IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 6, DECEMBER

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1 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 6, DECEMBER Eddy Current Volume Heating Thermography and Phase Analysis for Imaging Characterization of Interface Delamination in CFRP Yunze He, Member, IEEE, and Ruizhen Yang, Member, IEEE Abstract Imaging inspection is highly demanded in the optimization of industry processes. Optical imaging inspection is not applicable for inside defects, while infrared (IR) imaging inspection can provide information about internal structure of objects. Eddy current thermography is an emerging IR imaging inspection technique for conductive materials or objects. This paper presents eddy current volume heating thermography (ECVHT) and phase analysis for delamination inspection in carbon fiber reinforced plastics (CFRPs) based on the previously proposed eddy current pulsed phase thermography (ECPPT). The proposed method has been verified through experimental studies under both transmission and reflection modes. After discrete Fourier transform (DFT) of temperature responses, the phasegram and phase spectra can be used to image and characterize interface delamination in CFRP due to elimination of nonuniform heating effect and carbon fiber structures. With the whole temperature response processed by DFT, carbon fiber structures and delamination can be differentiated due to periodic oscillation of phase spectra. With temperature response in cooling phase processed by DFT, some characteristic features can be extracted to construct the new phase images according to the shape of phase spectra. In all, using ECVHT and phase analysis, imaging characterization for delamination can get much better performance than conventional visible optical inspection system and eddy current pulsed thermography. Index Terms Carbon fiber reinforced plastic (CFRP), delamination, imaging inspection, induction heating, infrared (IR) imaging, nondestructive testing, phase analysis, volume heating thermography (VHT). I. INTRODUCTION I MAGING inspection for defect or flaw is highly demanded in optimization of industry processes [1] [9]. Naso described a fuzzy-logic optical sensor for online weld defect detection. The data processing algorithm encompasses a Kalman filter to reduce the heavy amount of noise affecting the measured signals, and an intelligent fuzzy system to assess the degree of acceptability of the weld [10]. Acciani presented a Manuscript received May 15, 2015; revised August 07, 2015 and August 18, 2015; accepted August 29, Date of publication September 16, 2015; date of current version December 02, This work was supported by the National Natural Science Foundation of China under Grant and Grant Paper no. TII Y. He is with the Department of Instrumentation Science and Technology, College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha , China ( hejicker@163.com; hejicker@gmail.com). R. Yang is with the Department of Civil and Architecture Engineering, Changsha University, Changsha , China ( xbaiyang@163.com). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TII neural network-based automatic optical inspection system for the diagnosis of solder joint defects on printed circuit boards assembled in surface-mounting technology [4]. Tsai proposed a dissimilarity measure based on the optical-flow technique for surface defect detection for light-emitting diode (LED) wafer die inspection [11]. Gao designed an automated optical inspection (AOI) system for E-shaped magnetic core elements and poposed several novel algorithms to realize defects detection by this system [1]. Bai proposed a method to effectively and efficiently detect defects in images using saliency-based phase spectrum analysis [3]. All these methods recognizing surface defects are based on the visible optical system. Unfortunately, visible optical imaging system is not applicable for inside defects detection. Carbon fiber reinforced plastics (CFRPs) have been widely used to replace metals in industrial electronic fields including renewable energy, electric power generation, and energy storage, etc., due to their excellent advantages such as low cost, light weight, high strength/weight, and high stiffness/weight ratios. However, delamination and disbond are inevitable during either fabrication or lifetime of a composite structure. This flaw, usually difficult or even impossible to detect from the surface using visible optical system, severely degrades the loadbearing capacity of structures. Thus, the new imaging inspection techniques for interface delamiation are very important and attractive subjects. Infrared (IR) thermography became an important imaging diagnostic technique for quality control, condition monitoring, nondestructive testing, and fault diagnosis [5], since the availability of first commercial IR camera system in Currently, it has attracted a lot of attentions in many emerging industrial fields including microelectronics, renewable energy [12], and electric power, etc. With active thermography, an external thermal stimulation is brought to object under test and the thermal response is recorded by an IR camera to provide information (such as thermal properties and presence of defects) about the internal structure of objects. Induction heating is the process of heating an electrically conducting object by electromagnetic induction, through heat generated by eddy currents. Combining induction heating and IR thermography, eddy current thermography (ECT) is proposed specific for conductive materials, which has lot of advantages, such as noncontact, and fast and high resolution [13]. It can be applied in terms of eddy current pulsed thermography (ECPT) [14], [15], eddy current step heating thermography, eddy current lock-in thermography, and eddy current pulsed phase thermography (ECPPT) IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.

2 1288 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 6, DECEMBER 2015 Fig. 1. (a) ECPPT principle schematic. (b) Excitation signal. (c) Temperature response. (d) Phase spectrum. Recently, ECT has been investigated for CFRP evaluation. In modeling of ECT on CFRPs, a methodology based on shell elements was presented to model the electromagnetic thermal behavior of multilayered conductive composite materials [16]. A model taking into account the influence of different fiber orientations on the electromagnetic parameters was presented [17]. Then, a multiscale approach was used to calculate the electromagnetic and thermal field distribution. The relevance of this technique was then discussed for different positions of flaws and the optimal frequency was estimated [18]. In experimental investigation, ECT was investigated for artificial crack evaluation in CFRP and the preliminary results show the significant potential [19]. In order to quantitatively evaluate impact damage, deviation was extracted from the optical flow for transient thermal images to make a dynamic assessment about impacts [20]. Principal components analysis (PCA) and independent components analysis (ICA) were used to process one-dimensional (1-D) transient responses in order to reconstruct two-dimensional (2-D) image and improve the sensitivity of delamination [21], [22]. The detection mechanisms for carbon fiber structure and impact were analyzed through theoretic analysis and validated by experimental studies under reflection and transmission modes [23]. ECPPT is a powerful ECT involving pulsed inductive heating, thermal wave conduction, and Fourier analysis, which was proposed for subsurface defect evaluation in steel using the phasegram and features extracted from phase spectra. The results have illustrated that the nonuniform heating effect by the shape of induction coil can be eliminated and defect detectability can be significantly improved. In addition, ECPPT has shown the performance to reduce the variation in surface emissivity [24]. In 2015, ECPPT based on volume heating for CFRP has been proposed and cooling phase has been used as the input data [25]. However, ECPPT has not been systematically and deeply investigated in nondestructive testing of delamination in CFRPs. In this work, ECPPT was introduced for imaging characterization of inside delamination in CFRP under both transmission and reflection modes. Due to unique volumetric induction heating, the proposed methods were also named as eddy current volume heating thermography (ECVHT) and phase analysis. The results indicated that ECVHT and phase analysis are capable of improving the delamination detectability over conventional ECPT. This paper is organized as follows. The principle of ECPPT for CFRP is explained first in Section II. Then, experimental setup is introduced in Section III, and experimental studies using ECVHT and phase analysis under transmission and reflection modes are carried out in Section IV. Finally, conclusion and future work are outlined in Section V. II. METHODOLOGY The principle of ECPPT for subsurface evaluation in steel was based on surface heating due to small skin depth. They are named as eddy current surface heating thermography (ECSHT) and phase analysis. However, the principle of ECPPT for CFRP is totally different from that for steel in heating style, thermal wave propagation style, characterization methods under configuration modes, while has some similarity in signal processing. Due to the volumetric heating, the proposed ECPPTs for CFRP were named as ECVHT and phase analysis in order to show the difference. A. Eddy Current Volume Heating Thermography Fig. 1(a) shows the principle schematic of ECPPT for CFRPs. The excitation module and IR camera are controlled by synchronization module. Excitation signal generated by the excitation module is a small period of high-frequency electric current, as shown in Fig. 1(b). It is driven to the inductive coil near the CFRP. Then, the electric current passing through the coil will induce eddy currents and generate resistive heat pulse in the CFRP. These eddy currents induced by excitation current

3 HE AND YANG: ECVHT AND PHASE ANALYSIS FOR IMAGING CHARACTERIZATION 1289 has a penetration depth in homogeneous material (or called skin depth based on skin effect), which can be calculated by δ = 1 πμσf (1) where f is the frequency of excitation signal, σ is the electrical conductivity (S/m), and μ is the magnetic permeability (H/m). It is concluded that the skin depths vary for different materials. For steel with great conductivity and permeability, the skin depth is very small on the order of micrometers and the heating style is surface heating. For CFRP (σ = 1000 S/m and no magnetic), the skin depth is significantly greater (about 50 mm). Thus, the heating style for CFRP is volumetric heating. The resistive heat will diffuse as the time delay till the heat balances in material. At the same time, the temperature distribution on the surface of material is captured by an IR camera, and then the image sequence is transformed to PC. As we know, the generated inductive heat in object depends on the parameters of material and excitation signal. The resistive heat Q can be expressed as μf Q = Pt I 2 t (2) σ where P is the heating power, I is the current amplitude, and t is the heating time. The increase in temperature ΔT under volumetric heating is given approximately by ΔT = Pt ρcv where ρ is the mass density, C is the specific heat of the material, and V is the volume in which thermal changes. Considering that P, ρ, C, and V are constant, it is concluded that temperature increases linearly with time. One typical temperature response for a point on the surface of CFRP is shown in Fig. 1(c). It is noticed that the temperature response can be divided into two phases: heating phase and cooling phase. The temperature increases linearly in heating phase and then decreases slowly in cooling phase due to volumetric heating. B. Phase Analysis Based on Thermal Wave Propagation Section II-A illustrates that there is a heat pulse generated in CFRP. It is well known that any waveform, periodic or not, can be approximated by the sum of purely harmonic waves oscillating at different frequencies. The frequency content of an ideal temporal pulse of null duration has a frequency spectrum with uniform energy distribution between all frequencies from 0 to. Of course, a real thermal pulse in ECPPT is different from that of an ideal temporal pulse since its duration and amplitude are finite. Therefore, the temperature response in the sample can be recognized as a sum of thermal waves. Thermal wave propagation styles for CFRP is different from that of steel due to different heating style. For steel, thermal wave is generated on the surface and propagate to inside. However, thermal wave is generated inside under volumetric heating and propagate to surrounding area. Each of them has a different frequency ω, (3) thermal diffusion length μ, and speed v. According to thermal wave theory, the thermal diffusion length μ can be expressed by 2 k 2α μ = ωρc = (4) ω where k is thermal conductivity, ρ is density, C is heat capacity, and α is thermal diffusivity. The propagation speed v of these waves is obtained as v = 2ωα. (5) The above two equations indicate that higher frequency thermal waves propagate nearer but faster, while lower frequency thermal waves propagate farther but more slowly. If there is a defect at depth d, the parameters of thermal waves will be changed. However, not all variations for thermal waves can propagate to surface. If the thermal diffusion length μ is greater than d, the variation in thermal wave can be observed on temperature distribution on the surface. That is to say, the defect with greater depth will cause variation on the low-frequency components. The amplitude A and phase ϕ of thermal wave can be expressed as A = T 0 e z/μ ϕ = z/μ (6) where T 0 is the temperature on surface and z is the depth. Obviously, amplitude has a strong dependence of surface temperature, which is decided by environmental reflections, emissivity variations, and nonuniform heating, as well as surface geometry and orientation. On the contrary, phase is independent on these parameters. Thus, phase analysis has more advantages, such as elimination of nonuniform heating and surface emissivity variations compared with those in conventional thermal images. C. Fourier Transform-Based Phase Extraction The well-known Fourier transform is a mathematical tool between the temporal and frequency domains. The procedure for ECPPT data analysis is based on the Fourier transform. For temperature response at each pixel, the 1-D discrete Fourier transform (DFT) is computed according to the well-known formula [26] N 1 F n =Δt T (kδt)e i2πnk/n = Re n + Im n (7) k=0 where Δt is the sampling interval, n designates the frequency increment, and Re n and Im n are, respectively, the real and imaginary components of F n. Then, the amplitude and phase can be computed by the following equations: A n = Re 2 n + Im 2 n [ ] ϕ n =tan 1 Imn. (8) Re n

4 1290 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 6, DECEMBER 2015 The frequency components f n and frequency resolution f r can be derived from the time sequence as follows: f n = n N Δt 1 f r = N Δt. (9) Then, the amplitude and phase at some frequencies for all pixels are extracted to obtain amplitude image and phase image (phasegram). The fast Fourier transform (FFT) algorithm available in MATLAB allows processing the signal more effectively. In previous work, the temperature response in cooling phase was used as the input data of Fourier transform, and the phase spectra are shown in Fig. 1(d). Several features can be extracted and used to form the new phase images [25]. Min phase is the minimum value of phase spectrum; frequency to min phase means the frequency when the spectrum is the smallest. If we use the whole temperature response including heating phase and cooling phase as input data of Fourier transform, the phase spectrum will show periodic oscillation. The oscillation period is the reciprocal of the heating time. This phenomenon means that the influence of the nonuniform heating can be periodically suppressed in the phase images and the defect detectability can be improved [27]. The work [22] has found that carbon structure can be observed on the early stage of heating phase, while delamination can be characterized using the late stage of cooling phase. Thus, in this paper, both the temperature response in cooling phase and the whole temperature response (including heating phase and cooling phase) are processed and the results are compared. D. Characterization Methods Under Transmission and Reflection Modes As shown in Fig. 2(a), there are two configuration modes for conventional optical thermography: 1) reflection mode where thermal excitation (lamp) heats the surface of MUT; and 2) IR camera records the temperature on the same side, and transmission mode where MUT is stimulated from one side, while data are recorded on the opposite side. Usually, there is no direct access to both sides of MUT. Hence, reflection mode configuration is more practical. Another advantage for reflection mode is being easy to quantify defect depth d based on heat conduction from surface to inside. However, transmission mode generally yields high contrast results than reflection mode for some components (like thin plate) due to the block of heat conduction. Also, the excitation does not block the IR camera view to sample surface under transmission mode. For ECVHT, both modes are also available. As shown in Fig. 2(b), the induction coil will heat the whole CFRP due to volumetric induction heating, as mentioned in Section II-A. The defect will interrupt the heat conduction inside CRFP and lead to the abnormal thermal waves. If the diffusion length μ of thermal wave is greater than depth (dr for reflection mode and dt for transmission mode), the abnormal thermal wave can be observed from the surface through IR camera. Thus, it is concluded that characterization methods using ECVHT under transmission and reflection modes are the same. Fig. 2. Characterization principle under transmission and reflection modes for (a) conventional optical thermography and (b) ECVHT. III. EXPERIMENTAL STUDIES A. ECPPT Setup Experimental system for ECPPT was developed as shown in Fig. 3(a). A precision induction heating device, Easyheat 224 from Cheltenham Induction Heating, Ltd., was used for induction heating, with a maximum excitation power of 2.4 kw, a maximum current of 400 Å, and an excitation frequency range of khz. The excitation coil was made of 6.35-mm high-conductivity hollow copper tubing. In order to fit the sample under test, the excitation coil was designed as rectangular shape in plane. The state-of-the-art IR system Flir SC7500 was used to record the temperature change, which is a Stirling cooled camera with a array of µm InSb detectors. The pitch between detectors is 30 µm. Also, it has a sensitivity of < 20 mk, a maximum full frame rate of 383 Hz. The radiation of the object was sampled using the commercial thermography software Altair and the unit of radiation is digital level (DL). A nonlinear transfer function after calibration can convert the radiation (unit: DL) into temperature (unit: K), which requires an operator setting several parameters (emissivity, background temperature, transmission, etc.). In order to simplify the procedure, we used DL as the unit of temperature in experimental studies. The image sequences recorded by IR camera were transmitted to PC and the phase analysis was implemented in MATLAB through Function fft() on PC. B. CFRP Sample The photo and section schematic of CFRP sample with inside delamination provided by ALSTOM are shown in Fig. 3(b) and (c), respectively. The lateral dimension is mm 2.From left to right, the sample has different thicknesses from 3.48 to

5 HE AND YANG: ECVHT AND PHASE ANALYSIS FOR IMAGING CHARACTERIZATION 1291 Fig. 4. Thermal images for delamination in 2.97-mm-thickness area at 20, 200, and 500 ms. Fig. 3. (a) ECPPT experimental setup. (b) Front and back side of CFRP sample. (c) Section schematic of CFRP sample. (d) Transmission and reflection mode in experiments. 1 mm. There are two man-made delamination defects with lateral size of 36 and 100 mm 2 in each thickness area. Man-made delamination defects were manufactured by inserting a polytetrafluoroethylene (PTFE) film between fiber layers. PTFE with small conductivity (about 0.25 W/(m K)) was selected to simulate the air in delamination. These delamination defects have the same distance to back side (0.5 mm) but different distance to front side (from about 3 to 0.5 mm). IV. EXPERIMENTAL RESULTS AND DISCUSSION The results and discussion are given under transmission mode first and then reflection mode. A. Transmission Mode In the experiment under transmission mode, the induction coil was placed on the front side of sample, while IR camera was placed on the back side, as shown in Fig. 3(d). The heating time was set as 200 ms followed by cooling time 800 ms. The sampling frequency of IR camera was 200 Hz and sampling interval was s. The 100-mm 2 delaminations in and 3.48-mm-thickness areas were tested. The distances of two delamination defects to front side are about 2.5 and 3 mm, respectively. The distances of delamination to back side (depth dr) are the same (0.5 mm). 1) ECVHT: Fig. 4 shows the thermal images for delamination in 2.97-mm-thickness area at 20, 200, and 500 ms, respectively. The unit of temperature is DL and the temperature is normalized to [0, 1]. Clearly, in three images, the nonuniform heating effect is dominated in x-axis, because the coil is along the y-axis. In image at 20 ms, fiber-woven structure can be observed from the hot and dark patterns. In detail, the carbon fiber is highlighted, because the eddy current (EC) directly heats the carbon fiber. In image at 200 ms, as the heat diffuses laterally, the carbon fiber structure is blurred but still visible. In image at 500 ms, a low-temperature area in y-axis direction appears. Fig. 5(a) shows the temperature profile of line 1 shown in Fig. 4, which is a line crossing the delamination along x-axis direction. The nonuniform heating effect is so dominating that the temperature in the middle of line 1 is the highest. Fig. 5(b) shows the temperature profile of line 2 shown in Fig. 4, which is a line crossing the delamination in y-axis direction. Because line 2 is parallel to coil, there is no nonuniform heating effect. The temperature profile at 20 ms just shows the periodic fiber structures; although the temperature at 200 ms has an obvious increase, it still shows the periodic fiber structures. The delamination area has an obvious decrease than other defect-free area at 500 ms. In all, it is difficult to identify the delamination due to severe influence from nonuniform heating effect and carbon fiber structures. The temperature responses over time for four points (A, B, C, and D) were observed, as shown in Fig. 6. Their locations are listed in Table I. As mentioned, the temperature responses can be divided into two phases: 1) rising phase; and 2) falling phase. The period of rising phase equals heating time (0.2 s). This phenomenon is totally different from that of steel, where the period of rising phase is much greater than heating time. Also, the temperature increases almost linearly in rising phase

6 1292 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 6, DECEMBER 2015 TABLE I LOCATION OF POINTS A, B, C, AND D Fig. 5. Temperature profiles over delamination in 2.97-mm-thickness area at 20, 200, and 500 ms for (a) line 1 and (b) line 2. Fig. 7. Phase images of delamination in 2.97-mm-thickness area at 2, 4, 6, 7, and39hz. Fig. 6. Temperature responses for points A, B, C, and D. and decreases slowly in cooling phase due to volumetric heating, while temperature increases and decreases exponentially for steel. The locations of points also have influence on temperature responses. Points B and D have similar linear increase and higher temperature than points A and C in heating phase, because they are on the carbon fiber which generate the Joule heat. Points A and C also have the similar change in the heating phase due to being on the polymer matrix. However, points C and D show lower temperature than points A and B in cooling phase, because the delamination does not generate heat. It is noticeable that the difference between delamination and defectfree area in heating phase is very small; however, it becomes remarkable in late stage of cooling phase. 2) Phase Analysis With Whole Temperature Response as Input Data: The whole temperature responses including heating phase and cooling phase were processed by DFT. The total number N for the whole temperature response is 200. We did the 200-point DFT in MATLAB. The frequency resolution f r is 1 Hz. The phase images for 100-mm 2 delamination in 2.97-mm thickness area at 2, 4, 6, 7, and 39 Hz are selected and shown in Fig. 7. The unit of phase is radian and the phase is normalized to [0, 1]. Comparing ECPPT phase images with ECVHT thermal images in Fig. 4, the nonuniform heating effect has been reduced. Fig. 8 shows the phase profiles at 4, 6, and 7 Hz for the same lines 1 and 2 in Fig. 4. Comparing with temperature profile in Fig. 5, the nonuniform heating effect is reduced and then the size of delamination can be characterized from phase profile for line 1. In the phase image and profile at 4 Hz, the fiberwoven structures (texture) can be observed as the hot patterns. In the phase image and profile at 6 Hz, the matrix structures can be observed as hot spots. In the phase images and profiles at 2 and 7 Hz, the fiber and matrix structures are suppressed and the delamination can be observed. This means that fiber structures and delamination can be separated by selecting the appropriate frequency. In the phase image at 39 Hz, nothing but noise can be found due to energy damping. The phase spectra for points B and D are shown in Fig. 9. Obviously, phase spectra are modulated by a frequency f m, which is about 5.5 Hz. The analytical results revealed that the phase value oscillates with a period of the reciprocal of the heating duration [27]. In the experiments, the heating time is set as 0.2 s, while the real heating time for points B and D is

7 HE AND YANG: ECVHT AND PHASE ANALYSIS FOR IMAGING CHARACTERIZATION 1293 Fig. 10. Phase images of delamination in 3.48-mm thickness at 2, 4, and 39 Hz. Fig. 8. Phase profiles over the delamination in 2.97-mm thickness at 4, 6, 7, and 39 Hz for (a) line 1 and (b) line 2. Fig. 9. Phase spectra for points B and D in the delamination in 2.97-mmthickness area s from Fig. 6. Thus, the value of modulated frequency is 1/0.185 s =5.4 Hz, which agrees with the observed modulated frequency 5.5 Hz in Fig. 9. In Fig. 7, both 2 and 7 Hz are located on the peak of phase spectra in Fig. 9, at which frequency delamination can be found. Both 4 and 6 Hz are located on the valley of phase spectra in Fig. 9, at which frequency carbon fiber structures can be found. The phase images for 100-mm 2 delamination in 3.48-mm thickness at 2, 4, and 39 Hz are shown in Fig. 10. Clearly, there is no nonuniform heating effect. In the phase image at 4 Hz, the fiber-woven and matrix structures can be observed as hot patterns. On the contrary, in the phase image at 2 Hz, the fiber and matrix structures are suppressed and the delamination area can be observed. In the phase image at 39 Hz, nothing but noise can be found. This agrees with that of delamination in 2.97-mm thickness. It can be concluded from the above results using the whole temperature response in heating and cooling phases as input data under transmission mode that phasegram and phase spectra can be used to improve the delamination detectability, nonuniform heating effect caused by coil shape and position can be eliminated, due to oscillation phenomenon, the fiber-woven structures and delamination can be separated, and phase image at peak frequency can highlight the delamination, and the characteristic features like min phase and frequency-to-min phase are difficult to extract. 3) Phase Analysis With Temperature Response in Cooling Phase as Input Data: The temperature responses in cooling phase for delamination in 2.97-mm thickness were processed by DFT. The total number N for the temperature response in cooling phase is 156. We did the 156-point DFT in MATLAB. The frequency resolution f r is Hz. The phase spectra for points B and D are shown in Fig. 11(a). As we can see, the oscillation phenomenon disappears. Due to the spectra shape, two characteristic features (min phase and frequency-to-min phase) can be extracted from phase spectra. The spectra arrive at the lowest at about 7.7 Hz. The phase profiles for the same lines 1 and 2 at about 7.7, 11.5, and 70.5 Hz are shown in Fig. 11(b) and (c). Obviously, the nonuniform heating effect is eliminated and the delamination size can be characterized easily from 7.7 to 70 Hz. Fig. 12(a) and (b) shows the phase images at and Hz. Fig. 12(c) and (d) shows the phase images formed by min phase and frequency-to-min phase after denoising the phase spectra. It is noticeable that not only nonuniform heating effect but also carbon fiber structures can be eliminated from phase images. Then, the delamination detectability can be improved compared with ECVHT results in Fig. 4. Actually, the phasegram from to about 70 Hz can be used to highlight the delamination. But the phase image at Hz has the best performance, because it is at the valley frequency of phase spectra. See Fig. 12, the phase image at Hz in Fig. 12(a) is better than that at Hz in Fig. 12(b). In

8 1294 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 6, DECEMBER 2015 Fig. 12. Phase images of delamination in 2.97-mm-thickness area formed by (a) phase at Hz; (b) Hz; (c) min phase; and (d) frequency-to-min phase. Fig. 11. (a) Phase spectra for points B and D on the delamination in 2.97-mmthickness area. (b) Phase profile of line 1 at 7.7, 11.5, and 70.5 Hz. (c) Phase profile of line 2 at 7.7, 11.5, and 70.5 Hz. addition, some sound area also shows the similar pattern with delamination in Fig. 12. The reason may be that there is damage area in fabrication of sample. The temperature responses in cooling phase for delamination in 3.48-mm thickness were processed by DFT. Fig. 13(a) shows the phase images at Hz. Fig. 13(b) and (c) shows the phase images formed by min phase and frequency-to-min phase after denoising the phase spectra. It is noticeable that not only nonuniform heating effect but also carbon fiber structures have been almost completely eliminated from phase images. It can be concluded from the ECPPT results using the temperature response in cooling phase as input data under transmission mode that phasegram and phase spectra can be used to improve the delamination detectability, nonuniform heating effect caused by coil shape and position can be eliminated, the fiber-woven structures can be suppressed better than that using the whole response as input data, and the characteristic features Fig. 13. Phase images of delamination in 3.48-mm-thickness area formed by (a) phase at Hz, (b) min phase, and (c) frequency-to-min phase. like min phase and frequency-to-min phase can be extracted to construct the new phase image. B. Reflection Mode In the experiment under reflection mode, both induction coil and IR camera were placed on the back side, as shown in Fig. 3(d). The 100-mm 2 delamination in and 3.48-mm thickness has been tested. The distances of delamination to back side (depth dr) are the same (0.5 mm). The heating time is 200 ms followed by cooling time 300 ms. The sampling frequency is 200 Hz and sampling interval is s. 1) ECVHT: Fig. 14 shows the thermal images using ECVHT for delamination in 2.97-mm thickness at 20, 200, and 500 ms, respectively. The temperature is normalized to [0, 1]. Clearly, there is severe nonuniform heating effect along x-axis. In thermal image at 20 ms, the fiber-woven structures can be

9 HE AND YANG: ECVHT AND PHASE ANALYSIS FOR IMAGING CHARACTERIZATION 1295 Fig. 14. Thermal images delamination in 2.97-mm-thickness area at 20, 200, and 500 ms. Fig. 15. Phase images of delamination in 2.97-mm-thickness area at 2, 4, and 39 Hz under reflection mode. Fig. 16. Phase images of delamination in 2.97-mm-thickness area formed by (a) phase at Hz; (b) min phase; and (c) frequency-to-min phase. 3) Phase Analysis With Temperature Response in Cooling Phase as Input Data: The temperature responses in cooling phase for delamination in 3.48-mm thickness were processed by DFT. The total number N for the temperature response in cooling phase is 56. We did the 56-point DFT in MATLAB. The frequency resolution f r is Hz. Fig. 16(a) shows the phase images at Hz. Fig. 16(b) and (c) shows the phase images formed by min phase and frequency-to-min phase after denoising the phase spectra. It is noticeable that not only nonuniform heating effect but also carbon and matrix structures can be eliminated from phase images. Then, the delamination detectability can be improved compared with ECVHT results in Fig. 14. This agrees with that in Fig. 12 under transmission mode. No matter using the whole temperature response or temperature response in cooling phase, the same conclusion can be obtained under transmission mode and reflection modes. observed from the hot and dark patterns. In thermal image at 200 ms, the carbon fiber structures are blurred but still visible. In thermal image at 500 ms, the relatively low temperature area appears which presents the delamination in the sample. In all, it is very difficult to identify the delamination due to the influence of nonuniform heating effect and carbon fiber structures through thermal images. 2) Phase Analysis With Whole Temperature Response as Input Data: The whole temperature response including heating phase and cooling phase for 100-mm 2 delamination in 3.48-mm thickness has been processed by DFT. The total number N for the whole temperature response is 100. We did the 100-point DFT in MATLAB. The frequency resolution f r is 2 Hz. The phase images at 2, 4, and 40 Hz are shown in Fig. 15. The phase is normalized to [0, 1]. Clearly, the nonuniform heating effect is eliminated. In phase image at 2 Hz, the fiber and matrix structure are suppressed and the delamination area can be observed. But in phase image at 40 Hz, nothing but noise can be found. This agrees with that in Fig. 7 under transmission mode. V. CONCLUSION In this work, ECPPT was presented for imaging characterization of interface delamination in CFRP laminates, involving volumetric induction heating, thermal wave propagation, and Fourier transform-based phase analysis. The proposed method was named as ECVHT and phase analysis, which were verified through experimental studies under both transmission and reflection modes. The conclusions are as follows. 1) The phasegram from ECVHT and analysis can reduce the nonuniform heating effect caused by coil shape and position and periodic carbon fiber structures, which is helpful to imaging characterization of delamination in CFRPs. 2) With whole temperature response including heating and cooling phases processed by DFT, fiber structures and delamination can be separated due to periodic oscillation of phase spectra with frequency of reciprocal of heating time. 3) With temperature response in cooling phase processed by DFT, some features (min phase and frequency-to-min phase) can be extracted to construct new phase images

10 1296 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 6, DECEMBER 2015 and carbon fiber structures can be eliminated better than that using the whole response as input data. 4) Delamination characterization methods using ECVHT under transmission and reflection modes are the same, and the similar results can be obtained due to volumetric heating. This is helpful to flexible configuration of ECVHT in manufacturing and maintenance process. All conclusions illustrate that ECVHT and phase analysis are capable of improving the imaging characterization of delamination over conventional ECPT and optical inspection methods. However, this work did not find out the relation between depths and characteristic frequencies through experiments. This could be solved out by improving the experiments (using longer time or greater sampling frequency) or signal processing methods. Although the methods did not completely separate the mixed information of layer, texture, and defect, it has shown the potential to map the texture; see Figs. 7 and 10. Future work will be focused on the quantification of defect depth using the characteristic frequencies, tomography for CFRP laminates through designing new specific samples, and comparison study of different signal processing methods [28] with phase analysis. ACKNOWLEDGMENT The authors would like to thank Prof. G. Y. Tian with Newcastle University, U.K., for help in experimental studies. They would also like to thank ALSTOM for providing the experimental CFRP samples, and the China Scholarship Council for sponsoring Dr. Y. He to Newcastle University, and Dr. R. Yang to University of British Columbia, Canada, for joint study. REFERENCES [1] H. Gao, C. Ding, C. Song, and J. Mei, Automated inspection of E-shaped magnetic core elements using K-tSL-center clustering and active shape models, IEEE Trans. Ind. Informat., vol. 9, no. 3, pp , Aug [2] W.-C. Li and D.-M. Tsai, Defect inspection in low-contrast LCD images using hough transform-based nonstationary line detection, IEEE Trans. Ind. Informat., vol. 7, no. 1, pp , Feb [3] X. Bai, Y. Fang, W. Lin, L. Wang, and B.-F. Ju, Saliency-based defect detection in industrial images by using phase spectrum, IEEE Trans. Ind. Informat., vol. 10, no. 4, pp , Nov [4] G. Acciani, G. Brunetti, and G. Fornarelli, Application of neural networks in optical inspection and classification of solder joints in surface mount technology, IEEE Trans. Ind. Informat., vol. 2, no. 3, pp , Aug [5] D. M. Tsai, S. C. Wu, and W. Y. Chiu, Defect detection in solar modules using ICA basis images, IEEE Trans. Ind. Informat., vol. 9, no. 9, pp , Feb [6] D.-M. Tsai and J.-Y. Luo, Mean shift-based defect detection in multicrystalline solar wafer surfaces, IEEE Trans. Ind. Informat., vol. 7, no. 1, pp , Feb [7] D. You, X. Gao, and S. Katayama, Multisensor fusion system for monitoring high-power disk laser welding using support vector machine, IEEE Trans. Ind. Informat., vol. 10, no. 2, pp , May [8] M. Win, A. Bushroa, M. Hassan, N. Hilman, and A. Ide-Ektessabi, A contrast adjustment thresholding method for surface defect detection based on mesoscopy, IEEE Trans. Ind. Informat., vol. 11, no. 3, pp , Jun [9] C. Benedek, O. Krammer, M. Janóczki, and L. Jakab, Solder paste scooping detection by multilevel visual inspection of printed circuit boards, IEEE Trans. Ind. Electron., vol. 60, no. 6, pp , Jun [10] D. Naso, B. Turchiano, and P. Pantaleo, A fuzzy-logic based optical sensor for online weld defect-detection, IEEE Trans. Ind. Informat., vol. 1, no. 4, pp , Nov [11] D. M. Tsai, I. Y. Chiang, and Y. H. Tsai, A shift-tolerant dissimilarity measure for surface defect detection, IEEE Trans. Ind. Informat., vol.8, no. 1, pp , Feb [12] S. Stipetic, M. Kovacic, Z. Hanic, and M. Vrazic, Measurement of excitation winding temperature on synchronous generator in rotation using infrared thermography, IEEE Trans. Ind. Electron., vol. 59, no. 5, pp , May [13] B. Gao, A. Yin, G. Tian, and W. L. Woo, Thermography spatialtransient-stage mathematical tensor construction and material property variation track, Int. J. Therm. Sci., vol. 85, pp , [14] B. Gao, L. Bai, W. L. Woo, G. Y. Tian, and Y. Cheng, Automatic defect identification of eddy current pulsed thermography using single channel blind source separation, IEEE Trans. Instrum. Meas., vol. 63, no. 4, pp , Apr [15] B. Gao, L. Bai, W. L. Woo, and G. Tian, Thermography pattern analysis and separation, Appl. Phys. Lett., vol. 104, p , [16] B. Ramdane, D. Trichet, M. Belkadi, T. Saidi, and J. Fouladgar, Electromagnetic and thermal modeling of composite materials using multilayer shell elements, IEEE Trans. Magn., vol. 47, no. 5, pp , May [17] G. Wasselynck, D. Trichet, B. Ramdane, and J. Fouladgar, Microscopic and macroscopic electromagnetic and thermal modeling of carbon fiber reinforced polymer composites, IEEE Trans. Magn., vol. 47, no. 5, pp , May [18] B. Huu Kien, G. Wasselynck, D. Trichet, B. Ramdane, G. Berthiau, and J. Fouladgar, 3-D modeling of thermo inductive non destructive testing method applied to multilayer composite, IEEE Trans. Magn., vol. 49, no. 5, pp , May [19] L. Cheng and G. Y. Tian, Surface crack detection for carbon fibre reinforced plastic (CFRP) materials using pulsed eddy current thermography, IEEE Sensors J., vol. 11, no. 12, pp , Dec [20] L. Cheng and G. Y. Tian, Transient thermal behavior of eddy-current pulsed thermography for nondestructive evaluation of composites, IEEE Trans. Instrum. Meas., vol. 62, no. 5, pp , May [21] L. Cheng, B. Gao, G. Y. Tian, and W. L. Woo, Impact damage detection and identification using eddy current pulsed thermography through integration of PCA and ICA, IEEE Sensors J., vol. 14, no. 5, pp , May [22] M. Pan, Y. He, G. Y. Tian, D. Chen, and F. Luo, Defect characterisation using pulsed eddy current thermography under transmission mode and NDT applications, NDT E Int., vol. 52, pp , [23] Y. He, G. Tian, M. Pan, and D. Chen, Impact evaluation in carbon fiber reinforced plastic (CFRP) laminates using eddy current pulsed thermography, Compos. Struct., vol. 109, pp. 1 7, [24] R. Yang, Y. He, B. Gao, and G. Y. Tian, Inductive pulsed phase thermography for reducing or enlarging the effect of surface emissivity variation, Appl. Phys. Lett., vol. 105, p , [25] R. Yang and Y. He, Eddy current pulsed phase thermography considering volumetric induction heating for delamination evaluation in carbon fiber reinforced polymers, Appl. Phys. Lett., vol. 106, p , [26] Y. He, M. Pan, G. Y. Tian, D. Chen, Y. Tang, and H. Zhang, Eddy current pulsed phase thermography for subsurface defect quantitatively evaluation, Appl. Phys. Lett., vol. 103, p , [27] M. Ishikawa et al., Inspection of CFRP laminates using phasetransformed induction heating thermography, in Proc. 11th Eur. Conf. Non-Destr. Test., Prague, Czech Republic, [28] Y.He,G.Y.Tian,M.Pan,D.Chen,and H.Zhanget al., An investigation into eddy current pulsed thermography for detection of corrosion blister, Corros. Sci., vol. 78, p. 1 6, Yunze He (M 11) finished the Joint Ph.D. study in electrical and electronic engineering from Newcastle University, Newcastle upon Tyne, U.K., in 2012; he received the Bachelor degree from Xi an Jiaotong University, Xi an, China, in 2006; and the Ph.D. degree in instrument science and technology from the College of Mechatronics Engineering and Automation from the National University of Defense Technology (NUDT), Changsha, China, in He is a Lecturer with NUDT. In the past 5 years, he has chaired or participated in more than 20 projects including the National Natural Science Foundation of China (NSFC), the

11 HE AND YANG: ECVHT AND PHASE ANALYSIS FOR IMAGING CHARACTERIZATION 1297 Engineering and Physical Sciences Research Council (EPSRC), etc. He has published more than 40 academic papers in journals and conferences, which have been cited more than 650 times in Google scholar and have an h-index of 14. He has authored the book Eddy Current Thermography Nondestructive Testing (in Chinese, National Defense Industry Press, 2013), which is the first monograph focusing on eddy current thermography nondestructive testing research in China and is collected by many university libraries in China. He is also a co-author of Inductive Thermography Pattern Separation-Nondestructive Testing And Evaluation Method Using Inductive Thermography (in English, LAP Lambert Academic Publishing, 2014). He is a Reviewer for over 25 academic journals. His research interests include eddy current testing, thermography, and composite testing in the field of nondestructive testing and structural health monitoring. Ruizhen Yang (M 15) finished the Joint Ph.D. study in wood science from the University of British Columbia, Vancouver, BC, Canada, in 2012, and received the Ph.D. degree in structural engineering from the College of Civil Engineering of Hunan University, Changsha, China, in She is a Lecturer with Changsha University, Changsha. In the past 5 years, she has chaired or participated in more than 10 projects including NSFC. She has authored more than 30 academic papers in journals and conferences. Her research interests include bamboo wood composite materials, nondestructive testing, and structural health monitoring.