same time. Among major machine processes including milling, drilling and turning, grinding is known as the most complicated. Grinding wheel wear mechanism is still not fully understood today. Grinding wheel wear monitoring is thus necessary in grinding process. The most popular method in grinding wheel condition monitoring is called indirect method, which relies on machining process parameters and sensor signals such as vibration, forces, current, power, temperature, and acoustic emission. In contrast, direct methods use vision sensors such as scanning electron microscope (SEM) to measure grinding wheel surface or its replica directly. Direct methods have good measurement flexibility, high spatial resolution, and good accuracy. However, they always require interrupting

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machining process and have a strict demand on the environment. In addition, vision systems are costly. As a result, they are more applicable in laboratory at the present stage. In this paper, indirect method is used to monitor wheel wear in surface grinding process. A general procedure of indirect grinding conditions monitoring method includes the following steps: (1) sensor selection; (2) signal processing; (3) feature extraction; (4) feature selection; and (5) pattern recognition strategies to match the selected features with grinding wheel conditions. A brief review over the past decade on this specific issue is given below. In 2009, Oliveira et al. [1] presented a CIRP keynote paper titled “Industrial Challenges in Grinding”, in which a similar CIRP keynote paper published by Kegg [2] 26 years ago in 1983 was mentioned. Although almost three decades passed, some grinding problems are still unsolved in industry due to their high complexity. Better predictability of grinding process is one of those issues. Efforts have been made to attain this target and substantial achievements have been obtained in the past several years. In another keynote paper on grinding process monitoring, Tönshoff et al. [3] reviewed measuring techniques to monitoring process quantities as well as output quantities. The micro and macro topography of the active abrasive layer of the grinding wheel were considered as the dominant features of the process. Lee et al. [4] developed a grinding wheel topographic mapping system based on AERMS. AE signals were obtained from the contact between diamond tool and grinding wheel and converted into root mean square level. To measure the contact of the diamond tool with each abrasive grain, a fast time constant was calculated as the average time spent for two consecutive hits between abrasive grains and the diamond tool. Results showed that the AE mapping system could generate an image similar to the surface topography presented on the grinding wheel surface, which was an “L” mark. Lee et al. [4] provided a new approach for grinding wheel topography mapping using indirect methods. However, it could not be applied in grinding processes due to wheel topography mapping was obtained during the dressing process. Most of the other indirect methods focused on grinding wheel conditions, e.g., wheel wear, burn or end of wheel life, rather than wheel topography. Lezanski [5] developed an intelligent grinding wheel condition monitoring system, in which neural network and fuzzy logic were used to classify the conditions of the grinding wheel cutting abilities in the external cylindrical grinding process. Multiple sensors were used and features were extracted in both time and frequency domain. An 8–3– 1 (8 nodes in input layer, 3 nodes in hidden layer, 1 node in output layer) three layers feed forward back propagation neural network was built for feature selection and eight input features were selected as inputs of the neuro-fuzzy

Int J Adv Manuf Technol (2012) 62:107–121

model. The best performance index of 83.3% was obtained. In their four papers on grinding wheel condition monitoring, Liao et al. [6] kept studying on various signal processing and classification methodologies. Autoregressive modeling and discrete wavelet decomposition were employed as the effective approaches for feature extraction. Hidden Markov model [6], adaptive genetic clustering algorithm [7], boosted minimum distance classifiers and four different wrapper approach classifiers [8] were introduced respectively to distinguish different states of grinding wheel condition, mainly sharp and dull. The results were encouraging. For the high material removal rate (MRR) condition, clustering accuracy of 100% was obtained using Hidden Markov model [6], while clustering accuracy of an adaptive genetic clustering algorithm was 97% [7]. On the other hand, for the low MRR condition, Hidden Markov model could achieve 75% in clustering accuracy [6], while an adaptive genetic clustering was 86.7% [7]. For the boosted minimum distance classifiers, the best average classification accuracy of 91.9% was obtained using Adaptive Boosting-Minimum distance classifier (AdaBoost-MDC) [8]. In another report, Liao [9] used ant colony optimization-based method and the sequential forward floating selection method to choose the best feature subsets. Five classification methods, i.e. the nearest mean (NM), k-nearest neighbor (KNN), fuzzy k-nearest neighbor (FKNN), center-based nearest neighbor (CBNN) and k-means-based nearest prototype (KMNP), were introduced as classifiers. Among all five classifiers, the lowest classification error of 7.81% was achieved using CBNN for the dataset of wavelet energy feature, while the lowest classification error was 6.875% using the dataset of AR coefficients. Cakan [10] studied the real-time monitoring of flank wear of an alumina-based ceramic cutting tool using a photo electronic sensor and the results were quite encouraging. Subrahmanya and Shin [11] pointed out that although various methods had been reported for grinding wheel condition monitoring, no widespread applications in industry had been found as none single method or feature had been demonstrated to be successful for all setups and wheel– workpiece combinations. Considering of this circumstance, Subrahmanya and Shin [11] developed an automated sensor selection and fusion combine with parameter-free model training approach for monitoring of burn, chatter and wheel wear. Combination of embedded sensor selection algorithm and an approximate estimate of the leave-one-out (LOO) error for hyperparameter tuning of least squares–support vector machines (LS-SVM) were proposed for automated feature selection and sensor fusion. Sun et al. [12] provided a systematic procedure to select training data. The quality of the training data was estimated by the performance evaluation through support vector machine. Inasaki [13] developed a monitoring and controlling system using the fusion of AE and power sensor to detect and optimize the cylindrical


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grinding process. A summary of viable solutions of tool condition monitoring (mainly grinding wheel wear) is reported in Table 1. Three key conclusions can be drawn from efforts mentioned above: 1. The prediction accuracy of wheel condition is relatively low, which limits its application in industry. For example, prediction results reported in the mentioned studies are generally lower than 95%, except Liao’s [6]. 2. One of the critical issues of grinding wheel conditions monitoring is on-line industrial application. Unfortunately, such kind of efforts is rarely seen in papers reviewed above. 3. As to signal processing methods using wavelet analysis, the wavelet base is generally selected arbitrarily without any explanation. Only Jemielniak and Kossakowska [14] compared features extracted using 22 wavelets and their sensitivity on tool wear conditions based on AE sensors, and the optimal wavelet was selected because it yielded the best results after comparison with other wavelet bases. This study focuses on the development of grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine, in order to make an important step towards the on-line industrial applications. In the next section, grinding experiment setup and AE signals acquisition system are presented. Section 3 introduces the preprocessing methods as well as discrete wavelet decomposition algorithm used to analyze AE signals and extract features. In Section 3, a contact detection method is proposed for signal preprocessing to extract signals when grinding is actually performed, which should be considered in on-line industrial applications. The optimal wavelet is selected to avoid arbitrary selection, after analyzing wavelet

properties. Support vector machine rather than neural networks or clustering algorithms is introduced for classification of sharp and worn wheel states in Section 4. Section 5 shows the classification results. Different parameters that may affect the performance of the proposed grinding wheel wear monitoring system are discussed. Performance of a backpropagation (BP) neural network is also presented in the discussion section and its comparison with the proposed method is talked. Conclusions are made at the end of the paper. The overall structure of this paper is shown in Fig. 1.

2 Experimental setup and procedure 2.1 Experimental setup The grinding tests were performed on an ABA Grinding Technology Z&B Multiline series surface grinder using a white fused alumina wheel (WA60LmV) to grind carbon steel materials (ASTM 1045). The workpiece has a dimension of 300 mm in length and 50 mm in width. The maximum grinding wheel speed could reach 30 m/s. An oil-based coolant was sprayed onto the workpiece during grinding. The grinding process was monitored by an acoustic emission sensor (SOUNDWEL SR150C) mounted on the side face of the worktable through magnetic force. Its working frequency was 60–400 kHz and the AE signal was collected at 1 MHz sampling rate using a PC-based data acquisition card (ADLINK DAQ-2010). A schematic diagram of the experimental setup is shown in Fig. 2. 2.2 Experimental procedure A white fused alumina-based vitrified bonded grinding wheel with 100 mm in width was used in this study. The

Table 1 Summary of grinding wheel condition monitoring and related solutions Referance

Sensor(s)

Signal analysis

Features

Classification algorithm

Test results

Lezanski [5]

Power spectrum and time domain Wavelet analysis

8 features

Neuro-fuzzy

83.3%

Liao et al.[6]

AE, forces, and vibration AE

Wavelet energy

Liao et al.[7]

AE

Wavelet analysis

Wavelet energy

Liao et al.[8] Liao [9]

AE AE

AR modeling AR modeling and Wavelet analysis

AR order AR order and wavelet energy

HMM-based clustering methods Adaptive genetic clustering algorithm Boosted classifiers NM, KNN, FKNN, CBNN and KMNP

100% (high MRR), 75% (low MRR) 97% (high MRR), 86.7% (low MRR) 91.9% 92.19% (wavelet features), 94.125% (AR coefficient)

Subrahmanya and Shin [11]

AE, Power and accelerometer

Time domain and frequency domain

AE: 7 features, Accelerometer and powermeter: 8 features

LOO error, LS-SVM

97.42%

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Fig. 1 Overall structure of the grinding wheel wear monitoring system

wheel was trued into 500 mm in diameter right before the experiment. Then, dressing process was performed using a table dresser (ABA TAG) magnetically held on the worktable. A carbon steel specimen was grinded several times prior to the data acquisition to stabilize the grinding wheel. Then the monitoring system was turned on to collect signals in the steady state. After that, AE signals were collected when the grinding wheel reached a worn state. Whether the wheel was worn or not was determined based on the grinding sounds and sparks with the operator’s experience. More grinding cycles were performed to make sure the wheel is completely worn out. Note that the grinding wheel is two times wider than the workpiece. In order to make full use of the wheel and avoid localized wheel wear, the grinding process involved having a wheel advance along the width direction, while the workpiece moved in the orthogonal direction simultaneously to make the grinding wheel fully contact with the workpiece during one grinding cycle. Table 2 shows two sets of grinding parameters.

3 Signal processing and feature extraction The sampling rate of AE signals was 1 MHz, which was relatively high for continuous collection. In this experiment, in order to capture the full grinding process, signals Fig. 2 Experimental setup

generated during the spark-in and spark-out stages were collected as well. So each signal contains both idle running (before wheel/workpiece contact) and grinding period (after wheel/workpiece contact) signals, with duration of about 4 s. However, only signal segment during grinding is useful for us. The proposed signal processing and feature extraction methodology have the following steps: signal preprocessing, discrete wavelet decomposition and feature extraction. More details are described in the following subsections. 3.1 Preprocessing There are three purposes in signal preprocessing step: to extract signals during which grinding was actually performed, segment signals into proper length, and filter noise signals generated by coolant and other sources. A novel approach was proposed to extract contact signals during grinding period and eliminate signals in idle running. Webster et al. [15] suggested that there were two stages during wheel and workpiece contact, designated as the grit contact and wheel contact. Grit contact means that few higher grits are engaging the grinding, while wheel contact indicates that the whole wheel is in contact with the workpiece continuously rather than intermittently. Traditional methods predominately use AERMS signal as an indicator of wheel and workpiece contact. However, Webster et al.


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Table 2 Grinding parameters Level

Depth of cut (μm)

1 2

10 20

Grinding velocity (m/s)

Feed velocity (mm/min)

20 20

24 24

Workpiece Material ASTM 1045 ASTM 1045

[15] pointed out that AERMS signals in grit contact stage were too weak and the burst AE signals generated by grit cutting could be easily confused with other noises. In this paper, a wheel and workpiece contact detection method through discrete wavelet decomposition of AE raw signals was proposed to extract signals when grinding was actually performed. The process of discrete wavelet decomposition is described in Section 3.2. Raw AE signals containing idle running signal segment and grinding signal segment are shown in Fig. 3. It is noticed that AE signal amplitude rises notably as soon as the wheel and workpiece make contact. However, there are distinguished fluctuations prior to the contact, which could be generated by coolants sprayed onto the workpiece during grinding. Misjudgment might occur if contaminated signals were used directly to forecast wheel and workpiece contact. Therefore, a denoising method based on discrete wavelet decomposition was used to eliminate the fluctuations. Daubechies wavelet family is compactly supported and orthogonal with maximum regularity, which is preferable to detect abrupt signals. After four-level decomposition and single-level reconstruction using wavelet db5, the original signal and the decomposition levels 1, 2, 3 and 4 are shown in Fig. 4. It was observed that fluctuations during idle running period declined dramatically in decomposition level 2. Therefore, the decomposition level 2 was then extracted for further analysis. Yao et al. [16] suggested that the standard deviation and energy of proper wavelet decomposition could be used to

Fig. 3 Raw AE signal

forecast chatter in boring operation. Chatter and wheel/ workpiece contact are similar to some extent because both of them generate a sudden change when the two phenomena took place. In this study, after wavelet transform of raw AE signals, the energy of decomposition level 2 was calculated using time constant of 0.5 ms. It was found that the energy signals of the idle running period were close to zero, while the grinding period signals increased significantly. Therefore, energy signals were selected as the criterion to extract grinding period signals. Results are shown in Fig. 5. Three conclusions could be drawn from the Fig. 5: (1) energy of the idle running period signals was close to zero; (2) energy increased significantly after the wheel engaging the workpiece and (3) there was a time advantage of energy signal compared with raw AE signal. Thus, energy value prior to contact was selected as reference datum, which could be obtained when the wheel and workpiece were not in contact. A threshold was set as 10 dB of the reference datum. Signals lower than the threshold was set as zero and discarded. Signals obtained using this method were believed to be generated in grinding period, which are shown in Fig. 6. After extracting grinding signals from the raw data, it was found that the data length of each grinding period was still very large (nearly 500,000 samples). Redundant amount of information could increase the processing time and it was not necessarily needed. In order to shorten the calculation time and retain adequate amount of information, AE signal was segmented into ten adjacent parts so there were 50,000 data points in each segment. The raw AE signals contain grinding information as well as noises. Most of the proposed wheel wear monitoring systems were performed without coolant. It was not quite in accord with actual conditions. Coolant brings more noises and uncertainties than dry grinding. In this paper, it was found that useful grinding AE signals for wear detection were contaminated at lower frequencies when the grinding

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Fig. 4 Discrete wavelet decomposition results of raw AE signals

was accompanied with coolants. Fast Fourier Transform was performed on signals during idle running and grinding period. The results are shown in Fig. 7. It is noticed that the frequency spectrums of idle running period signals mainly concentrate at frequency domains lower than 90 kHz. Frequency spectrums higher than 90 kHz are

relatively weak. Besides, the amplitude is smaller than grinding signals. On the contrary, frequency spectrums of grinding period signals higher than 90 kHz are distinct and visible. Meanwhile, frequency at 30 kHz increases significantly compared with idle running signals. Therefore, an infinite impulse response (IIR) Butterworth high-pass filter


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Fig. 5 Energy of decomposition level 2 and raw AE signal in time domain: a a full view; b a zoom view of the non-grinding section; c a zoom view of the onset section; and d a zoom view of the ending section

with cut-off frequency of 30 kHz was designed. Considering that AE signals contain more valuable information in higher frequency domain, 90 kHz was finally used as cut-off frequency and it performed better than 30 kHz in the subsequent classification process. Details of the performance using different cut-off frequencies, i.e. signals high-pass filtered at cut-off frequency of 30 kHz and signals high-pass filtered at cut-off frequencies of 90 kHz were presented in Section 6.1. So far, the preprocessing procedure was accomplished and the each filtered AE signal was processed in the subsequent section for further analysis. 3.2 Feature extraction based on wavelet Conventional signal processing methodologies are generally carried out in time domain, frequency domain or both. Time domain approaches have been employed in earlier grinding

wheel condition monitoring researches. Features commonly used in time domain were signal amplitude, root mean square, mean value, standard deviation [5], skewness and kurtosis [11], etc. For AE signals in particular, Jemielniak and Kossakowska [14] used features such as AE event, ringdown count and AE signal duration in AE-based monitoring system. Although time domain approaches have advantages of time-saving and convenience in calculation, lots of valuable information is omitted. As a matter of fact, time domain methods are mostly used in real-time circumstances. Among various approaches that have been taken to analyze acoustic emission signals, Fourier transform method has been considered as an effective approach for grinding wheel wear monitoring. In the traditional Fourier transform, the frequency is defined for the sine or cosine function spanning the whole data length. Therefore, the disadvantage of Fourier transform is that it is impossible to have a good resolution both in time domain and in frequency domain.

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Fig. 6 Contact signals: a a full view; b a zoom view of the onset section; and c a zoom view of the ending section

That is why Fourier transform is unsuitable for identifying non-stationary transient information. Time-frequency analysis is the most popular method for non-stationary signals, such as the short-time Fourier transform (STFT). The STFT could provide time-frequency representations for the signal by successively sliding the window along the time axis. However, since the STFT relies on the traditional Fourier analysis, the signals should be piecewise stationary. On the other hand, the problem with STFT is that the window for the analysis of the entire signal could not change. Therefore, STFT provides a constant resolution for all frequencies. It is still impossible to have good resolutions in time as well as frequency at the same time. Wavelet transform was proposed to overcome these drawbacks. Wavelet transform allows long time intervals with more precise low frequency information and shorter regions with high frequency information. Due to this advantage, wavelet transform was widely used in signal processing in the past decade and it is still a stimulating approach nowadays. The basics of discrete wavelet transforms are briefly presented and the use of discrete wavelet transform to extract feature is described in the next subsection. 3.2.1 Discrete wavelet transform Wavelet transform allows one to unfold a signal into both space and scale. Compared with the STFT, the

time-frequency resolution of the continuous wavelet transform (CWT) depends on the frequency of the signal. The quality factor of CWT is constant in the timefrequency plane. CWT is an effective tool for signal analysis. However, it involves much redundant information and is computationally slow. In discrete wavelet transform (DWT), the wavelets are discretely sampled, and signals can be separated into several approximation signals and detail signals. It is less time-consuming compared with the CWT. Three levels DWT decomposition tree of a signal with frequency range 0 to fn is shown in Fig. 8.

3.2.2 Extraction of wavelet coefficient features There are various wavelet base functions available. Teti et al. [17] pointed out that in most process monitoring studies, the type of wavelet was arbitrarily selected without any explanation. Different base functions have different properties, such as order of approximation, vanishing moments, orthogonality, and compact support. The result of wavelet transform strongly depends on the type of the wavelet base, which makes it critical to choose a proper wavelet base to match the signal to be analyzed for wavelet transform applications. There are several factors which


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Fig. 7 The frequency spectrum of: a idle running and b grinding period

should be considered in choosing the wavelet basic function. (1) Orthogonality. In orthogonal wavelet analysis, the number of convolutions at each scale is proportional to the width of the wavelet base at that scale [18]. Orthogonal wavelet could reduce the redundant information and improve calculation speed. (2) Compact support. Most compactly supported wavelets are designed to have a rapid fall-off so that they can be considered as band-limited. Compactly supported wavelet could reduce computation complexity and have better time resolution.

Fig. 8 Three level DWT decomposition

(3) Shape. The wavelet base should reflect the type of features present in the time series. To analyze an impulse signal, base wavelet with similar shape should be employed to perfectly extract the components of the signal. For example, Singh and Tiwari [19] selected the optimal wavelet to analyze electrocardiogram (ECG) signal by choosing the maximum cross correlation coefficient between ECG signal and the wavelet filter. Other properties such as symmetry, regularity and vanishing moments are also important while choosing wavelet bases. In this study, the coif2 wavelet with level of 5 was chosen for its orthogonality, compactly supported and its

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similarity to the shape of acoustic emission signals. Considering that coif2 is not the only wavelet base which possesses these properties, performance comparisons of different wavelet bases were presented in the Section 6.2. Teti et al. [17] showed that wavelet transform coefficients were usually treated as separate signals, each characterized by features used for time domain signals. In this paper, after five-level decomposition on each AE signal segment, features of the root mean square and variance of each separate coefficient were calculated as they represented signal’s effective value and fluctuations. As a result, for each decomposition coefficient, feature vector of SFRMS is obtained and defined as sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi nd 1 X SFRMS ðns ; nc Þ ¼ coef 2 ði; nc Þ ð1Þ nd i¼1 in which ns is the specimen size, and nd indicates the length of each decomposition coefficient, while coef is the decomposition coefficient matrix of ns rows and nc columns. nc is given by nc ¼ n þ 1

ð2Þ

where n is the decomposition level. Feature vector of SFVAR is defined as SFVAR ðns ; nc Þ ¼

nd 1 X ðcoef ði; nc Þ μðnc ÞÞ2 nd i¼1

ð3Þ

in which μ is the mean value matrix of nc columns. Table 3, 4, 5, and 6 show SF1 and SF2 values of a specified segment with cut depths of 10 and 20 μm. It can be observed that except several outliers at decomposition level d1, d2, d3, and d4, SFRMS and SFVAR values of worn wheel conditions are generally higher than sharp conditions in each decomposition coefficient. Therefore, feature vector is constructed by [SFRMS, SFVAR] under two

Table 3 SFRMS values extracted by five-level coif2 wavelet, depth of cut010 μm

cutting depths and set as inputs for the SVM classification system in the next section.

4 Support vector machine for classification SVM is based on statistical learning theory. Conventional classification method and artificial neural network (ANN) are studied that the sufficient samples are available, which is difficult to obtain in practice. SVM based on statistical learning theory has better generalization than ANN for a smaller number of samples. The Vapnik–Chervonenkis theory (VC theory) and structural risk minimization guarantee the local and global optimal solution are exactly the same, and disadvantages like over-fitting problem and poor generalization in small samples which are commonly seen in neural network and other machine learning theories have been overcome in SVM approach. Besides, SVM is good at two-class tasks. It is proved to be an effective tool for classification, regression and function estimation and it is widely used in the field of data-driven modeling. In fact, Burges [20] showed that SVM performed well when finite samples were available and it had been successfully implemented in many fields including handwritten digit recognition, object recognition, speaker identification, text categorization and other pattern recognition cases as well as regression estimation cases. In this study, due to the fact that sufficient grinding wheel wear samples are not always available in practice, as well as it has high accuracy and good generalization for a smaller number of samples, SVM algorithm was used to predict grinding wheel wear. A radial basis function (RBF) kernel was selected for its good performance, which was necessary to map the original finite-dimensional space into a much higher-dimensional space. In standard SVM, the penalty parameter and kernel function parameter of RBF kernel were arbitrarily selected, or selected by trial-and-error

No.

States

d1

d2

d3

d4

d5

a5

1 2 3 4 5 6 7 8 9 10 11

Sharp Sharp Sharp Sharp Worn Worn Worn Worn Worn Worn Worn

0.0306 0.0366 0.0285 0.0361 0.0588 0.0384 0.0532 0.0476 0.0568 0.0396 0.0467

0.1402 0.1611 0.1251 0.1450 0.2728 0.1932 0.2392 0.2126 0.2215 0.1868 0.2049

0.1668 0.1676 0.1378 0.1503 0.3008 0.2334 0.2549 0.2348 0.2247 0.2054 0.2051

0.0485 0.0445 0.0373 0.0416 0.0871 0.0676 0.0746 0.0575 0.0591 0.0512 0.0504

0.0178 0.0163 0.0138 0.0149 0.0310 0.0250 0.0259 0.0253 0.0227 0.0206 0.0194

0.0561 0.0098 0.0166 0.0104 0.0686 0.0294 0.0287 0.0500 0.0293 0.0104 0.0162

Int J Adv Manuf Technol (2012) 62:107–121 Table 4 SFVAR values extracted by five-level coif2 wavelet, depth of cut010 μm

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No.

States

d1(×10−4)

d2(×10−2)

d3(×10−2)

d4(×10−3)

d5(×10−4)

a5(×10−5)

1 2 3 4 5 6 7 8 9 10 11

Sharp Sharp Sharp Sharp Worn Worn Worn Worn Worn Worn Worn

9.3396 1.3362 8.1455 13.057 34.565 14.763 28.337 22.684 32.219 15.667 21.848

1.9664 2.5963 1.5662 2.1019 7.4449 3.7338 5.7229 4.5200 4.9047 3.4909 4.1986

2.7812 2.8089 1.9001 2.2591 9.0466 5.4451 6.4990 5.5120 5.0480 4.2174 4.2065

2.3490 1.9796 1.3927 1.7345 7.5922 4.5674 5.5699 3.3066 3.4945 2.6207 2.5404

3.1738 2.6583 1.8974 2.2259 9.6167 6.2529 6.6841 6.3783 5.1704 4.2316 3.7603

312.91 9.5469 27.436 10.810 469.13 85.612 82.162 250.17 85.249 10.910 26.073

method, which made it hard to get the satisfied classification results. Therefore, we introduced genetic algorithm (GA) to select the optimal parameters automatically. The classification accuracy was designated as fitness function in GA, and then the optimal penalty parameter and kernel function parameter would be obtained when they yielded the best classification accuracy. The population size was set as 100 and the maximum number of generations was set as 25. The states of grinding wheel are classified into two categories: sharp and worn, which were designated as the output of our pattern recognition system. The output result of ‘1’ represents that the wheel is sharp and ‘−1’ means the wheel is worn. Feature vector [SFRMS, SFVAR] of each signal segments under two cutting depths was scaled to the range of [0, 1] using equation: x!y¼

x xmin 2 ½0; 1 xmax xmin

ð4Þ

where x is the original value, and xmin, xmax are the minimum and maximum values in the feature vector. The scaled feature vector was then input into the SVM classification system using a Matlab SVM toolbox modified based on

Table 5 SFRMS values extracted by five-level coif2 wavelet, depth of cut020 μm

Chang and Lin [21]. Result of the GA-SVM classification is presented in the next section. The comparison between GA-SVM and BP neural network was discussed in Section 6.3.

5 Results Two sets of feature vectors extracted from AE signals using the previous methods were input in the SVM classification system. The first feature vector had 40 records of sharp conditions and 70 records of worn conditions with the cut depth of 10 μm, while the other set had 80 records of sharp conditions and 20 records of worn conditions with the cut depth of 20 μm. Half records of each feature vector were taken out for training and the other half for testing. Each classification was repeated for five times and the mean value was obtained. Table 7 shows the classification results under the two cutting conditions. The classification accuracy was 100% under the cutting depth of 20 μm. With the cutting depth of 10 μm, the accuracy is a little lower, which was 99.39%. The classification results indicate that

No.

States

d1

d2

d3

d4

d5

a5

1 2 3 4 5 6 7 8 9 10

Sharp Sharp Sharp Sharp Sharp Sharp Sharp Sharp Worn Worn

0.0450 0.0529 0.0520 0.0753 0.0495 0.0698 0.0564 0.0764 0.0959 0.1123

0.2081 0.2266 0.2486 0.3139 0.2388 0.3021 0.2463 0.3157 0.4252 0.4829

0.2597 0.2600 0.3050 0.3564 0.2782 0.3333 0.2599 0.2921 0.4937 0.5583

0.0760 0.0681 0.0887 0.0950 0.0758 0.0955 0.0649 0.0748 0.1435 0.1447

0.0279 0.0260 0.0289 0.0356 0.0283 0.0341 0.0250 0.0274 0.0519 0.0536

0.0666 0.0066 0.0445 0.0681 0.0280 0.0324 0.0248 0.0248 0.1148 0.0598

118 Table 6 SFVAR values extracted by five-level coif2 wavelet, depth of cut020 μm


No.

States

d1(×10−3)

d2(×10−2)

d3(×10−2)

d4(×10−3)

d5(×10−4)

a5(×10−5)

1 2 3 4 5 6 7 8 9 10

Sharp Sharp Sharp Sharp Worn Worn Worn Worn Worn Worn

2.0270 2.8030 2.7039 5.6678 2.4495 4.8713 3.1834 5.8368 9.2066 12.617

4.3297 5.1340 6.1811 9.8526 5.7044 9.1244 6.0691 9.9652 18.079 23.318

6.7423 6.7606 9.3026 12.701 7.7383 11.107 6.7573 8.5284 24.372 31.159

5.7708 4.6391 7.8762 9.0347 5.7524 9.1254 4.2186 5.5959 20.612 20.949

7.7765 6.7662 8.2666 12.663 8.0022 11.661 6.2564 7.5272 26.854 28.697

443.59 4.3223 197.38 463.03 78.228 103.98 61.345 60.979 1311.7 356.16

the grinding wheel wear monitoring system performs quite well on wheel wear prediction.

6 Discussion There are several parameters that could affect the performance of the proposed grinding wheel wear monitoring system. Related to high-pass filter in preprocessing procedure is the cut-off frequency. Butterworth highpass filter with cut-off frequency of 90 kHz was used. With respect to the discrete wavelet decomposition procedure are the wavelet base and decomposition level. Coif2 wavelet with decomposition level of 5 was selected. Performance of BP neural network and its comparison with the proposed method were also discussed. 6.1 Performance under different cut-off frequencies As mentioned in Section 3.1, frequencies of idle running signals are generally lower than 90 kHz. Meanwhile, it is noticed that grinding signals frequency at 30 kHz increases significantly compared with idle running. An IIR Butterworth high-pass filter with cut-off frequency of 30 kHz was designed. Butterworth rather than Chebyshev filter was employed as it yielded better results in further classification. Performance using different cut-off frequencies, i.e., signals high-pass filtered at cut-off frequency of 30 kHz and signals high-pass filtered at cut-off frequencies of 90 kHz was compared and the classification result is shown in Table 8.

Table 7 Classification results

Apparently, both cutting conditions reveal better classification results with cut-off frequency of 90 kHz. The results show that the signals filtered out in higher frequency should contain more information about the wheel wear. As can be seen in Fig. 7, power in both the idle running and grinding period signals is mainly concentrated at lower frequencies, which could submerge the useful wheel wear information at higher frequencies. If, therefore, the AE signals are high-pass filtered out at cut-off frequency of 30 kHz, the results are poorer, since the part of the wheel wear information stored in the high frequencies are submerged by lower frequencies. It has to be mentioned that cut-off frequency of 90 kHz is not always the best for all wheel wear monitoring. As can be seen in Fig. 7, there is no apparent change at 90 kHz. The cut-off frequency was employed since it performed much better than lower frequencies. Moreover, the optimal cut-off frequency should be different at various cutting conditions. The results indicate that it is better to extract wheel wear information from the high frequencies than the low frequencies since useful information seemed to be submerged by noises at low frequencies. 6.2 Influence using different base wavelets The coif2 wavelet with level of 5 was employed in Section 3 for its orthogonality, compactly supported and its similarity to the shape of acoustic emission signals. However, as noted earlier, coif2 is not the only wavelet compliant with the above mentioned requirements. To evaluate the influence

Depth of cut is 10 μm States Sharp Worn

Prediction amount 19 35

Depth of cut is 20 μm Target amount

Result

States

20 35

99.39%

Sharp Worn

Prediction amount 40 10

Target amount

Result

40 10

100%


119

Table 8 Classification results using different cut-off frequencies Depth of cut is 10 μm

Depth of cut is 20 μm

States

Prediction amount

Target amount

Sharp Worn

19 35

20 35

Result 30 kHz 92.73%

90 kHz 99.39%

of different base wavelets and decomposition levels, we compared the classification results using db1, db2, db3, coif1, coif2, coif3, sym2, sym3, and sym4. The decomposition levels are varied from 2 to 12. Each classification process was repeated three times and the mean value was computed. Fig. 9 shows the results with the cut depth of 10 μm. It indicates that the classification accuracy is affected to some extent, but not very seriously. Coif3 at level 6, coif1 at level 6, db3 at level 7 and sym3 at level 7 perform not as well as others, but still higher than 90%. coif2, db1, db2, sym1, and sym2 maintain high classification accuracy at different levels, which could be used as the optimal wavelet bases. There are no obvious changes with the cut depth of 20 μm. Classification accuracy of 100% can be obtained using different base wavelets, except coif2 at level 4, coif3 at levels higher than 6 and db1 at levels higher than 8. The worst result of coif3 and db1 is 96%. Although several reports showed that the result of wavelet transform strongly depends on the type of the wavelet bases, results presented in our study indicate that the relationship is to some extent, not very serious. We attribute this to the signal preprocessing, to be more precise, the high-pass filtering process, in which noises were eliminated and useful information concerning wheel wear was extracted. 6.3 Comparison with BP neural network Before we employ support vector machine for classification, several other popular classification methods were considered,

States

Prediction amount

Target amount

Sharp Worn

40 10

40 10

Result 30 kHz 98%

90 kHz 100%

e.g., the standard SVM, BP neural network. It is noticed that Lezanski [5] and Salgado et al. [22] used neural network related methods for grinding wheel wear monitoring, and the results are quite encouraging. Hence, in this study, we constructed a wheel monitoring system using the popular BP neural network, as a comparison with the proposed GA based SVM method. The same input data sets as for the support vector machine system has been used in the neural network system. The input data was scaled to the range of [0, 1] using Eq. 4 and divided into two sets—a training set and a testing set. The goal of training is to optimize the network connection weights and minimize the difference between the outputs generated by the trained network and the outputs from the testing set. The inputs and outputs of the network have been determined yet, so the number of hidden layers and the number of nodes in hidden layers have to be selected and optimized. It has been proved that the three-layer network with sufficient number of nodes is able to model any mathematical function [23], and it was also found out that there was no improvement given by having two hidden layers in the network [22], so the hidden layer is limited to one. The number of nodes in the hidden layer is decided using weight pruning method [5]. The initial weights and the number of nodes are determined randomly and training is continued until a satisfied level of the RMS error is reached. Then, the number of nodes can be optimized by elimination of nodes whose absolute value of output weight is smaller than a certain percent of weights with the smallest value.

Fig. 9 Classification accuracy at different decomposition levels using: a coif1, coif2, coif3, b db1, db2, db3, c sym1, sym2 and sym3 with the cut depth of 10 μm

120

The initial network structure was set as 12–20–2. After pruning, the number of nodes in the hidden layer was reduced to 4 and the classification results under the two cutting conditions were obtained. The classification accuracy was 96% under the cutting depth of 20 μm, while however, the accuracy is a much lower at the cutting depth of 10 μm, which drops to 90.91%. There might be two reasons to explain the results. First, as discussed by Silva [24], the cutting condition interval is too narrow, and the neural network could hardly classify tool wear under such condition. On the other hand, the training samples are not sufficient enough to obtain the optimal network structure. It reveals that SVM performs better when sufficient grinding wheel wear samples are not available.

7 Conclusions In this study, a wavelet and support vector machine based grinding wheel wear monitoring system was proposed for the classification of sharp and worn wheels. Acoustic emission signals generated from the grinding zone were collected for further analysis. A new preprocessing method was developed based on wavelet decomposition to extract signals when grinding was actually performed and eliminate grinding noises. It was found that the further classification results performed better after denoising the signals using high-pass filter with cut-off frequency of 90 kHz. Each segmented signal was processed using five-level coif2 wavelet. The wavelet was chosen based on their properties and the performance in the classification procedure. Influence of different wavelet and decomposition levels were discussed in Section 6. The results presented in our study indicate that the classification accuracy is affected by different wavelet bases, but not as seriously as previous reports. We attribute this to the high-pass filtering process, in which noises were eliminated and useful information concerning wheel wear was extracted. The RMS and variance value of each decomposition coefficient were extracted to form feature vector [SFRMS, SFVAR]. After data scaling, the vector was input into the classification system constructed using SVM algorithm. GA method was introduced and the classification accuracy was designated as fitness function in GA method, so the optimal penalty parameter and kernel function parameter could be obtained. The experimental results show that classification accuracy could reach up to 99.39% with a cut depth of 10 μm and 100% at the cut depth of 20 μm. This indicates that the proposed method has a good performance for wheel wear prediction. The proposed methods are quite encouraging. The good classification accuracy guarantees its reliability, however,


only under the specific conditions. Since industrial applications are much more complicated, further improvement should be carried out in more complicated grinding conditions. Developing a more robust and universal on-line wheel wear monitoring system will be part of our future effort. Acknowledgments This study has been made possible with the supports from National Natural Science Foundation of China (Grant Nos. 71071138 and No. 50835008).

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