Hierarchical strategies in tool wear monitoring

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Keywords: tool condition monitoring, turning, neural network. 1 INTRODUCTION ... Most often used quantities in commercial TCM sys- tems are cutting force ...
SPECIAL ISSUE PAPER 375

Hierarchical strategies in tool wear monitoring ´ ski* K Jemielniak and S Bombin Warsaw University of Technology, Warsaw, Poland The manuscript was received on 5 January 2005 and was accepted after revision for publication on 15 August 2005. DOI: 10.1243/095440505X32841

Abstract: The paper presents a comparison of efficiency of tool wear monitoring strategies based on one signal feature, on a single neural network with several input signals, and on a hierarchical algorithm and a large number of signal features. In the first stage of the hierarchical algorithms, the tool wear was estimated separately for each signal feature. This stage was carried out using either simple neural networks or polynomial approximation. In the second stage, the results obtained in the first one, were integrated into the final tool wear evaluation. The integration was carried out by the use of either a neural network or averaging. The paper shows a considerable advantage of the hierarchical models over conventional industrial solutions (single signal feature) and typical laboratory solutions (single, large neural network). Keywords: tool condition monitoring, turning, neural network 1

INTRODUCTION

The search for process automation, stimulated by growing demands for higher quality and productivity, and the reduction of human supervision of a machining process resulted in the development of tool condition monitoring (TCM) systems. The existing systems, both experimental and commercially available, are based on the measurements of physical phenomena that are correlated with tool wear, and thus can be exploited as tool wear symptoms. Most often used quantities in commercial TCM systems are cutting force components, acoustic emission (AE) and vibration [1]. The review of earlier developments can be found in reference [2]. Since then, the possibility of a reliable tool wear evaluation based on one signal feature (SF) has been questioned because the measured feature depends not only on the tool wear but also on the variety of other process parameters of a random nature, making the relationship between tool wear and measured value very complex; it has a statistical rather than a strict, predictable nature. Despite that, most commercially available [1] as well as experimental [3] systems apply ‘one process–one SF’ strategies. Moreover, these strategies are based on the *Corresponding author: Faculty of Production Engineering, Warsaw University of Technology, Narbutta 86, Warsaw, 02-524, Poland. email: [email protected]

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assumption that the feature is a monotonic function of the tool wear, which is not always the case. This might be the reason why there is still no satisfactory performance of the systems applied in industry [4]. The combination of different signal features today is ever increasing in order to overcome drawbacks of a single sensor approach. The information extracted from one or several sensor signals has to be combined into one tool condition estimation. This can be achieved by various means, such as statistical methods, autoregressive modelling, pattern recognition, expert systems, and others [5]. The neural network approach has recently been the most intensively studied method for feature fusion [6–9]. Usually, a single neural network is used, where several SFs are fed into the network inputs, while the tool wear estimation is the network output. In some works, however, hierarchical tool wear monitoring strategies were proposed. The system presented in reference [5] employs several measures of the cutting force, acoustic emission, and vibration. It consists of two modules where the first one estimates the tool wear from all SFs taken from one sensor and the cutting parameters. A single radial basis function artificial neural network was used here. The results obtained in the first module were integrated into the final system’s response in the second module, in which a fuzzy neural network was used.

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Fig. 1

Turning centre Venus 450 with installed sensors

Fig. 2 Direct (in volts) cutting forces and acoustic emission signals during the first operation and the second cut of the first tool life

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EXPERIMENTAL CONDITIONS AND RESULTS

The experiments have been performed on a turning centre VENUS 450 equipped with an industrial cutting force sensor (Kistler 9601A31) and an acoustic emission (AE) sensor (Kistler 7815B121) (see Fig. 1). The workpieces were steel C 45 bars, 160 mm in diameter, machined in subsequent cuts with depths of cut ap ¼ 1.5 mm (13 cuts) and ap ¼ 2 mm (9 cuts), feed f ¼ 0.1 mm/rev, and cutting speed vc ¼ 150 m/min, down to a diameter of 85 mm. Thus every ‘operation’ consisted of 22 cuts; 86 such operations were carried out, during which eight tools were worn out. Figure 2 presents the direct signals (in volts) obtained from the cutting force and AE sensors

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during the first operation of the first tool life. In this case, the characteristics were high values within the feed force signal at the beginning of each cut. Owing to the construction, the sensor could not be positioned close to the tool (see Fig. 1), which resulted in negative values of Fc force signals, seen in Fig. 2. A strong cross-talk in the cutting force sensor resulted in a complex relation between the actual cutting force components and the values of signals obtained from the sensor. It was decided not to use a complex sensor signal calibration, because in the monitoring system the digitized signals would be used directly. It was assumed that the monitoring system should be learned (trained) during the first tool life so that it

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Hierarchical strategies in tool wear monitoring

would be ready to monitor the tool condition during subsequent tool lives. Thus, the results obtained in the first tool life were used for learning, while the other seven were used for an estimation of tool wear monitoring accuracy.

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FEATURE SELECTION

In the laboratory systems, the tool wear measures [flank wear (VB) and crater wear (KT)] are usually used as tool condition indicators. However, under factory floor conditions, these measures are seldom used. On the other hand, commercial tool condition monitoring (TCM) systems use a simplified approach – the signal feature (SF) value is compared with the predetermined (learned) limit and crossing the limit is signalled as a tool failure. As an intermediate estimation of the tool condition, the user has only instantaneous values of the measured SF. Here, the used-up portion of the tool life (DT), defined as the ratio of the cutting time as performed so far (t) to the overall tool life span (T) was used as the tool condition measure: DT ¼ t/T. Numerous SFs were calculated out of the four obtained signals (three cutting force components and AE): average value, root mean square (r.m.s.), several distribution parameters, maximum and minimum values from selected time periods of cuts, and increments of the measures. Having an ample number of features, originating from one or more signals, those that are correlated with the tool wear should be selected, which means that there is a clear dependence of the feature on the tool wear. Here the following method was used. Firstly, to make an SF correlation with DT comparable, SFs were normalized according to the formula SFn ¼

SF  SFmin SFmax  SFmin

ð1Þ

where SFmin is the minimal SF value in the first tool life, SFmax is the maximum SF value in the first tool life, and SFn is the normalized SF value. Each signal feature (SF) was than correlated with DT, using a two-degree polynomial approximation. It was noticed that application of a higher polynomial degree can result in achieving good scores by the features with two extremes. Taking into account the frequency of a small number of operations in tool life occurring, such a result was considered as unacceptable. A polynomial of the second degree is very simple and fits poorly to a non-linear SF(DT) relationship, but it enables a rough approximation of simple non-monotonic functions (with one extreme). The SF for which the root mean square error (r.m.s.e.) of such a correlation was the smallest was selected for further consideration.

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Dealing with large numbers (several tens) of signal features, it usually appears that among those that meet the criteria, there are groups of SFs that are similar (correlated) to each other. To eliminate these similar features, which do not contain additional information, the r.m.s.e. between the selected feature and all others was calculated, and those with r.m.s.e. values less than 0.1 were rejected. From among the remaining signal features, again the best one was selected and the SFs correlated with it were eliminated. The procedure was repeated for all remaining SFs until the r.m.s.e of the SF(DT) correlation was higher than 0.05. Values of four selected SFs – the variance and median of the Ff signal from the middle (second) of every 22 cuts, the median and minimum value of AERMS from the middle (second) of every 22, versus DT – were presented in Fig. 3. It is worth mentioning that the feature most often used in TCM systems – mean value of the feed force – was very close to the median of the Ff signal from the second cut presented in Fig. 3. The r.m.s.e. value for Ff,av was larger than that obtained for Ff,Med,2nd, so Ff,av, which is closely correlated with Ff,Med,2nd, was eliminated. 4

TOOL WEAR ESTIMATION USING A SINGLE NEURAL NETWORK

Apart from the natural dispersion of each SF, for some tools (tool lives) their values are significantly different, e.g. Ff,Med,2nd for tools 4 and 8 and Ff,Var,2nd and AEMed,2nd for tool 2. In such cases the tool condition estimation based on one feature – DT(SF) – would lead to erroneous results. On the other hand, it is much less likely that most SFs will have uncharacteristic values. That is why a combination of different SFs, i.e. evaluations of DT based on the DT(SF1 . . . SFN) relationship, may be a means to overcome drawbacks of the single feature approach. As a single neural network is the most often used method for feature integration, this method was first tried out. The feedforward–back-propagation neural network was used here. As the number of learning data was not high enough, only the four best SFs were taken into account (the network has four inputs). Moreover, verifying data were generated as average values of each adjoining pair of learning data. The number of hidden neurons was selected to minimize the r.m.s.e. of network output for the learning set of data (the first tool life). Networks with 4 to 20 neurons in the hidden layer were tested, and finally 10 neurons were selected. The same method (minimizing the r.m.s.e. ) was used to select values of momentum (0.14) and learning coefficient (0.65). The obtained neural network was tested using the data from all tool lives. The results of this test are

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Fig. 3 Selected signal features (normalized, dimensionless) versus the used-up portion of the tool life DT; Ff,Var,2nd, variance of Ff from the second of every 22 cuts; Ff,Med,2nd, median of Ff from the second; AEMed,2nd, median of the AERMS from the second; AEMin,2nd, minimum value of AERMS from the second

used as the DT estimation accuracy. Estimation of DT for the second tool was completely erroneous, which can be attributed to too small a number of learning data (natural in these circumstances) in relation to the necessary network size. The impact of those SFs, which do not run characteristically (compare Figs 3 and 4) dominated the network’s output and could not be compensated by the typical ones. Also estimation of DT for the fourth tool was rather unsatisfactory. The remaining estimations were better. 5 Fig. 4 Used-up portion of the tool life (DTNN) estimated using a single neural network versus actual (DT)

presented in Fig. 4 as the used-up portion of the tool life estimated by the neural network (DTNN) versus the actual value of DT ¼ t/T. If the estimation was exact, they would form a straight line, also drawn in Fig. 4, marked as DTNN ¼ DT. The r.m.s.e. can be

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HIERARCHICAL ALGORITHM OF TOOL CONDITION MONITORING

To improve accuracy of the tool condition estimation, the hierarchical strategy of the tool condition estimation was applied. As previously, the system was trained (learned) using the data from the first tool life only; then it was tested using all data. The strategy consisted of two stages. In the first one, the used-up portion of the tool life (DT) was evaluated using each signal feature (SF) separately. Firstly, the

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Fig. 5 Used-up portion of the tool life based on Ff,Med,2nd and the FF-BP neural network (DTNN, left) and a polynomial approximation of the third degree (DTpoly, right) versus actual (DT)

direct relationship SF ¼ f(DT) was determined. Two methods were used here: (a) FF-BP neural network with one input (the signal feature), three hidden neurons, and DT as the output; (b) polynomial approximation of third degree. Ff,Med,2nd presented in Fig. 3 (and many other SFs, not shown there) is a non-monotonic function of the used-up portion of the tool life (the tool wear). The TCM strategies are usually based on the reverse function, where the value of the signal feature determines the tool wear estimation. As the non-monotonic functions are not reversible, all TCM systems using such strategies must fail if the signal feature appears to be non-monotonic. Therefore, in the proposed strategy the following method was applied to overcome this difficulty: 1. The SF ¼ f(DT) function values were calculated and put into a matrix of 100 elements (every 1 per cent of DT). 2. During DT evaluation, the matrix was searched for the SF value closest to the measured one; however, the search was limited to 30 values (30 per cent of DT), starting with the value obtained in the previous operation. The results obtained from both methods for the average value of the feed force are presented in Fig. 5. The used-up portion of the tool life DT was evaluated fairly appropriately for most tools, especially at the end of tool lives, which proves that the problem of the non-monotonous SF ¼ f(DT) function has been overcome. Errors (r.m.s.e.) in both methods appeared to be very close, so both can be

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used alternatively. Of course, as expected, when the SF (Ff,Med,2nd in this case) course was significantly different (tool 4; see also Fig. 3), the DT estimation was erroneous. The purpose of the second stage of the hierarchical TCM strategy is the integration of all DT estimations obtained in the first stage using the polynomial approximation of third degree. Two methods were also used here: (a) a neural network of the same type as described above for direct DT estimation from the SF values; (b) averaging of the values obtained in the first stage, with expunging outliers, i.e. values differing more than 3s from the mean value. The results are presented in Fig. 6. In this stage, the neural network application appeared to be less effective. This time a much simpler solution (polynomial approximation) proved to be much better, namely it gave a smaller r.m.s.e.

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CONCLUSIONS

1. The used-up portion of the tool life (DT ¼ t/T) is a convenient indicator of a tool condition, is much more informative than the direct value of a sensor signal or any signal feature, and is more practical than tool wear measures (VB, KT), which are not usually measured at the factory floor conditions. 2. The assumption about the monotonic, increasing character of signal feature (SF) dependence on the tool wear, being the foundation of most TCM

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Fig. 6 Used-up portion of the tool life estimation based on hierarchical strategy: polynomial approximation þ neural network (DTpolyþNN, left), polynomial approximation þ averaging (DTpolyþavg, right) versus actual (DT)

strategies, excludes many useful features. Taking full advantage of all SFs correlated with the tool condition, including those decreasing or even non-monotonic, can significantly improve the reliability of TCM systems. 3. The difficulties related to reversing non-uniform functions SF(DT), could be overcome by transforming them into a matrix and a limited range search of SF values closest to the measured one. 4. The signal feature integration minimizes the diagnosis uncertainty, reducing the randomness in one SF and providing a more reliable tool condition estimation. 5. Decomposition of the multi SF tool wear estimation into hierarchical algorithms has several advantages over the single-step approach: (a) Many more SFs can be used, as the SF(DT) function for a single feature is simple, easy to determine and then reverse, while the direct determination of the DT(SF1 . . . SFN) function needs numerous learning data and a long learning time. (b) Non-monotonic SF(DT) functions can be used, as explained above.

REFERENCES 1 Jemielniak, K. Commercial tool condition monitoring systems. Int. J. Adv. Mf. Technol., 1999, 15, 711–721 2 Byrne, G., Dornfeld, D., Inasaki, I., Ko¨nig, W., and Teti, R. Tool condition monitoring (TCM) – the status of research and industrial application. Ann. CIRP, 1995, 44(2), 541–567.

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3 Dimla, E., Dimla, Sr., and Lister, P. M. On-line metal cutting tool condition monitoring. I: force and vibration analyses. Int. J. Machine Tools Mf., 2000, 40, 739–768. 4 Ketteler, G. Analysis of requirements for monitoring systems. In Proceedings of the Second International Workshop on Intelligent manufacturing systems, Leuven, Belgium, 1999, pp. 721–725. 5 Kuo, R. J. and Cohen, P. H. Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network. Neural Networks, 1999, 12. 6 Das, S., Bandyopadhyay, P. P., and Chattopadhyay, A. B. Neural-networks-based tool wear monitoring in turning medium carbon steel using a coated carbide tool. J. Mater. Processing Technol., 1997, 63, 187–192. 7 Jemielniak, K., Kwiatkowski, L., and Wrzosek, P. Diagnosis of tool wear based on cutting forces and acoustic emission measurements as inputs to a neural network. J. Intell. Mfg, 1998, 9, 447–455. 8 Karpuschewski, B., Wehmeier, and Inasaki, I. Griding monitoring system based on power and acoustic emision sensors. Ann. CIRP, 2000, 49(1), 235–240. 9 Tansel, I.N., et al. Tool wear estimation in micromachining, Part I: tool usage–cutting force relationship. Int. J. Machine Tools Mf., 2000, 40, 599–608.

APPENDIX Notation ap AE AEMed,2nd

depth of cut acoustic emission median of the AERMS from the second of every 22 cuts

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AEMin,2nd AERMS f Fc Ff Ff,av Ff,Med,2nd Ff,Var,2nd Fp FF-BP KT

minimum value of AERMS from the second of every 22 cuts root mean square of acoustic emission feed cutting force feed force mean value of the feed force median of Ff from the second of every 22 cuts variance of Ff from the second of every 22 cuts passive force feedforward neural network with backpropagation learning algorithm crater wear

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rev r.m.s. r.m.s.e. SF t T TCM vc VB

revolution root mean square root mean square error signal feature cutting time tool life span tool condition monitoring cutting speed flank wear

DT DTNN

used-up portion of the tool life used-up portion of the tool estimated by the neural network standard deviation

s

381

life

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