Approaches for efficient tool condition monitoring based on support vector machine

Chapter Introduction CHAPTER Introduction 1.1 Research background One of the most important trends in modern manufacturing systems has been the relentless efforts towards minimizing the cost, maximizing productivity and improving product quality. Tool condition sensing greatly contributes towards the optimization of the cutting process, efficient tool change policies, improvement on product quality, and reducing tool cost (Kumar et al., 1997). Thus tool condition monitoring (TCM) is critical in manufacturing systems. The major objective of a sensor-based TCM system is to determine the cutting tool conditions (such as tool wear, breakage etc.) from the sensor data. Much research (Elbestawi et al., 1991; Dornfeld, 1990; Tansel and McLauglin, 1993; Wong et al., 1997) has been undertaken in these fields, since cutting tools are both an important factor in manufacturing costs and the quality of the workpiece (Pfeifer and Wiegers, 2000). Despite intensive research during the past two decades, successful and effective TCM in automated machining systems remains an engineering challenge (Li and Mathew, 1990). The developed systems often have narrow ranges of performance, require substantial training or setup time to function correctly (Byrne and Dornfeld, 1995). Therefore further research is needed. In the following section, the basic architecture of sensor-based TCM systems is Chapter Introduction presented, which includes sensing method, feature extraction and selection, and decision-making techniques. 1.2 Architecture of TCM system TCM System is basically an information flow and processing system (as shown in Figure 1.1), in which the information source selection and acquisition (sensing data collection), information processing and refinement (feature extraction and selection) and decision making based on the refined information (condition identification) are fully integrated. Signal Selection and Acquisition Information Processing and Refinement Decision Making Tool Condition Figure 1.1 Information flow and processing scheme in TCM 1.2.1 Signal acquisition by sensing methods Sensing is the first part of the information-driven TCM system, which provides the primary information inputs. The basic requirements in the selection of sensing signals are: 1. The signals should directly or indirectly provide information that is closely related to the changes in the tool conditions. 2. The signals should have high signal to noise ratio (SNR), and not interfere with the machining process. 3. The acquired sensing information should indicate or detect all significant events in the cutting process. 1.2.2 Signal processing Signal processing is the core function of the information-driven TCM system, Chapter Introduction which includes feature extraction and feature selection. It basically performs a transformation process in which a large flow of sensor signals is streamlined to a compact tool-condition-informative feature vector in time and frequency domain. The key challenge in this technique is to derive features, which contain not only as much tool condition information as possible, but also compact in nature. Feature extraction Since sensed signals are typically noisy, these signals have to be further processed i.e. feature extraction, to yield useful features that are highly sensitive to tool conditions. The widely used feature extraction approaches include: 1. Time domain analysis such as derivative of signal (Li and Mathew, 1990), statistical value of waveform (Kannatey-Asibu and Dornfeld, 1982). 2. Advanced signal processing techniques such as neural network (Tansel and McLauglin, 1993), wavelet analysis (Tansel and McLauglin, 1993, Wu and Du, 1995). 3. Power spectrum analysis such as FFT, cross spectrum (Emel and KannateyAsibul, 1988). 4. Time series analysis, such as autoregressive (AR) and autoregressive moving average (ARMA) (Liang and Dornfeld, 1989). Feature selection Feature selection is to select an optimum subset of features from potentially useful features which are available in a given problem domain (Gose et al., 1996). It is a challenging task to select the characteristic features that not only represent the characteristics of the process (information), but also contains less noise. This method outputs a subset of all available features, therefore, the dimensionality of the final input feature set may be reduced. Its intention is not only to discover all the features Chapter Introduction relevant to the concept and determine how relevant they are, but also to find a minimum feature subset for effective classification with good generalization performance. In addition, feature selection may also speed up the classifier for time critical applications, and make feature discovery possible. The optimum feature subset has been defined as the subset that performs the best under a classification system (Jain and Zongker, 1997). "Performs the best" here may be explained in two slightly different ways: 1. The subset of features which gives the lowest classification error (an unconstrained combinatorial optimization problem); or 2. The smallest subset of features for which the classification error proportion is below a set threshold (constrained combinatorial optimization) (Siedlecki and Sklansky, 1988). The latter is widely employed in many practical applications including this research. 1.2.3 Decision making The decision-making strategy is to map the signal features to a proper class (machining tool conditions) i.e. pattern recognition (Li and Mathew, 1990). The output of the decision-making process includes one or more of the following: 1. Identification of tool conditions (such as tool wear/breakage etc.). 2. Evaluation of the severity of certain abnormal tool conditions. 3. Prediction of tool conditions and control of machining process. This research focuses on the first item, i.e. binary tool conditions identification (fresh or worn) and multiclassification of tool conditions (sharp, workable and worn). The robustness of decision-making depends not only on the identification techniques, Chapter Introduction but also on the features’ quality. The better the inter-class separation capability of the features, the more robust the identification results will be. 1.3 Literature review Tool wear in the metal cutting process results in a loss in dimensional accuracy of the finished product and some possible damage to the work-piece. With the increasing use of machining centers and flexible manufacturing systems, on-line tool wear monitoring has become a challenging research field. The following session provides a comprehensive review about every component of the above mentioned scheme. 1.3.1 Overview of sensing method To achieve greater reliability and robustness in turning operation, both single and multiple sensing, coupled with various signal processing and pattern recognition techniques, have been investigated for single or multiple tool condition identification. As aforementioned, the potentially most economical scheme for TCM is to employ a single-sensor approach for multiple tool conditions identification from the viewpoint of information utilization. The sensing methods in TCM can be categorized into direct or indirect methods according to the signal obtained (Micheletti et al., 1976). The direct sensing method estimates tool conditions through the measurement of tool geometry directly, such as shape or position of cutting edge, optical scanning of the tool tip, electrical measurement of the contact resistance between the tool and workpiece, and radioactive analysis of the chip, analyzing the vision of the tool, measuring the volume of wear particles or the distance between workpiece and tool or tool holder. The limitation of these methods lies in that it is difficult to collect the relevant Chapter Introduction information under actual cutting process. The indirect methods are those concerned with detecting some process-borne signals about tool wear and establishing the relationship between these signals and tool wear (Elbestawi et al., 1991). Indirect methods include measurement of cutting force (Elbestawi et al., 1991; Hong et al., 1996; Santanu et al., 1996; Bao and Tansel, 2000), acoustic emission (Diei and Dornfield, 1987; Sampath and Vajpayee, 1987; Liu and Liang, 1991; Zizka, 1996; Wilcox et al., 1997; Niu et al., 1998; Xu, 2001), vibration of tool or tool post (Lee et al., 1987; Elwardany et al., 1996; Moore and Kiss, 1996; Li and Dong, 2000), ultrasonic vibration (Ultrasonic Energy) (Hayashi et al., 1988; Coker and Shin, 1996; Abuzahra and Yu, 2000), acoustic wave (sound) (Takata et al., 1986), current of spindle or feed motor (power input) (Matsushima et al., 1982; Rangwala and Dornfeld, 1987; Altintas, 1992; Lee et al., 1995) and optical signal (Cuppini et al., 1986; Oguamanam et al., 1994; Wong et al., 1997). These indirect methods have the advantages of less complexity and suitability for practical application (Byrne and Dornfeld, 1995), thus they have been used by many researchers. Of all the signals, acoustic emission (AE), and cutting force are most commonly used. An introduction about them is provided as follows. AE sensing AE signals reflect the microscopic activities (friction, fracture etc.) of the cutting process. It naturally contains multiple tool condition information such as tool wear, fracture etc. Through proper processing, it can be more economically (compared with multi-sensing approach) used for multiple tool condition identification. The merit of using AE to detect tool wear lies in its frequency range is much higher than that of the machine vibrations and environment noises (Sata et al., 1973). Hence, relatively precise signal can easily be obtained by applying high-pass filter. Moreover, AE can Chapter Introduction be obtained by using a piezoelectric transducer mounted on the tool holder which does not interfere with the cutting operation, thereby makes continuous monitoring tool condition possible. However, other researchers held a different idea. They believed that AE signals cannot be independently used to provide reliable tool wear detection in TCM. Blum and Inasaki (1990) performed experiments to determine the relationship between flank wear and AE signals. They were particularly interested in the use of the AE mode, a parameter describing the ‘whole’ characteristics of the cutting process, and then concluded that extracting tool wear information from the AE signal was difficult. The reason causing the two opposing views did not lay on the sensing technology, but on the ensuing analysis (Lister, 1993). Based on this opinion, this thesis first discusses the application of AE signals in TCM system when steel is used as workpiece. Cutting force sensing Measuring cutting forces is one of the most common techniques to monitor tool condition, since they are more sensitive to tool wear than vibration or power measurements (Lee et al., 1989). The reliability of force measurements is another factor for their popularity in tool wear monitoring applications. As a cutting tool shears the workpiece, high stresses and strain rates give rise to forces with dynamic behavior across a broad spectrum of frequencies. The relationship between tool wear (e.g. flank wear and crater wear) and increasing cutting force is well known for a long time (Dornfeld, 1990; Oraby and Hayhurst, 1991; Lee et al, 1992; Ravindra et al., 1993b; Tarng et al., 1994). Although many investigators agreed that the change of cutting forces represents an accurate and reliable approach to estimate tool condition, they still argued which component is the most sensitive; dynamic component, static component, or both of Chapter Introduction them. Cutting condition is also an argued issue. Cuppini et al. (1990) implemented a continuous monitoring method and established relationships between wear and cutting power without cutting conditions. While, Choudhury and Kishore (2000) believed that cutting speed, feed and depth of cut should be taken into account in tool condition recognition. This work has tried to clear up the above arguments according to cutting force from titanium machining. 1.3.2 Overview of signal processing AE signal processing Due to AE signals’ high frequency nature and sensitivity to the micro-structural behavior of material, it is widely employed to extract the useful information in TCM. Iwata and Moriwaki (1977) pioneered the method of using AE signals to monitor tool wear condition in a cutting process, and they found that the power of spectrum of AE signals up to 350kHz increased with tool wear and then it reached saturation. Since the AE signal associated with the tool flank wear is stationary in nature, fast fourier transform (FFT) is still the best tool for the analysis of this type of signals. The spectral density of AE signals has been found to be the most informative feature for TCM in turning (Emel and Kannatey-Asibu, 1988). Naerheim and Arora (1984) used continuous and discontinuous AE in turning operations to test gradual wear and intermittent degradation of cutting tools, respectively. Roget et al (1988) concluded that AE parameters such as root mean square (RMS), mean, and peak values and their corresponding variance, kurtosis and skew could provide sufficient warning information of tool breakage and tool wear in various cutting condition. Jemielniak and Otman (1988) considered that the skew and kurtosis to be better indicators of tool Chapter Introduction failure than RMS values. Another approach for improving the reliability of the wear related AE signal was proposed by Blum and Suzuki (1988). A feature called “AE mode” has been observed to be quite sensitive to tool wear condition. Time series analysis focuses on the stochastic nature in the dynamics process of AE generation. Liang and Dornfeld (1989) employed time series modeling techniques to extract AE features such as autoregressive (AR) parameters and AR residual signals for testing and monitoring tool wear. Moriwaki and Tobito (1990) proposed statistical features (mean, variance and the coefficient of RMS) as inputs of a pattern recognition system to identify and predict the ensuing tool life for coated tool life estimation. Zheng et al. (1992) used an optic fiber sensor and a commercially available PZT AE sensor to conduct drilling and milling operations. Results from the two experiments showed a reasonable degree of agreement. Using AE features, König et al. (1992) performed tests to monitor small drillings and detect tool fracture, and reported that AE features were sensitive to tool chipping. Dornfeld (1992) presented compelling reviews on the application of AE sensing techniques in tool wear detection in machining. He observed that the changes in skew and kurtosis of AE RMS signals could effectively indicate tool wear. Kakade et al. (1994) reported that AE parameters (ring-down count, rise time, event duration, event rate and frequency) could distinguish clearly the cutting action of a sharp and worn or broken tool. Kannatey-Asibu and Dornfeld (1982) found that the changes in skew, kurtosis and of the AE RMS signal effectively indicate the tool wear in machining. Kamarthi et al. (1995) considered that AE features extracted by the wavelet transform were very sensitive to gradually increasing flank wear. The magnitude of the AE in the frequency domain was employed by Li and Yuan (1998) to monitor the change of tool states. Choi et al. (1999) fused AE and cutting forces to develop a real-time TCM for turning operations. Chapter Introduction The recorded data were analyzed through a fast block-averaging algorithm for features and patterns indicative of tool fracture. Similar work was conducted by Jemielniak and Otman (1998), who used a statistical signal-processing algorithm to identify RMS, skew and kurtosis of the AE signals and detect catastrophic tool failure. Inspection of the results indicated that the skew and kurtosis were better indicators of catastrophic tool failure than the RMS values. Cutting force processing Shi and Ramalingam (1990) investigated the feasibility of different force components, and observed that the feed force to cutting force ratio was sensitive to flank wear but insensitive to process changes (cutting speed and depth of cut). Dornfeld (1990) and Ko and Cho (1994) focused on the dynamic characteristic of the cutting force, due to the friction variation between tool and workpiece in tool wear process. Oraby and Hayhurst (1991) developed a model to build the relationship between the feed force, radial force and flank wear in a turning operation. Elbestawi et al. (1991) employed FFT to compute the sensitivity of cutting harmonics (cutting force signals) to flank wear. Lee et al. (1992) found that the components of feed and tangential dynamic force bore a good relationship to flank wear trend. Lister (1993) analyzed the power spectra of dynamic cutting forces and found that the power level of certain frequency band increases with tool wear. In the orthogonal milling, Caprino et al. (1996) concluded that both the horizontal and vertical forces undergo large variations with tool wear. Lee et al. (1989) analyzed the dynamic force signals of a coated grooved tool by FFT, and found that the percentage increase of dynamic tangential force could give a promising threshold for the prediction of tool failure. Choudhury and Kishore (2000) observed that the ratio between the feed force and cutting force provided a practical method to quantify tool wear in turning. Dimla and 10 Chapter Conclusions and Recommendations scheme can be illustrated as shown Figure 8.1. Firstly an SOM or some clustering tool can be used to partition the feature input space into several disjoint regions. Then for each particular region, different SVMs (or other neural networks) compete until the most adequate one is used to tackle partitioned regions. Since the partitioned regions have more uniform distributions than that of the entire input space, it is easier for an SVM to capture a more stationary input-output relationship. Moreover, less number of training data points in each region makes the system more efficient for both training and classifying. SVMs Feature Input Signal Extraction Self-Organizing Feature Map Region SVMs Output SVMs n SVMs Region SVMs Output SVMs n SVMs Region SVMs Output SVMs n Figure 8.1 Proposed hybrid system with SVM and SOM 132 References Author’s Publications List Journal Papers 1. Sun, J., Hong, G. S., Rahman, M., Wong, Y.S. Identification of Feature Set for Effective Tool Condition Monitoring by Acoustic Emission Sensing, International Journal of Production Research, Vol.42, No.5, pp.901–918, 2004. 2. Sun, J., Rahman, M., Wong, Y.S., Hong, G.S. Multi-classification of Tool Wear with Support Vector Machine by Manufacturing Loss Consideration, International Journal of Machine Tools and Manufacture, Vol. 44, No.11, pp.1179–1187, 2004. 3. Sun, J., Hong, G. S., Rahman, M., Wong, Y. S. Improved Performance Evaluation of Tool Condition Identification by Manufacturing Loss Consideration, International Journal of Production Research, Vol. 43, No. pp. 1185 – 1204, 2005 4. Sun, J., Hong, G. S., Wong, Y. S., Rahman, M. and Z.G. Wang Effective Training data selection in Tool Condition Monitoring System, appear in International Journal of Machine Tools and Manufacture 5. Sun, J., Wong, Y. S., Rahman, M., Hong, G. S. and Z.G. Wang Tool Condition Identification Framework in Titanium Machining, submit to Journal of Engineering Manufacture, Proceedings of the Institution of Mechanical Engineers, Part B 6. Z.G. Wang, M. Rahman, Y.S. Wong and J. Sun Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing, submit to International Journal of Machine Tools and Manufacture 7. Z.G. Wang, Y.S. Wong, M. Rahman and J. Sun Multi-objective optimization of high-speed milling of titanium alloys, submit to International Journal of Advanced Manufacturing Technology. 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In Condition Monitoring and Diagnostic Engineering Management COMADEM 96 Conference Proceedings, pp.377-387, 1996. 147 [...]... different category of tool wear conditions Therefore this tool condition identification method is extended to multiclassifying tool conditions Finally, a framework which generalizes sensing signal selection, feature analysis, performance evaluation and decision making is proposed in this study Two case studies are provided to demonstrate the above proposed methods: one based on AE signal from machining... naturally contain multiple tool condition information (tool wear, fracture), have been proven to effective in TCM Compared with multi-sensing approach, AE sensing can be more economically Thus, it is used as references for developing tool condition identification system During metal cutting the workpiece undergoes considerable plastic deformation as the tool pushes through it Within the deformation zones,... effective feature set to identify tool conditions From the analyzed results, cutting force sensing is considered as a suitable monitoring signal, and its effective feature set is used as input for tool condition identification in titanium machining Then, a performance evaluation function with manufacturing loss consideration is introduced to determine whether a tool is due for replacement other than merely... learning and prediction is the most popular method to perform decision making on tool conditions These issues are further discussed in great details in the following chapters The value of this research is to improve the application of NNs -based methods in TCM so as to realize reliable tool condition identification over a range of cutting conditions 1.4 Research objective and contributions The objective... Chapter 1 Introduction an effective training data set with reliable recognition performance in tool condition identification The benefits of multiclassification of tool wear are described in chapter 6 The problems that existed in the application of NNs in TCM are analyzed, and the performance evaluation function introduced in chapter 5 is extended to multilevel classification of tool condition Finally three... other based on cutting force from machining titanium In short, the major contributions of this thesis include: (1) Develop a method to identify feature set from various extracted features 17 Chapter 1 Introduction (2) Propose a new performance evaluation function by manufacturing loss consideration (3) Propose an effective decision making method in tool condition identification (4) Improve the performances... number of neurons, and can be retrieved almost instantaneously in practical application NNs can also perform decision making based on incomplete and noisy information, which makes it suitable for the diagnostic function in a manufacturing system (Rangwala and Dornfeld, 1990) Rangwala and Dornfeld (1987) pioneered the use of Back-propagation (BP) to classify AE and force signal for tool wear monitoring. .. algorithms impose a great challenge for feature extraction techniques (Niu et al., 1998) However, extracting compact tool- wear-sensitive but condition- 15 Chapter 1 Introduction independent features is still an ongoing research issue In this thesis, support vector machine (SVM) is proposed to learn the correct tool wear information in the extensive cutting conditions Compared with other learning algorithms,... signals Generally, the AE signals from machining process may contain both types because of the co-existence of different tool conditions 2.4 Cutting forces and tool wear Understanding the basic force trend in the metal-cutting process that is related to tool wear or failure will enable one to know tool conditions The variation of cutting forces to tool wear has been widely established (Oraby and Hayhurst,... the cutting tool When a tool is used under normal cutting conditions, flank wear is usually the primary factor that determines the life of an insert, while crater formation is more important under high-temperature and high speed cutting conditions (Boothroyd and Knight, 1989) In this experiment, due to cutting conditions within the normal range, only flank wear is considered to determine tool life This . of tool wear conditions. Therefore this tool condition identification method is extended to multiclassifying tool conditions. Finally, a framework which generalizes sensing signal selection,. tool conditions identification (fresh or worn) and multiclassification of tool conditions (sharp, workable and worn). The robustness of decision-making depends not only on the identification. refinement (feature extraction and selection) and decision making based on the refined information (condition identification) are fully integrated. Figure 1.1 Information flow and processing