Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

Full text

(1)

Advances in Material Processing Mechanism and Monitoring Technology - Original Research Article

Prediction of tool wear in CFRP

drilling based on neural network

with multicharacteristics

and multisignal sources

Guoqiang Zhu

1

, Shanshan Hu

1

and Hongqun Tang

2

Abstract

Carbon fiber-reinforced polymer (CFRP) drilling is a typical process in the aircraft industry. Because the components of CFRP are different and uneven, it is difficult to extract tool wear characteristics from the machining signals, which are composed of the processing characteristics of various materials and the tool state characteristics. The aim of this work is to present a new comprehensive approach based on multicharacteristics and multisignal sources to predict the tool wear state during CFRP drilling through a combination of a backpropagation (BP) artificial neural network (ANN) model and an efficient automatic system depending on the sliding window algorithm. It was verified that the peak factor and Kurtosis coefficient of different signals and the energy value of the d5 layer of the thrust force signal and the d3 layer of the vibration signal after wavelet decomposition were related to tool wear. Among them, the energy value of the d3 layer of the vibration signal was selected as the wear indicator and was able to describe the state of the tool during the CFRP drilling process regardless of the drilling conditions and individual tool differences. A confirmatory drilling experiment using 6-mm-diameter polycrystalline diamond twist drilling under different processing parameters was conducted to verify the ANN model based on multicharacteristics and multisignal sources. A lower feed speed and a higher cutting speed were both highly correlated with the VB value of flank wear. Drill wear accelerated because of the occurrence of adhesive wear when the number of drilled holes reached around 90. The accuracy of the neural network model is 80–87% when using the value of only one characteristic but clearly increases based on multicharacteristics and multisignal sources in real time, indicating that the BP ANN model has higher accuracy in predicting the tool state in CFRP drilling through the sensor signal fusion method.

Keywords

CFRP, tool wear, BP artificial neural network, automatic system, characteristic value, wear indicator

Introduction

Carbon fiber-reinforced polymer (CFRP) has made great progress in the aerospace and automotive fields due to its characteristics of lightweight, low pollution, and low energy consumption at the same time. In the field of aero-nautics and astroaero-nautics, CFRP drilling is not only the most frequent application in the machining process but also the most economical, efficient, and widely used method in hole-making production.1

However, due to the anisotropy, high strength, and toughness of carbon fiber, fast wear and failure of CFRP drilling tools is an important problem, which greatly affects the machining accuracy and surface roughness of

1

Department of Mechanical Manufacturing, School of Mechanical Engineering, Guangxi University, Nanning, Guangxi, People’s Republic of China

2Guangxi Key Laboratory of Processing for Non-Ferrous Metal and

Featured Materials, Guangxi University, Nanning, Guangxi, People’s Republic of China

Date received: 4 February 2020; accepted: 18 December 2020

Corresponding author:

Shanshan Hu, Department of Mechanical Manufacturing, School of Mechanical Engineering, Guangxi University, Nanning, Guangxi, People’s Republic of China, 530004.

Emails: chloee_2000@163.com; hsswhh@gxu.edu.cn

Composites and Advanced Materials Volume 30: 1–15 ªThe Author(s) 2021 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/2633366X20987234 journals.sagepub.com/home/acm

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Composites

and Advanced

(2)

workpieces. Bayraktar and Turgat2,3 found that uncoated drills caused less delamination than TiN- and TiAN-coated high speed steels (HSS) drills, and the most effective para-meter in hole delamination of composites was determined by the feed rate. Even though the minimum quantity lubri-cation environment cannot prevent the premature failure of drill bits during the machining of CFRP stacks,4 it was found that the delamination factor decreases under dry drilling in some conditions.5 In the aviation industry, dimensional tolerance and surface integrity of various workpieces are strictly required. Therefore, when the cut-ting tools should be replaced has become a problem requir-ing attention.

In recent years, many research methods have been con-sidered to extract characteristic values from sensor signals in the cutting process for tool condition monitoring, such as finding more suitable influence factors6 through cutting force signals, vibration signals, acoustic emission signals, and so on. Rmili et al.7proposed an automatic tool wear detection system based on vibration analysis. According to the particularity of acoustic emission in micromachining, Ren et al.8 proposed a fuzzy tool state detection system based on acoustic emission. Neugebauer et al.9identified the machining position of the tool in drilling CFRP through acoustic emission signals to achieve a better machining effect. To study the machining mechanism of CFRP, Mo¨hr-ing et al.10considered various machining defects that may occur in actual operation, observed the acoustic emission spectrum, and discussed the influence of different drilling mechanisms on chips. In these studies, only a single sensor provides a single signal source without considering the influence of other characteristic values on the whole processing.

As CFRP is a kind of nonmetallic material, its process-ing signals are mixed with the characteristic information of fiber breakage and epoxy resin melting, which makes it very difficult to extract information about tool wear. Therefore, very little research on the extraction and mon-itoring of CFRP tool wear has been carried out so far. Kim and Ramulu11 found the relationship between the sensor signal and the characteristics of the drilled composite hole quality by analyzing the frequency from multiple signal sources. Caggiano et al.12 monitored the tool wear of CFRP drilling by establishing an artificial neural network (ANN) model based on the special peak values of drilling force and torque found in frequency domain analysis. According to the research of Amini et al.,13the vibration and drilling force signals have more influence than other processing parameters and signals. Wang et al.14 found that multisignals and multifeatures can better correlate the predicted value with the actual value by analyzing the characteristic values of thrust force and vibration in microdrilling research. Jain and Lad15 proposed a new integrated tool condition monitoring system to help improve system performance and diagnostic reliability and predict tool wear better.

In this article, a multicharacteristic and multisource backpropagation (BP) ANN model was established to predict the tool state in CFRP small hole drilling by simultaneously collecting thrust force signals and tion signals. The characteristics of thrust force and vibra-tion signals in the CFRP drilling process were obtained after wavelet decomposition. With the characteristic val-ues, a BP ANN model was trained and predicted through samples of tool wear and blade breakage characteristics. The prediction accuracy can be dramatically improved by fusion of different sensor sources using evidence the-ory. Finally, a high-efficiency automatic detector was used to evaluate this tool state monitoring model. This research is expected to provide a theoretical method and realizable path for online tool monitoring of composite materials processing, which is not expensive for industry and can be used to monitor important stations on produc-tion lines.

Modeling method of tool wear based on BP

neural network

The error BP algorithm is an important neural network model16proposed by Rumelhart, which has the advantages of a simple learning algorithm and high computational effi-ciency. As shown in Figure 1, the BP ANN is composed of three layers: an input layer, a hidden layer, and an output layer. The technical route of neural network modeling is shown in Figure 2.

The learning algorithm of the BP ANN was implemen-ted according to the following steps:

1. The characteristic values that are sensitive to tool wear are selected as state characteristic values and put into the input layer.17The BP neural network model is trained by characteristic sample data under the conditions of normal, blunt, and dam-aged to obtain a prediction result for the tool state. The prediction state values, [1,0,0], [0,1,0], and [0,0,1], represent the normal, blunt, and damaged tool states.

To determine the number of hidden nodes in the hidden layer in the BP neural network model,18 the number of nodes with the smallest error is obtained through trial and error by continuously increasing the value of the positive integer a in equation (1). The number of hidden layers is finally determined to be 13

Number ¼pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiinputnumberþoutputnumberþ a ð1Þ 2. Based on the regression principle, network weight, and threshold method, the characteristic value from the input layer is processed by a sigmoid function using equations (2) and (3)

(3)

Output layer ik¼ f netð kÞ; netk¼ Xm j¼0 wjkhj k¼ 1; 2; 3 . . . ; l ð2Þ

Hidden layer hj¼ f netj

  ; netj¼ Xn j¼0 vijxi j¼ 1; 2; 3 . . . ; m ð3Þ

Among them, wjkis the weight connecting the j’th node of the hidden layer and the k’th node of the output layer, vij is the weight connecting the i’th node of the input layer and the j’th node of the hidden layer, f(x) is a sigmoid function, and x0and y0are the thresholds of the input layer and the hidden layer, respectively.

3. When the actual state is not equal to the target state, the output error E is expanded and equation (4) is obtained. The error can be reduced by adjusting wjk and vij, where dkis the ideal output value

E¼ 1=2 ( dk f " Xm j¼0 wjkf Xn 0 vijxi !#)2 ð4Þ 4. The error of each node of the output layer and

hidden layer is calculated as

rk¼ dð k ikÞ ikð1 ikÞ k¼ 1; 2; 3 . . . ; l

ð5Þ bj¼ rjwjkhj 1hj

 

j¼ 1; 2; 3 . . . ; m ð6Þ

Figure 2. Flowchart of modeling based on BP neural network. BP: backpropagation. Figure 1. BP neural network structure diagram. BP: backpropagation.

(4)

5. The weights and thresholds of the connection layers are updated using the errors of the nodes of the output layer and the hidden layer as follows

wjkðN þ 1Þ ¼ wjkð Þ þ arN khj ð7Þ

vijðN þ 1Þ ¼ vijð Þ  arN k ð8Þ

xjkðN þ 1Þ ¼ xjkð Þ þ bbN jxi ð9Þ

yijðN þ 1Þ ¼ yijð Þ  bbN j ð10Þ

where N is the number of corrections. The iterations are executed m times.a and b are random vectors in the range of [0,1].

6. When the specified error limit has not been reached, the calculation starts again from step (2). 7. When the number of iterations has been completed or a specified error limit has been reached, the update is stopped.

Case study

Experimental scheme and platform

Workpiece. The CFRP (Wuxi Jiabo Composites Co., Wuxi,

Jiangsu, China) with a size of 60 50  5 mm3was formed

by stacking 10 layers of unidirectional prepreg composed of AG-8 epoxy resin and T700 carbon fiber (TORAY Co., Tokyo, Japan) according to the angle sequence of [0/ 45/45/90], as shown in Figure 3. The fiber angle of

each layer can be reduced by choosing the fiber direction between each layer during customization, which can effec-tively prevent delamination of layers and ensure a good machining process. The fiber number of T700 carbon fiber is 12 K/beam with a monofilament diameter of 1–2 m. The tensile strength of fiber is 4900 MPa and the tensile elastic modulus is 230 GPa. AG-8 epoxy resin with a den-sity of 1.1–1.2 g/cm3and a Poisson ratio of 0.30–0.39 were adopted, and its maximum processing temperature was 150–170C. A polycrystalline diamond (PCD) twist drill brazed with two separated PCD inserts (Ningbo Yongzuan Co., Ningbo, Zhejiang, China) was selected, as shown in Figure 4.

Experimental scheme. Cutting speeds of Vc¼ 113, 132, 150, and 170 m/min and feed speeds of Vf¼ 80, 90, 100, and 110 mm/min were adopted in this experiment, wherein a brand new PCD twist drill was used for continuous drilling under each group of parameters. Through-hole dry drill-ing19without coolant was adopted.

Data acquisition and experimental platform. The experiment

was carried out on a FANUC ROBODRILL_a-T14iFLb vertical machining center with a maximum spindle speed of 24,000 r/min and a rated power of 11 kW. The thrust force signal was collected by a Kistler 9257B piezoelectric dynamometer (Kistler Group, Inc., Bern, Kanton Bern, Switzerland) and a Kistler 5070A charge amplifier (Kistler Group, Inc., Bern, Kanton Bern, Switzerland). The vibra-tion signal was collected by a PCB 356A01 triaxial sensor (PCB Piezotronics, Inc., Buffalo, State of New York, USA) and recorded by an NI 9234 sound and vibration module (National Instruments Corporation, Austin, State of Texas, USA). Based on Shannon’s sampling theory, the sampling frequency of the test was chosen as 10 kHz. The original signal was denoised by a finite-impulse response low-pass filter to delete the noise signal and distinguish the tool state during processing. Figure 5 shows a diagram of the experi-mental and test system.

Figure 3. CFRP workpiece. CFRP: carbon fiber-reinforced polymer.

(5)

Measurement method of tool wear. Because the resin wraps

around the fiber, the CFRP chips stick to the drill bit, mak-ing it hard to clean, and thus, it is difficult to measure the tool wear through changes in quality. Furthermore, the hardness of the PCD insert is significantly higher than that of the tool shank made of cemented carbide, which means the flank wear on the tool shank becomes a major wear. Therefore, the state of the cutting tool was monitored on the basis of the tool wear width (VB). After drilling of every 10 consecutive holes, a scanning electron micro-scope (SEM) was used to observe the surface wear and measured the average VB. The flank wear value and the wear degree classification were obtained accordingly.20 To investigate the complex wear mechanisms and hard-ness difference between different parts of the drill bit in the high-speed process,21,22the flank wear was measured after drilling 10 consecutive holes at 1/12 and 4/12 of the diameter on both the left and the right cutting edge, as shown in Figure 6, and the calculation method was as follows

V B¼ ðV B1þV B2þV B01þV B02V B0Þ=4 ð11Þ

where VB0is the new drill bit’s total value of VB1, VB2, V B01, and V B02.

Extraction of characteristics

Classification of tool wear characteristics. Figure 7 showed the

SEM morphology at different drilling times under the pro-cessing conditions of Vc¼ 132 m/min and Vf¼ 100 mm/ min. It can be seen from Figure 7(a) and (b) that the wear of

the tool shank was quite obvious because the hardness of the tool shank made of cemented carbide was significantly lower than that of the PCD insert. Cracks and fractures were often caused by stress concentration at the connection between the PCD insert and the tool shank, as shown in Figure 7(c).

Using equation (11), the VB value of flank wear under various cutting parameters was obtained and the increasing trend of flank wear is shown in Figure 8. According to the calculated VB values of all drills, a VB value greater than 0.05 mm was considered to indicate the blunt state. The normal state was marked as type I and labeled as [1,0,0], the blunt state was marked as type II and labeled as [0,1,0], and the damaged state was marked as type III and labeled as [0,0,1].

Figure 5. Experimental and test system diagram.

Figure 6. Tool wear types for drill bit.

(6)

The delamination factor (Fd) has been widely used to characterize the degree of damage of the workpiece at the entrance and exit during drilling of composites. It may be calculated from the ratio of the maximum diameter (Dmax) of the delamination zone to the drill diameter (D), as expressed by equation (12)

Fd¼Dmax=D ð12Þ

Figure 9 shows the drilled surface of the entrance and exit under different cutting parameters. Unlike the surface morphology of the entrance, there was certain delamination at the exit of the hole and the fiber at the bottom was torn out dramatically. This result can be explained by the fact that as the cutting speed is increased, the cutting edge action decreases as the number of passes across the same region, and the friction between the cutting edges and the board will cause temperature elevation and softening of the matrix phase, thus reducing damage.23–26 And it can be seen that a low feed rate was advantageous in drilling of

CFRP composite to reduce delamination. It was noted that the material removal rate increased with the feed rate, which led to a greater thrust force and sped up the tool wear. It further indicated that the delamination factor had a negative correlation with Vcand had a positive correlation with Vfand the thrust force. The rationality of the VB value can also be verified through delamination factor’s change trend.

Figure 10 shows the energy spectrum of the drill bit before and after it became blunt. The substrate was exposed, as confirmed by the increased presence of the drill bit matrix element W at both points A and B. Increases in the elements N and P, the main elements of epoxy resin, indicated that the adhesive wear occurred when the number of holes reached 90 at both points A and B. The adhesive wear increased the friction to some degree, and the rapidly increasing VB value in Figure 8 proved the influence of adhesive wear from another angle.

Figure 7. Tool wear at different drilling times (conditions: Vc¼ 132 m/min, Vf¼ 100 mm/min). (a) The flank wear after 30 holes, (b) the flank wear after 80 holes, and (c) the state of the broken edge.

                         ) m m( B V Number of holes Vf=100mm/min, Vc=113m/min Vf=100mm/min, Vc=132m/min Vf=100mm/min, Vc=150m/min Vf=100mm/min, Vc=170m/min                          ) m m( B V Numbers of holes Vc=132m/min, Vf=80mm/min Vc=132m/min, Vf=90mm/min Vc=132m/min, Vf=100mm/min Vc=132m/min, Vf=110mm/min

(a)

(b)

(7)

Analysis and extraction of time-domain characteristics.

Dimen-sionless time-domain characteristics are a commonly used feature. Within the scope of this study, they were not affected by drilling conditions and can reflect the inherent characteristics of the signal. Therefore, dimensionless time-domain feature parameters27can be selected to extract tool features. The peak factor, waveform factor, and

Kurtosis coefficient in the time domain were selected to determine whether they had a decisive influence on the tool state. The definitions of these characteristics were given in equations (13) to (15)

Peak factor C¼Xpeak=Xrms ð13Þ

Waveform factor S¼ Xrms=Xav ð14Þ

Figure 9. The drilled surface of the entrance and exit under different cutting parameters at hole 90.

Figure 10. The energy spectrum of the flank wear drill bit surface before and after the blunt state. (a) The new drill bit and (b) the blunt drill bit.

(8)

Kurtosis coefficient K ¼ 1 nS n k¼1xk4 . X4rms1 ð15Þ where Xavis the absolute average value of the signal, Xpeak is the peak value, Xrms1is the root mean square value of X in the time domain signal, xkis the sample value of the k’th time-domain signal, and n is the number of time-domain signals, n¼ 1, 2, . . . , n.

Figure 11 showed the relationship between the charac-teristic value of the signal in the time domain and the number of holes. It is known that the number of holes has a positive correlation with the tool flank wear, which is clearly reflected by the peak factor and Kurtosis coefficient in Figure 11. Therefore, these two characteristic parameters can be considered to represent the tool state.

Analysis and extraction of frequency-domain characteristics.

Although the time domain signal has the advantages of intuition and accuracy after filtering the influence of environment and other factors on the original signal, it cannot solve the influence of processing parameters, such as cutting speed, feed speed, and depth of cut, on the original signal. Thus, further frequency domain anal-ysis of the thrust force and vibration signal was necessary.

The DB10 wavelet basis function was selected to decompose the original signal into multiple layers. Because the characteristics of a7 layer were the closest to the original signal, the number of decomposition layers was set at 7. The frequency used in wavelet decomposition must be halved, and then, the corresponding frequency distribution ranges of a7, d7, d6, d5, d4, d3, d2, and d1 were (0–39.0625 Hz), (39.0625–78.125 Hz), (78.125– 156.25 Hz), (156.25–312.5 Hz), (312.5–625 Hz), (625–1250 Hz), (1250–2500 Hz), and (2500–5000 Hz), as shown in Figure 12.

As an expression of the average energy of the signal,28 the wear state of the tool can also be identified by moni-toring the changes of the X0rmsvalue of the thrust force and

vibration signals in the frequency domain. The X0rmsin the

frequency domain was as follows X0rms ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1=nX n i¼1 xi2 s ð16Þ where xiis the amplitude of the i’th original frequency domain signal and n is the number of signals, n¼ 1, 2, . . . , n.

Wavelet decomposition was carried out on force signals and vibration signals for the normal, blunt, and damaged states, respectively. Figure 13 shows the relationship between X0rmsand the decomposition layer under different

tool conditions. With the increase of tool wear, X0rms

chan-ged noticeably, especially the d5 layer of the thrust force signal and the d3 layer of the vibration signal. Therefore, the energy values of these two decomposition layers in equation (17) were selected as characteristic values

Ei¼ ð jxi;jj2dt¼ Xr j¼1jxi;jj 2 ð17Þ

where xi, jis the discrete point values, r is the number of sampling points, and i is the number of decomposition layers. Figure 14 shows the relationship between the decomposition layers and energy values. Due to the increase of tool wear and microcrack propagation, the inter-nal crystal of the drill was seriously damaged, and the potential energy release was enhanced, which revealed the obvious energy difference before and after the blunt state. It can be seen that the difference between normal state and abnormal state (blunt and damaged) of the d5 layer in Figure 14(a) and the d3 layer in Figure 14(b) is the most significant, indicating that these two layers were the most sensitive to abnormal state.

           ec r of ts ur ht f o e ul a v cit si re t ca ra h C Number of holes Peak factor Kurtosis coefficient Waveform factor           n oit ar bi v f o e ul a v cit si r et c ar a h C Number of holes Peak factor Kurtosis coefficient Waveform factor

(a)

(b)

Figure 11. Relationship between the characteristic value of the signal and the number of holes in the time domain (conditions: Vc¼ 150 m/min, Vf¼ 100 mm/min). (a) Thrust force signal and (b) vibration signal.

(9)

d1 d2 d3 d4 d5 d6 d7 1 2 3 4 5 RMS Layer Normal Blunt Damaged d1 d2 d3 d4 d5 d6 d7 1 2 3 4 5 RMS Layer Normal Blunt Damaged

(a)

(b)

Figure 13. Relationship between root mean square and decomposition layer under different tool conditions (conditions: Vc¼ 150 m/ min, Vf¼ 100 mm/min). (a) Thrust force signal and (b) vibration signal.

d1 d2 d3 d4 d5 d6 d7 a7 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Energy value Layer Normal Blunt Damaged d1 d2 d3 d4 d5 d6 d7 a7 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Energy value Layer Normal Blunt Damaged (a) (b)

Figure 14. Relationship between energy and decomposition layer under different tool conditions (conditions: Vc¼ 150 m/min, Vf¼ 100 mm/min). (a) Thrust force signal and (b) vibration signal.

Figure 12. Wavelet decomposition of the original force signal (conditions: Vc¼ 150 m/min, Vf¼ 100 mm/min).

(10)

Prediction of tool state

Prediction of tool state based on thrust force signals.

Twenty-five groups of data samples (10 groups of normal state samples, 10 groups of blunt state, and 5 groups of damaged state) were used for training the network 1000 times with a minimum error of 0.001. The variance approximation curve of the learning process after training is shown in Figure 15. It can be seen that the BP network reached the error limit value after only 11 iterations, which indicated that the train-ing effect was ideal.

After the network training, the prediction state was pre-dicted by inputting actual experimental data. The predic-tion results of tool wear state based on thrust force can be seen in Table 1. Only the results of groups 3 and 14

deviated from the actual state, while the other output values were relatively close to the actual state. The tool wear state was predicted with an accuracy of 87%.

Prediction of tool state based on vibration signals. With the

same method mentioned above, the sample data of the vibration signal were trained by the neural network and the learning process variance approximation curve was obtained, as shown in Figure 16. The training times of vibration signals were greater than those of thrust force signals, which indicated the need for fusion of different sensor signals in different scales to improve the accu-racy. After obtaining the training results, the neural net-work model was used to predict the state of the tool. Table 2 lists the prediction results of tool wear state

Figure 15. BP network signal training process based on thrust force signal. BP: backpropagation.

Table 1. The thrust force signal prediction results.

Group number BP network output Accuracy Actual state

1 0.0041 0.6656 0.3304 p [0,1,0] 2 0.0029 0.6924 0.405 p [0,1,0] 3 0.7827 0.1499 0.0674  [0,1,0] 4 0.0181 0.8564 0.1255 p [0,1,0] 5 0.2344 0.5449 0.2211 p [0,1,0] 6 0.0321 0.8468 0.1211 p [0,1,0] 7 0.8036 0.0021 0.1945 p [1,0,0] 8 1.7767 0.5532 0.2237 p [1,0,0] 9 0.8895 0.1033 0.0074 p [1,0,0] 10 0.8821 0.1011 0.0169 p [1,0,0] 11 0.7709 0.2211 0.0084 p [1,0,0] 12 1.9123 0.6778 0.2349 p [1,0,0] 13 0.2218 0.0037 0.7749 p [0,0,1] 14 0.7289 0.1231 0.1483  [0,0,1] 15 0.0737 0.1329 0.7934 p [0,0,1] BP: backpropagation.

Figure 16. BP network signal training process based on vibration signal. BP: backpropagation.

Table 2. The vibration signal prediction results.

Group number BP network output Accuracy Actual state

1 0.4519 1.9821 0.5304 p [0,1,0] 2 0.0329 0.9566 0.0106 p [0,1,0] 3 0.1049 0.8499 0.0452 p [0,1,0] 4 0.0756 0.799 0.1254 p [0,1,0] 5 0.8344 0.0446 0.1211  [0,1,0] 6 0.3221 0.6234 0.0543 p [0,1,0] 7 0.897 0.0021 0.1013 p [1,0,0] 8 0.6653 1.8324 0.1671  [1,0,0] 9 0.8895 0.0106 0.0999 p [1,0,0] 10 0.8721 0.1132 0.0147 p [1,0,0] 11 0.7709 0.2211 0.0078 p [1,0,0] 12 0.7223 0.2178 0.0593 p [1,0,0] 13 0.2218 0.0031 0.7749 p [0,0,1] 14 0.1231 0.8289 0.0482  [0,0,1] 15 0.0338 0.0668 0.8994 p [0,0,1] BP: backpropagation.

(11)

based on the vibration signal. The accuracy of the pre-diction result was 80% based on the vibration signal. The prediction accuracy was obviously different based on different sensors.

Multifeature and multisensor fusion. To reduce the difference

between different sensors, it is necessary to fuse the pre-diction results of different sensors to improve the predic-tion accuracy and stability. Evidence theory was used to fuse the signals of thrust force and vibration. The basic trust allocation29was established by predicting the failure sam-ples in the previous results, where

m1ð Þ¼yAi 1ð Þ= yAi ð 1ð Þþ yAi 1ð ÞBi Þ m1ð Þ¼ yBi 1ð Þ= yBi ð 1ð Þþ yAi 1ð ÞBi Þ ð18Þ m2ð Þ¼yAi 2ð Þ= yAi ð 2ð Þþ yAi 2ð ÞBi Þ m2ð Þ¼ yBi 2ð Þ= yBi ð 2ð Þþ yAi 2ð ÞBi Þ ð19Þ Here, m1(Ai) represents the prediction result of the nor-malized thrust force signal sample at the i’th node; m2(Bi) is

the prediction result of the normalized vibration signal sample at the i’th node; y(Ai) and y(Bi) are the i’th node of thrust force and vibration in the network output layer, respectively, i ¼ 1, 2, 3. Combining equation (18) with equation (19) gives m Aið Þ ¼ X Ak\Bj¼A m1 Akð Þm2 Bjð Þ , 1 X Ak\Bj¼Ø m1 Akð Þm2 Bjð Þ ! ð20Þ m Bið Þ ¼ X Ak\Bj¼B m1 Akð Þm2 Bjð Þ , 1 X Ak\Bj¼Ø m1 Að Þm2 Bjð Þ ! ð21Þ Using the above fusion method, the samples that were incorrectly predicted by the single sensor signal are repre-dicted in Table 3.

Finally, the comparison of different model results is summarized in Table 4. The prediction accuracy of the fused data from the multicharacteristics and multisignal sources was higher than that of any previous single sensor. Therefore, the BP neural network model based on multi-characteristics and multisignal sources can effectively pre-dict the tool wear and the blunt and damaged states in the CFRP drilling process.

Table 3. Reprediction results obtained by fusion of signals of thrust force and vibration.

Group number m(A1) m(A2) m(A3) m(B1) m(B2) m(B3) Accuracy Actual state

3 0.1033 0.8682 0.0285 0.1944 0.6667 0.1388 p [0,1,0]

5 0.0329 0.9566 0.0105 0.2050 0.6840 0.1110 p [0,1,0]

8 0.1049 0.8499 0.0453 0.1050 0.8573 0.0376 p [0,1,0]

14 0.0181 0.7254 0.2566 0.1406 0.7390 0.1204 p [0,1,0]

Table 4. Comparison of different models.

BP network based on single force signals BP network based on single vibration signals BP network based on forecasting results fusion Accuracy 87% 80% 100% Number of iterations 11 16 8 Error 0.001 0.001 0.001 BP: backpropagation.

Figure 17. The flowchart of the automatic detection.

                      e ul a v y gr e n E Number of holes Drill A Drill B Drill C

Figure 18. Relationship between energy value of the d3 layer of the vibration signal and number of holes (conditions: Vc¼ 132 m/ min, Vf¼ 100 mm/min).

(12)

Model verification by automatic detector

Automatic detector principle

According to the above analysis, the tool state can be detected and predicted using the collected characteristic values with consideration of different processing para-meters. However, the prediction accuracy decreased when single vibration data of different processing parameters were input into the ANN prediction model above. It is necessary to reduce the influence of processing parameters on the accuracy of the prediction model. Therefore, a wear indicator was proposed to detect the change tendency of the vibration signal’s energy value of the d3 layer.

The energy values of the d3 layer of the vibration signal collected in 16 groups of process parameters of this article were selected for temporal signal processing based on equation (22). The value “1” corresponds to the presence

of possible transitions noted in the tool lifespan, and the value “0” indicates that no changes have been detected. Based on the sliding window algorithm,30the energy value of the vibration signals is input into the sliding window algorithm and then divided into sections to compare the energy values with each other to find out the abnormal change

I lN eð Þ / I l  1ð ÞN e ) Detector ¼ 1 I lN eð Þ >/ I l  1ð ÞN e ) Detector ¼ 0 

ð22Þ where I is the wear indicator, Ne is the number of samples when the analyzed signal is stable, l is the parameter indi-cating the sliding window, and/ is a proportionality factor between 1 and 2. The initial value of / is set to 1. The detector will be numerically optimized until the abnormal change tendency of energy is found. A flowchart of the automatic detection is shown in Figure 17.

Verification of the wear indicator

Influence of individual tool differences on automatic detection.

To make a general assessment of tool wear, the sensitivity of individual tool differences to characteristic values was first verified. Three drills (labeled A, B, and C) of the same specification were investigated in the same experiment, as shown in Figures 18 and 19. No matter which drill was adopted, it represented a clear tendency of accelerated wear when 45 holes had been drilled. Therefore, the tool manu-facturing error has little influence on the wear indicator. After around 90 holes had been drilled, the energy value of the d3 layer suddenly changed dramatically. Figure 19 shows the high correlation between the energy value of the d3 layer of the vibration signal and the VB value of flank wear. Hence, the energy value of the d3 layer of the vibra-tion signal can be defined as the wear indicator, since it can detect a small amount of wear before the blunt state is reached.                  e ul a v y gr e n E Number of holes h1=40 Vc=150m/min Vf=110mm/min                  e ul a v y gr e n E Number of holes h1=46 Vc=113m/min Vf=110mm/min

(a)

(b)

Figure 20. (a, b) The relationship between the energy value of the d3 layer of the vibration signal and the number of holes.                e ul a v y gr e n E VB(mm) Drill A Drill B Drill C

Figure 19. Relationship between energy value of the d3 layer of the vibration signal and flank wear (conditions: Vc¼ 132 m/min, Vf ¼ 100 mm/min).

(13)

Influence of processing parameters on automatic detection.

Secondly, to avoid the influence of different processing parameters, the unused 15 sets of different processing parameters in this article were tested by the automatic detector. Figure 20 shows the relationship between the energy value of the d3 layer of the vibration signal and the flank wear under two randomly selected parameters. The transition from stable wear to accelerated wear before the blunt state was obviously free from the influence of processing parameters. The results indicated that the energy value of the d3 layer of the vibration signal was an effective indicator for monitoring the change of the wear during the CFRP drilling process regardless of the processing parameters.

Verification of automatic detector. With the application of the

automatic detector, data from all the experiments were used to verify the effectiveness of the automatic detector. Fig-ure 21 shows the wear state identification by automatic detector, which displays the effective sensitivity to tool wear and can automatically predict the accelerated wear

state before the blunt state, in spite of individual differences in the drill bit and different processing parameters.

From the above discussion, we could verify that the BP neural network for the prediction of tool wear from multi-characteristics, multisource sensors, and the wear indicator can find the tool wear moment accurately.

Conclusion

A BP ANN model based on multicharacteristics and multi-sources was built to predict the normal, blunt, and damaged tool states after extracting characteristic values reflecting the tool states during machining of composite materials. An experiment was conducted with various processing para-meters to train and predict the tool state of CFRP small hole drilling by simultaneously collecting the thrust force sig-nals and vibration sigsig-nals. The main results were as follows:

1. Five types of characteristic values (time-domain of peak factor and Kurtosis coefficient, the energy value of the d5 layer of the thrust force signal, and

(a)

           

 Drill A

Number of holes

Wear stabilization Wear acceleration

α=1.44 Vc=132m/min Vf=100mm/min Wear transition

(b)

             Drill B Number of holes

Wear stabilization Wear acceleration

α=1.34 Vc=132m/min Vf=100mm/min Wear transition

(c)

             Number of holes

Wear stabilization Wear acceleration

α=1.38 Vc=150m/min Vf=110mm/min Wear transition

(d)

             Number of holes

Wear stabilization Wear acceleration

α=1.51

Vc=113m/min

Vf=110mm/min

Wear transition

Figure 21. (a–d) Wear state identified under various processing conditions.

(14)

the d3 layer of the vibration signal after wavelet decomposition) were extracted to reflect the tool states during machining. A BP ANN model was established to train and predict the normal, blunt, and damaged tool states.

2. There was certain delamination at the exit of the hole and the fiber at the bottom was torn out dra-matically. Either a lower feed speed or a higher cutting speed could effectively reduce the delami-nation factor, which was highly correlated to the VB value of flank wear. The wear of the drill accelerated because of the occurrence of adhesive wear when the number of drilled holes reached around 90.

3. The accuracy of single sensor source training and prediction varied among different sensor sources: the prediction accuracy based on thrust force was 87% and had faster convergence, whereas the accu-racy of the prediction result was 80% based on the vibration signal.

4. Two sources of prediction results from thrust force and vibration were fused by evidence theory. Com-pared with the prediction accuracy based on force signal and vibration signal alone, the final fusion result showed that the prediction accuracy of sensor fusion data using multicharacteristics and multi-signal sources was improved by 13% and 20%, respectively.

5. By setting up an automatic detector, the energy value of the d3 layer of the vibration signal was set as a wear indicator for sensitively detecting a small amount of wear before the blunt state was reached, in spite of individual differences in the drill bit and different processing parameters. The BP neural network model based on multicharac-teristics, multisignal sources, and the wear indi-cator can effectively predict the normal, blunt, and damaged tool states in the CFRP drilling process.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Nature Science Foundation of China [Nos 51965004 and 51565005] and the Guangxi Key Laboratory of Processing for Non-Ferrous Metal and Featured Materials [Grant No. 2019GXYSOF02].

ORCID iD

Shanshan Hu https://orcid.org/0000-0002-7984-0170

References

1. Li H, Liebscher M, Ranjbarian M, et al. Electrochemical modification of carbon fiber yarns in cementitious pore solu-tion for an enhanced interacsolu-tion towards concrete matrices. Appl Surf Sci 2019; 487: 52–58.

2. Bayraktar S¸ and Turgut Y. Determination of delamination in drilling of carbon fiber reinforced carbon matrix composites/ Al 6013-T651 stacks. Measurement 2020; 154: 107493. 3. Bayraktar S¸. Assessment of cutting performance on drilling

with different drilling methods of fiber reinforced polymer composites: a literature review. J Fac Eng Archit Gazi Univ 2018; 33(2): 629–647.

4. Xu J, Ji M, Davim JP, et al. Comparative study of minimum quantity lubrication and dry drilling of CFRP/titanium stacks using TiAlN and diamond coated drills. Composite Struct 2020; 234: 111727.

5. Khanna N, Pusavec F, Agrawal C, et al. Measurement and evaluation of hole attributes for drilling CFRP composites using an indigenously developed cryogenic machining facil-ity. Measurement 2020; 154: 107504.

6. Mohan NS, Ramachandra A and Kulkarni SM. Influence of process parameters on cutting force and torque during drilling of glass–fiber polyester reinforced composites. Composite struct 2005; 71(3–4): 407–413.

7. Rmili W, Ouahabi A, Serra R, et al. An automatic system based on vibratory analysis for cutting tool wear monitoring. Measurement 2016; 77: 117–123.

8. Ren Q, Balazinski M, Baron L, et al. Type-2 fuzzy tool con-dition monitoring system based on acoustic emission in micromilling. Inf Sci 2014; 255: 121–134.

9. Neugebauer R, Ben-Hanan U, Ihlenfeldt S, et al. Acoustic emission as a tool for identifying drill position in fiber-reinforced plastic and aluminum stacks. Int J Mach Tool Manuf 2012; 57: 20–26.

10. Mo¨hring HC, Kimmelmann M, Eschelbacher S, et al. Process monitoring on drilling fiber-reinforced plastics and aluminum stacks using acoustic emissions. Procedia Manuf 2018; 18: 58–67.

11. Kim D and Ramulu M. Frequency analysis and process mon-itoring in drilling of composite materials. Adv Composite Lett 2004; 13(4): 185–192.

12. Caggiano A, Centobelli P, Nele L, et al. Multiple sensor monitoring in drilling of CFRP/CFRP stacks for cognitive tool wear prediction and product quality assessment. Proce-dia CIRP 2017; 62: 3–8.

13. Amini S, Baraheni M and Mardiha A. Parametric investiga-tion of rotary ultrasonic drilling of carbon fiber reinforced plastics. Proc Inst Mech Eng E J Process Mech Eng 2018; 232(5): 540–554.

14. Wang LP, Wang JS, Ye PQ, et al. A theoretical and experi-mental investigation of thrust and torque in vibration micro-drilling. Proc Inst Mech Eng B J Eng Manuf 2001; 215(11): 1539–1548.

15. Jain AK and Lad BK. A novel integrated tool condition monitoring system. J Intell Manuf 2019; 30(3): 1423–1436.

(15)

16. Wang JP, Li XM and Hang Y. Motor failure diagnosis based on ant colony algorithm and BP neural network. Noise Vib Worldwide 2011; 42(11): 51–56.

17. Vassilopoulos AP, Georgopoulos EF and Dionysopoulos V. Modelling fatigue life of multidirectional GFRP laminates under constant amplitude loading with artificial neural net-works. Adv Composite Lett 2006; 15(2): 43–51.

18. Xie N, Ma F, Duan M, et al. Tool wear condition monitoring based on principal component analysis and c-support vector machine. J Tongji Univ (Nat Sci) 2016; 44(3): 434–439. 19. Mkaddem A, Demirci I and El Mansori M. A micro–macro

combined approach using FEM for modelling of machining of FRP composites: cutting forces analysis. Composite Sci Technol 2008; 68(15–16): 3123–3127.

20. Lazar MB and Xirouchakis P. Experimental analysis of drill-ing fiber reinforced composites. Int J Mach Tool Manuf 2011; 51(12): 937–946.

21. Kuppuswamy R, Zunega J and Naidoo S. Flank wear assess-ment on discrete machining process behavior for Inconel 718. Int J Adv Manuf Technol 2017; 93(5–8): 2097–2109. 22. Dolinˇsek S, ˇSuˇstarˇsiˇc B and Kopaˇc J. Wear mechanisms of

cutting tools in high-speed cutting processes. Wear 2001; 250(1–12): 349–356.

23. Davim JP, Rubio JC and Abrao AM. A novel approach based on digital image analysis to evaluate the delamination factor

after drilling composite laminates. Composite Sci Technol 2007; 67(9): 1939–1945.

24. Gaitonde VN, Karnik SR, Rubio JC, et al. Analysis of para-metric influence on delamination in high-speed drilling of carbon fiber reinforced plastic composites. J Mater Process Technol 2008, 203(1–3): 431–438.

25. Rubio JC, Abrao AM, Faria PE, et al. Effects of high speed in the drilling of glass fibre reinforced plastic: evaluation of the delamination factor. Int J Mach Tool Manu 2008; 48(6): 715–720.

26. Davim JP, Reis P and Antonio CC. Experimental study of drilling glass fiber reinforced plastics (GFRP) manufactured by hand lay-up. Composite Sci Technol 2004; 64(2): 289–297. 27. Peigne G, Kamnev E, Brissaud D, et al. Self-excited vibratory drilling: a dimensionless parameter approach for guiding experiments. Proc Inst Mech Eng B J Eng Manuf 2005; 219(1): 73–84.

28. Li CB, Wan T, Chen XZ, et al. On-line monitoring method of tool wear for NC turning in batch processing based on cutting power. Comput Integr Manuf Syst 2018; 24(8): 1910–1919. 29. Huang CL and Wang CJ. A GA-based feature selection and

parameters optimization for support vector machines. Expert Syst Appl 2006; 31(2): 231–240.

30. Koc¸ CK. Analysis of sliding window techniques for exponen-tiation. Comput Math Appl 1995; 30(10): 17–24.

Figure

Updating...