Clustering of acoustic emission signals collected during drilling process of composite materials using unsupervised classifiers

Abstract

Conventional methods of analysis for drilling of composite materials usually study the amount of damaged area, thrust force, and effective parameters. However, these methods do not provide the investigator with sufficient information about drilling mechanisms. In the current investigation, a procedure for diagnosing different drilling mechanisms based on the analysis of the signals of acoustic emission is presented. According to the number of time domain acoustic emission parameters, using multi-variable methods of analysis is unavoidable. In this work, unsupervised pattern recognition analyses (fuzzy C-means clustering) associated with a principal component analysis are the tools that are used for the classification of the recorded acoustic emission data. After classification of acoustic emission events, the resulting classes are correlated with the different drilling stages and mechanisms. Acoustic emission signal analysis provides a better discrimination of drilling stages than mechanic-based analyses.

Keywords

Drilling composites acoustic emission fuzzy C-means clustering principal component analysis

Introduction

Drilling of composite materials

The development of composite materials offers several advantages over homogeneous and isotropic conventional materials such as high specific stiffness, high strength, resistance to fatigue loads, tolerance to temperature extremes, dimensional stability, and weight minimization.^1,2 These superior mechanical properties have developed the field of composite materials ranging from automobile components to sporting goods. Processes such as hand lay-up, filament winding, autoclave curing, etc. are employed to fabricate composite materials. However, machining operations have to be performed to manufacture the finished components.³

Drilling is one of the most common and acceptable machining processes performed in the final stages of assembly of sub-components. Any defect resulting in rejection of the part represents an expensive loss. It has been reported that drilling-induced delamination accounts for 60% of all part rejections during assembly of an aircraft.⁴ There are several forms of damage that occur during drilling of composite materials; among these, matrix cracking, fiber pull out, fiber breakage, matrix burning, and delamination are the most crucial.

Delamination is one of the serious problems that occur due to localized bending in the zone sited at the point of drill contact. Delamination leads to poor assembly tolerance and has the potential to reduce the lifetime of structures as it reduces the mechanical properties of the components. Two forms of delamination have been identified by EI-Sonbaty et al.,⁵ called peel-up at the drill entrance and push-down at the exit side of the work piece. In order to quantitatively evaluate the amount of delamination, Chen⁶ proposed an approach to obtain the value of the conventional delamination factor which assumes the form as equation (1). The conventional delamination factor is not appropriated owing to the fact that the crack size does not represent the damage magnitude conveniently. Furthermore, this procedure does not indicate the damage area. A novel approach devised to measure the delamination factor was proposed by Davim et al.,⁷ namely the adjusted delamination factor, and calculated through equation (2). The first part of equation (2) represents the size of the crack contribution and the second part represents the damage area contribution.

F_{d} = \frac{D_{max}}{D_{0}}

(1)

F_{da} = α \frac{D_{max}}{D_{0}} + β \frac{A_{max}}{A_{0}}

(2)

The coefficients α and β can be calculated from the following equation

β = \frac{A_{d}}{A_{0} - A_{max}} and α = 1 - β

(3)

where D₀ is the nominal diameter of the hole, D_max is the maximum diameter of the damage hole, A₀ is the area related to the nominal hole, A_max is the area related to the maximum diameter of the delamination zone, and A_d is the delaminated area.

Until now many researchers have attempted to minimize the delamination on drilled components of composite materials, considering the speed, feed and point angle as the affecting parameters with various methods such as Taguchi, multi-objective optimization, neural network, genetic algorithm, and others.^8–13 Krishnamoorthy et al.¹⁴ used grey relational analysis to choose the optimal combination of drilling parameters and improve the quality of the drilled holes. The analysis method was based on five different output performance characteristics, namely, thrust force, torque, entry delamination, exit delamination, and eccentricity of the holes. Analysis of variance (ANOVA) is also used to find the percentage contribution of the drilling parameters. Their results show that feed rate is the most significant factor in drilling of carbon fiber-reinforced plastic composites. Palanikumar¹⁵ used Taguchi’s L₁₆, 4-level orthogonal array to design the experimentations. The cutting parameters were optimized with consideration of thrust force, workpiece surface roughness, and delamination factor. The analysis of the grey relational grade shows that feed rate is a more significant parameter than spindle speed.

AE and clustering

Drilling-induced damage affects the mechanical properties of the part and the demand for monitoring techniques. Non-destructive damage assessment techniques include offline and online methods. Offline methods include digital scanning, optical microscopy, ultrasonic scanning, and radiography which cannot be used to assess damage under loading. In the field of machining, acoustic emission is considered one of the most acceptable and accurate online methods.^16–20

Acoustic emissions (AE) are transient ultrasonic waves generated by sudden movement in a material under stress.²¹ When a component is subjected to mechanical load, discontinuities in materials may release AE energy. AE energy travels in the form of high-frequency stress waves. These waves propagate through the specimen and are transformed into electrical signals by piezoelectric transducers (AE sensors). The electrical signals are then amplified and further processed as AE signal data. A precise processing of the AE signals can lead to the discrimination of the different cutting mechanisms occurring during drilling of composites. Different processing methods are used in compliance with the type of signals.²²

Ravishankar et al.²³ used acoustic emission root mean square (AE-RMS) index to interpret drilling stages from entry to exit. They mentioned that total AE signals gathered from drilling of composite materials are from four important sources, namely, fiber cutting, matrix cutting, friction, and delamination. They tried to identify signal sources by examining the signal characteristic of each source when drilled separately to quantify the energy level and then compare them with the total signals, which contain energy due to all sources. In the next stage, they looked for the typical parameter/parameters that can characterize the individual sources. In a similar approach,²⁴ they found frequency characteristics of emission from contact friction (rubbing action due to the rotating chisel edge touching the specimen), drilling (generation of microchips), and peripheral friction (rubbing action of the rotating drill body against the hole walls). Velayudham et al.²⁵ used acoustic emission for condition monitoring of composite drilling process. They applied wavelet packet transform (WPT) on AE data and extracted features to monitor tool wear condition with number of holes. The results show that the monitoring index increases with the number of holes due to rubbing of worn drill flank.

The major problem associated with AE signal processing is the discrimination between the different acoustic emission sources. Among numerous processing methods, cluster analysis is a robust tool for investigating and interpreting data. The main objective of cluster analysis is to separate a set of data into several classes reflecting the internal structure of the data. Godin et al.²⁶ classified recorded AE signals collected during tensile tests on cross-ply glass/epoxy composites in order to distinguish damage mechanisms. They used a combination of the self-organizing map (SOM) and the k-means methods to classify recorded AE in three clusters. They chose six time domain AE parameters: amplitude, duration, rise time, counts, counts to peak, and energy as an input vector for clustering problem. According to their results amplitude distribution of different damage mechanisms, i.e. matrix cracking, interfacial debonding, and delamination was achieved. In a similar study, Godin et al.²⁷ used two different methods of classification: a supervised and unsupervised classification (Kohonen’s map) for AE signals recorded during tensile tests on glass/polyester composites. They combined two techniques: the k-means algorithm and the k-nearest neighbors. Three different specimens with different damage mechanisms were used, i.e. pure resin samples, 90° and 45° off-axis unidirectional composite samples. These different specimens were expected to produce different damage modes during tensile tests. Based on the results, the characteristics of signals for each damage mode were identified. These characteristics are duration, rise time, amplitude distribution and number of hits. Huguet et al.²⁸ used acoustic emission data as input in a Kohonen self-organizing map, which automatically clusters the acoustic emission signals, making a correlation with the failure modes possible. Marec et al.²⁹ used multivariable analysis and wavelet transform for clustering acoustic emission data. The clustering methods were fuzzy C-means (FCM) clustering coupled with a principal component analysis (PCA). The continuous wavelet transform and discrete wavelet transform were used on typical matrix cracking and fiber–matrix debonding AE signals. Different frequency distributions of these two kinds of signals were noticeably recognized. Matrix cracking mainly has frequency range of 50–150 kHz, while the frequency range for debonding is 170–350 kHz. Pappas et al.³⁰ applied a k-mean algorithm on AE recorded data during the quasi-static tensile loading of center-hole carbon/carbon composites. They clustered AE data into five classes related to five damage mechanisms, namely, short fiber/matrix debonding, interlaminar matrix cracking, single fiber failure, fiber pullout, and multi fiber failure.

Philippidis et al.³¹ used neural network techniques on AE signals to characterize damage of carbon/carbon laminates. They used the modified learning vector quantization (M-LVQ) technique which is suitable for types of AE data emitted by composites. Bar et al.³² used PVDF sensors to identify failure modes in glass fiber reinforced plastic (GFRP) based on the artificial neural network (ANN) approach. Their results show that the characteristics of the AE signals are not affected by the stacking sequence of a laminate but are dependent on the failure mechanisms. ANN can also classify the AE signals which are highly overlapping in their parameters. Omkar et al.³³ used ant colony optimization to classify AE signals to their respective sources. Their experimental results show that this method is able to generate straightforward rules to classify the AE data set accurately. Moevus et al.³⁴ studied damage mechanisms and associated acoustic emission in two SiCf/[Si–B–C] composites exhibiting different tensile behaviors. They applied the k-mean classification method to find the AE characteristic of different damage mechanisms. They successfully distinguished different types of matrix cracking in the composite by the time domain AE analysis. Liu et al.³⁵ used fuzzy pattern recognition of AE signals for detecting grinding burn. They applied wavelet packet transform to extract features from AE signals and fuzzy pattern recognition for optimizing features and identifying the grinding status.

In this study, different drilling stages are distinguished based on the analysis of the signals of acoustic emission. Unsupervised pattern recognition techniques, i.e. FCM with PCA, are used for the analysis of AE data. The aim of the work is to enhance analysis efficiency in discriminating various AE sources and to establish an automated procedure of AE signal classification to evaluate AE activity from future tests using similar devices.

Clustering methodology

Clustering is a general methodology and a remarkably rich conceptual and algorithmic framework for data analysis and interpretation.^36,37 In this section, unsupervised pattern recognition analyses (FCM clustering) associated with a PCA are explained as the tools used for the classification of the monitored AE events.

Fuzzy C-means

FCM is defined as a data clustering technique in which a dataset is grouped into c clusters with every datapoint in the dataset belonging to every cluster to a certain degree. This technique was firstly introduced by Dunn in 1973³⁸ and generalized by Jim Bezdek in 1981³⁶ as an improvement on earlier clustering methods. The main purpose of FCM is clustering n data (n AE signals) into c classes. Sample data is defined by A matrix, $A = {a_{1}, a_{2}, \dots, a_{n}}$ , and each value of this matrix has m features, $a_{i} = {a_{i}^{1}, a_{i}^{2}, \dots, a_{i}^{m}}$ . Some of these features for a typical AE signal are amplitude, energy, rise time, etc. The objective function guiding the search through the data space is expressed by equation (4). The main aim of the objective function is to minimize the Euclidian distance between each data point in the cluster and its cluster center, and to maximize the Euclidian distance between other cluster centers.

J (u, v) = \sum_{k = 1}^{n} \sum_{i = 1}^{c} (υ_{ik}) 2 (d_{ik}^{2})

(4)

where u is the membership matrix, v is the cluster center matrix and

υ_{ik}

(

υ_{i} (a_{k})

) is the membership value of the k-th pattern vector to i-th class, with the unity constraint imposed on the membership grades.

$d_{ik}^{2}$ is the Euclidean distance between the pattern vector a_k and the cluster center C_i assumes the form

d_{ik}^{2} = (a_{k} - C_{i}) T (a_{k} - C_{i})

(5)

The cluster centers and updated membership matrix are calculated from

C_{i} = \frac{\sum_{k = 1}^{n} (υ_{ik}) 2 . a_{k}}{\sum_{k = 1}^{n} (υ_{ik}) 2}

(6)

υ_{ik}^{(r + 1)} = [\sum_{j = 1}^{c} (\frac{d_{ik}^{r}}{d_{jk}^{r}}) 2] - 1

(7)

The fuzzy algorithm is expressed by the flowchart shown in Figure 1.

The last two steps are iterated until the improvement over the previous iteration is below a threshold ɛ, whereas r is the iteration step

‖ J r + 1 - J r ‖ \leq ɛ

(8)

Principal component analysis

One of the difficulties inherent in multivariate statistics is the problem of visualizing data that have many variables. The problem can be simplified by replacing a group of variables with one or more new variables. The basic goal in PCA is to reduce the dimension of the data. The method generates a new set of variables, called principal components. Each principal component is a linear combination of the original variables. All the principal components are orthogonal to each other, so there is no redundant information. The principal components as a whole form an orthogonal basis for the space of the data.

The first and second principal components are two axes in space perpendicular to each other. By projecting each observation on these axes, the resulting values form new variables, and the variances of these variables are the maximum among all possible choices of these two axes. The fundamental and basic equations of PCA are given in the previous studies.^39,40

Experimental procedure

Material and specimen preparation

High strength E-glass woven fiber was used as reinforcement in epoxy resin to prepare the laminate slabs used in this study. The properties of the epoxy resin as a matrix material are as follows: density of 1.12 g/cm³, ultimate tensile strength of 80 MPa, failure strain of 3%, and elastic modulus of 2.7 GPa. The woven fabric has a density of 292 g/m², ultimate tensile strength of 2150 MPa, and elastic modulus of 74 GPa. The composite laminates were prepared by hand lay-up technique in the lab. GFRP specimens were in rectangular form and had dimensions of 100 mm × 100 mm. The specimens were made of 13 laminae with thickness of 5 mm. The fiber volume fraction was approximately 60% and the Poisson’s ratio was measured as 0.3.

Testing procedure

Drilling processes were conducted on glass fiber reinforced epoxy composites using an FP4M vertical machining center supplied by ANILAM (maximum r/min, 2500 and feed rate, 200 mm/min). The thrust forces generated during drilling tests were measured with a Kistler two-component piezoelectric dynamometer (model 9255B). A proper clamping system was used to fix the specimens in the drilling machine shown in Figure 2. All drilling tests were conducted without coolant. In order to study the influence of the feed rate on the thrust force and delamination, two test conditions were selected as mentioned in Table 1. The levels of feed rate were selected based on preliminary research.²² In the present study, the drilling tests were carried out using commercial HSS twist drills with constant geometry (Table 2). To avoid the effect of drill wear, each hole was implemented using a new drill.

Table 1.

Drilling test conditions.

Feed rate (mm/rev)	Spindle speed (rpm)	Drill point angle (degree)
A1	0.11	260	118
A2	0.5	260	118

Table 2.

Specification of HSS twist drill.

Drill diameter (mm)	Point angle (degree)	Helix angle (degree)	Average rake angle (degree)	Clearance angle (degree)	Cutting edge length (mm)	Chisel edge length (mm)	Chisel edge angle (degree)	Land of margin (mm)
10	118	30	21.8^a	12^a	4.75	2	51	0.8

^aMeasured at the outer diameter.

AE equipment

In the present work, a two-channel data acquisition system from Physical Acoustics Corporation (PAC) with a sampling rate of 1 MHz and a 40 dB pre-amplification was used to record AE data. AE measurements were achieved by using a broadband, resonant-type, single-crystal piezoelectric transducer, called Pico, as an acoustic emission sensor, with an optimum frequency range 100 kHz to 1 MHz, fixed on the specimen with a fixed distance of 30 mm from the center of hole. Silicon grease used as an acoustic couplant to improve the signal transmission between specimens and sensor. An amplitude threshold of 30 dB was used to avoid background noise during sampling. The amplitude distribution ranges from 0 to 100 dB in which 0 dB corresponds to 1 mV. After installation of the sensor, a pencil lead break procedure⁴¹ was used to ensure the sensor was properly coupled to the specimen.

Results and discussion

Drilling induced delamination is a serious defect, which has the tendency to reduce the structural strength. The main key for decreasing delamination lies in reducing the thrust force. The thrust forces generated during drilling of two specimens used in this investigation (A1 and A2) are shown in Figure 3. The cutting conditions for tests A1 and A2 are reported in Table 2. According to the figure, different drilling conditions do not alter the general shape but do alter the peak value of thrust force. An increase in the feed rate represents an increase in the impact of the cutting edges against the fibers, which may be the main source of the highest thrust force values recorded.

It has been proved that both delamination mechanisms at drill entrance and exit have a direct relation with the thrust force generated during drilling of composite materials.^{4,5,13,42–45} When the drill enters the laminate it has a tendency to pull the abraded material along the flute and the material spirals up prior to being efficiently cut (peel-up delamination). When the drill reaches the last few uncut plies, the thrust force induced stress exceeds the interlaminar bond strength and delamination occurs before the laminate is completely penetrated by the drill (push-out delamination). Figure 4 shows the scanned delaminated area of specimens A1 and A2 at both entry and exit sides. The values of the adjusted delamination factor stated in the figure were calculated based on equation (2).

Among numerous types of monitoring tools, acoustic emission is considered as one of the most powerful methods in the field of composite drilling. Acoustic emission is more sensitive than thrust force to study drilling conditions because it gathers signals produced by different mechanisms or from different damage sources with different features. In this paper, multivariable analysis was applied in order to discriminate the drilling stages according to their AE patterns. The AE signals are complex objects that must be characterized by multiple pertinent descriptors in order to be processed. Six time domain AE features are used as the component of an input vector. These are rise time, amplitude, average frequency, energy, counts and duration of the signals. Sometimes it makes sense to compute principal components for raw data. This is appropriate when all the variables are in the same units. Standardizing the data is preferable when the variables are in different units or when the variance of the different columns is substantial (as in this case). The feature values are normalized by dividing each column by its standard deviation. In order to visualize the results, a PCA is used in a two-dimension subspace. The percent of the total variability explained by each principal component is shown in Figure 5.

The preceding figure shows that the only clear break in the value of variance accounted for by each component is between the first and second components. However, one component by itself cannot visualize the data. The first two principal components contain more than 80% of the total variability in the signal space for two specimens, so this might be a reasonable way to reduce the dimensions in order to visualize the data. In other words, the first PCA components have a 2D projection that contains more than 80% of the variance of the 6D data.

Based on the studies by Ravishankar et al.,^23,24 the total energy release or AE obtained during drilling contains information about the different sources. The main sources are fiber cutting, matrix cutting, friction and delamination. According to the fact that four main sources for AE signals generated during drilling of composite materials are reported, the classification to be made was initially considered as a four-class problem. However, no good results were achieved in such a classification. In the novel approach, the AE signals are clustered into three clusters and each one is correlated with a different drilling stage as follows: (i) drill entry stage, (ii) cutting stage, (iii) delamination-friction stage. In the next step the cutting signal itself is split into two clusters: (i) fiber cutting and (ii) matrix cutting. Figure 6 visualizes the PCA analysis of the FCM clustering on the sample of AE signals. PCA projection shows that the distribution of the data does not overlap. Thus, classifying the data and separating the drilling stages seems easy.

The remaining problem is attribution of each class to a specific drilling stage. To solve this problem, time-based features of AE signals were plotted versus time. By feature extraction, amplitude was selected as the best candidate as it is strikingly similar to thrust force. After the classification of data, each datum was marked with a special color and an amplitude diagram was regenerated. Figure 7 shows amplitude versus time plot for two drilling tests. This reveals that AE data clustered in the first class are related to drill entry stage, data in the second class are related to cutting stage and data in the third class are related to delamination-friction stage. In both diagrams delamination at drill entrance and drill exit are evident. This proves the ability of AE to detect both pull-up and push-out delamination mechanisms.

The different distribution percentages of AE signals generated during drilling of composite materials with two different feed rates are shown in Table 3. As can be seen from the table, cutting stages for both feed rates have almost equal percentages, meaning that the cutting action mostly generates special amounts of energy with regard to other stages. By contrast, drill entry and delamination-friction stages are more sensitive to feed rate. When feed rate is increased, the time for the entering stage decreases; consequently the percentage of this stage decreases. Furthermore, by increasing the feed rate, the drilling process behavior will approach impact of a conic indentor tip on a composite plate. Considering the fact that there is not enough time for the cutting operation and chip formation, the drill penetrates the laminate by punching. This concentrated force produces greater bending and consequently results in more interlaminar crack growth. These cracks generate more AE events; hence, the percentage of AE signals associated with the delamination-friction stage increases.

Table 3.

Different distribution percentages of AE signals.

Percentage of first class (entry stage)	Percentage of second class (cutting stage)	Percentage of third class (delamination and friction)
A1	24	45	31
A2	18	46	36

After classification, it is possible to define the AE characteristics of each cluster. The ranges and averages of AE parameters (peak amplitude, rise time, duration, energy, count, and average frequency) for these different classes are shown in Tables 4 to 6. It is obvious from the tables that the entry stage has distinguishing features which discriminate it from the other two stages. This stage has the lowest amplitude, duration, energy and average frequency ranges. In contrast, time-scale features of AE signals for cutting and delamination-friction stages show approximately similar ranges except for energy. The cutting operation calls for higher mechanical energy with respect to delamination and friction, which results in higher AE energy. These results can be used as identification features of the composite drilling process.

Table 4.

Amplitude, rise time, duration, energy, count, and average frequency distribution of first class (entry stage).

Amplitude (dB)	Rise time (µs)	Duration (µs)	Energy (aJ)	Count	Ave. frequency (Hz)
Ranges	A1	30–71	1–542	1–43,500	0–819	1–4688	0–222
Ranges	A2	30–54	1–224	4–813	0–4	1–57	5–250
Averages	A1	45	73	2520	30	229	66
Averages	A2	36	100	385	0.9	20	84

Table 5.

Amplitude, rise time, duration, energy, count, and average frequency distribution of second class (cutting stage).

Amplitude	Rise time	Duration	Energy	Count	Ave. frequency
Ranges	A1	88–99	1–559	104,856	39,042–65,535	13,183–14,581	126–139
Ranges	A2	93–100	4–568	104,856	17,010–65,535	13,385–14,752	128–141
Averages	A1	95	148	104,856	63,924	13,804	131
Averages	A2	98	169	104,856	61,201	13,877	132

Table 6.

Amplitude, rise time, duration, energy, count, and average frequency distribution of third class (delamination-friction stage).

Amplitude	Rise time	Duration	Energy	Count	Ave. frequency
Ranges	A1	74–90	1–496	5–104,856	0–38,457	2–14903	124–400
Ranges	A2	80–98	4–281	104,856	2658–28,676	11,871–14,641	113–140
Averages	A1	82	131	103,494	10,925	14,030	139
Averages	A2	92	96	104,856	19,687	14,048	134

After identification of the drilling stages, in order to discriminate fiber and matrix cutting mechanisms, AE signals associated with the cutting stage are clustered into two classes. Figure 8 illustrates discrimination of fiber and matrix cutting signals generated during the cutting stage. Observing the figure, assignment of the cutting mechanisms to the classes is a challenging problem because of simultaneous fiber and matrix cutting; and it requires an in-depth understanding of the cutting process. The fiber volume fraction of the composite materials which are used in this study shows a higher contribution percentage of fiber. Hence, a higher proportion of AE signals generated during cutting stage must be attributed to the fiber cutting mechanism.

Different distribution percentages of AE signals generated during the cutting stage with two different feed rates are shown in Table 7. As can be seen from the table, portions of fiber and matrix cutting signals generated during drilling are independent of feed rate and are possibly dependent on material properties, like fiber volume fraction.

Table 7.

Different distribution percentages of AE signals.

Percentage of first class (matrix cutting)	Percentage of second class (fiber cutting)
A1	34	66
A2	35	65

The ranges and averages of AE parameters for fiber and matrix cutting mechanisms are shown in Tables 8 and 9. Time-scale features of AE signals for these two mechanisms show approximately similar ranges except for the rise time shown in Figure 9. As can be seen, fiber cutting has a lower rise time range than matrix cutting. This is mainly due to the fact that fiber cutting emitted burst signals which have a shorter rise time; while matrix cutting has homogenous cutting behavior which is emitted as a continuous signal with longer rise time.

Table 8.

Amplitude, rise time, duration, energy, count, and average frequency distribution of fist class (fiber cutting).

Amplitude	Rise time	Duration	Energy	Count	Ave. frequency
Ranges	A1	88–99	1–181	104,856	39,042–65,535	13,183–14,581	126–139
Ranges	A2	93–99	4–128	104,856	17,010–65,535	13,385–14,752	128–141
Averages	A1	94	81	104,856	63,986	13,780	131
Averages	A2	97	42	104,856	58,444	13,848	132

Table 9.

Amplitude, rise time, duration, energy, count, and average frequency distribution of second class (matrix cutting).

Amplitude	Rise time	Duration	Energy	Count	Ave. frequency
Ranges	A1	89–99	185–559	104,856	42,533–65,535	13,236–14,499	126–138
Ranges	A2	96–100	152–568	104,856	65,535	13,417–14,659	128–140
Averages	A1	94	288	104,856	63,794	13,854	132
Averages	A2	98	249	104,856	65,535	13,924	133

Conclusions

FCM is coupled with a PCA to discriminate different drilling stages from AE signals and to visualize the classification into classes. Clustering is first done in order to discriminate different drilling stages. The signals are clustered in three different classes. Assignment of the classes to different drilling stages (entry stage, cutting stage and delamination-friction stage) is done based on different amplitude ranges. The AE characteristics of each stage are then determined. According to results, the entry stage has the lowest amplitude, duration, energy and average frequency ranges. The cutting stage has higher AE energy than the delamination-friction stage. Feed rate level does not affect AE hit percentage of the cutting stage, while it affects entry and delamination-friction stages. After identification of the drilling stages, in order to discriminate fiber and matrix cutting mechanisms, AE signals associated with the cutting stage are clustered into two classes. It is found that portions of fiber and matrix cutting signals are independent of feed rate. Time-scale features of AE signals for these two mechanisms show approximately similar ranges except for the rise time. Fiber cutting mechanism has a lower rise time range than matrix cutting.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest

None declared.

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