Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network

Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

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Multi-sensor integration for on-line tool wear estimation through arti®cial neural networks and fuzzy neural network R.J. Kuo* Department of Industrial Engineering, National Taipei University of Technology, Taipei 106, Taiwan Received 1 March 1999; accepted 1 January 2000

Abstract On-line tool wear estimation plays a critical role in industrial automation for higher productivity and product quality. In addition, an appropriate and timely decision for tool change is required in machining systems. Thus, this paper develops an estimation system through integration of two promising technologies, arti®cial neural networks (ANNs) and fuzzy logic. The proposed system consists of ®ve components: (1) data collection, (2) feature extraction, (3) pattern recognition, (4) multi-sensor integration, and (5) tool/work distance compensation. Two di€erent networks, a feedforward neural network with an error backpropagation learning algorithm and a counterpropagation neural network, are employed to recognize the extracted features and provide a comparison of these two networks based on accuracy and speed. Meanwhile, in order to enhance the accuracy of the estimation result, this research work applies multiple sensors for detection. The data from multiple sensors are integrated through the proposed fuzzy logic model. Such a model is self-organizing and self-adjusting, learning from experience. Physical experiments of the metal cutting process are implemented to evaluate the proposed system. The results showed that the proposed system can signi®cantly increase the accuracy of the product pro®le when compared to the conventional approaches, like multiple regression and a single ANN. 7 2000 Elsevier Science Ltd. All rights reserved. Keywords: Production and process control; Metal cutting; Multi-sensor integration; Fuzzy neural networks; Arti®cial neural networks; Fuzzy logic

1. Introduction Wear of the cutting tool is an inevitable result of the metal cutting process. Since undesirable e€ects of tool wear include: (1) a loss in the dimensional accuracy of the ®nished product and (2) possible damage to the workpiece, the on-line prediction of cutting tool wear becomes crucial. To date, it remains one of the major obstacles in the optimization of the metal cutting process and in the full implementation of unmanned machining. It is especially important for precision ¯exible manufacturing systems (PFMS). Thus, developing an intelligent estimation system for tool wear is important. * Tel.: +886-2-2771-2171; fax: +886-2-2731-7168. E-mail address: [email protected] (R.J. Kuo).

Arti®cial neural networks (ANNs), fuzzy logic, and genetic systems constitute three independent research ®elds regarding sixth generation systems (SGS). Motivated by the results in each of these areas and the potential for mutual progress in computational modeling, an integration of these concepts is very important (Kosko, 1992). ANNs and the fuzzy model have been used in many application areas (Lippmann 1987; Lee 1990a, 1990b), and each pairing has its own advantages and disadvantages. Therefore, how to successfully integrate these two approaches, ANNs and fuzzy modeling, for the machining systems is the main focus of this research. This research compares two di€erent neural networks, a feedforward network with an error backpropagation (EBP) learning algorithm and a counterpropagation network, for pattern recognition. Moreover, a fuzzy neural network (FNN) is proposed which

0952-1976/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 2 - 1 9 7 6 ( 0 0 ) 0 0 0 0 8 - 7

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R.J. Kuo / Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

Nomenclature e meij sij cij sij we‰ j Š n

the sample number the ijth membership function value for sample e the scalar of the ijth membership function the center of the ijth membership function the variance or width of the ijth membership function the intersection of the membership function values for rule [ j ], sample e the number of the input variables

is self-organizing and self-adjusting, and is able to learn from experience. For the FNN, the inputs and outputs are partitioned by Kohonen's feature mapping and the premise and consequence parameters are updated via an EBP-type learning algorithm. Physical experiments will be used in order to evaluate the proposed system. Three di€erent kinds of sensors (force, vibration, and acoustic emission) are used to detect sensory information about the cutting tool wear. The basic structure of the estimation system is to acquire the sensory signal pattern from each sensor ®rst and then extract the features from the sensory signal pattern. ANNs are employed to recognize the features from each sensor. Each ANN provides a decision, the amount of tool wear, for each sensor. Then the multi-sensor integration method (FNN) is applied to combine all the decisions from di€erent sensors. The proposed method is able to predict the continuous amount of tool wear better than multiple regression and ANN both in terms of speed and accuracy. In addition, the predicted amount of tool wear is used to adjust the distance between the working material and cutting tool in order to increase the precision of the ®nished product. The rest of this paper is organized as follows. In Section 2, the literature survey is presented, while Section 3 presents the methodology which discusses the proposed estimation system. Section 4 explains how the experiments are performed. The data obtained from the experiments described in Section 4 are used to evaluate the proposed system and the results and discussions are given in Section 5. Finally, Section 6 presents the conclusions.

2. Background ANNS and fuzzy logic have been widely applied in many areas (Hertz et al., 1992; Freeman and Skapura, 1991; Kosko, 1992). In the following, the integration

m ze fe‰ j Š a‰ j Š, i b‰ j Š x ei E Oe Z

the number of the categories of each input variable the desired output for sample e the ®ring strength of rule [ j ] for sample e the coecient of input x i the coecient of input rule j the ith input of sample e the cost function the actual output of sample e the training rate

of ANNs and fuzzy logic and their applications in tool wear monitoring will be discussed. 2.1. ANNs in fuzzy modeling Generally, the traditional fuzzy system, as mentioned above, is based on the experts' knowledge, however, it is very subjective. Besides, it is very dicult to acquire sound knowledge and ®nd the required expertise (Jang, 1992). Recently, the ANN learning algorithm has been applied to improve the performance of a fuzzy system and shown to be a new and promising approach. Takagi and Hayashi (1991) introduced a feedforward ANN into fuzzy inference. Each rule is represented by an ANN, while all the membership functions are represented by only one ANN. Jang (1991, 1992) and Jang and Sun (1993) proposed a method which transforms the fuzzy inference system into a functional equivalent adaptive network, and then employ the EBP-type algorithm to update the premise parameters and a least squares method to identify the consequence parameters. Meanwhile, Wang and Mendal (1992), Shibata et al. (1992) and Fukuda and Shibata (1992) also presented similar methods. Nakayama et al. (1992) proposed a so-called FNN which has a special structure for realizing a fuzzy inference system. Each membership function consists of one or two sigmoid functions for each inference rule. Lin and Lee (1991, 1994) proposed the so-called neural-network-based fuzzy logic control system (NN-FLCS). They introduced the low-level learning power of neural networks in the fuzzy logic system and provided high-level human-understandable meaning to the normal connectionist architecture. Recently, Kuo and Xue (1999a, 1999b) proposed a novel fuzzy neural network whose inputs, outputs, and weights are all non-symmetrical Gaussian functions. The learning algorithm is a EBP-type learning prodedure. Furthermore, the EBP-type learning algorithm is integrated with a genetic algorithm to speed up the training time and to avoid local minima (Kuo et al., 2000).

R.J. Kuo / Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

2.2. Tool wear monitoring through ANNs and fuzzy logic There has already been much research done applying ANNs in the area of machining. Tansel (1990) developed two ANN systems to represent cutting dynamics. Then, Tansel and Laughlin (1991) used adaptive resonance theory ART2 for the detection of tool breakage in milling operations, which provided a 97.2% success rate. Guillot and Oua® (1991) provided time domain inputs to a feedforward, three-layer ANN which identi®ed tool breakage at its output for milling. Similar applications can also be found, e.g., Malakooti and Zhou (1992), Khanchustambham and Zhang (1992), Elanayar and Shin (1992) and Dan et al. (1996). However, all of these e€orts consider the case of a single sensor only. Rangwala and Dornfeld (1987, 1989, 1990) and Rangwala (1988) applied ANNs for monitoring tool wear states in a turning operation. A multiple sensor scheme utilizing cutting force and acoustic emission information was presented. The results showed a 95% success rate for classifying binary tool wear states, fresh and worn. Chryssolouris and Domroese (1988, 1989) proposed an intelligent controller which uses a multi-sensor approach for process monitoring. The study focuses on the module which integrates the sensor-based information to provide the controller with the best possible estimates for the tool wear and wear rate. Tests indicated that when compared to the group method of data handling GMDH and least-squares regression techniques, ANNs were more e€ective at learning a relationship for providing parameters and estimates, especially when the relationship between the sensorbased information and the actual parameter is nonlinear. Thereafter, a statistical approach (Chryssolouris et al., 1991, 1992) was used. Before applying the above mentioned statistical approach for integration, the con®dence distance measure for the support of sensor i by sensor j, de®ned as dij ˆ 2A, where A is the area under the probability density curve Pi …yjx i † between x i and x j , is used to eliminate the non-consensus sensory values ®rst. Masory (1991) proposed a tool wear model based on the EBP learning algorithm of ANNs. Though this research tried to predict the continuous amount of tool wear, only a single cutting condition was considered. Similarly, ANNs were also applied by Kamarthi et al. (1991) as the pattern recognizer, while the input vector was the parameter of ARMA(8, 8) and the network used was a Kohonen's feature mapping. Force and vibration sensors were used in this application. Leem and Dreyfus (1992) also applied Kohonen's feature map for sensor fusion in turning. The results showed that the proposed network achieves

251

94 and 92% accuracy for classi®cation into two and three levels of tool wear, respectively. Tansel (1992) used ART2 to combine the information from the dynamometer and laser vibrometer in drilling. The proposed system accurately detects the pre-failure phase for all of the cases. Recently, fuzzy models have also been employed in tool wear monitoring where input is always divided into several groups and the boundary between these groups is vague, It is very similar to tool wear whose status is fuzzy, especially when multiple sensors are used. It has been shown how to use fuzzy models to recognize the fuzziness of tool wear status (Kuo, 1994). Similarly, the tool wear monitored is the tool wear state instead of the continuous value. 3. Methodology In this section, a system is proposed, which can predict the amount of tool wear on-line. The system consists of ®ve components: (1) data acquisition, (2) feature extraction, (3) pattern recognition, (4) multisensor integration, and (5) tool/work distance compensation. Fig. 1 shows the general structure of the estimation system. Each component is discussed in the following subsections. 3.1. Data acquistion This system ®rst collects a sensory signal pattern, which corresponds to particular characteristics of the process. In Fig. 1, it is assumed that there are three sensors used. From these three sensors, three sensory signal patterns can be collected through the acquisition system. 3.2. Feature extraction However, di€erent sensory signal patterns or signatures correspond to the state of the tool as it wears. In order to analyze the relationship between the signal patterns and the amounts of tool wear, it is critical to extract the features of the sensory signal patterns in advance. Two di€erent kinds of feature extraction methods, time series analyzer and frequency analyzer, are used because of the sensitivity of the signals. Intuitively, the sensory signal patterns obtained from sensors are a time series. By analyzing the time series data, a simple mathematical time series model can be constructed. This model is able to represent the time series data in a concise way. Then, the coecients of the model can be treated as the features. There are three di€erent kinds of models that can be used. Thus, it is necessary to determine which model, autoregressive model (AR), moving average model (MA), or

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ARMA, is more suitable for the current data. For frequency analysis, the Fourier transformation (FT) is used to transform the time series data into the frequency domain. The algorithm used is the FFT. Five peak values of the frequency data points are selected as the features. 3.3. Pattern recognition In this component, ANNs are employed to recognize the features corresponding to di€erent amounts of tool wear. The inputs of the network are from the above feature extraction algorithms. Thus, if the number of features is n, then the number of input nodes is also n. The number of outputs, which is the amount of tool wear, is one. There have already been several ANNs developed

and applied to practical problems. Each poses its own advantages and disadvantages. Thus, this research will try to test and compare two ANNs, a feedforward network with an error backpropagation (EBP) learning algorithm and a counterpropagation network (CPN) (Hertz et al., 1992). For the feedforward network, the EBP learning algorithm is applied to recognize the features extracted from the above section, feature extraction, since it is easy to understand and implement. Due to the slow training speed of the EBP learning algorithm, three fuzzy decision tables developed by Kuo (1995) are employed to dynamically adjust three training parameters (training rate, momentum, and steepness of activation function). It should be reiterated that a single network is used for each sensor. Thus, if there are n sensors, then n total ANNs should be implemented. Through these

Fig. 1. Multi-sensor integration system.

R.J. Kuo / Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

networks, n di€erent predictions can be obtained. Next, a new approach for multi-sensor integration, FNN, will be employed in order to integrate these predicted amounts of tool wear. 3.4. Multi-sensor integration In this component, a new multi-sensor integration method (FNN) is employed. Basically, FNN consists of two stages as follows. 3.4.1. Self-organizing stage (SOS) In this stage, the membership function and the consequence are de®ned. The procedures of SOS are as follows. 3.4.1.1. Clustering. Kohonen's feature mapping is used to divide the training data into a speci®ed number of groups. Based on the results of Kohonen's feature mapping, each group corresponds to an inference rule. 3.4.1.2. Shape and position determination. Di€erent membership functions can be chosen depending on the domain. Based on Wang's (1992) discussion, fuzzy modeling with membership functions of scaled Gaussian functions is actually a universal approximator that can approximate any non-linear input±output data arbitrarily well on a compact set. Thus, three parameters of a scaled Gaussian function should be determined. For each group or rule, the mean of the group is set to be the center of the membership function. The width of the membership is set to be three times the standard deviation of each group. 3.4.1.3. Consequence determination. The consequence, which is the control action of each inference rule, is determined for each group, or rule, by using regression. 3.4.2. Self-adjusting stage (SAS) The basic idea of this stage is to employ the learning capability of the ANN to update the parameters, which have already been predetermined by the SOS, of fuzzy modeling. Consequently, fuzzy modeling should be represented as a form of an ANN. The proposed architecture of the fuzzy model used is based on Takagi's (1983) fuzzy system. Then the fuzzy model is trained by using the EBP-type algorithm. The proposed fuzzy ANN consists of ®ve layers. Layer 1 is the input layer which consists of the realvalued input variables. Every node in layer 2 is the value of the membership function: ÿ
1 xÿc 2 ÿ …1† mA …x† ˆ se 2 s where x is the input variable and s, c, and s are the

253

parameters, and A is the linguistic term. As mentioned, the shape and position of the membership function will change when any of s, c, or s are changed. Every node in layer 3 possesses the capability of multiplication. It is equivalent to the meaning of the ®ring strength in fuzzy modeling. Layer 4 calculates the ith ®ring strength proportional to the sum of all the ®ring strengths, while layer 5 is the output layer which combines all the control action values from all the inference rules. The weights connecting any two nodes are 1 except the weights between layers 4 and 5. They are the consequences for each rule. Since Takagi's model is employed, these consequences are regression models which are denoted as fi , ! X ai, j x j ‡ bi …2† fi ˆ j

where j is number of input, aij is the coecient, x is the independent variable or predicted amount of tool wear, and bi is the constant term. The proposed learning algorithm for the fuzzy model is trained by using the EBP-type algorithm. Since the structure is much di€erent from the standard EBP, modi®cation should be made. In order to clearly describe the learning algorithm, the variables used are de®ned in the Nomenclature. The fuzzy model, in the form of an ANN, can be derived from the following equations:  e 2 mei, j ˆ si, j e

Membership function:

We‰jŠ ˆ

Firing strength:

X Final output:

‰jŠ

iˆn Y iˆ1, j2‰l, mŠ

x i ÿci, si, j

j

mei, j

…3†

…4†

we‰jŠ fe‰jŠ

Oe ˆ X ‰jŠ

Regression model:

1 ÿ2

fe‰jŠ ˆ

…5†

we‰jŠ X a‰jŠ, i x ei i

! ‡ b‰jŠ

…6†

Since the EBP-type algorithm is employed to selfadjust the parameters, the inference rules are updated so as to minimize the cost function E which is de®ned as: Eˆ

1X e …z ÿ Oe †2 2 e

…7†

where e is the training example number. Each par-

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ameter of the control action function is updated by an amount proportional to the partial derivative of E with respect to that parameter. The updated learning rule is of the form ˆ W…t† W…t‡1† i, j i, j ÿ Z

@E @wi, j

…8†

where wij is the updated parameter. All the above-mentioned equations are used to ®ne-tune the parameters which have been determined by the SAS of the fuzzy inference system. Thus, this section shows how to employ the EBP-type learning algorithm for fuzzy inference system. For a detailed derivation, refer to (Kuo, 1994). 3.5. Tool/work distance compensation The ®nal decision, the amount of tool wear which is made by the multi-sensor integration technique, is used to decide whether the tool is fresh or worn out and how to adjust the distance between the tool and working material. The approach used to keep the distance between the working material and tool constant is to adjust the depth of cut during the operation of CNC turning machines. For example, in the CNC turning machine program, the depth of cut is adjusted according to the following equation: D…t ‡ 1 † ˆ D…t† ‡ DD…t†

…9†

where W(t ) = the amount of tool wear at time t D(t ) = the depth of cut at time t DW…t† ˆ fW…t† ÿ W…t ÿ 1†g DD…t† ˆ DW…t† tan…clearance angle† for each monitoring iteration. Fig. 2 shows that when the amount of ¯ank wear is W(t ), then the depth of cut should be added by DD…t†: If W(t ) is larger than a predetermined constant, then a tool change is made.

Fig. 2. Tool/work distance compensation

4. Experimental setup In order to evaluate the proposed system, the experiments were run using a 20HP LeBlonde lathe. Three types of sensor: force, vibration, and acoustic emission, were employed. For forces in the feed, radial and main cutting directions, a three-axis Kistler Z3392/b piezoelectric force dynamometer was used, while two PCB accelerometers were employed for vibrations in the feed and main cutting directions. A physical acoustics emission sensor was located at the center of the tool for monitoring acoustic emission signals. Force sensors, vibration sensors, and an acoustic emission sensor were connected to the Kistler three-channel model 5804 charge ampli®er, PCB charge ampli®ers, and DECI AE preampli®er, respectively. Thereafter, force sensory outputs were connected to a National Instruments acquisition board which was connected to an IBM compatible PC with the Labview software package, while vibration sensory outputs were connected to a Tektronix 2630 Fourier Analyzer which was connected to an IBM compatible PC with a Fourier Analyzer package. The acoustic emission output was connected to an ANALOGIC acquisition board which was connected to an IBM compatible PC with ANALOGIC FAST Series package. The sampling rates of forces, vibrations, and acoustic emission were 3 KHz, 25.6 KHz, and 1 MHz, respectively, after carrying out some research (Chryssolouris et al., 1992; Rangwala and Dornfeld, 1990; Karmarthi et al., 1991) and doing some tests. In addition, in order to make sure that all the three acquisition systems were triggered at the same time, an automatic trigger was connected to the three systems. Once the trigger was initiated, it started all three data acquisition systems simultaneously. This allowed sensor data to be taken at the end of a cut and the measured wear correlated to the sensor data obtained. Meanwhile, a chip breaker was also mounted on the top of the insert in order to avoid the sensors being damaged by the chips. The ¯ank wear was measured by a Baush & Lomb toolmaker's microscope, while surface roughness was measured using a Federal Systems Pocket Surf. A Starrett micrometer caliper was used for measuring the diameter of the workpiece. In this experiment, all the sensory signals from three cutting forces in the feed, radial and main cutting directions, two vibrations in the feed and main cutting directions, and acoustic emission were collected for each cut and saved as three ®les. Flank wear of the tool, diameter of the workpiece, and surface roughness were then measured o€-line. All of the sensor measurements were sequenced using a common trigger just prior to the end of the cut as described. This experiment used SAE 6150 chromium-vanadium alloy steel as the test workpiece. The work-

R.J. Kuo / Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

piece's dimensions are 7.5 in. diameter by 36 in. length. The quench and tempered heat treatment procedures of the workpiece are as follows: 1. 2. 3. 4.

heated to 15508F, oil quenched, tempered at 6008F, air cooled.

The resultant hardness ranges from 350 to 390 BHN. The Kennamental KSBR-164C tool holder was used for machining, while the cutting insert used was a Kennamental K68 grade carbide insert SPG 422 mounted on the tool holder. The cutting conditions were varied in order to obtain more robust data sets. Feed rates were varied from 0.0064 to 0.0156 ipr. Levels 0.0064, 0.0088, 0.0112, 0.0136, and 0.0156 ipr were selected. For speed, three di€erent cutting speeds, 100, 130, and 160 sfpm were used. The depth of cut was kept as a constant, 0.05 in. A full factorial experiment was performed. In total, there were 15 di€erent cutting conditions, or treatments (3 speeds  5 feeds). The experimental procedures are as follows: 1. Mount the tool insert and chip breaker on the tool holder. 2. Set up the cutting conditions and calibrate the ANALOGIC FAST Series package for the acoustic emission acquisition. 3. Cut the workpiece for 1 min and initiate the trigger at the end of the cut, for approximately 55 s, in order to collect the sensory signals for forces in three directions vibrations in two directions, and acoustic emission. 4. Save the sensory signals for forces, vibrations and acoustic emission in the three di€erent ®les. 5. Remove the tool insert from the tool holder and measure the ¯ank wear with the Baush & Lomb toolmaker's microscope. 6. Measure the diameter with the micrometer and surface roughness using the Federal Pocket Surf. 7. Remount the tool insert and chip breaker, and continue repeating steps 3±6 until severe wear, about 0.018 in., is reached. There are 4096 data points for each cutting experiment, but only 1024 data points will be used in the next section, model validation. These sensory signal patterns are processed for feature extraction.

5.1. Data acquisition The data is obtained from three forces in the feed, radial, and main cutting directions, two vibrations in the feed and main cutting directions, and acoustic emission. For the purpose of explanation, the cutting condition and the corresponding tool number are shown in Table 1. In total, there are 15 tools employed in this experiment. Each tool has 10±13 samples. The total number of samples is 168. However, these 15 tools are divided into two groups for training and testing. Thus, the shadowed cells represent those data used for training ANNs and multi-sensor integration methods. Otherwise, they are for the validation of multi-sensor integration methods. As a result of this division, the training samples can cover the range of the cutting conditions. Therefore, the numbers of training and testing samples are 89 and 79, respectively. 5.2. Feature extraction Two di€erent analyzers are used to extract the features from the sensory signal pattern. 5.2.1. Time series analyzer The sensory signal pattern obtained from the sensor is a time series. In the following discussion, the sensory signal patterns mentioned are the magnitudes of the signals. The test developed by Dickey and Fuller is ®rst applied to test for non-stationarity. For each sensor, 10 signal patterns are randomly selected. The results showed that most of them are stationary. Then, by examining the autocorrelation function (ACF) and partial ACF (PACF) of the time series data, AR(5) is able to represent all the sensory signal patterns of force, vibration, and acoustic emission. Since the constant term is also included in the model, there are six total features for all the sensory signal patterns. For a detailed discussion, refer to (Kuo, 1994). 5.2.2. Frequency analyzer Since the sensory signal patterns obtained from the experiments are time domain data, these patterns are Table 1 The cutting conditions and the corresponding tool numbers Feed (in.)

5. Model validation results and discussion An on-line estimation system and experimental setup have been discussed in the previous two sections. In this section, the data obtained from the physical experimentation will be used to evaluate the proposed system.

255

0.0064 0.0088 0.0112 0.0136 0.0156

Speed (fpm) 100

130

160

T1 T2 T3 T4 T5

T6 T7 T8 T9 T10

T11 T12 T13 T14 T15

256

R.J. Kuo / Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

transformed into frequency domain patterns through the FFT in order to observe the frequency spectrum. Then the data is sorted and the ®ve greatest values are selected for each pattern as the features of that pattern. 5.3. Pattern recognition In the previous section, the features were extracted through two di€erent analyzers, time series and frequency. The time series analyzer employs AR(5) for all of the patterns, while ®ve peak values are chosen as the features for the frequency analyzer. However, it is very important to ®nd out the better analyzer for this application. Thus, the features obtained from the above two feature extraction analyzers are used as the inputs to the EBPN as well as CPN, and the following three alternatives will be tested for each sensor: 1. features extracted by the frequency analyzer; 2. features extracted by the time series analyzer; and 3. features extracted by both frequency and time series analyzers. In addition, cutting conditions, feed and speed, and cutting time are also employed as features. 5.3.1. Error backpropagation network (EBPN) For the features extracted from the time series analyzer, the EBPN architecture consists of nine input units, cutting time, speed, feed, and six time series features, which are connected to the hidden units which are connected to one output unit (predicted amount of ¯ank wear). Regarding the number of hidden units, some alternatives are testi®ed. The results showed that 18-hidden-unit can provide the smallest MSE value. Therefore, the network structure is 9(input)±18(hidden)±1(output). For the frequency analyzer, the EBPN architecture consists of eight input units which are connected to 16 hidden units which are connected to one output unit. Similarly, 16 is the result of implementing some tests. If both of the di€erent analyzers are combined in order to provide the inputs to the EBPN, the network architecture consists of 14 input units which are connected to 28 hidden units which are connected to one output unit. Based on the above three di€erent kinds of feature extraction algorithms, the computation is implemented by using the C programming language. Before training, the features, cutting conditions, cutting time, and measured amounts of tool wear are normalized within (0, 1). For all the di€erent cases, the training is considered complete when the the number of training epochs is 1,500,000. The training rate and momentum are set to 0.1 and 0.9, respectively, while the steepness of the activation function is set to 0.5. A 586 IBM compatible PC is utilized.

Table 2 lists all the computational results and the mean square error (MSE) values. The radial force with a combination feature extraction method of 8.11Eÿ9 MSE value is the least when compared with the other two analyzers. However, they are very close. Basically, it is not necessary to choose the combination feature extraction method, since its training time is much longer than the other two. Therefore, both the testing and training results indicate that the radial force with frequency features will be the candidate for integration. A similar result exists for the feed and main cutting forces. However, in this research only one will be selected among three di€erent directions for force sensors. Thus, the radial force with frequency features will be used in the integration. Based on similar concepts, the main cutting vibration using the time series analyzers will be selected. For acoustic emission, features extracted from the frequency analyzer only, have the lowest MSE value, 1.71Eÿ8. 5.3.2. Counterpropagation network The setup of CPN is similar to EBPN. The computational results are shown in Table 3. Thus, the radial force with time series features will be used in the integration. Based on similar concepts, the main cutting vibration using the frequency analyzers having the lowest MSE value will be selected. For acoustic emission, features extracted from the frequency analyzer have the lowest MSE value, 1.05Eÿ5. In the next section, a fuzzy model will integrate only the best sensors' results for force, vibration, and acoustic emission. The above results indicate that the EBPN can always provide more accurate results than CPN. The only advantage of CPN is that the training time is shorter. Since the computational speed of EBPN is enough to provide a timely decision, it is selected instead of CPN. 5.4. Multi-sensor integration The amounts of tool wear obtained from three senTable 2 The MSE values of EBPN for three feature extraction methods Sensors

Force Feed Radial Main cutting Vibration Feed Main cutting Acoustic emission

Feature extraction algorithm Frequency

Time domain

Combination

7.39Eÿ8 8.34Eÿ9 5.89Eÿ7

2.65Eÿ8 9.68Eÿ8 1.34Eÿ8

2.11Eÿ8 8.11Eÿ9 9.21Eÿ9

8.38Eÿ8 5.64Eÿ8 1.71Eÿ8

7.33Eÿ8 4.74Eÿ8 6.82Eÿ7

7.19Eÿ8 4.58Eÿ8 5.35Eÿ7

R.J. Kuo / Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

sors, or ANNs, are combined through three di€erent multi-sensor integration methods: multiple regression, ANN, and FNN, for the purpose of comparison. The predicted and measured amounts of tool wear for training samples will train these three integration methods. In order to make a comparison of these three integration methods, the value of the mean square error (MSE) is calculated as the criterion. MSE was also used by Chryssolouris and Domroese (1989) for the purpose of comparison. By using MSE, the absolute distance between the predicted amount of tool wear and the measured, or actual, amount of tool wear can be observed. 5.4.1. Multiple regression For the regression model, there are three independent variables: predicted amounts of tool wear from radial force, main cutting vibration, and acoustic emission. Meanwhile, the dependent variable is the measured amount of tool wear. The multiple regression model will also include the constant term. The multiple regression model is determined by using the MINITAB statistical software. The ®tted model is as follows: y^ ˆ 0:569032x 1 ‡ 0:432042x 2 ‡ 0:004549x 3 ‡ 0:004022

…10†

where yà is the predicted amount of tool wear after integration, x 1 , x 2 , and x 3 are the predicted values from the radial force, main cutting vibration, and acoustic emission, respectively. The MSE of the predicted amounts of tool wear is equal to 4  10ÿ9.

computational result shows that the value of MSE for the predicted amounts of tool wear is equal to 1.2  10ÿ7 as the number of epochs is 1,500,000. 5.4.3. FNNs FNNs consist of two stages, SOS and SAS. Kohonen's feature mapping divides the data into three groups in SOS representing small, medium, and large amounts of tool wear, since the wear curve may be easily segmented in this manner for physical reasons. For each group or rule, the mean and standard deviation are calculated. The center is set to be the mean. The width is set to be three times the standard deviation and the scale is set to be one for the initial state. Moreover, the regression model, which is the consequence of each rule or group, is also found. Through the above initial set-up, the self-adjusting stage will ®ne-tune all of these parameters, scales, centers, widths, and coecients of the regression models for all of these three rules. Thus, by using the learning algorithm proposed in Section 2, these parameters are updated epoch by epoch. After epoch 1000, the MSE drops to 2  10ÿ9. If training is continued, MSE will decrease to 1.32  10ÿ9 when the number of epochs is 10,000. This implies that it is not necessary to train the FNN for a long time, since FNN is able to learn the training samples in a short time and its accuracy is not signi®cantly improved if training is continued. The regression models for each rule before and after ®ne-tuning are: Rule 1 IF

5.4.2. ANNs For the network which integrates the results of radial force, main cutting vibration, and acoustic emission, the ANN architecture consists of three input units and which are connected to six hidden units, which are ®nally connected to one output unit. The Table 3 The MSE values of CPN for three feature extraction methods Sensors

Force Feed Radial Main cutting Vibration Feed Main cutting Acoustic emission

Feature extraction algorithm Frequency

Time domain

Combination

2.44Eÿ5 6.47Eÿ6 7.25Eÿ5

2.39Eÿ5 6.68Eÿ6 6.83Eÿ5

9.74Eÿ6 6.18Eÿ6 1.22Eÿ5

1.64Eÿ5 8.76Eÿ6 1.05Eÿ5

7.22Eÿ5 9.55Eÿ6 4.33Eÿ5

1.33Eÿ5 8.43Eÿ6 1.39Eÿ5

257

the amount of tool wear predicted by radial force is small AND the amount of tool wear predicted by main cutting vibration is small AND the amount of tool wear predicted by acoustic emission is small

THEN the amount of tool wear is: before y^ ˆ 062963x 1 ‡ 0:22951x 2 ÿ 0:11758x 3 ÿ 0:000055 after y^ ˆ 0:681134x 1 ‡ 0:219751x 2 ÿ 0:142628x 3 ÿ 0:00081 Rule 2 IF

the amount of tool wear predicted by radial force is medium AND the amount of tool wear predicted by main cutting vibration is medium AND the amount of tool wear predicted by acoustic emission is medium

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THEN the amount of tool wear is: before y^ ˆ 0:268101x 1 ‡ 0:077119x 2 ‡ 0:731269x 3 ‡ 0:000538 after y^ ˆ 0:266292x 1 ‡ 0:066931x 2 ‡ 0:681062x 3 ÿ 0:000221 Rule 3 IF

the amount of tool wear predicted by radial force is large AND

the amount of tool wear predicted by main cutting vibration is large AND the amount of tool wear predicted by acoustic emission is large THEN the amount of tool wear is: before y^ ˆ 0:0115781 ‡ 0:993291x 2 ‡ 0:004241x 3 ÿ 0:004433 after y^ ˆ 0:0098215x 1 ‡ 1:101938x 2 ‡ 0:0056218x 3 ÿ 0:008821 So far, three di€erent methods have been developed.

Fig. 3. Measured and predicted amount of tool wear through FNN for cutting SAE6150(370BHN) with Kennamental K68/SPG422 insert.

R.J. Kuo / Engineering Applications of Arti®cial Intelligence 13 (2000) 249±261

In the next section, these three methods will be evaluated by 79 test samples. 5.4.4. Testing/validation The testing results via FNN are shown in Fig. 3, while Fig. 4 shows the pro®les with and without adjusting the depth of cut using FNN results. 5.5. Discussions For time series models, based on the results of nonstationarity tests, all the sensory signal patterns are stationary. After examining the ACFs and PACFs based on Kendall and Ord (1990), AR(5) is best able to represent most of the patterns for force, vibration, and acoustic emission. Though some of the patterns' ACFs and PACFs may show that AR(6) or AR(7) is an appropriate model, the majority agree with AR(5). If the ARMA model is used, it will not improve the speed of learning for an ANN, but will increase the number of the features. The reason for this is that the coecients from MA are not able to represent the patterns, and it can be found that there are no spikes for ACF. For pattern recognition, this paper tries to testify two di€erent neural networks, EBPN and CPN. The training and testing results indicate that EBPN can provide much better forecasting results than CPN. However, the disadvantage of EBPN is that it requires a longer training time. Since the di€erence in MSE values between EBPN and CPN is very large, it is not reasonable to select CPN. In addition, three fuzzy models developed by Kuo (1995) signi®cantly decrease the training time. Therefore, in the integration, only the results from EBPN are choosen. For a single sensor, radial force is more closely related to the amount of tool wear based on the testing

259

samples. It is also identical to the training results, since the network trained by radial force features can converge fastest. In multi-sensor integration, it is shown that FNN can predict the amount of tool wear more accurately than the other two multi-sensor integration methods (multiple regression and ANN) when no sensor is defective. FNN also outperformed multi-sensor fusion using ANN. Without doubt, the MSE value of FNN is less than the multiple regression's MSE value. If the training samples are divided into several groups and use a multiple regression model for each group, then the result will be better than the single multiple regression model. Similarly, FNN is very similar to this method which divides the training patterns into groups and uses a multiple regression model for each group. One di€erence is that FNN with its membership functions can more accurately model the relationship between the inputs and outputs. Also, the learning capability of FNN can ®ne-tune the parameters in order to improve accuracy. From the plotting of measured and predicted amounts of tool wear, it can be found that some amounts of tool wear can not be predicted very well because the hardness of the workpiece is not uniformly distributed. The sudden change in the hardness will signi®cantly change the sensory signal patterns. Meanwhile, a crater is also found under conditions of high speed and/or feed. This will also a€ect the sensory signal patterns. For instance, ¯ank wear will increase the force, while crater wear will decrease the force in some cases. Based on the above discussions, FNN, which is a kind of fuzzy model, is able to predict the amount of tool wear more accurately compared with the other methods. Though Ko and Cho (1994) have applied the

Fig. 4. Pro®le with and without compensation.

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fuzzy model for tool wear monitoring in diamond turning, the study only considers three wear states (i.e. initial, gradual, and severe) instead of the continuous amount of tool wear. In addition, Chryssolouris et al. (1992) employ an ANN and statistical method to ascertain the continuous amount of tool wear. However, the amount of experimental data is insucient since only seven tool inserts are used. For the current study, there is more sucient experimental data, which is enough for both training and testing.

6. Conclusions In this paper, an on-line estimation system applied in the area of tool wear monitoring has been proposed. The proposed system is able to accurately predict the amount of tool wear. The comparison result also indicates that EBPN can provide a better forecast than CPN. In addition, utilization of the EBP-type learning algorithm for a fuzzy model is pretty promising. The proposed multi-sensor integration method, FNN, is found to be more accurate than other methods including multiple regression and an ANN on the basis of MSE. It signi®cantly reduces the training time when compared with ANN, since the ®rst stage of FNN, SOS, has divided the training samples into several groups or rules and the second stage, SAS, only needs to ®ne-tune the parameters. Regarding the pro®le of the cutting material, its precision is higher since the predicted tool wear value is applied to adjust the depth of cut.

Acknowledgements This research work was ®nancially supported in part by the National Science Council of Taiwan, ROC, under Contract NSC 85-2213-E-214-006. Their support has been appreciated. In addition, the experimental data were collected in the Machining Research Lab, Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, USA. Thus, the support of Professor P.H. Cohen is also appreciated. The author would also like to thank the anonymous referees for reading the paper and o€ering many helpful comments.

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