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Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator
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International Journal of Machine Tools & Manufacture 47 (2007) 1011–1020 www.elsevier.com/locate/ijmactool
Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator A.M. Bassiunya, Xiaoli Lib, a
Department of Mechanical Engineering, Faculty of Engineering, Helwan University, Helwan-Cairo, Egypt b Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China Received 3 April 2006; received in revised form 19 June 2006; accepted 20 June 2006 Available online 21 August 2006
Abstract Real time tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable monitoring techniques are required to provide fast response to the unexpected tool failure. Milling is one of the most fundamental machining operations. During the milling process, the current of feed motor is weakly related to the cutter condition, the change of power consumption is not significant to identify tool condition. Thus, current of motor-based tool condition still requires some new approaches to sort out significant pattern that could be employed to indicate tool condition. In this paper, a new approach is proposed to detect end mill flute breakage via the feed-motor current signals, which implements Hilbert–Huang transform (HHT) analysis and a smoothed nonlinear energy operator (SNEO) to extract the crucial characteristics from the measured signals to indicate tool breakage. Experiments on a CNC Vertical Machining Centre are presented to show the algorithm performance. The results show that this method is feasible and can accurately and efficiently monitor the conditions of the end mill under varying cutting conditions. r 2006 Elsevier Ltd. All rights reserved. Keywords: End milling; Tool flute breakage; Hilbert–Huang transform; Smoothed nonlinear energy operator; Feed-motor current
1. Introduction Sophisticated and expensive CNC milling machines are widely used in modern industry for improved productivity, better precision and variety of products. Failure of cutting tools in milling significantly decreases machining productivity and quality. Therefore, in-process tool condition monitoring and replacement of the damaged tool at the right time are very important to assure machining quality, system reliability and achieving fully automated manufacturing. Unfortunately, tool breakage detection in milling is difficult due to the complex nature of machining processes and the variable cutting conditions that affect the collected signals. It is desirable to develop a low cost and reliable tool breakage monitoring system for milling process. Corresponding author.
E-mail addresses: [email protected] (A.M. Bassiuny), [email protected] (X. Li). 0890-6955/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijmachtools.2006.06.016
Several methods have already been applied to effectively monitor tool conditions [1–5]. These methods can be classified into two categories: direct and indirect methods. Direct methods detect the actual tool breakage relying on optical sensing while indirect methods monitor the tool condition through the existing relationships between its parameters. Since it is difficult to employ direct methods due to inaccessibility of the machining area, indirect methods are preferred. In these methods, various signals such as cutting forces [1,2], acoustic emission (AE) [3], and vibrations [4] are employed to detect tool states. In order to improve the performance of existing tool condition monitoring system, computational intelligence was applied, such as back propagation net (BPN) [6], self-organized neural networks [7] as well as adaptive-network-based fuzzy inference system (ANFIS) [8] and support vector machine (SVM) [9]. Measurement of cutting forces and AE are the most common method that has been widely used to detect
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abnormalities during the cutting process [10,11]. Cutting force measurement generally requires the use of a dynamometer which is highly expensive and quite difficult to install for milling machine. The use of AE in end milling, however, has been less straightforward for some reasons. The end milling process is interrupted cutting operations where shock pulse loading occurs during the entry and exit of each individual tooth to the workpiece. The magnitude of these shock pulses may equal to those generated during tooth fractures. Accordingly, AE-based flute breakage monitoring leads to missed breakage events and excessive false alarms during end milling [12]. On the other hand, how to locate AE sensor to obtain AE signals from the rotating cutting tools is a difficult question. Vibration analysis also is a valuable method, which was widely used for tool condition monitoring [4]. However, its application in end milling is somewhat limited by the nature of an end milling process like AE-based method. To avoid the above-mentioned shortcomings, spindle motor current and feed driver motor current were used as monitoring signature for tool breakage [13]. Driver current monitoring technique is the best approach to acquire signals without additional sensors because the machine is not modified, even when current sensors are used [14,15]. In addition, the current from servomotors is available, in almost all of modern machine tools, directly from the servo-driver [16]. Briefly, the motor current based monitoring system is the best from the view of application and cost. The core issue of this method is to require a novel signal processing method to extract the useful pattern from current signals that will be able to indicate the state of cutting tools. Evaluation of cutting tool conditions from the acquired current signal is challenging because the current signals do not significantly reflect the cutting tool condition. Consequently, it is desirable to develop a novel method for providing an accurate interpretation about the cutting tool conditions. Previously, fuzzy and neuro-fuzzy systems were employed for monitoring of milling cutters using spindle motor current (e.g. [17]). In [10], wavelet analysis has proven its efficiency to determine tool breakage in drilling. However, there are some drawbacks in the wavelet-based technique. Wavelet is a priori basis method; it needs spurious harmonics to represent nonlinear waveform deformations and there is uncertainty principle limitation on time or frequency resolution from the convolution pairs based also on a priori basis. Since it deals with time–frequency domain, it needs a large amount of computation memory for its calculation process and a long processing time. Moreover, continuous wavelet transform is not appropriate for feature extraction. Standard discrete wavelet transform (DWT) is a very powerful tool for many non-stationary signal-processing applications, but it suffers from three major limitations: shift sensitivity, poor directionality, and the absence of phase information. The Hilbert–Huang transform (HHT) is a new method for analyzing nonlinear and non-stationary systems in
time–frequency space, which was proposed by Huang et al. [18,19]. In this method the complicated signal can be adaptively decomposed into a set of monocomponent signals, defined as intrinsic mode functions (IMFs) through empirical mode decomposition (EMD) process. With Hilbert transform, the IMFs yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. The final presentation of the results is a time–frequency-energy distribution, designated as the Hilbert spectrum [20]. The HHT method has many advantages over other methods, including wavelets and other extensions of Fourier analysis. It provides more precise definition of particular events in time–frequency space than wavelet analysis, and also a more physically meaningful interpretation of the underlying dynamic processes [21]. Because of its excellence, HHT has been studied extensively and widely applied in many fields [22,23]. In this paper, a new detection technique based on HHT analysis and a nonlinear energy operator is introduced for detecting tool breakage during end milling. Four-flute end mills are used during the experiments on a CNC Vertical Machining Centre (Mazak AJV 25/405). The feed-motor current signals are measured. Experimental results showed that the proposed method is feasible and can accurately and efficiently determine the conditions of the end mill under varying cutting conditions. The rest of the paper is organized as follows. In Section 2, the proposed detection method is presented and tested via simulation. In Section 3, the experimental setup is described and the main results are discussed. Finally we summarize our results and make some conclusions in Section 4. 2. The detection algorithm 2.1. Hilbert–Huang transform The HHT is an empirically based data-analysis method. Its basis of expansion is adaptive, so that it can produce physically meaningful representations of data from nonlinear and non-stationary processes [18]. This technique is based on direct extraction of the energy associated with the intrinsic time scales in the signal. This process generates a set of components, called the IMFs. The HHT consists of two steps: data ‘‘sifting’’ to generate the intrinsic modes (IMF), and application of the Hilbert transform to the intrinsic modes [19]. The algorithm to create IMFs establish with the definitions of local maxima and minima of the time series of the signal s(t) . The local maxima smax ðtÞ and minima smin ðtÞ are connected by a cubic spline line to produce respectively upper envelope us ðtÞ and lower envelope l s ðtÞ. Their mean is denoted as m1 ðtÞ and is given by m1 ðtÞ ¼
us ðtÞ þ l s ðtÞ . 2
(1)
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The difference between the original signal s(t) and the socalled ‘‘running mean’’ m1 ðtÞ is the first component h1 ðtÞ: h1 ðtÞ ¼ sðtÞ m1 ðtÞ.
(2)
The sifting process has to be repeated up to k times, as it is required to reduce the extracted signal to an IMF: h1k ðtÞ ¼ h1ðk1Þ ðtÞ m1k ðtÞ,
(3)
where subsequent component h1(k1)(t) is treated as the original signal. The resulting time series is the first IMF: c1(t) ¼ h1k(t). To check if h1k(t) is an IMF, the following conditions must be fulfilled [18,19]: (1) the difference between the numbers of extrema and zero-crossings is of p1; and (2) the mean of the upper envelop (linked by local maxima) and the lower envelop (linked by local minima) is zero at every point. The criterion for the sifting process to stop can be the size of the standard deviation, computed from the two consecutive sifting results as " # T jh1ðk1Þ ðtÞ h1k ðtÞj2 1X s¼ , (4) m t¼0 h21ðk1Þ ðtÞ where m is the maximum number of the original signal digitizing rate cells, T is the total length of the signal. A typical value for s can be smaller than 0.3. The first IMF c1(t) is subtracted from the original signal: r1 ðtÞ ¼ sðtÞ c1 ðtÞ. This difference is called as the residue r1(t). It is treated as the new signal and subjected to the same sifting process. The process of finding intrinsic modes cj continues until the final residue rn(t) will be a constant or a monotonic function. Then it is achieved a decomposition of the original signal into n-empirical modes and a residue: sðtÞ ¼
n X
cj þ r n .
(5)
j¼1
The second step is to apply the Hilbert transform to the decomposed IMFs. Each IMF component has its Hilbert transform yi ðtÞ: Z 1 1 cj ðtÞ dt. (6) yj ðtÞ ¼ p 1 t t With this definition the analytic signal zi ðtÞ is defined as zj ðtÞ ¼ cj ðtÞ þ iyj ðtÞ ¼ aðtÞeiyðtÞ .
(7) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The instantaneous amplitude aj ðtÞ ¼ c2j ðtÞ þ y2j and
phase yj ðtÞ ¼ arctan½yj ðtÞ=cj ðtÞ of each IMF of the motor current signal can be gained by above Hilbert transforms. To analyze the analytic signal zj(t) the Hilbert amplitude spectrum H(o, t) is used. Therefore, one can define an instantaneous frequency oj given by dyj ðtÞ . dt Thus the original signal can be expressed as R hX i sðtÞ ¼ Re aj ðtÞei oj ðtÞ dt . oj ðtÞ ¼
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This equation enables us to represent the amplitude (or the energy) and the instantaneous frequency of the motor current signal in a three-dimensional plot. In the Hilbert spectrum we can see the distribution of the signal energy in the time domain. A peak in the spectrum indicates that is highly probable that a wave of that frequency appeared at that particular point in the time interval considered [19–22]. 2.2. Energy operator The nonlinear energy operator NEO, which uses the Teager–Kaiser Energy Operator, is regarded as an efficient tool for detecting abrupt changes of the signal because of its sensitivity to instantaneous changes in frequencydependent energy [23]. The output of the NEO is proportional to the product of the instantaneous amplitude and frequency of the input signal, and hence highlights the breakage events in the signal. For a discrete-time series, the nonlinear energy operator is defined as [23] C½xðnÞ ¼ x2 ðnÞ xðn þ 1Þxðn 1Þ
(10)
Using Bartlett window applied to the output of the NEO can help in better localizing the local maxima [24–26]. The process that combines the NEO and the windowing is called smoothed nonlinear energy operator (SNEO). Bartlett window, when applied to the output of the NEO, magnifies the local pointed peaks. The SNEO can be defined as follows [25]: Cs ½xðnÞ ¼ C½xðnÞ oðnÞ,
(11)
where is the convolution operator and o(n) is a smoothing window function. SNEO is dependent on the square of both the amplitude and the frequency of the signal, and shows high energy for a high-frequency condition. Consequently, the output of the SNEO can be considered as the instantaneous energy of the highpass filtered version of the signal. For a linear combination of source signal resulting from the normal cutting conditions and the transient signals caused by the tool breakage, i.e., yðnÞ ¼ xðnÞ þ sðnÞ, where x and s are uncorrelated, the expected energy applying SNEO to is expressed by syðnÞ ¼ Cs ½yðnÞ ¼ Cs ½xðnÞ þ Cs ½sðnÞ.
(12)
For tool breakage cases, Cs ½xðnÞ ¼ Cs ½sðnÞ; syðnÞ Cs ½sðnÞ, while for normal conditions, Cs ½sðnÞ 0; syðnÞ Cs ½xðnÞ. Using this property of SNEO, the detection problem is reduced to finding an appropriate threshold T that separates the high energy regions from background signal regions by the equation: syðnÞ4T.
(8) 2.3. Thresholding (9)
The output of the SNEO has to be threshold to indicate the tool condition. The value of the threshold is selected
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experimentally. Simply calculate the peak value of the current data from SNEO, and compare it with the peak value in the case of normal cutting conditions. The peak value is computed as p ¼ maxfCs ½yðnÞ; 1; 2; . . . ; ng.
(13)
2.4. The proposed method The proposed method for tool breakage detection in end milling consists of the following main steps:
Sampling current signals by a frequency of 1000 Hz, and collecting a segment of data of 0.064 s for processing. The segment is very important for real time system; the selection should consider the flute number and rotation speed of spindle. Decompose the sampled signal into a set of monocomponent signals, IMFs, as described in Section 2.1. Remove the IMF components with small amplitudes and that with high frequencies from analysis (in tool breakage, signal amplitude is the most crucial). De-noise the remaining components, if necessary. Compute phase, instantaneous amplitude and instantaneous frequency of each IMF. Compute the Hilbert energy of the combined IMF. This is done by summing the instantaneous amplitudes (or squared amplitudes) of the selected IMFs. Applying SNEO to the combined Hilbert energy. The window size is determined based on a series of experiments. (Bartlett window of size 20 is appropriate for most cases, except for minor cutting the size is 120). Thresholding: the extracted signals result from SNEO is thresholded to indicate the tool condition.
2.5. Simulation study For the purpose of evaluating the performance of the proposed method we use the following synthetic signal: sðtÞ ¼ xðtÞ þ vðtÞ,
(14)
where x(t) and v(t) are the background signal and the artificial fault set, respectively. The background is chosen: xðtÞ ¼ sinðotÞ sinð2ot þ fÞ þ sinð4otÞ, fo ¼ 0:027p; f ¼ p=2g. Fig. 1(a) shows 1200 samples of x(t). The faults signal v(t) shown in Fig. 2(b) includes two peaks at instants 360 and 850 and are distributed over the background signal as shown in Fig. 2(c). The algorithm, when applied to the signal, yields seven IMF components (C1–C7) as shown in Fig. 2 with the last component indicating the residual. Because the time scales of the signal s(t) are different, the IMF components can be easily divided into two groups: the high-frequency signal representing the x(t); and the last low-frequency components representing v(t).
Fig. 1. The simulated signal.
The instantaneous energy of the IMF components are calculated and used to compute Hilbert power. Fig. 3 gives the Hilbert power and the output of the SNEO and its time frequency. As it can be seen, all faults have been successfully detected. It is obvious that the proposed method can efficiently indicate the faults for the simulated signal at their occurrence times. 3. Experiment The schematic diagram of the experimental set-up used for investigating the above method is shown in Fig. 4. The experiments are performed on a CNC Vertical Machining Centre (Mazak AJV 25/405) with AC permanent magnet synchronous motors. Three current sensors (PCB Mounting Hall Effect Current Transducer, Stock No. 286-327 (RS Components Ltd)) are used to measure the three-phases currents of feed-motor: IU, IV, and IW. The signals are first passed though low-pass filters with a cut-off-frequency of 50 Hz and then sent via an A/D converter to a personal computer. A good breakage detection algorithm must be sensitive to the changes in tool conditions and insensitive to the variations of cutting parameters. To evaluate the performance of the proposed method, a series of experiments are conducted under different cutting conditions. Table 1 shows the tool parameters and cutting conditions used in this work. These tests included entry/exit cuts, flute breakage during steady-state cutting, changed cutting parameters; and flute breakage on entry/exit cuts. All of the tests were performed under dry conditions in down milling mode; the sampling frequency was set as 1 kHz. Additionally, the maximum tooth frequency used in the tests should be less than 67 Hz; this is determined by the bandwidth of feed servo system.
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Fig. 2. The seven IMF components for the signal sðtÞ ¼ xðtÞ þ nðtÞ.
The root mean square (RMS) of feed-motor current, I RMS , is used for converting the AC current to the equivalent DC current and can be calculated by rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2 ½I þ I 2v þ I 2w . I RMS ¼ 3 u For the feed-motor current generated during end milling under the bandwidth of the feed drive system, the frequency is equal to the reciprocal of the tooth passing frequency, such that: f p ¼ N f N s =60, where Nf is the number of cutting flutes, Ns is the cutter rotational speed in r/min (rpm). Given the sampling frequency fs, the number of data points per period is: M ¼ f p =f s . Thus the number of periods contained in a complete sample data is: P ¼ N=M [12]. 4. Results 4.1. Analysis of feed-motor current signal Fig. 5 displays the feed-motor current signals collected at a spindle speed of 600 rpm and the feed speed is 120 mm/min using four flute, 8 mm end mill. The radial depth of cut and axial depth of cut are 2 and 4 mm, respectively. The first part of the signal represents the current at no load. The cutting process starts at 5 s where the current signal jumps up, and the machine begins the period of steady-state cut (5–35 s). The signal stability is interrupted, after 35 s, when flute breakage occurs as shown in Fig. 5. This change in the feed-motor current signature may occur due to two reasons. First the broken flute encounters a reduced chip load, while the other flutes must absorb an increased chip
load for compensating for the broken flute. Second, the fracture tool material becomes ‘‘jammed’’ between the workpiece and subsequent flutes. This greatly increases the cutting force [7], and consequently increasing the feedmotor current [12]. The cutting ends at about 42 s, where the feed-current drops down again. During the steady-state cutting, the pattern of the feedmotor current signal is similar to that of feed cutting force as shown in Fig. 6. The figure shows that the variations of motor current and cutting force are similar to a regular periodic signal. Fig. 7 shows the current signature of normal and broken tools. The figure shows that the period for one rotation of spindle is approximately 0.1 s corresponding to a spindle speed of 600 rpm. There are four peaks during one revolution where each one represents the cutting process of one individual tooth (Fig. 7(a)). When breakage occurs, the profile of the signature is distorted due to the effect of tooth breakage as shown in Fig. 7(b). The reduction of the current amperage in this case may be due to the reduced thickness of chip for the broken tooth. 4.2. HHT-SNEO tool breakage detection To test the performance of the new detection method, a lot of real machining signals are analyzed. Fig. 8 shows the nine components of the IMFs (C1–C9) obtained using the sifting method for normal cutting operation (right) and in the case of tool breakage (left). The components are listed from high to low frequency. The last one (res.) is a monotonic function and represents the central tendency of the signal.
ARTICLE IN PRESS A.M. Bassiuny, X. Li / International Journal of Machine Tools & Manufacture 47 (2007) 1011–1020 a-Signal 2 0 -2 0
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b-Hilbert Power 8 6 4 2 0
200
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SNEO 4 2 0 TF-Representation 0.25
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1016
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Fig. 3. The output of the HHT-SNEO.
Fig. 4. The schematic diagram of experimental setup.
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It can be seen that the amplitudes of Ci(t) varies from one IMF to another, and more particularly the amplitudes of some IMFs (e.g. C8–C9) are much smaller than other IMFs. Because these amplitudes of C are very small, they have no physical sense and can be removed from the analysis. Fig. 8 also shows that most of the noise in the data is contained in C1 and C2 component and can be easily separated from the data. The first three modes are almost similar in the two cases and thus it may represent the information about the background signal. The differences in the waveform of the fourth mode are quite clear. It can be observed that the fourth IMF component has higher amplitude in the case of tool breakage than the corresponding one in the case of normal cutting. Obviously, C4 contains the main information on defects. Fig. 9 shows the instantaneous amplitudes of C4 components. The most informative IMFs, however, depends on the cutting conditions. To automate the selection of these IMFs, a thorough analysis of feed-motor current under different cutting conditions was carried out. Based on this analysis, it was found that the number of IMF components varies by changing the cutting conditions. The most informative IMFs that indicate the tool condition can be selected automatically by first removing the residual and
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considering only the components in the range of (Nimf-6 to Nimf-4). Here Nimf refers to the total number of IMF components in the decomposed signal. Thus the signal to disturbance ratio can be greatly improved and the rest of the IMF components are then pre-processed. However, to enhance the required signal, wavelet denoising, level two is applied to reduce the noise level in the selected IMFs. The instantaneous energy of each component is calculated and combined to form a new signal for further analysis as shown in Fig. 10(b). Smoothed nonlinear energy operator (SNEO) is then applied on the energy signal to emphasize the breakage-waves that corrupt the pure feedmotor current signals. A 120-point Bartlett window was found to be appropriate for all cutting conditions listed in
Fig. 6. Feed-motor current signal (left) and cutting force signal (right) during the same period of normal cutting.
Table 1 Experimental condition Tool Material Cutting conditions
Milling method
HSS Flute/diameter: 2/8; 4/8; 4/6 45# Steel. Spindle speed 300, 450, 600, 900, 1200 rpm Feed rate 90, 120 mm/min Axial depth of cut: 1–6 mm Radial depth of cut: 1–4 mm Down cut—without coolant
Fig. 7. Feed-motor current signals for normal tool (left) and broken tool (right).
Fig. 5. Feed-motor current signals from normal tool condition to tool flute breakage in end milling.
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Fig. 8. Decomposition by the sifting method of the feed-motor current signal shown in Fig. 5 for normal (left) and broken (right) tools.
Fig. 9. Instantaneous amplitudes of C4 for normal and breakage tools.
Fig. 10. Output of the detection algorithm: (a) original signal, (b) combined instantaneous energy, (c) SNEO output and (d) threshold result.
Table 1. Fig. 10(c) shows how the SNEO highlights the breakage events in the energy signal. The output of the SNEO is threshold to separates the breakage regions from background signal regions as shown
in Fig. 10(d). The value of the threshold depends on the energy level in SNEO output signal, Emax, which is strongly, related the cutting conditions. A threshold value of 0.37Emax was found to be suitable for all investigated
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conditions. Thus the process of IMFs and threshold selection can be automated. It is obvious for the analysis of the case that the proposed method can indicate the breakage accurately. The sensitivity of the algorithm for the slight changes in feed-motor current, as in the case of minor cutting edge fracture, is investigated. Fig. 11 shows the wave form during one revolution when one flute of cutting tools is broken. The flute fracture occurs at around second 45.15 during the steady cutting state. Fig. 12(a) shows a segment of feedmotor current signals during the period (44–46.5 s). Fig. 12(b) shows the marked features derived from the feedmotor current signal as an output of SNEO. The time– frequency representation is shown in Fig. 12(c). As shown from the output, Fig. 12(d), a peak at around second 45.4 is found to indicate the small fracture of cutting edge. This results in a delay of about 0.35 s. Clearly, the algorithm can indicate the small variations of feed-current signal due to small edge fracture successfully and at the proper time during end milling.
Fig. 11. Wave forms of one insert breakage.
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The results displayed in Figs. 10–12 show that the HHTSNEO method is powerful for analyzing the breakage data. It provides a more precise definition and meaningful interpretation of the breakage in time–frequency space than other analysis methods.
5. Conclusions In this paper we present a new method for tool condition monitoring in end milling. The primary goal is to detect flute breakage during such operations. The method is based on the application of a new time–frequency signal processing method called the ‘‘Hilbert-Huang method’’ for decomposition of the original signal. To highlights the breakage events in the signal, we apply a smoothed nonlinear energy operator on the signal representing Hilbert power. The method is verified by simulation and experimentally. Experimental results on a CNC Vertical Machining Centre are presented to show the performance of the algorithm. The analysis verified that the feed-motor current is directly related to the cutting force and hence, the motor current signature is used to extract the crucial characteristics of the end mill during machining operation. The most informative IMFs that indicate the tool condition and the threshold value are automatically selected. The results reveal that the combination of the HHT and SNEO can successfully detect the tool breakage even in the case of small fracture. The feasibility of the proposed method to accurately and efficiently determine the conditions of the end mill under varying cutting conditions is proved. However, real time implementation of the proposed method still needs a little work in the near future.
Fig. 12. Feed-motor current signals and detection results for small cutting edge fracture.
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Acknowledgements The authors would like to thank the Editor and two anonymous reviewers for constructive comments that led to an improved manuscript. The part work is supported by National Natural Science Foundation of China (No. 60575012). References [1] P.-T. Huang, J.C. Chen, C.-Y. Chou, A statistical approach in detecting tool breakage in end milling operations, Journal of Industrial Technology 15 (3) (1999) 1–7. [2] R. Romero-Troncoso, G. Herrera-Ruiz, I. Terol-Villalobos, C. Jauregui-Correa, FPGA based on-line tool breakage detection system for CNC milling machines, Mechatronics 14 (2004) 439–454. [3] E. Govekar, J. Gradisek, I. Grabe, Analysis of acoustic emission signals and monitoring of machining processes, Ultrasonic 38 (2000) 598–603. [4] I. Yesilyurt, End mill breakage detection using mean frequency analysis of scalogram, International Journal of Machine Tools & Manufacture 46 (2006) 450–458. [5] D. Dimla Snr, Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods, International Journal of Machine Tools & Manufacture 40 (2000) 1073–1098. [6] P.-T. Huang, J.C. Chen, Neural network-based tool breakage monitoring system for end milling operations, Journal of Industrial Technology 16 (2) (2000). [7] S. Zhang, T. Asakura, S. Hayashi, Drill breakage detection and prediction using self-organized neural networks, SICE Annual Conference in Sapporo, August 4–6, Hokkaido Institute of Technology, Japan, 2004, pp. 153–158. [8] S.-P. Lo, The application of an ANFIS and grey system method in turning tool-failure detection, International Journal of Advanced Manufacturing Technology 19 (2002) 564–572. [9] C. Sohyung, A. Shihab, O. Arzu, K. Nandita, Tool breakage detection using support vector machine learning in a milling process, International Journal of Machine Tools & Manufacture 45 (2005) 241–249. [10] Xiaoli Li, D. Shen, Y. Zhejun, Discrete wavelet transform for tool breakage monitoring, International Journal of Machine Tools & Manufacture 39 (1999) 1935–1944. [11] G. Pontuale, F.A. Farrelly, A. Petri, L. Pitolli, A statistical analysis of acoustic emission signals for tool condition monitoring (TCM), Acoustics Research Letters Online, Acoustical Society of America, 2003. [12] Xiaoli Li, Detection of tool flute breakage in end milling using Feedmotor current signatures, IEEE/ASME Transactions on Mechatronics 6 (4) (2001) 491–498.
[13] E. Jantunen, A summary of methods applied to tool condition monitoring in drilling, International Journal of Machine Tools & Manufacture 42 (2002) 997–1010. [14] R. Romero-Troncoso, G. Herrera-Ruiz, I. Terol-Villalobos, J. Jauregui-Correa, Driver current analysis for sensorless tool breakage monitoring of CNC milling machines, International Journal of Machine Tools & Manufacture 43 (2003) 1529–1534. [15] Xiaoli Li, R. Du, B. Denkena, J. Imiela, Tool breakage monitoring using motor current signals for machine tools with linear motors, IEEE Transactions on Industrial Electronics 52 (5) (2005) 1403–1408. [16] A. Luis, H. Gilberto, P. Rocio, R. Rene, L. Wbaldo, Sensorless tool failure monitoring system for drilling machines, International Journal of Machine Tools & Manufacture 46 (2006) 381–386. [17] G.D. Kim, C.N. Liu, In-process tool fracture monitoring in face milling using spindle motor current fracture index, International Journal of Advanced Manufacturing Technology 18 (2001) 383–389. [18] N. Huang, S. Samuel, editors. Hilbert-Huang Transform and its Application, World Scientific Publishing, 2005 (Chapter 1) available at http://www.worldscibooks.com/mathematics/5862.html [19] P. Flandrin, P. Gonc- alve`s, Empirical mode decompositions as datadriven wavelet-like expansions, International Journal of Wavelets, Multiresolution and Information Processing 5 (2004) 1–28. [20] F. Robert, I. Eric, Application of Hilbert–Huang transform for improved defect detection in Terahertz NDE of Shuttle Tiles, SPIE International Symposia on Smart Structures & Materials/NDE, San Diego, California, March 6–10, 2005 /http://techreports.larc.nasa.govS. [21] J.N. Yang, Y. Lei, S. Lin, N. Huang, Hilbert-Huang based approach for structural damage detection, Journal of Engineering Mechanics 130 (2004) 85–95. [22] N. Huang, W. Man-Li, Q. Wendong, R. Steven, S. Samuel, E. Jin, Applications of Hilbert–Huang transform to non-stationary financial time series analysis, Applied Stochastic Models in Business and Industry 19 (2003) 245–268. [23] H. Hamid, M. Mostefa, B. Boualem, EEG Spike Detection Using Time–frequency Signal Analysis. V-421–V-424, ICASSP IEEE, 2004. [24] S. Mukhopadhyay, G. Ray, A new interpretation of nonlinear energy operator and its efficacy in spike detection, IEEE Transactions on Biomedical Engineering 45 (1998) 180–187. [25] P. Hae-Jeong, J. Do-Un, P. Kwang-Suk, Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method, IEEE Transactions on Biomedical Engineering 49 (12) (2002) 1526–1533. [26] S. Cui, X. Li, G. Ouyang, X. Guan, Detection of epileptic spikes with empirical mode decomposition and nonlinear energy operator. International symposium on Neural Networks, Chongqing, China, May 2005, Lecture Notes in Computer Science, Springer, Vol. 3498/ 2005, pp. 445–450.
International Journal of Machine Tools & Manufacture 47 (2007) 1011–1020 www.elsevier.com/locate/ijmactool
Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator A.M. Bassiunya, Xiaoli Lib, a
Department of Mechanical Engineering, Faculty of Engineering, Helwan University, Helwan-Cairo, Egypt b Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China Received 3 April 2006; received in revised form 19 June 2006; accepted 20 June 2006 Available online 21 August 2006
Abstract Real time tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable monitoring techniques are required to provide fast response to the unexpected tool failure. Milling is one of the most fundamental machining operations. During the milling process, the current of feed motor is weakly related to the cutter condition, the change of power consumption is not significant to identify tool condition. Thus, current of motor-based tool condition still requires some new approaches to sort out significant pattern that could be employed to indicate tool condition. In this paper, a new approach is proposed to detect end mill flute breakage via the feed-motor current signals, which implements Hilbert–Huang transform (HHT) analysis and a smoothed nonlinear energy operator (SNEO) to extract the crucial characteristics from the measured signals to indicate tool breakage. Experiments on a CNC Vertical Machining Centre are presented to show the algorithm performance. The results show that this method is feasible and can accurately and efficiently monitor the conditions of the end mill under varying cutting conditions. r 2006 Elsevier Ltd. All rights reserved. Keywords: End milling; Tool flute breakage; Hilbert–Huang transform; Smoothed nonlinear energy operator; Feed-motor current
1. Introduction Sophisticated and expensive CNC milling machines are widely used in modern industry for improved productivity, better precision and variety of products. Failure of cutting tools in milling significantly decreases machining productivity and quality. Therefore, in-process tool condition monitoring and replacement of the damaged tool at the right time are very important to assure machining quality, system reliability and achieving fully automated manufacturing. Unfortunately, tool breakage detection in milling is difficult due to the complex nature of machining processes and the variable cutting conditions that affect the collected signals. It is desirable to develop a low cost and reliable tool breakage monitoring system for milling process. Corresponding author.
E-mail addresses: [email protected] (A.M. Bassiuny), [email protected] (X. Li). 0890-6955/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijmachtools.2006.06.016
Several methods have already been applied to effectively monitor tool conditions [1–5]. These methods can be classified into two categories: direct and indirect methods. Direct methods detect the actual tool breakage relying on optical sensing while indirect methods monitor the tool condition through the existing relationships between its parameters. Since it is difficult to employ direct methods due to inaccessibility of the machining area, indirect methods are preferred. In these methods, various signals such as cutting forces [1,2], acoustic emission (AE) [3], and vibrations [4] are employed to detect tool states. In order to improve the performance of existing tool condition monitoring system, computational intelligence was applied, such as back propagation net (BPN) [6], self-organized neural networks [7] as well as adaptive-network-based fuzzy inference system (ANFIS) [8] and support vector machine (SVM) [9]. Measurement of cutting forces and AE are the most common method that has been widely used to detect
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abnormalities during the cutting process [10,11]. Cutting force measurement generally requires the use of a dynamometer which is highly expensive and quite difficult to install for milling machine. The use of AE in end milling, however, has been less straightforward for some reasons. The end milling process is interrupted cutting operations where shock pulse loading occurs during the entry and exit of each individual tooth to the workpiece. The magnitude of these shock pulses may equal to those generated during tooth fractures. Accordingly, AE-based flute breakage monitoring leads to missed breakage events and excessive false alarms during end milling [12]. On the other hand, how to locate AE sensor to obtain AE signals from the rotating cutting tools is a difficult question. Vibration analysis also is a valuable method, which was widely used for tool condition monitoring [4]. However, its application in end milling is somewhat limited by the nature of an end milling process like AE-based method. To avoid the above-mentioned shortcomings, spindle motor current and feed driver motor current were used as monitoring signature for tool breakage [13]. Driver current monitoring technique is the best approach to acquire signals without additional sensors because the machine is not modified, even when current sensors are used [14,15]. In addition, the current from servomotors is available, in almost all of modern machine tools, directly from the servo-driver [16]. Briefly, the motor current based monitoring system is the best from the view of application and cost. The core issue of this method is to require a novel signal processing method to extract the useful pattern from current signals that will be able to indicate the state of cutting tools. Evaluation of cutting tool conditions from the acquired current signal is challenging because the current signals do not significantly reflect the cutting tool condition. Consequently, it is desirable to develop a novel method for providing an accurate interpretation about the cutting tool conditions. Previously, fuzzy and neuro-fuzzy systems were employed for monitoring of milling cutters using spindle motor current (e.g. [17]). In [10], wavelet analysis has proven its efficiency to determine tool breakage in drilling. However, there are some drawbacks in the wavelet-based technique. Wavelet is a priori basis method; it needs spurious harmonics to represent nonlinear waveform deformations and there is uncertainty principle limitation on time or frequency resolution from the convolution pairs based also on a priori basis. Since it deals with time–frequency domain, it needs a large amount of computation memory for its calculation process and a long processing time. Moreover, continuous wavelet transform is not appropriate for feature extraction. Standard discrete wavelet transform (DWT) is a very powerful tool for many non-stationary signal-processing applications, but it suffers from three major limitations: shift sensitivity, poor directionality, and the absence of phase information. The Hilbert–Huang transform (HHT) is a new method for analyzing nonlinear and non-stationary systems in
time–frequency space, which was proposed by Huang et al. [18,19]. In this method the complicated signal can be adaptively decomposed into a set of monocomponent signals, defined as intrinsic mode functions (IMFs) through empirical mode decomposition (EMD) process. With Hilbert transform, the IMFs yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. The final presentation of the results is a time–frequency-energy distribution, designated as the Hilbert spectrum [20]. The HHT method has many advantages over other methods, including wavelets and other extensions of Fourier analysis. It provides more precise definition of particular events in time–frequency space than wavelet analysis, and also a more physically meaningful interpretation of the underlying dynamic processes [21]. Because of its excellence, HHT has been studied extensively and widely applied in many fields [22,23]. In this paper, a new detection technique based on HHT analysis and a nonlinear energy operator is introduced for detecting tool breakage during end milling. Four-flute end mills are used during the experiments on a CNC Vertical Machining Centre (Mazak AJV 25/405). The feed-motor current signals are measured. Experimental results showed that the proposed method is feasible and can accurately and efficiently determine the conditions of the end mill under varying cutting conditions. The rest of the paper is organized as follows. In Section 2, the proposed detection method is presented and tested via simulation. In Section 3, the experimental setup is described and the main results are discussed. Finally we summarize our results and make some conclusions in Section 4. 2. The detection algorithm 2.1. Hilbert–Huang transform The HHT is an empirically based data-analysis method. Its basis of expansion is adaptive, so that it can produce physically meaningful representations of data from nonlinear and non-stationary processes [18]. This technique is based on direct extraction of the energy associated with the intrinsic time scales in the signal. This process generates a set of components, called the IMFs. The HHT consists of two steps: data ‘‘sifting’’ to generate the intrinsic modes (IMF), and application of the Hilbert transform to the intrinsic modes [19]. The algorithm to create IMFs establish with the definitions of local maxima and minima of the time series of the signal s(t) . The local maxima smax ðtÞ and minima smin ðtÞ are connected by a cubic spline line to produce respectively upper envelope us ðtÞ and lower envelope l s ðtÞ. Their mean is denoted as m1 ðtÞ and is given by m1 ðtÞ ¼
us ðtÞ þ l s ðtÞ . 2
(1)
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The difference between the original signal s(t) and the socalled ‘‘running mean’’ m1 ðtÞ is the first component h1 ðtÞ: h1 ðtÞ ¼ sðtÞ m1 ðtÞ.
(2)
The sifting process has to be repeated up to k times, as it is required to reduce the extracted signal to an IMF: h1k ðtÞ ¼ h1ðk1Þ ðtÞ m1k ðtÞ,
(3)
where subsequent component h1(k1)(t) is treated as the original signal. The resulting time series is the first IMF: c1(t) ¼ h1k(t). To check if h1k(t) is an IMF, the following conditions must be fulfilled [18,19]: (1) the difference between the numbers of extrema and zero-crossings is of p1; and (2) the mean of the upper envelop (linked by local maxima) and the lower envelop (linked by local minima) is zero at every point. The criterion for the sifting process to stop can be the size of the standard deviation, computed from the two consecutive sifting results as " # T jh1ðk1Þ ðtÞ h1k ðtÞj2 1X s¼ , (4) m t¼0 h21ðk1Þ ðtÞ where m is the maximum number of the original signal digitizing rate cells, T is the total length of the signal. A typical value for s can be smaller than 0.3. The first IMF c1(t) is subtracted from the original signal: r1 ðtÞ ¼ sðtÞ c1 ðtÞ. This difference is called as the residue r1(t). It is treated as the new signal and subjected to the same sifting process. The process of finding intrinsic modes cj continues until the final residue rn(t) will be a constant or a monotonic function. Then it is achieved a decomposition of the original signal into n-empirical modes and a residue: sðtÞ ¼
n X
cj þ r n .
(5)
j¼1
The second step is to apply the Hilbert transform to the decomposed IMFs. Each IMF component has its Hilbert transform yi ðtÞ: Z 1 1 cj ðtÞ dt. (6) yj ðtÞ ¼ p 1 t t With this definition the analytic signal zi ðtÞ is defined as zj ðtÞ ¼ cj ðtÞ þ iyj ðtÞ ¼ aðtÞeiyðtÞ .
(7) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The instantaneous amplitude aj ðtÞ ¼ c2j ðtÞ þ y2j and
phase yj ðtÞ ¼ arctan½yj ðtÞ=cj ðtÞ of each IMF of the motor current signal can be gained by above Hilbert transforms. To analyze the analytic signal zj(t) the Hilbert amplitude spectrum H(o, t) is used. Therefore, one can define an instantaneous frequency oj given by dyj ðtÞ . dt Thus the original signal can be expressed as R hX i sðtÞ ¼ Re aj ðtÞei oj ðtÞ dt . oj ðtÞ ¼
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This equation enables us to represent the amplitude (or the energy) and the instantaneous frequency of the motor current signal in a three-dimensional plot. In the Hilbert spectrum we can see the distribution of the signal energy in the time domain. A peak in the spectrum indicates that is highly probable that a wave of that frequency appeared at that particular point in the time interval considered [19–22]. 2.2. Energy operator The nonlinear energy operator NEO, which uses the Teager–Kaiser Energy Operator, is regarded as an efficient tool for detecting abrupt changes of the signal because of its sensitivity to instantaneous changes in frequencydependent energy [23]. The output of the NEO is proportional to the product of the instantaneous amplitude and frequency of the input signal, and hence highlights the breakage events in the signal. For a discrete-time series, the nonlinear energy operator is defined as [23] C½xðnÞ ¼ x2 ðnÞ xðn þ 1Þxðn 1Þ
(10)
Using Bartlett window applied to the output of the NEO can help in better localizing the local maxima [24–26]. The process that combines the NEO and the windowing is called smoothed nonlinear energy operator (SNEO). Bartlett window, when applied to the output of the NEO, magnifies the local pointed peaks. The SNEO can be defined as follows [25]: Cs ½xðnÞ ¼ C½xðnÞ oðnÞ,
(11)
where is the convolution operator and o(n) is a smoothing window function. SNEO is dependent on the square of both the amplitude and the frequency of the signal, and shows high energy for a high-frequency condition. Consequently, the output of the SNEO can be considered as the instantaneous energy of the highpass filtered version of the signal. For a linear combination of source signal resulting from the normal cutting conditions and the transient signals caused by the tool breakage, i.e., yðnÞ ¼ xðnÞ þ sðnÞ, where x and s are uncorrelated, the expected energy applying SNEO to is expressed by syðnÞ ¼ Cs ½yðnÞ ¼ Cs ½xðnÞ þ Cs ½sðnÞ.
(12)
For tool breakage cases, Cs ½xðnÞ ¼ Cs ½sðnÞ; syðnÞ Cs ½sðnÞ, while for normal conditions, Cs ½sðnÞ 0; syðnÞ Cs ½xðnÞ. Using this property of SNEO, the detection problem is reduced to finding an appropriate threshold T that separates the high energy regions from background signal regions by the equation: syðnÞ4T.
(8) 2.3. Thresholding (9)
The output of the SNEO has to be threshold to indicate the tool condition. The value of the threshold is selected
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experimentally. Simply calculate the peak value of the current data from SNEO, and compare it with the peak value in the case of normal cutting conditions. The peak value is computed as p ¼ maxfCs ½yðnÞ; 1; 2; . . . ; ng.
(13)
2.4. The proposed method The proposed method for tool breakage detection in end milling consists of the following main steps:
Sampling current signals by a frequency of 1000 Hz, and collecting a segment of data of 0.064 s for processing. The segment is very important for real time system; the selection should consider the flute number and rotation speed of spindle. Decompose the sampled signal into a set of monocomponent signals, IMFs, as described in Section 2.1. Remove the IMF components with small amplitudes and that with high frequencies from analysis (in tool breakage, signal amplitude is the most crucial). De-noise the remaining components, if necessary. Compute phase, instantaneous amplitude and instantaneous frequency of each IMF. Compute the Hilbert energy of the combined IMF. This is done by summing the instantaneous amplitudes (or squared amplitudes) of the selected IMFs. Applying SNEO to the combined Hilbert energy. The window size is determined based on a series of experiments. (Bartlett window of size 20 is appropriate for most cases, except for minor cutting the size is 120). Thresholding: the extracted signals result from SNEO is thresholded to indicate the tool condition.
2.5. Simulation study For the purpose of evaluating the performance of the proposed method we use the following synthetic signal: sðtÞ ¼ xðtÞ þ vðtÞ,
(14)
where x(t) and v(t) are the background signal and the artificial fault set, respectively. The background is chosen: xðtÞ ¼ sinðotÞ sinð2ot þ fÞ þ sinð4otÞ, fo ¼ 0:027p; f ¼ p=2g. Fig. 1(a) shows 1200 samples of x(t). The faults signal v(t) shown in Fig. 2(b) includes two peaks at instants 360 and 850 and are distributed over the background signal as shown in Fig. 2(c). The algorithm, when applied to the signal, yields seven IMF components (C1–C7) as shown in Fig. 2 with the last component indicating the residual. Because the time scales of the signal s(t) are different, the IMF components can be easily divided into two groups: the high-frequency signal representing the x(t); and the last low-frequency components representing v(t).
Fig. 1. The simulated signal.
The instantaneous energy of the IMF components are calculated and used to compute Hilbert power. Fig. 3 gives the Hilbert power and the output of the SNEO and its time frequency. As it can be seen, all faults have been successfully detected. It is obvious that the proposed method can efficiently indicate the faults for the simulated signal at their occurrence times. 3. Experiment The schematic diagram of the experimental set-up used for investigating the above method is shown in Fig. 4. The experiments are performed on a CNC Vertical Machining Centre (Mazak AJV 25/405) with AC permanent magnet synchronous motors. Three current sensors (PCB Mounting Hall Effect Current Transducer, Stock No. 286-327 (RS Components Ltd)) are used to measure the three-phases currents of feed-motor: IU, IV, and IW. The signals are first passed though low-pass filters with a cut-off-frequency of 50 Hz and then sent via an A/D converter to a personal computer. A good breakage detection algorithm must be sensitive to the changes in tool conditions and insensitive to the variations of cutting parameters. To evaluate the performance of the proposed method, a series of experiments are conducted under different cutting conditions. Table 1 shows the tool parameters and cutting conditions used in this work. These tests included entry/exit cuts, flute breakage during steady-state cutting, changed cutting parameters; and flute breakage on entry/exit cuts. All of the tests were performed under dry conditions in down milling mode; the sampling frequency was set as 1 kHz. Additionally, the maximum tooth frequency used in the tests should be less than 67 Hz; this is determined by the bandwidth of feed servo system.
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Fig. 2. The seven IMF components for the signal sðtÞ ¼ xðtÞ þ nðtÞ.
The root mean square (RMS) of feed-motor current, I RMS , is used for converting the AC current to the equivalent DC current and can be calculated by rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2 ½I þ I 2v þ I 2w . I RMS ¼ 3 u For the feed-motor current generated during end milling under the bandwidth of the feed drive system, the frequency is equal to the reciprocal of the tooth passing frequency, such that: f p ¼ N f N s =60, where Nf is the number of cutting flutes, Ns is the cutter rotational speed in r/min (rpm). Given the sampling frequency fs, the number of data points per period is: M ¼ f p =f s . Thus the number of periods contained in a complete sample data is: P ¼ N=M [12]. 4. Results 4.1. Analysis of feed-motor current signal Fig. 5 displays the feed-motor current signals collected at a spindle speed of 600 rpm and the feed speed is 120 mm/min using four flute, 8 mm end mill. The radial depth of cut and axial depth of cut are 2 and 4 mm, respectively. The first part of the signal represents the current at no load. The cutting process starts at 5 s where the current signal jumps up, and the machine begins the period of steady-state cut (5–35 s). The signal stability is interrupted, after 35 s, when flute breakage occurs as shown in Fig. 5. This change in the feed-motor current signature may occur due to two reasons. First the broken flute encounters a reduced chip load, while the other flutes must absorb an increased chip
load for compensating for the broken flute. Second, the fracture tool material becomes ‘‘jammed’’ between the workpiece and subsequent flutes. This greatly increases the cutting force [7], and consequently increasing the feedmotor current [12]. The cutting ends at about 42 s, where the feed-current drops down again. During the steady-state cutting, the pattern of the feedmotor current signal is similar to that of feed cutting force as shown in Fig. 6. The figure shows that the variations of motor current and cutting force are similar to a regular periodic signal. Fig. 7 shows the current signature of normal and broken tools. The figure shows that the period for one rotation of spindle is approximately 0.1 s corresponding to a spindle speed of 600 rpm. There are four peaks during one revolution where each one represents the cutting process of one individual tooth (Fig. 7(a)). When breakage occurs, the profile of the signature is distorted due to the effect of tooth breakage as shown in Fig. 7(b). The reduction of the current amperage in this case may be due to the reduced thickness of chip for the broken tooth. 4.2. HHT-SNEO tool breakage detection To test the performance of the new detection method, a lot of real machining signals are analyzed. Fig. 8 shows the nine components of the IMFs (C1–C9) obtained using the sifting method for normal cutting operation (right) and in the case of tool breakage (left). The components are listed from high to low frequency. The last one (res.) is a monotonic function and represents the central tendency of the signal.
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b-Hilbert Power 8 6 4 2 0
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Fig. 3. The output of the HHT-SNEO.
Fig. 4. The schematic diagram of experimental setup.
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It can be seen that the amplitudes of Ci(t) varies from one IMF to another, and more particularly the amplitudes of some IMFs (e.g. C8–C9) are much smaller than other IMFs. Because these amplitudes of C are very small, they have no physical sense and can be removed from the analysis. Fig. 8 also shows that most of the noise in the data is contained in C1 and C2 component and can be easily separated from the data. The first three modes are almost similar in the two cases and thus it may represent the information about the background signal. The differences in the waveform of the fourth mode are quite clear. It can be observed that the fourth IMF component has higher amplitude in the case of tool breakage than the corresponding one in the case of normal cutting. Obviously, C4 contains the main information on defects. Fig. 9 shows the instantaneous amplitudes of C4 components. The most informative IMFs, however, depends on the cutting conditions. To automate the selection of these IMFs, a thorough analysis of feed-motor current under different cutting conditions was carried out. Based on this analysis, it was found that the number of IMF components varies by changing the cutting conditions. The most informative IMFs that indicate the tool condition can be selected automatically by first removing the residual and
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considering only the components in the range of (Nimf-6 to Nimf-4). Here Nimf refers to the total number of IMF components in the decomposed signal. Thus the signal to disturbance ratio can be greatly improved and the rest of the IMF components are then pre-processed. However, to enhance the required signal, wavelet denoising, level two is applied to reduce the noise level in the selected IMFs. The instantaneous energy of each component is calculated and combined to form a new signal for further analysis as shown in Fig. 10(b). Smoothed nonlinear energy operator (SNEO) is then applied on the energy signal to emphasize the breakage-waves that corrupt the pure feedmotor current signals. A 120-point Bartlett window was found to be appropriate for all cutting conditions listed in
Fig. 6. Feed-motor current signal (left) and cutting force signal (right) during the same period of normal cutting.
Table 1 Experimental condition Tool Material Cutting conditions
Milling method
HSS Flute/diameter: 2/8; 4/8; 4/6 45# Steel. Spindle speed 300, 450, 600, 900, 1200 rpm Feed rate 90, 120 mm/min Axial depth of cut: 1–6 mm Radial depth of cut: 1–4 mm Down cut—without coolant
Fig. 7. Feed-motor current signals for normal tool (left) and broken tool (right).
Fig. 5. Feed-motor current signals from normal tool condition to tool flute breakage in end milling.
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Fig. 8. Decomposition by the sifting method of the feed-motor current signal shown in Fig. 5 for normal (left) and broken (right) tools.
Fig. 9. Instantaneous amplitudes of C4 for normal and breakage tools.
Fig. 10. Output of the detection algorithm: (a) original signal, (b) combined instantaneous energy, (c) SNEO output and (d) threshold result.
Table 1. Fig. 10(c) shows how the SNEO highlights the breakage events in the energy signal. The output of the SNEO is threshold to separates the breakage regions from background signal regions as shown
in Fig. 10(d). The value of the threshold depends on the energy level in SNEO output signal, Emax, which is strongly, related the cutting conditions. A threshold value of 0.37Emax was found to be suitable for all investigated
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conditions. Thus the process of IMFs and threshold selection can be automated. It is obvious for the analysis of the case that the proposed method can indicate the breakage accurately. The sensitivity of the algorithm for the slight changes in feed-motor current, as in the case of minor cutting edge fracture, is investigated. Fig. 11 shows the wave form during one revolution when one flute of cutting tools is broken. The flute fracture occurs at around second 45.15 during the steady cutting state. Fig. 12(a) shows a segment of feedmotor current signals during the period (44–46.5 s). Fig. 12(b) shows the marked features derived from the feedmotor current signal as an output of SNEO. The time– frequency representation is shown in Fig. 12(c). As shown from the output, Fig. 12(d), a peak at around second 45.4 is found to indicate the small fracture of cutting edge. This results in a delay of about 0.35 s. Clearly, the algorithm can indicate the small variations of feed-current signal due to small edge fracture successfully and at the proper time during end milling.
Fig. 11. Wave forms of one insert breakage.
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The results displayed in Figs. 10–12 show that the HHTSNEO method is powerful for analyzing the breakage data. It provides a more precise definition and meaningful interpretation of the breakage in time–frequency space than other analysis methods.
5. Conclusions In this paper we present a new method for tool condition monitoring in end milling. The primary goal is to detect flute breakage during such operations. The method is based on the application of a new time–frequency signal processing method called the ‘‘Hilbert-Huang method’’ for decomposition of the original signal. To highlights the breakage events in the signal, we apply a smoothed nonlinear energy operator on the signal representing Hilbert power. The method is verified by simulation and experimentally. Experimental results on a CNC Vertical Machining Centre are presented to show the performance of the algorithm. The analysis verified that the feed-motor current is directly related to the cutting force and hence, the motor current signature is used to extract the crucial characteristics of the end mill during machining operation. The most informative IMFs that indicate the tool condition and the threshold value are automatically selected. The results reveal that the combination of the HHT and SNEO can successfully detect the tool breakage even in the case of small fracture. The feasibility of the proposed method to accurately and efficiently determine the conditions of the end mill under varying cutting conditions is proved. However, real time implementation of the proposed method still needs a little work in the near future.
Fig. 12. Feed-motor current signals and detection results for small cutting edge fracture.
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Acknowledgements The authors would like to thank the Editor and two anonymous reviewers for constructive comments that led to an improved manuscript. The part work is supported by National Natural Science Foundation of China (No. 60575012). References [1] P.-T. Huang, J.C. Chen, C.-Y. Chou, A statistical approach in detecting tool breakage in end milling operations, Journal of Industrial Technology 15 (3) (1999) 1–7. [2] R. Romero-Troncoso, G. Herrera-Ruiz, I. Terol-Villalobos, C. Jauregui-Correa, FPGA based on-line tool breakage detection system for CNC milling machines, Mechatronics 14 (2004) 439–454. [3] E. Govekar, J. Gradisek, I. Grabe, Analysis of acoustic emission signals and monitoring of machining processes, Ultrasonic 38 (2000) 598–603. [4] I. Yesilyurt, End mill breakage detection using mean frequency analysis of scalogram, International Journal of Machine Tools & Manufacture 46 (2006) 450–458. [5] D. Dimla Snr, Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods, International Journal of Machine Tools & Manufacture 40 (2000) 1073–1098. [6] P.-T. Huang, J.C. Chen, Neural network-based tool breakage monitoring system for end milling operations, Journal of Industrial Technology 16 (2) (2000). [7] S. Zhang, T. Asakura, S. Hayashi, Drill breakage detection and prediction using self-organized neural networks, SICE Annual Conference in Sapporo, August 4–6, Hokkaido Institute of Technology, Japan, 2004, pp. 153–158. [8] S.-P. Lo, The application of an ANFIS and grey system method in turning tool-failure detection, International Journal of Advanced Manufacturing Technology 19 (2002) 564–572. [9] C. Sohyung, A. Shihab, O. Arzu, K. Nandita, Tool breakage detection using support vector machine learning in a milling process, International Journal of Machine Tools & Manufacture 45 (2005) 241–249. [10] Xiaoli Li, D. Shen, Y. Zhejun, Discrete wavelet transform for tool breakage monitoring, International Journal of Machine Tools & Manufacture 39 (1999) 1935–1944. [11] G. Pontuale, F.A. Farrelly, A. Petri, L. Pitolli, A statistical analysis of acoustic emission signals for tool condition monitoring (TCM), Acoustics Research Letters Online, Acoustical Society of America, 2003. [12] Xiaoli Li, Detection of tool flute breakage in end milling using Feedmotor current signatures, IEEE/ASME Transactions on Mechatronics 6 (4) (2001) 491–498.
[13] E. Jantunen, A summary of methods applied to tool condition monitoring in drilling, International Journal of Machine Tools & Manufacture 42 (2002) 997–1010. [14] R. Romero-Troncoso, G. Herrera-Ruiz, I. Terol-Villalobos, J. Jauregui-Correa, Driver current analysis for sensorless tool breakage monitoring of CNC milling machines, International Journal of Machine Tools & Manufacture 43 (2003) 1529–1534. [15] Xiaoli Li, R. Du, B. Denkena, J. Imiela, Tool breakage monitoring using motor current signals for machine tools with linear motors, IEEE Transactions on Industrial Electronics 52 (5) (2005) 1403–1408. [16] A. Luis, H. Gilberto, P. Rocio, R. Rene, L. Wbaldo, Sensorless tool failure monitoring system for drilling machines, International Journal of Machine Tools & Manufacture 46 (2006) 381–386. [17] G.D. Kim, C.N. Liu, In-process tool fracture monitoring in face milling using spindle motor current fracture index, International Journal of Advanced Manufacturing Technology 18 (2001) 383–389. [18] N. Huang, S. Samuel, editors. Hilbert-Huang Transform and its Application, World Scientific Publishing, 2005 (Chapter 1) available at http://www.worldscibooks.com/mathematics/5862.html [19] P. Flandrin, P. Gonc- alve`s, Empirical mode decompositions as datadriven wavelet-like expansions, International Journal of Wavelets, Multiresolution and Information Processing 5 (2004) 1–28. [20] F. Robert, I. Eric, Application of Hilbert–Huang transform for improved defect detection in Terahertz NDE of Shuttle Tiles, SPIE International Symposia on Smart Structures & Materials/NDE, San Diego, California, March 6–10, 2005 /http://techreports.larc.nasa.govS. [21] J.N. Yang, Y. Lei, S. Lin, N. Huang, Hilbert-Huang based approach for structural damage detection, Journal of Engineering Mechanics 130 (2004) 85–95. [22] N. Huang, W. Man-Li, Q. Wendong, R. Steven, S. Samuel, E. Jin, Applications of Hilbert–Huang transform to non-stationary financial time series analysis, Applied Stochastic Models in Business and Industry 19 (2003) 245–268. [23] H. Hamid, M. Mostefa, B. Boualem, EEG Spike Detection Using Time–frequency Signal Analysis. V-421–V-424, ICASSP IEEE, 2004. [24] S. Mukhopadhyay, G. Ray, A new interpretation of nonlinear energy operator and its efficacy in spike detection, IEEE Transactions on Biomedical Engineering 45 (1998) 180–187. [25] P. Hae-Jeong, J. Do-Un, P. Kwang-Suk, Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method, IEEE Transactions on Biomedical Engineering 49 (12) (2002) 1526–1533. [26] S. Cui, X. Li, G. Ouyang, X. Guan, Detection of epileptic spikes with empirical mode decomposition and nonlinear energy operator. International symposium on Neural Networks, Chongqing, China, May 2005, Lecture Notes in Computer Science, Springer, Vol. 3498/ 2005, pp. 445–450.