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Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture http://pib.sagepub.com/ Tool wear monitoring−−an intelligent approach

Ch Srinivasa Rao and R. R. Srikant Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2004 218: 905 DOI: 10.1243/0954405041486028 The online version of this article can be found at: http://pib.sagepub.com/content/218/8/905

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Tool wear monitoring—an intelligent approach Ch Srinivasa Rao* and R R Srikant Department of Mechanical Engineering, GITAM, Visakhapatnam (AP), India

Abstract: Tool wear monitoring is one of the most crucial and inevitable processes in present-day manufacturing systems. With the growth of unmanned factories, the need for on-line monitoring systems is well recognized. Artificial intelligence techniques such as artificial neural networks, fuzzy logic and the neuro-fuzzy technique have proved their potential in monitoring the manufacturing processes. In shop-floor control, the condition of the cutting tool is of more concern than the actual tool wear value but, in research activities, the estimation of the actual value of the tool wear occupies a prominent place. The present work is concerned with the assessment of the tool condition and also the estimation of the tool wear value. In the present paper, artificial intelligence techniques are applied to estimate the tool condition and tool wear value on line. Kohonen’s selforganizing map is applied in neural networks for estimating the tool condition. Fuzzy logic and the neuro-fuzzy technique are implemented by triangular membership functions. To assess the tool wear value, a back-propagation neural network is applied. In fuzzy logic and neuro-fuzzy techniques, the centroid method of defuzzification is applied to obtain the flank wear value. Experimental data are generated by machining EN-8 steel with a high-speed steel cutting tool. The obtained data are used to train and test the networks. To make the monitoring system user friendly, a user interface is developed using Microsoft Visual Basic 6.0. Keywords: tool wear monitoring, neural networks, Kohonen’s self-organized map (SOM), fuzzy logic, triangular membership functions, neuro-fuzzy technique, flank wear, radial force

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INTRODUCTION

The need for increased flexibility and high quality of the product, economically, paved the way for computerintegrated manufacturing that aims at increasing the productivity by effective utilization of the equipment through the reduction of idle time and scraps by adopting different process-monitoring methodologies. Tool wear is an important factor directly affecting the quality of the machined products. Hence, it is important to observe and monitor the condition of the cutting tool. The present work attempts to provide an intelligent approach for estimating the tool condition via the implementation of artificial intelligence techniques. Several eminent researchers suggested different toolwear-monitoring strategies in the literature. These strategies can be broadly divided into two types: direct and indirect methods. Direct methods involve the measurement of tool wear directly, while indirect methods involve the measurement of a parameter that is easy to measure, such as the cutting temperature and The MS was received on 31 October 2003 and was accepted after revision for publication on 26 April 2004. *Corresponding author: Department of Mechanical Engineering, GITAM, Visakhapatnam (AP), India 530045. E-mail: [email protected] B21003

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forces, which can be used to assess tool wear. Although direct methods measure tool wear directly, their implementation is costly and cannot be applied on line [1–4]. Hence indirect methods are more popular. Rao [5] pointed out that the radial force showed better correlation with tool wear than the other two components of cutting force. Youn and Yang [6] showed that the cutting force data could eliminate the influence of cutting conditions such as cutting speed, feed rate and depth of cut. For the past few years, artificial intelligence has emerged as an important tool to monitor the processes in any unmanned factory and to replace human intelligence. Rangwala [7] used cutting forces and acoustic emission signals as inputs to a multi-layer perceptron. Burke [8] employed unsupervised networks to classify the tool condition. This method of classification is found to be adaptive to the cutting conditions. Mesina and Langari [9] used a neuro-fuzzy technique to predict the tool condition in a milling process. For fuzzifying the inputs, a triangular fuzzy membership function is applied. The results obtained show that the proposed system predicts the tool condition with a success rate of 97 per cent. Purushothaman and Srinivasa [10] trained a multilayer perceptron with a back-propagation algorithm to estimate tool flank wear on line. Kuo and Cohen [11] Proc. Instn Mech. Engrs Vol. 218 Part B: J. Engineering Manufacture


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Ch SRINIVASA RAO AND R R SRIKANT

proposed a neural-network-based fuzzy controller to monitor the tool wear. Balazinski et al. [12] compared three artificial intelligence techniques, namely backpropagation neural networks, fuzzy logic and the neuro-fuzzy technique in tool wear monitoring by monitoring the cutting forces. The encouraging results obtained in the implementation of the artificial intelligence techniques has provoked much work in this direction. 2

NEURO-FUZZY TECHNIQUES

2.1

Artificial neural networks

An artificial neural network (ANN) is an informationprocessing paradigm that is inspired by the procedure in biological nervous systems. An ANN consists of multiple layers of simple processing elements called neurons. Various inputs to the network are represented by In . Each of these inputs is multiplied by a connection weight, represented by Wn and added to the bias to compute activation an which is converted into the output On via a transfer function [13]: an ¼ Wn InT þ

ð1Þ

On ¼ f ðan Þ

ð2Þ

Although all artificial neural networks contain the basic building block, namely the artificial neuron, the difference in the arrangement of neurons and transfer function applied drastically change the behaviour of the network. Learning is the process by which random-valued parameters (weights and bias) of a neural network are adapted through a continuous process of simulation by the environment in which network is embedded. Learning may be categorized as unsupervised learning, supervised learning and reinforced learning [13]. 2.2

The Kohonen self-organizing map

For classification of various input patterns, unsupervised networks can be effectively used. Kohonen’s selforganizing map (SOM) is used in the present work due to its outstanding merits [14]. Activation is computed as explained above. The output is obtained by using a competitive law. Weight modification in Kohonen’s SOM can be expressed as i Wðk

þ 1Þ ¼ i WðkÞ þ ½I i WðkÞ

ð3Þ

i WðkÞ ¼ old weight structure ¼ learning rate I ¼ input

In the present work, the network classifies the tool state as in condition or worn out; i.e. the network converges to produce the aforementioned two classes. 2.3

Back-propagation neural network

Supervised networks are best suited to estimate the exact value of tool wear. Supervised learning compares the obtained output with the expected target value and adapts the network to obtain a stable weight structure. In the present work, a back-propagation neural network is used for on-line tool wear estimation owing to its distinguished features [13]. The data for training the networks are obtained through experimentation. The obtained data are normalized; i.e. each entity of a particular input vector, say machining time, is divided by the length of the vector to scale it down from 0 to 1 to prevent the abnormal growth of weight structures during successive iterations. These data are fed as input to the network. Weights are updated using the generalized delta rule Wnew ¼ Wold ET I

ð4Þ

where Wnew ¼ weight after modification Wold ¼ weight structure before modification ¼ learning rate, usually taken between 0 and 1 ET ¼ error obtained Weight change is calculated for all connections. Errors for all patterns are summed and the algorithm is run until the error falls below a specified value. To reach the global minimum of the error of the network, the aid of heuristic optimization techniques is sought in present work. The present work uses the concept of momentum to overcome local minima [13]. The technique lies in adding a portion of previous weight changes in weight modification: W2 ðk þ 1Þ ¼ W2 ðkÞ þ ET O1 þ W2 ðkÞ

ð5Þ

where ¼ momentum rate, usually taken to be around 0.5– 0.8 k ¼ number of the present iterations The obtained stable weight structures are used with new input patterns to obtain the tool flank wear values.

where i 2 Ni ðdÞ Ni ðdÞ ¼ neighbourhood of the winning neuron within distance d Wðk þ 1Þ ¼ modified weight structure i

2.4

Fuzzy logic

Fuzzy logic has a great capability to capture human common-sense reasoning, decision making and other

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TOOL WEAR MONITORING—AN INTELLIGENT APPROACH

aspects of human cognition. It overcomes the limitations of classic logical systems, which impose an inherent restriction on representation of imprecise concepts [15]. A general fuzzy controller involves a fuzzy inference engine and fuzzification–defuzzification modules [16]. Fuzzification expresses the input variables in the form of fuzzy membership values based on various membership functions [16]. In the present work, triangular membership functions are used. Governing rules in linguistic form such as ‘if the cutting force is high and the machining time is high, then tool wear is high’ are formulated on the basis of experimental observations [12]. For example, the above-mentioned rule is formulated if, for cutting force and machining time values falling in the high group, the corresponding tool wear is found to fall in the high fuzzy group. Based on each rule, an inference can be drawn on output grade and membership value. Inferences obtained from various rules are combined to arrive at a final decision. In shop-floor control, the aim is to classify the tool condition as ‘worn out’ or ‘in condition’; a dividing factor of membership value 0.85 in the high-wear group is selected. The obtained membership values are defuzzified using various techniques to obtain the true value of the flank wear. In the present work, the centroid method of defuzzification is used [16].

2.5

Fig. 1 Experimental set-up

Neuro-fuzzy technique

In realistic applications, a controlling system has to rely on a non-fuzzy computer that processes numerical information [17]. Hence, the possibility of replacing linguistic rules in fuzzy logic with neural networks is considered by the implementation of a back-propagation neural network fortified with heuristic methods of optimization.

3

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Fig. 2 Lathe tool dynamometer

are compared with the initial tool profile and the flank wear is determined. Machining is carried out under constant cutting conditions with an average cutting speed of 100 m/min, a feed rate of 0.2 mm/rev and two depths of cut equal to 1 mm and 0.5 mm. Figure 3 shows a worn tool. The obtained

EXPERIMENTAL SET-UP AND EXPERIMENTATION

Turning is carried out on a lathe using EN-8 steel as workpiece material and a high-speed steel (HSS) tool bit with ISO nomenclature 0–5–30–15–10–10–0.5 mm. Machining is carried out with three different cutting edges of the HSS tool. Cutting force signals from the cutting tool are measured by a lathe tool dynamometer. The experimental set-up is as shown in Fig. 1. The readings recoded by the dynamometer are accurate with a standard error of 5 per cent. The analogue signals recorded are converted to digital form internally in the instrument (Fig. 2). Machining is interrupted at intervals of 1 min; the tool profile is analysed under an optical projector employing a lens of 20 magnification. The obtained tool profiles B21003

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Fig. 3 Profile of a tool flank in the optical projector Proc. Instn Mech. Engrs Vol. 218 Part B: J. Engineering Manufacture


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Fig. 4 Variation in the flank wear with machining time

values of the tool flank wear and radial force are used to fabricate various tool flank wear-monitoring systems. 4

RESULTS AND DISCUSSION

Tool flank wear is measured at different intervals of machining time with increase in the machining time. Figure 4 shows the variation in flank wear with increase in the machining time. Observations indicate that the flank wear growth is initially rapid up to machining for nearly 8 min and slowly the increase in tool flank wear value becomes constant for a machining time of up to 20 min. On further machining, accelerated growth is observed in flank wear. In the present work, the radial component of the cutting force is chosen to estimate the tool flank wear. Figure 5 shows the variation in the radial force with increase in the machining time. Initially, for a machining time of up to 15 min the radial force is found to increase rapidly. Then the radial force is observed to be nearly constant until a machining time of 25 min is reached. On further machining, an increase in the radial force is observed.

Normalized values of the radial force and the machining time are presented as input patterns to train the Kohonen network. Hence the number of input layer neurons is two. The rest of the data is presented to the network for testing the network and monitoring the tool condition. The network converges, thus classifying the tool state into two groups; thus the number of output layer neurons is two. The learning rate is taken to be 0.45 and the number of iterations to be 20 000. It can be seen that the cutting tool is said to be in condition for the first nine samples and is classified as worn out from the tenth sample (Fig. 6). Although neural networks provide an effective means of classifying the tool condition, the use of user-friendly linguistic rules is an added advantage for fuzzy logic and thus enhances its credibility. Because of its effectiveness, fuzzy logic is employed in the present work to monitor the tool condition. Outputs obtained are in the form of fuzzy grades of the tool flank wear in three groups: low, medium and high. A criterion of membership value of 0.85 of the high group is chosen to decide the tool condition. Results obtained from fuzzy logic are represented in Fig. 7.

Fig. 5 Variation in the radial force with machining time

Fig. 6 Classification of the tool condition by neural networks Proc. Instn Mech. Engrs Vol. 218 Part B: J. Engineering Manufacture


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Fig. 7

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Classification of the tool condition by fuzzy logic or the neuro-fuzzy technique

Fig. 8 Determination of the number of inputs

A cutting tool is said to be in condition for the first nine samples and the tenth sample is said to be worn out. It is observed that the classification schemes, by employing neural networks and fuzzy logic show a similar trend under identical cutting conditions. In addition to the above techniques, the neuro-fuzzy technique is employed in the present work to classify the tool condition. The inputs to the network are the fuzzy grades of cutting force and machining times, the outputs being the fuzzy grades of tool wear. Hence the number of input layer neurons is six and the number of output layer neurons is three. By trial and error, the number of hidden layer neurons is fixed to be three for minimum error. The learning rate is taken as 0.45. The number of iterations is 25 000. Similar results are obtained irrespective of the amount of processor time taken compared with Kohonen’s SOM. The back-propagation network is trained and tested in two different cases; in one including the cutting conditions as the inputs (five inputs) and in the other excluding

them (two inputs). A comparison of the results obtained from the two cases indicates that better estimation of tool flank wear is obtained using only the cutting forces and the machining time as the inputs and suggests that cutting conditions may be excluded from the network as the cutting forces embed in themselves the characteristics of the cutting conditions (Fig. 8). The size of the network is optimized by presenting the input data. The number of hidden layer neurons is fixed as 3 and the number of iterations as 25 000. Hence the topology of the network is fixed as 2–3–1. Normalized values of the radial force and the machining time are presented as input patterns to train the network. The rest of the data are presented to the network for testing the network and estimating the tool wear. The obtained results are compared with the experimental values as shown in Fig. 9. The results indicate that, although initially the variation between the experimental values and the estimated values from neural networks is significant, the values

Fig. 9 Comparison of the neural network (NN) results with the experimental values B21003

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Fig. 10 Comparison of the results obtained from the three techniques (NN, neural network; NFT, neurofuzzy technique; FL, fuzzy logic)

Fig. 11 Percentage normalized errors of the three techniques (NN, neural network; NFT, neuro-fuzzy technique; FL, fuzzy logic)

become identical as the machining time increases. This may be attributed to the fact that neural networks tend to estimate the values by approximating the machining system and the approximation may not hold in the initial stages. In the present work, fuzzy logic and the neuro-fuzzy technique are employed for estimating tool flank wear. In fuzzy logic, the centroid method of defuzzification is applied to obtain the true value of the tool wear. The topology of the network in the neuro-fuzzy technique is the same as in the case of tool condition estimation. The results obtained from fuzzy logic and the neurofuzzy technique are compared with the experimental values in Fig. 10. Figure 11 indicates that the percentage error is high in the initial stages. On further increase in machining time, disparity decreases and the results from all three techniques are close to the experimental values.

used for testing the networks. Figure 12 represents the implementation of three artificial intelligence techniques in Soft ‘Wear’ for monitoring the tool condition. The MONITOR button is used to activate the Cþþ program. The SHOW RESULT button shows the results obtained from the techniques. The REPORT button in the neuro-fuzzy technique gives the report on the number of iterates of the network and the weights structures of the network. The BACK button navigates the user to make a choice of the technique to be used. Figure 13 depicts the implementation of Soft ‘Wear’ for estimation of the tool flank wear value. A backpropagation neural network is used. The significance of the various buttons is as explained above. The COMPARE button is used to compare the results obtained from the three techniques and the experimental values (Fig. 10). Soft ‘Wear’ was developed with a view to providing a user-friendly package that can be applied to estimate the amount of tool wear or to monitor the tool condition. Basic understanding of the networks and tool wear is sufficient to apply Soft ‘Wear’ for monitoring the tool wear efficiently. The package can be obtained free of cost for academic purposes by request from the authors through email. However, the package is still at the development stage and is being improvised by extending it to accommodate different parameters and different machining processes. 6

5

SOFT ‘WEAR’ PACKAGE

In the present work, application of advanced techniques, namely neural networks, fuzzy logic and the neuro-fuzzy technique, for tool wear monitoring is made user friendly by developing a front end using Microsoft Visual Basic 6.0. The developed package is called Soft ‘Wear’. In the present work, Cþþ programs are executed at the back end. In the programs, some of the experimental data are used for training and the rest of the data are

CONCLUSIONS

1. Tool flank wear can be effectively monitored via the radial cutting force. 2. For a tool wear estimation, results obtained by using only the cutting forces and the machining time are more accurate than those obtained by including the cutting conditions as inputs to the network. This also decreases the processor time drastically. 3. Of the three techniques used, the neuro-fuzzy technique produces better results than the other two techniques in a tool flank wear value estimation.



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Fig. 12 Tool condition monitoring using Soft ‘Wear’: (a) neural networks; (b) fuzzy logic; (c) the neuro-fuzzy technique

Fig. 13 Tool wear estimation using Soft ‘Wear’: (a) neural networks; (b) fuzzy logic; (c) the neuro-fuzzy technique B21003

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4. For shop-floor control, where tool condition monitoring is of the utmost importance, Kohonen’s SOM and fuzzy logic can be applied with the advantage of low processor time compared with the neurofuzzy technique.

7

RECOMMENDATIONS FOR FUTURE WORK

1. The developed package Soft ‘Wear’ can be extended to monitor tool wear by employing different monitoring parameters such as tool tip temperature, acoustic emissions, and vibrations. Relevant data must be provided to train and test the networks. 2. Different machining processes can be considered and tool wear can be monitored by presenting the required data. Processes such as milling and broaching involve the use of multi-point cutting tools and hence monitoring is more complex. 3. Tool wear monitoring can be achieved for different combinations of workpiece materials and cutting tools. Experimental data are to be obtained for these combinations and presented to the networks as input.

REFERENCES 1 Du, R., Zhang, B., Hungerford, W. and Pryor, T. Tool condition monitoring and compensation in finish turning using optical sensor. In Proceedings of the ASME Symposium on Mechatronics, 1993, PED-Vol. 63, pp. 245–251 (American Society of Mechanical Engineers, New York). 2 Giusti, F., Santochi, M. and Tantussi, G. On-line sensing of flank and crater wear of cutting tools. Ann. CIRP, 1987, 36(1), 41–44. 3 Li, D. and Mathew, J. Tool wear and failure monitoring techniques for turning—a review. Int. J. Mach. Tools Mf., 1990, 30(4), 579–598. 4 Cook, N. H. and Lang, A. B. Criticism of radioactive tool life testing. Trans. ASME, J. Engng for Industry, 1963, 85(5), 381–387.

5 Rao, D. N. Investigations into tool-wear monitoring in turning. PhD thesis, Department of Mechanical Engineering, Indian Institute of Technology, Delhi, India, 1991. 6 Youn, J.-W. and Yang, M.-Y. A study on the relationships between static/dynamic cutting force components and tool wear. Trans. ASME, J. Mfg Sci. Engng, May 2001, 123, 196–205. 7 Rangwala, S. Machining process characterization and intelligent tool condition monitoring using acoustic emission signal analysis. PhD thesis, Mechanical Engineering Department, University of California, Berkeley, California, 1988. 8 Burke, L. I. Automated identification of tool wear states in machining processes: an application of self-organising neural networks. PhD thesis, Department of Industrial Engineering and Operations Research, University of California, Berkeley, California, 1989. 9 Mesina, O. S. and Langari, R. A neuro-fuzzy system for tool condition monitoring in metal cutting. Trans. ASME, J. Mfg Sci. Engng, May 2001, 123, 312–318. 10 Purushothaman, S. and Srinivasa, Y. G. A back-propagation algorithm applied to tool wear monitoring. Int. J. Mach. Tools Mf., 1994, 34(5), 625–631. 11 Kuo, R. J. and Cohen, P. H. Intelligent tool wear estimation system through artificial neural networks and fuzzy modelling. Artif. Intell. Engng, 1998, 12, 229–242. 12 Balazinski, M., Czogala, E., Jemielniak, K. and Leski, J. Tool condition monitoring using artificial methods. Engng Applic. Artif. Intell., 2002, 15, 73–90. 13 Hagan, M. T., Demuth, H. B. and Beale, M. Neural Network Design, 1996 (Thomson Learning, Vikas Publishing House, New Delhi, India). 14 Bose, N. K. and Liang, P. Neural Networks Fundamentals with Graphs, Algorithms and Applications, 1998 (Tata McGraw-Hill, New Delhi, India). 15 Kosko, B. Neural Networks and Fuzzy Systems—A Dynamical Approach to Machine Intelligence, 1997 (Prentice-Hall of India, New Delhi, India). 16 Klir, G. J. and Yuan, B. Fuzzy Systems and Fuzzy Logic— Theory and Practise, 1998 (Prentice-Hall of India, Englewood Cliffs, New Jersey). 17 Nie, J. and Linkens, D. Fuzzy Neural Control: Principles, Algorithms and Applications, 1998 (Prentice-Hall of India, Englewood Cliffs, New Jersey).



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