Micro-end-milling—III. Wear estimation and tool breakage detection using acoustic emission signals

International Journal of Machine Tools & Manufacture 38 (1998) 1449–1466

Micro-end-milling—III. Wear estimation and tool breakage detection using acoustic emission signals I. Tansel*, M. Trujillo, A. Nedbouyan, C. Velez, Wei-Yu Bao, T.T. Arkan, B. Tansel Mechanical Engineering Department, Florida International University, Miami, FL 33199, USA Received 24 November 1997

Abstract Acoustic Emission (AE) signals have been used to monitor tool condition in conventional machining operations. In this paper, new procedures are proposed to detect tool breakage and to estimate tool condition (wear) by using AE. The proposed procedure filters the AE signals with a narrow band-width, band-pass filter and obtains the upper envelope of the harmonic signal by using analog hardware. The envelope is digitized, encoded and classified to monitor the machining operation. The characteristics of the envelope of the AE were evaluated to detect tool breakage. The encoded parameters of the envelope of the AE signals were classified by using the Adaptive Resonance Theory (ART2) and Abductory Induction Mechanism (AIM) to estimate wear. The proposed tool breakage and wear estimation techniques were tested on the experimental data. Both methods were found to be acceptable. However, the reliability of the tool breakage detection system was higher than the wear estimation method.  1998 Elsevier Science Ltd. All rights reserved. Keywords: Micro-machining; Neural networks; Acoustic emission; Metal cutting; Monitoring; Tool breakage; Failure; Tool life; Wear; Pre-failure

1. Introduction Acoustic Emission (AE) signals have been successfully used for many years to monitor tool condition for conventional tools [1–13]. These techniques are good starting points to find similar methods for micromachining applications; however, they should be carefully modified by con* Corresponding author. 0890-6955/98/$19.00  1998 Elsevier Science Ltd. All rights reserved. PII: S 0 8 9 0 - 6 9 5 5 ( 9 8 ) 0 0 0 1 7 - 0

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sidering the operational conditions. In micromachining very small amounts of metal are removed at extremely high rotational speeds. Cutting forces are very small at these metal removal rates, and extensive machine vibration is created by the high-speed spindle. Since the cutting force signals of the force transducers (or dynamometer) include very high noise created by the inertia forces of the machine oscillations, AE signals are a good alternative for tool condition monitoring. However, the AE signal also includes extensive noise created by all the moving parts of the system. The developed tool condition monitoring procedures for micromachining should be able to work with very small and noisy AE signals. In this paper, two new procedures are proposed to detect tool breakage and to estimate wear by monitoring tool condition in micromachining applications. The proposed procedures use a single AE sensor, which can be easily attached between the workpiece, and the platform, which holds it. The AE activity is monitored at a very small frequency range to minimize the noise and to have very high sensitivity by using specially designed sensors and hardware. In the following sections the theoretical background, proposed procedures, experimental set-up, and results are presented. 2. Theoretical background In this section, the theoretical background of AE-based tool monitoring, Adaptive Resonance Theory (ART2) neural networks, and an Abductory Induction Mechanism (AIM) polynomial network will be introduced. 2.1. Monitoring tool condition by using AE signals There are several approaches to explain the generation of AE signals. One approach assumes that elastic stress waves are generated by rapid variation of strain energy during the machining. These waves reach the surface of the material and are monitored by piezoelectric transducers. Strain energy is generated by deformation, fracture or a combination of both factors. Some of the sources of AE were listed by Liu et al. [1,2], as plastic deformation in the shear zone near the cutting edges of the cutting teeth, rubbing between the rake faces of the cutting teeth and chips, rubbing between the flank faces of the cutting teeth and the workpiece, and rubbing between the flank faces of the end teeth of the cutter and the machined workpiece surface. Also, chip breakage and entanglements generate some strain energy. Many researchers used AE signals to monitor tool condition in turning [3–9], milling [1,2,10– 12] and drilling [13] operations. The general trend in AE is to monitor the sensory signal at a large frequency range [3,4,2,6,12], to obtain the RMS of the signal by using proper hardware [2,6,7,12,13], and to classify this signal by using various methods. Some researchers also have used the spectrum [3,6]. Recently, direct sampling of the signal, and sensor fusion [14] have also been proposed. 2.2. ART2 neural network Adaptive Resonance Theory (ART and ART2) neural network architecture was developed to classify systems by evaluating the binary and analog inputs respectively [15,16]. ART and ART2

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networks can be used to monitor the encoded data with minimal or no previous training. The sensitivity of the network is controlled by the vigilance parameter. This parameter should be carefully selected. A low vigilance does not allow the network to notice the differences in the input pattern with required accuracy; on the other hand, a high vigilance may create too many categories and may be confusing. ART2 has been used to monitor various machining operations [17]. 2.3. Abductory induction mechanism (AIM) AIM [18–21] is a highly automated, layered feed-forward network, which requires supervised training. AIM uses small modules to describe the relationship between the input and output. These modules are located in layers. Between the inputs and each output there is a series of layers. Each module has one output and one to three inputs. According to the number of inputs, modules are called single, double, or triple. At the lowest layer, each module is connected to either one, two, or three inputs. The outputs of these modules are connected to the inputs of the modules in the following layer. Some of the modules of a layer may be missing. This architecture continues until the output of the last module in the highest layer is connected to the output of the system. The algebraic form of the modules are the following: 1. Singles: Output = wo + (w1x1) + (w2x21) + (w3x31) 2. Doubles: Output = wo + (w1x1) + (w2x2) + (w3x21) + (w4x22) + (w5x31) + (w6x32) + (w10x1x2) 3. Triples: Output = wo + (w1x1) + (w2x2) + (w3x3) + (w4x21) + (w5x22) + (w6x23) + (w7x31) + (w8x31) + (w8x32) + (w9x33) + (w10x1x2) + (w11x1x3) + (w12x2x3) + (w13x1x2x3) where wn are the coefficients of the elements estimated by AIM and xn are the input variables. AIM automatically makes all the possible arrangements by using the different combinations of elements and estimates the parameters. It selects the optimal arrangement by considering the error and penalty factors selected by the operator. The speed is the main advantage of the network. The AbTech Corporation’s AIM program was used in this study. 3. Monitoring micro-tool condition by using AE In this section, the theoretical background of proposed procedures, a new wear estimation, and breakage detection methods are outlined.

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3.1. Theoretical background of the developed system and its operation The proposed AE-based monitoring system evaluates the characteristics of the impacts to the system. Impact occurs when the cutter rotates and the cutting edges hit the material surface. The number of impacts in each rotation are equal to the number of the cutting edges of the tool. The magnitude of the impact force is related to the material, tool and cutting condition. A new and sharp tool penetrates the surface of an aluminum or mild steel workpiece easily and requires a small force. The necessary penetration force increases when the tool gets dull. If the workpiece is hard to penetrate (such as hardened or stainless steel) impact forces are big and spikes are very distinctive. There will not be any contact between the tool and the material when the tool is broken and the penetration force is zero. The impacts excite all the frequencies of the system including the selected high frequencies above 20,000 Hz. The magnitude and interval of these impacts give important information about the tool condition. The meaningful information of the AE signals are mostly above 20,000 Hz. These signals require very fast sampling. Analog to digital (A/D) converters are expensive, but necessary to obtain thousands of samples of meaningful intervals such as several spindle rotations. These large amounts of data should be processed at several stages. To solve these problems most of the researchers have processed the AE signals at two stages. First, they preprocessed the AE signal with analog hardware and obtained a meaningful low frequency signal. In the second stage, this signal was digitized and interpreted digitally. In this study, AE signals were preprocessed either by using the DME Corporation’s SWAN 3000 commercial machine diagnostic system or a low cost system, which works with the similar principle. Forced vibrations created by motors, or other moving parts, excite fundamental frequencies and their harmonics. The fundamental frequencies are mostly at the operational frequency or natural frequencies of the components. The amplitude of the oscillations gradually decreases while the frequency increases. The fundamentals and the harmonics of the forced vibrations of most of the conventional mechanical systems and the machine tool structures are in the 0–1,000 Hz range. At 40,000 Hz, the effect of forced vibrations and audible sound are almost completely eliminated, while the impacts still excite the structure and create a tiny oscillation and acoustic energy. In this study, a specially designed piezoelectric AE sensor was used with 40,000 Hz natural frequency to detect these signals with the best possible sensitivity. The sensor was connected to a charge amplifier. The output of the charge amplifier was connected to a sharp band pass filter, which was designed to pass only a 40,000 Hz signal. After the filtering, the negative portion of the oscillations was eliminated by a rectifier and a specially designed amplitude modulation circuit was used to obtain the envelope of the AE signal. In the last stage two different approaches were used. The SWAN 3000 passed the envelope signal from a low pass filter. This filter was selected as the low cost version. Elimination of the low pass filter reduced the cost and allowed development of a simpler tool breakage detection algorithm. 3.2. Tool breakage detection The purpose of tool breakage detection systems is to warn the user when miniature tools are broken. A robust system should be able to detect tool breakage and not give false alarms when

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the cutting conditions change, as during the beginning and end phases of cutting or when increasing the depth of a cut. Under normal conditions, the activity of the AE signals drastically increases at the beginning of a cut. Later, the intensity of the signal slightly decreases and stabilizes. When the tool starts to leave the workpiece at the end of the operation, the tool vibrates, and the intensity of the AE signals vary. There is considerable AE activity for a short time interval as long as a tool workpiece contact exists; however, AE activity slowly diminishes and disappears when they do not touch each other. When a miniature tool breaks, the contact between the tool and workpiece immediately ceases. The preprocessed AE signal suddenly makes a big jump and AE activity immediately stops. However, this sudden jump and silent portion have different characteristics according to the selected analog preprocessing hardware in this study. Two different algorithms were developed to detect tool breakage depending upon the different hardware. 3.2.1. Detection of tool breakage using a simple system without a low pass filter at the final stage The tool breakage was indicated with a very sharp spike at the signal of the basic demodulation. Tool-breakage detection was completed in two steps (Fig. 1): 1. Compare each reading (xi) with a threshold value. If it is above a selected threshold go to the second step; if it is equal or below the threshold repeat the first step. 2. If (xi) is above the threshold, calculate the slope of the curve when the signal starts to drop (j) points later. If the data produce a negative slope, the tool is broken. 3.2.2. Detection of tool breakage using an advanced system with a low pass filter at the final stage The low pass filter at the final stage avoids aliasing; however, it also eliminates sudden jumps and decays. This characteristic prohibits use of a simple threshold to detect tool breakage. The diagram of the system is presented in Fig. 2.

Fig. 1. Basic decoding circuit for the analog signal processing of the AE signal.

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Fig. 2. Analog signal processing of the SWAN 3000 system.

To ascertain if the tool is leaving the workpiece or if it was broken, the following 6 steps were followed: 1. Calculate the average of the first 150 points (Xa). 2. Take each following data point (Xi) and compare it with the average. If Xi > (Xa − Xlow), update the average (Xa) by considering (Xi) and then repeat step 2. Otherwise go to step 3. 3. Take the following 150 points. Create 15 sections with 10 points each. For each section (j) obtain the linear interpolation between the most-left and most-right data point: For j = 0 to 14 For i = 0 to 9 Calculate the estimations for each point: X⬘j*10 + i = Xj*10 + i*(X(j + 1)*10 − Xj*10)/10 where i and j are indexes. 4. Total estimation error for 150 points:

冘冘 14

E=

9

(Xj*10 + i − X⬘j*10 + i)2

j=0 i=0

5. Compare the error with two reference values (Enormal, Ebreakage), and then give the decision: E > Enormal Enormal ⱖ E ⱖ Ebreakage

Tool is cutting normally. Tool is leaving the workpiece.

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Ebreakage > E

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Tool is broken.

6. If the tool is normal, go to step 2. Otherwise, the tool is broken or has left the workpiece, and the process should be stopped. 3.3. Wear estimation To estimate wear the most distinctive features of the preprocessed AE signals (the envelope) should be encoded and presented to a classification system such as ART2 neural network or AIM polynomial network. In this study, the following three features were found to be the most distinctive features related to tool wear: 1. The standard deviation of a segment with 500 data points of the upper envelope of the AE signal (Std). 2. The average of the same segment after normalization by considering the offset (Avg − Min). The preprocessed AE signal may have different offset values during digitization depending on hardware. To eliminate variation of the parameter with the offset, the minimum values of ten point segments were found and their average was calculated (Min). The second parameter was found by subtracting the Min from the average of 500 point long segments (Avg − Min). 3. The average of the absolute value of the difference of the values of consecutive data points in each segment (Avg − L). This value is very small if all the data points in the segment are the same. However, it increases when the data fluctuate. Three encoded parameters were normalized between 0 and 1000 before they were given to the ART2 and the AIM networks. The networks were used to classify the data at one of the following three categories: 쐌 New tool (the first 1/3 of tool life) 쐌 Used tool (approximately 1/3 to 2/3 of tool life) 쐌 Worn tool very close to tool breakage (the last 2/3 of tool life) Three encoded parameters were presented to ART2 network. ART2 classified these cases without any training. The encoded parameters and their categories (3 input–1 output) were used to train the AIM network. After the training the network classified the presented cases.

4. Experimental set-up The workpiece was clamped on the top of a dynamometer, which was assembled on the top of a linear table. The linear table was controlled by a microcomputer. The AE sensor was firmly tightened to the workpiece. Micro-end mills were High-Speed Steel (HSS) with two flutes HSS, and 0.38 mm (0.015 in.) diameter. A Bridgeport Series 1 Milling machine was used in the experiments. At low cutting speeds (3000 rpm), tools were attached to the spindle directly. To drive the micro-tool at higher speeds a Foredom motor Series MM (up to 18,000 rpm) was used with

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Table 1 Experimental conditions for tool breakage tests Experiment no.

Material

Cutting speed (rpm)

Feed rate(in./Sec)

Depth of cut (in.)

1 2 3 4

Mild Mild Mild Mild

30 000 30 000 3000 3000

0.004 0.004 0.015 0.015

0.016 0.016 0.005 0.005

steel steel steel steel

Table 2 Experimental conditions for wear estimation

Mild steel Aluminum

Cutting condition Feed rate (in./s) number

Depth of cut (in.)

Sampling interval rpm (␮s)

1 2 1 2

0.005 0.004 0.01 0.004

1000 500 1000 500

0.005 0.016 0.005 0.033

3000 30 000 3000 30 000

handpiece number 35. The handpiece was attached to the spindle of the milling machine with a specially designed aluminum block. The handpiece had a planetary multiplier mechanism with transmission ratio of 1:2.5. The experimental conditions are presented in Tables 1 and 2 and the diagram of the set-up is presented in Fig. 3.

Fig. 3. Experimental setup.

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The AE transducer was connected to either a SWAN System 3000 manufactured by the DME Corporation of Fort Lauderdale, FL, USA, or a specially designed similar hardware for preprocessing. Both systems used a specialized signal conditioner hardware, which amplifies, filters (40 kHz), rectifies, and demodulates the voltage from the AE transducer. The preprocessed signals were digitized and saved with a Nicolet 310 digital oscilloscope. 5. Results and discussion The performance of the proposed tool breakage detection and wear estimation methods were tested on experimental data and presented in this section. 5.1. Tool breakage detection The performance of the proposed tool-breakage detection mechanism was tested on two different types of material (mild steel, aluminum) at two different speeds. All of the high-speed machining cases were tested with the developed analog processing circuitry. The SWAN 3000 system was used for all the low-speed cutting experiments. In Figs 4 and 5 the variation of the AE signal is presented during machining and when the cutting conditions changed. Mainly three different cases were considered: initiation of machining, sudden force increases which might cause breakage, and termination of the pass with a healthy tool. The proposed breakage identification software correctly detected tool breakage in all of the test cases. In Fig. 5 the decision of the tool-breakage identification software is demonstrated at the bottom of each plot. First, for comparison the variance of data segments was inspected (Method 1 in the plot). Low variance indicated a change in the cutting conditions. This approach detected a change in the cutting conditions; however, it is not very reliable to distinguish the data of tool breakage from a healthy tool which is leaving the workpiece. The second line (Method 2) represents the decisions of the proposed tool-breakage identification software. When the program noticed a drop from the average value and found it suspicious, the program took 150 data points and evaluated them. If it estimated that the tool was normal, the line dropped and came back to the original level. When the program estimated that the tool was leaving the workpiece it made 1 unit drop and it stayed at that level. If it decided that the tool was broken, the line dropped 3 units and stayed at that level. The software correctly detected the tool condition in each case. 5.2. Estimation of tool wear To estimate the tool wear, experiments were repeated at 3000 and 30,000 rpm with mild steel and aluminum workpieces. Figure 6(a), (b) and (c) show the preprocessed AE signal when machining a mild steel workpiece at 30,000 rpm at the 0.25, 1.25 and 2.0 in. of tool life. At the beginning the AE activity was insignificant; however, it increased in time. The variation of the proposed three parameters to encode the AE envelope were calculated for mild steel and aluminum workpieces at 3000 and 30,000 rpm and presented in Figs 7 and 8. All the encoded parameters slightly decreased for a short period after the machining started; however, all of them sharply increased

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Fig. 4. AE activity during the machining of mild steel at 30 000 rpm with a micro-end mill. (a) Tool leaving the workpiece (Exp. No. 3). (b) Tool is broken (Exp. No. 4).

when the tool reached the last stages of its life. The graphs indicate that the selected three parameters were correlated with wear and can be used to estimate wear. ART2 and AIM networks were used to classify the encoded data. ART2 networks were used to classify 16–18 cases at each cutting condition. The ART2 neural network classified the encoded parameters of the experimental data without any training. When the characteristics of the parameters changed the network was supposed to create new categories. A perfect network would create three categories for new, used and worn tools. It was easier to

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Fig. 5. AE activity during the machining of mild steel at 3000 rpm with a micro-end mill. The tool condition estimation based on variance alone and the proposed system are presented at the bottom of the plot. (a) Tool leaving the workpiece (Exp. No. 7). (b) Tool is broken (Exp. No. 8).

classify the tool condition during the machining of the mild steel (see Table 3). 0.9890 was the best value for ART2 network. ART2 created 3 and 4 categories during the life of a tool without any error. It was more difficult to identify the tool condition during the machining of aluminum workpieces. The best vigilance was 0.9970 for machining of aluminum workpieces. ART2 created 8 and 10 categories without error. It was difficult to distinguish the encoded parameters of the new and used tool. Most of the errors were encountered when the network mixed new and used tools. However, the characteristics

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Voltage (v)

0.2 0.1 0.0 –0.1 –0.2 –0.3 –0.4 0

0.2

0.4

0.6

0.8

(a)

1

1.2

1.4

1.6

1.8

2

1.2

1.4

1.6

1.8

2

1.2

1.4

1.6

1.8

2

Time (sec)

0.5 0.4 0.3

Voltage (v)

0.2 0.1 0.0 –0.1 –0.2 –0.3 –0.4 0

0.2

0.4

0.6

0.8

(b)

1 Time (sec)

0.5 0.4 0.3

Voltage (v)

0.2 0.1 0.0 –0.1 –0.2 –0.3 –0.4 0

(c)

0.2

0.4

0.6

0.8

1

Time (sec)

Fig. 6. Preprocessed AE signal at the different stages of life for a mile steel workpiece. (a) 0.25 in. tool life. (b) 1.25 in. tool life. (c) 2 in. tool life.

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1000

800

600 Value

400

200 Avg-L

Avg-Min

0 0

5

10

15

20

25

Case number Std

(a)

Fig. 7. Variation of three critical parameters at the different stages of tool life during the machining of mild steel. (a) 3000 rpm. (b) 30 000 rpm.

of the encoded parameters were drastically changed when the tool reached the third stage of its life. In all the experiments, ART2 created a new category when the tool reached the last (worn) stage of its life and the network classified all the incoming cases in this category (with only one exception) until the tool was broken when the vigilance was above 0.987. The AIM polynomial network was tested on the same data. The encoded parameters of the experimental data with a mild steel workpiece were divided into three categories (new, used and worn). For the low speed experiment at 3000 rpm with an aluminum workpiece, again, three categories were used. However, for high spindle speed (30,000 rpm) tests with the aluminum workpiece, two categories (new-used together, and worn) were used. 16–18 cases were used for training. The trained network was tested on 6 cases the network never saw before (Table 4). The estimation accuracy of the AIM network was excellent.

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800

600

Value

400

200 Avg-L

Avg-Min

0 0

5

10

15

20

25

Std Case Number

(b)

Fig. 7. Continued.

6. Conclusions In this paper new methods were proposed to detect tool breakage and to estimate wear. The proposed methods interpreted the digitized envelope of the AE signals after they were processed by using an analog circuit to obtain the envelope of the filtered signal with a very narrow bandpass filter. The analog processing circuit reduced the data and effectively separated impact related information from all the other interfaces and environmental noise. The output of the processor had very low noise. The proposed tool breakage detection method was simple and reliable. It correctly identified each tool breakage case without creating any false alarms when the cutting conditions changed. Also, once the critical parameters were selected, they worked fine for all the materials we tested. To classify wear the processed AE signals were encoded by using their statistical properties. The three parameters were classified using the ART2 unsupervised neural network and the AIM polynomial network. When using the ART2 neural network, the cases preceding the breakage

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1000

800

600

Value

400

200 Avg-L

Avg-Min

0 0

5

10

15

20

25

Std Value

(a)

Fig. 8. Variation of the selected three characteristic parameters at the different stages of tool life during the machining of aluminum. (a) 3000 rpm. (b) 30 000 rpm.

were classified as belonging to one category, the rest were classified in different categories. When classifying the data using the AIM polynomial network, it was necessary to diminish substantially the value of the complexity penalty multiplier while working with the aluminum data, to obtain the appropriate response. The AIM polynomial network did not fail when classifying the parameters corresponding to the worst conditions of the tools. Both methods of classification showed similar behavior and can be used for industrial application. The main advantage of the AE is its independence from the cutting direction. The feed and the thrust direction forces should be separated for most of the monitoring applications by using the cutting forces. The weakness of this approach is very high noise at certain cutting conditions, such as when cutting an extremely thin section of material. This may cause extensive AE activity and problems. The AE based tool breakage detection system can be used with confidence. It requires a low cost monitoring system, response is very fast since the simple computations do not take processor time, and it is very reliable. The System detected tool breakage in all the test cases.

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800

600

Value

400

200

Avg-L

Avg-Min

0 0

5

Std

10

15

20

25

Case Number

(b)

Fig. 8. Continued.

Table 3 Performance of ART2 on acoustic emission parameters Mild Steel 2

Mild Steel 1

Aluminum 1

Aluminum 2

Vigilance

Cat.

Err.

Cat.

Err.

Cat.

Err.

Cat.

Err.

0.9850 0.9870 0.9890 0.9910 0.9930 0.9950 0.9970

1 2 3 3 3 4 6

12 4 0 0 0 0 0

4 4 5 6 9 11 14

0 0 0 0 0 0 0

4 4 4 4 4 5 8

9 9 9 9 9 3 0

1 2 2 4 7 8 10

8 5 5 5 0 0 0

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Table 4 Performance of AIM on acoustic emission parameters Material

Mild Steel 1

Mild Steel 2

Aluminum 1

Aluminum 2

Trained over (cases) Tested over (cases) Complexity Penalty Multiplier Average Abs. err. Err. Std. deviation Max. abs. err.

16 6 0.8 0.085450 0.20406 0.49630

18 6 0.95 0.11646 0.23430 0.58573

18 6 0.5 0.39397 0.39715 1.0000

18 6 0.5 0.073843 0.18088 0.44306

Case #

Exp.

Est.

Exp.

Est.

Exp.

Est.

Exp.

Est.

1 2 3 4 5 6

1.0000 1.0000 1.0000 2.0000 3.0000 3.0000

1.4963 1.0000 1.0000 1.9836 3.0000 3.0000

1.0000 1.0000 1.0000 1.0000 2.0000 2.0000

1.0000 1.0000 1.0000 1.0000 1.4143 1.8870

1.0000 1.0000 1.0000 2.0000 3.0000 3.0000

1.3029 1.6231 1.3762 3.0000 2.9384 3.0000

1.0000 1.0000 1.0000 1.0000 2.0000 2.0000

1.0000 1.0000 1.4431 1.0000 2.0000 2.0000

Acknowledgements This project was partially founded by Florida International University, National Science Foundation (Washington DC), Motorola (Ft Lauderdale, FL), and Southern Gear (Miami, FL). The authors would like to thank these organizations and their valuable employees who helped us with their expertise including Robert Shishler (of Motorola) and Allen Arch (Southern Gear). References [1] M. Liu, S.Y. Liang, Analytical modeling of acoustic emission for monitoring of peripheral milling process, International Journal of Machine Tools and Manufacture 31 (4) (1991) 589–606. [2] M. Liu, S.Y. Liang, Monitoring of peripheral milling using acoustic emission, Transactions of NAMRI/SME (1990) 120–127. [3] L. Zheng, Z.B. Luo, Y. Wu, Research and development on synthetic cutting tool monitoring with AE signal, Transactions of NAMRI/SME (1990) 360–365. [4] S. Vajpayee, A. Sampath, Acoustic emission as an indirect parameter for tools monitoring, in: Proceedings of Manufacturing International ’88, Symposium on Product and Process Design, vol. 1, Atlanta, Georgia, 1988, pp. 293–300. [5] J.R. Klaiher, D.A. Dornfeld, J.J. Liu. Acoustic emission feedback for diamond turning, Transactions of NAMRI/SME (1990) 113-119. [6] A.E. Diniz, J.J. Liu, D.A. Dornfeld, Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning, Wear 152 (1992) 395–407. [7] S. Rangwala, D. Dornfeld, A study of acoustic emission generated during orthogonal metal cutting—1: Energy analysis, International Journal of Mechanical Science 33 (6) (1991) 471–487. [8] S.Y. Liang, D.A. Dornfeld, Tool wear detection using time series analysis of acoustic emission, Journal of Engineering for Industry 111 (1989) 199–205.

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