Keywords

1 Introduction

Tool wear is a detrimental progressive phenomenon with negative impacts on productivity, efficiency, and safety of machining operations. Although tool wear mechanisms are diverse, flank wear is commonly used in milling processes. It is a consequence of the sliding contact between the flank face of the cutting edge and the newly machined surface of the workpiece. Flank wear can be estimated directly from geometric parameters of the wear land [1] or by means of indirect methods based on physical magnitudes that carry information of the development of the process [2, 3]. From the latter, the acoustic emission has been widely used for characterization of machining operations [4,5,6,7]. It has the advantage of being a non-intrusive method which does not require major modifications in the work area of the machine tool. Moreover, it can be recorded without interrupting the machining process and its frequency range is less influenced by machine vibrations or any surrounding sources. Regarding tool wear monitoring, the acoustic emission has been object of intensive research efforts [8,9,10,11,12], however the evidence is not conclusive and the methods developed are far from being implemented in practice.

In this contribution, an indirect method based on the acoustic emission signal is presented to estimate the progressive tool wear in peripheral milling operations. The measurement of flank wear and the cutting forces are considered in order to establish comparisons and to perform analysis of sensitivity to changes in tool condition.

2 Methodology

Due to causality between cutting speed and tool wear, a one-factor and two-level experiment is proposed as shown in Table 1. To improve the accuracy of the observations, four replicates were set up for each treatment. This gives a total of 8 experimental trials, which were randomized to minimize the occurrence of systematic errors.

The experiments were performed in a Deckel Maho DMC 1035 V Eco machine center under constant cutting conditions. They consist of a series of consecutive passes of peripheral milling on a prismatic AISI 4340 workpiece with a cutting length of 90 mm. Each treatment starts with a fresh tool, and it ends when a predetermined level of tool wear has been reached. A two-flute helical endmill is selected, however one of the cutting edges is removed to avoid runout, allowing to determine the progression of tool wear with a constant chip load.

Table 1. Experimental cutting conditions. Cutting tool: HSS helical endmill, 9 mm diameter, with \(2\) teeth and 30° helix angle. Nomenclature: cutting velocity \({V}_{c}\), spindle speed \(N\), cutting length of each pass \({L}_{c}\), axial cutting depth \({a}_{p}\), radial cutting depth \({a}_{e}\) and feed per tooth \({f}_{z}\).
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Visual inspections of the tool have been conducted after each cutting pass and a complete map of the cutting edge is captured by a digital microscope Dinolite AM4815ZTL. The acoustic emission is measured by a Kistler 8152C0050500 piezoelectric transducer coupled to a Kistler 5125C unit which provides an analog output within a frequency range of 50 and 400 kHz. The AE sensor is rigidly mounted to a fixing plate that holds the workpiece, providing a direct transmission path to the excitation source. The three components of the cutting forces are measured with a dynamometric platform Kistler 9257BA connected to a Kistler 5233A1 control unit which delivers analog outputs in a frequency range between 200 and 2000 Hz. The data acquisition system is based on a NI PXI platform equipped with a NI PXI-6132 card to record the acoustic emission signal with a sampling frequency of 2 MHz, and a NI PXI-4472B card dedicated to the cutting forces at a sampling frequency of 20 kHz.

The flank wear (VB) is determined from image processing and by following the recommendations of ISO 8688–2 [1]. The instantaneous mean and maximum values of the flank width, thereafter referred as \(V{B}_{mean}\) and \(V{B}_{max}\), are selected as characteristic values that represent the uniform and localized growth behavior of the wear land profile, respectively. On the other hand, the response of the acoustic emission and cutting forces to changes in tool condition is evaluated in terms of the peak amplitude of each cutting cycle, giving a measure of the prominence of the signals. A moving window is used as a data reduction method which allows to get the mean value and standard deviation of all instantaneous values within the zone where the tool is fully engaged.

3 Results

3.1 Flank Wear VB

Figure 1(a) shows the progression of flank wear. If the tool life criterion is set to 300 µm, one finds that tool life for the first treatment is 900 s, while for the second treatment it reduces to 400 s. The latter confirms the relationship between tool wear and cutting speed. Moreover, by examining the differences between the curves of \(V{B}_{max}\) and \(V{B}_{mean}\), it can be noticed that the cutting speed might also affect the shape of the wear land profile. As for the first treatment, both curves are found to be very close to each other, whereas in the second treatment, these two curves change at a different rate which is a clear indication of localized growth. This feature is also observed from the microscope images depicted in Fig. 1(b) and Fig. 1(c).

Fig. 1.
figure 1

(a) Evolution of progressive flank wear over cutting time. (b) Uniform wear land profile for Treatment No. 1 at \({t}_{c}=900\) s. (c) Non-uniform wear land profile with localized growth for Treatment No. 2 at \({t}_{c}=400s\).

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3.2 Analysis of Indirect Variables

Figure 2 shows the evolution of the mean peak value of the acoustic emission and the cutting force \({F}_{xy}\). A potential model for these two magnitudes has been adjusted to describe the trend of data points. In both cases, the amplitude of the mean peak value increases as a function of cutting time. This confirms that these variables can be considered as valid descriptors of the process and they can be readily applied in tool condition monitoring.

Fig. 2.
figure 2

Evolution of mean peak value. (a) Acoustic emission and (b) cutting force \({F}_{xy}\). Data points are marked with circles and outliers are highlighted with crosses.

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On the other hand, if one compares the rate of change of the indirect variables and the flank wear curves shown in Fig. 1(a), it can be noticed that the acoustic emission is not only more sensitive to flank wear than the cutting forces, but also it has the ability to distinguish whether the wear land profile follows a uniform behavior or not.

Fig. 3.
figure 3

Evolution of the dispersion of peak value. (a) Acoustic emission and (b) cutting force \({F}_{xy}\). Data points are marked with circles and outliers are highlighted with crosses.

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Analogously, Fig. 3 shows the standard deviation of the mean peak value of the magnitudes of interest. A potential model also has been adjusted to the experimental data. At first glance, the evolution of the dispersion of the acoustic emission and the cutting forces looks similar to the previous results, however, in this case the cutting forces reveal better agreement with the behavior of the flank wear curves. Physically, this means that a tool in good condition exhibits uniform cutting cycles, while in a degradation state it begins to differ from the average value given by normal operation. Therefore, the standard deviation of the mean peak value can be considered as a complementary indicator of flank wear.

Although the mean and standard deviation of the peak value constitute suitable indicators of tool wear progression, the definition of a tool life criteria based on process variables still requires establishing a threshold value. In this regard, the waveform analysis of the instantaneous peak value allows to incorporate additional information to strengthen the decision process. Figure 4 shows a waterfall plot of the instantaneous peak value of the acoustic emission signal as a function of the stationary cutting time and the cutting pass number for Treatment No. 2. For instance, by tracking the waveform amplitude changes between cutting passes, one can observe that the cutting tool starts to wear out at a more accelerated rate from pass 11, which is equivalent to approximately 50% of the lifetime given by the flank wear criterion of VB = 300 µm. This might appear as a qualitative analysis, but it opens the possibility to apply statistical methods to extract quantitative values representative of the tool life.

Fig. 4.
figure 4

Evolution of instantaneous peak value of the acoustic emission for Treatment No. 2.

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4 Conclusions

This work presents an indirect method to estimate tool wear in peripheral milling operations under stationary cutting conditions based on the acoustic emission and the cutting forces. Comparisons with direct flank wear measurements confirm that cutting speed has a strong influence in tool life and in the shape development of the wear land profile along the cutting edge. Experimental evidence suggests that feature extraction can be used to estimate flank wear and also to define a tool life criterion based on indirect variables of the process.