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Design and Integration of Flexible Sensor Matrix for In Situ Monitoring of Polymer Composites Yang Yang, Gabriele Chiesura, Bart Plovie, Thomas Vervust, Geert Luyckx, Joris Degrieck, Tsuyoshi Sekitani, and Jan Vanfleteren ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.8b00425 • Publication Date (Web): 16 Jul 2018 Downloaded from http://pubs.acs.org on July 23, 2018

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Design and Integration of Flexible Sensor Matrix for In Situ Monitoring of Polymer Composites Yang Yang,*,†,‡,§ Gabriele Chiesura,∥ Bart Plovie,† Thomas Vervust,† Geert Luyckx,∥ Joris Degrieck,∥ Tsuyoshi Sekitani,‡ Jan Vanfleteren*,† †

Centre for Microsystems Technology (CMST), imec and Ghent University, Technologiepark 15, Gent-Zwijnaarde 9052, Belgium The Institute of Scientific and Industrial Research, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047, Japan § Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99352, USA ∥Department of Materials Science and Engineering, Ghent University, Technologiepark 903, Gent-Zwijnaarde 9052, Belgium ‡

ABSTRACT: Sensory polymer composites are highly desirable for applications such as in situ and real-time production processes and structural health monitoring, and for technologies that include human-machine interfaces for the next generation of Internet of Things. However, the development of these materials is still in its infancy: these materials have been reported, but the large-scale fabrication of polymer composites with versatile and customizable sensing capabilities has yet to be demonstrated. Here, we report on a scalable fabrication strategy that enables such materials by designing and integrating PCB technology-inspired large-area flexible sensor matrices into polymer composites. The integrated sensor matrices successfully monitored in situ the production processes and structural health of an industrial polymer composite: from the application of vacuum, resin flow and polymerization, production defects, and temperature distribution. Our results demonstrate that the proposed strategy is a simple and effective solution as a distributed monitoring platform for polymer composites and shows the potential towards next generation of sensory polymer composites. KEYWORDS: flexible electronics, PCB technology, sensors, process monitoring, structural health monitoring Polymer composites (“composites”) have drawn the attention of many researchers in recent years due to the current demand for strong, lightweight materials;1-3 they are increasingly used as high-grade constituents of heavy-duty structures in a wide array of industrial application such as aviation, wind energy, and automotive.4 However, for the composites to live up to their full potential, a few requirements regarding the behavior during their production and operational life should be addressed. One requirement is understanding the material behavior during the production process, to reduce production cost and to avoid production flaws.5 Another requirement is the knowledge of their structural health during service,6 to avoid potentially severe economic consequences, and to ensure public safety.7 A useful monitoring system should be embedded to allow in situ and realtime measurements over the complete life cycle of the composite,8 and such that the production or operation of the composite is not interrupted or degraded. Over the last decade, substantial effort was devoted to the research and development of novel sensory polymer composites, i.e. composites with integrated sensing capabilities. The integration of sensors is highly desirable not only for applications such as in situ and real-time production processes and structural

health monitoring, but also for technologies that include humanmachine interfaces for Internet of Things. Carbon nanomaterials have received considerable attention for the creation of sensory polymer composites. For example, carbon nanotubes and graphene were dispersed in a polymer matrix to form conductive percolation networks as sensors to assess stress, strain and damage for structural health monitoring.9-10 However, these composites are restricted to (piezo) resistive effect-based sensing capabilities from the included nanomaterials. A lack of versatile and customizable sensing functionalities in a single platform poses an issue for large-scale industrial uptake of this technology. On the other hand, the approach of embedding conventional offthe-shelf sensors and electronics in composites was exploited,11-15 to monitor resin flow, to assess load, strain and temperature, and to monitor the production process and structural health. Nonetheless, the shape mismatch between complexly-shaped composites and planar rigid sensors and electronics results in a problem of large-scale high-volume integration of sensor network in a cost-effective manner. Recent progress in flexible and stretchable circuits opened up a new avenue for sensory polymer composites. Through materials and structure innovations, the circuits become stretchable, deformable, and conformable to curvilinear surfaces,16-19 and therefore are suitable for the integration in composites with unusual shapes and forms. Amongst the different strategies, PCB technology-inspired flexible and stretchable circuits are of particular interest.20 Besides their conformability with complexly shaped polymer composites,21-23 this technology is also compatible with an industrial scale PCB manufacturing environment and permits the use of standard PCB processes and off-the-shelf components for sensing, data storage and communication. In this study, large-area flexible sensor matrices (FSMs) with multi-sensing capabilities including but not limited to impedance and temperature measurements are designed, fabricated and integrated into an industrial polymer composite – a stiffened panel towards achieving sensory polymer composites. The FSM, inspired by PCB technology, allows the use of standard PCB processes for large-scale fabrication, and to integrate off-the-shelf components for versatile and customizable functionalities of the composites. It demonstrates the ability to monitor the production process and remains in the composite during its operation for structural health monitoring.

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Figure 1. Schematic illustration of composites with integrated flexible sensor matrix for in situ life cycle monitoring. (Image courtesy of Xiao-Meng Wu.) in the matrix. Here, a simple algorithm which takes about 100 ms Design of the Flexible Sensor Matrix (FSM) per sensor was implemented to rapidly and accurately measure the Figure 1 illustrates the proposed concept of sensory polymer capacitance. A total time of 2.8 seconds, including the time for composites through the integration of large-scale flexible, and temperature measurements and the delays encountered when stretchable sensor matrix in a stiffened panel for in situ life cycle switching between each sensor, is required to scan through the monitoring. The advantages of integrating PCB technologywhole matrix. inspired sensor matrix are twofold: First, the embedded electronic The designed FSM employs interdigital sensor-based circuits can conform to complexly shaped polymer composites; capacitance measurements for monitoring composite production second, the use of standard off-the-shelf components, such as processes.24 We propose a simple yet effective readout circuit sensors, data storage devices and communication chips is straightforward, enabling multifunctional sensor matrices with capable of rapid and accurate capacitance measurements for a data processing and transmission capabilities. A stiffened panel is network of sensors. The operating principle of the capacitance a representative semi-structural component found in many measurement is illustrated in Figure 2(c). A capacitive divider industrial applications, e.g. in automotive, transport, naval or system is built around the MCU for the impedance measurements. aeronautics applications. This panel was used as a demonstration The upper side of the divider represents the IDS with a of process monitoring during vacuum infusion and structural capacitance CMUT, and the lower side represents a reference health monitoring during service. capacitance of CS. The lossy part of the capacitor is modelled as a parallel resistor, where RMUT and RS are the lossy parts of CMUT Figure 2(a) depicts the system-level block diagram of the FSM. and CS respectively. An interdigital sensor, as shown in Figure The FSMs employ interdigital sensors and commercial 2(d), operating in the fringing electric field mode, was designed temperature measurement ICs (range: -55 to 125 °C) and custom for impedance sensing. In an IDS structure, L, W, g, N, η, designed, externally connected readout circuitry (hereafter respectively stand for the length of the fingers, the width of each referred to as the divider) for impedance and temperature finger, the spacing between fingers, the number of fingers and the measurements. The matrix of the IDS and temperature metallization ratio (η = W/(W+g)). These geometrical parameters measurement ICs was fabricated on a flexible copper-clad are important factors that influence the sensor’s nominal value laminate and connected externally to a microcontroller (MCU) and sensitivity. Details on how to select the design parameters with an on-chip analog multiplexer (MUX) driving a built-in 10were discussed elsewhere.25 To maximize sensor sensitivity bit analog-to-digital converter (ADC), and serial communication within the fabrication limitation, the unit area sensitivity of the capabilities through an interfacing board. The overall sensor, within our fabrication limit, was achieved with W = g = measurement scheme of the FSM is illustrated in the flowchart 0.1 mm. L and N was designed to be 15 mm and 60. To analyze shown in Figure 2(b). First, the MCU is initialized with hardware the behavior of the capacitive divider circuit, we start with both configuration; next, the program selects one channel at a time, capacitors discharged, and both input and output pins at 0 V. disabling other channels, and applies an excitation signal to the When a square wave,         , is selected channel. After the analog-to-digital conversion, the ADC applied at the input with amplitude of and duration of  value is read, and stored in a data register. Afterwards, the MUX (  is the Heaviside step function), a current will flow through will disable the selected channel and switch to the next channel, both capacitors. As a result, the output voltage,  , will repeating the same measurement sequence. After all 16 IDS are respond in accordance to the   , which is a function of read out, the program proceeds to read out the temperature  ,  ,  ,  , and  . By establishing the input-output measurement ICs. Finally, all the measurement data is transmitted relationship of the system, the relevant circuit parameters is to the PC through USB in one go. Since both capacitance and extracted:26 temperature are read in series, the time required to scan through the whole matrix is proportional to the number of sensor elements

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ACS Sensors

Figure 2. System overview of the proposed FSM and its readout circuitry: (a) System architecture of the FSM showing the interdigital sensors, temperature measurement ICs, microcontroller unit (MCU) for data processing and communication, USB transmission path, and the PC for data collection. (b) Simplified flowchart of the measurement scheme. (c) Circuit model of the readout circuitry for the capacitance measurements. (d) Structure of an interdigital sensor. (e) The accuracy of capacitance measurements for the fabricated FSM.

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 ,  The built-in ADC of the MCU converts the measured voltage,  , to a 10-bit digital value, ADC counts, with respect to the analog voltage reference, $78 . The relationship between these is: 9: ;< => (4)  

$78 1024 Since the analog voltage reference is internally connected to the MCU’s supply rail $78 . Inserting Eq. (4) into Eq. (3), and rearranging the resulting equation, we obtain:

9: ;< => A  (5) 1024  9: ;< => The proposed capacitance measurement using the capacitive divider readout circuitry was implemented by programming the connected MCU. First, the MCU selects the desired channel using the MUX. Then, an excitation voltage is supplied at the divider’s input. The software will then pause for a programmed time t1 until the transients stabilize before the ADC converts it into a digital value. Afterwards, the excitation is removed from the input; a delay time t2 is added before the start of the next measurement to ensure both capacitors are fully discharged. The duration of each measurement loop was determined by the sum of t1 and t2, which in the current implementation is 100 ms. The accuracy of the capacitance measurements was investigated; we used temperature independent precision capacitors (minimal temperature drift, 1% error) as  in the divider to measure this. The used capacitors were in the range of 10 to 1000 pF and built around the FSM and tested. The results are shown in Figure 2(e). An overall relative error of 5% was observed over the large capacitance range. In the measurements range of 20 to 100 pF, which is desired in our current work, relative errors of less than 2% were observed.

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Figure 3. Fabrication steps of the FSM and its integration in polymer composites. (a) Fabrication of the FSM: (1) start from Cu-PI substrate, (2) micro-structuring through photolithography and wet etching for creating the sensor and circuit pattern, (3a) assembly of the components onto the flexible circuit board. (b) Fabrication of the perforated FSM: (1-2) is the same from the standard FSM, (3b) laser cutting, (4) assembly of the components onto the stretchable circuit board. (c) Photograph of the fabricated FSM featuring 16 IDS and 16 temperature measurement ICs. (d) Integration of the FSMs in polymer composites using the vacuum assisted infusion process. Vacuum bag and glass mold are not shown in the figure for the sake of clarity. (e) Photograph of the fabricated sensory polymer composites. Fabrication and Integration of the FSM The fabrication steps of the FSM are shown in Figure 3. The process starts by structuring a copper pattern on a flexible polyimide (PI) substrate via photolithography and wet etching, followed by copper surface treatment using organic solderability preservative. PI is an ideal substrate material because of its relatively stable dielectric, thermal and mechanical properties over a wide temperature and frequency range, ensuring the stable operation of the interdigital sensor over a wide temperature range.24-25 The lead-free solder paste was manually dispensed using a dispensing system and 16 temperature measurement ICs were manually placed and soldered in a reflow oven. The fabrication of the perforated sensor matrix requires an additional step of laser cutting, as shown in Figure 3(b), to create the inplane meander-shaped interconnections by cutting through the polyimide. The photograph of the fabricated B4 sized FSM is shown in Figure 3(c). Figure 3(d) shows the integration of three FSMs in a glass fiber reinforced polymer composites using a vacuum assisted infusion process. One FSM was placed on top of a glass mold (not shown in the figure) and below the stiffened panel. Four layers of unidirectional glass fibers were stacked on top of the FSM as the stiffened panel. Two foam blocks, used for creating the U shapes, were placed on top of the panel, each with a perforated FSM glued on top. Another four layers of glass fibers were stacked on top of the foam and the perforated FSMs. Finally, the stack was sealed using a vacuum bag, and blended and degassed resin was

infused into the vacuum bag through the resin inlet. The resin outlet was connected with a tube to the vacuum line, and once the resin reaches the outlet, both tubes were clamped to stop the resin flow and to keep the vacuum. The stack was placed on a hotplate set to 70 °C to accelerate the curing of the epoxy resin. Figure 3(e) shows the fabricated sensory polymer composites. In situ monitoring of a stiffened panel The bending stiffness of the panel was significantly improved by attaching top U-shaped stiffeners on selected locations. The stiffeners can be bonded or welded to the panel after separately fabricating the components. Here, the stiffeners were cofabricated with the panel through a vacuum assisted resin infusion process. During this process, full impregnation of the fibers within the resin over the entire dry layup, i.e. both on the panel and on the stiffeners, should be ensured. Adequate process parameters (temperature, time, etc.) for the curing of the resin must be maintained to achieve the required physical and chemical properties of the produced composite parts.27-28 Amongst the proposed techniques for production process monitoring of composites, such as differential scanning calorimetry, dynamic mechanical analysis, and dielectric analysis (DEA). DEA is considered a promising technique due to its potential to perform the measurements in an industrial environment,27 and its suitability to be employed in a sensor matrix.29-31 Here, coplanar interdigital sensors, which combines geometrical stability, and the possibility to be accessed from a single side,32-33 were used in the FSM as the dielectric sensors.

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Figure 4. In situ and real-time production process monitoring on the panel section: (a) Top view image of the experimental setup with indications of the sensor locations. (b) Cross-sectional schematic shows the locations of the FSM. (c) Magnified cross-sectional schematic. (d) Measurements at the preparation and resin infusion stage, (e) and at the curing stage. Figure 4 shows the results of in situ and real-time production additional cross-linking that would further restrict the mobility of process monitoring of the stiffened panel on the panel section. the dipoles, thus indicating the vitrification of the resin or the end The exact location of each sensor from the FSM is indicated in of the cure. Throughout the production process, the temperature Figure 4(a), and the location of the FSM in the layup is shown in sensors on the plate show a smooth increasing trend, following the the cross-sectional schematic in Figure 4(b) (Figure 4(c): heating of the plate. However, no clear indications of reach of magnified cross-sectional schematic). The epoxy was infused vacuum sealing, flow front, or end of curing could be determined through a single tube inlet on the left side and vacuum was from the temperature measurement data. applied from the outlet on the right side. Figure 4(d) shows the To better understand the rate of curing within different sections dielectric constant ε', converted from the measured capacitance,25 of the composite part, which is an estimation of the speed of and temperature of the FSM during the resin infusion stage. At the polymerization, the rate of change for B C : log |d B C /dt| was beginning of the process (indicated by the blue shade in the calculated. The matrix data from the 16 sensors are presented figure), when the vacuum was applied to the production together as a contour plot, as shown in Figure 5. In general, only environment, ε' instantly increases from 1 to 2 for all the sensors a slight difference in curing speed is found for different sensors, because of the tight contact between the glass fiber and the indicating that different parts of the composites were cured at a sensors due to the vacuum. Afterwards, the epoxy resin and similar speed. hardener were mixed and degassed. One hour after the start of the measurements (indicated by the purple shade in the figure), the resin gradually progressed from the left to the right side; the resin flow front is indicated by the sharp increase of ε' from 2 to 6 ~ 8 when the resin flows over the sensor, due to the change of material under test from air to liquid epoxy. After the rise, the ε' stayed almost constant for most of the sensors except for sensors 6 and 8 where a vacuum leak was detected. The leak was located and fixed immediately, and the ε' restored to the high values observed through other sensors. Figure 4(e) shows the measurements of the FSM during the curing stage. Due to crosslinking of polymer chains through the opening of the epoxide ring, the three-dimensional polymer network grows with an increase in the viscosity of the resin.34 The resulting reduction in capacitance due to the reduced mobility of permanent dipole groups provides a basis for production monitoring of polymer composites using impedance spectroscopy. All the measurements Figure 5. B C rate of change, log |dB C /dt| unit: 1/hour, measured by followed a smooth sigmoidal decreasing trend and reached a the flexible sensor matrix. plateau after six hours. The asymptotic value suggests lack of any

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Figure 6. In situ and real-time production process monitoring on the stiffeners section: (a) Top view image of the experiment with indications of the sensor locations on the stiffeners, and cross-sectional schematic shows the locations of the perforated FSMs. (b) Measurements at the preparation and resin infusion stage and (c) at the curing stage. Figure 6 shows the measurements of the two perforated FSMs, located between the foam and glass fibers. The locations of each sensor is indicated in Figure 6(a). After resin infusion at the inlet, the resin continued to flow on the stiffened panel, and part of the resin climbed up to the stiffeners. Evident increases of ε' are seen for all the sensors upon achieving a vacuum seal and the arrival of the resin flow front, as shown in Figure 6(b). However, a discontinuity of the measurement, as indicated by a negative peak marked with an arrow in the figure, is seen between 1.2 and 1.3 hours. This drop of ε' is due to the loss of vacuum during the process, which caused the stiffeners to delaminate from the IDS. A vacuum leak could happen in industrial production processes as well and this could be detected in real time and located thanks to the sensor matrix. Therefore, the operator could intervene and fix the issue, avoiding dry spots and improving overall production yield. After fixing the leakage, the vacuum was reestablished, and the ε' restored to the increasing trend. Since the resin was preheated during the travel on the stiffened panel and because the stiffeners were far away from the hotplate, a gradient of temperature was observed between the resin and the stiffeners. Therefore, the temperature sensors could indicate the resin flow front in this particular case. The small time difference is due to different locations of the sensors. Figure 6(c) shows the

measurements during the resin curing stage. Discontinuities of the measurements were found for all the sensors after ~1.8 hours, indicated by a rapid drop of the ε'. It was visually confirmed that the stiffeners were completely detached from the foam. Curing curves show that detachment of the top glass fibers from the foam occurred at t = 1.8 hours. A thin layer of epoxy that remained on the IDS and continued to cure, resulting in a gradual decrease of the measured capacitances. Thanks to the integrated sensor system, the structural health of the composites during operation could be diagnosed. Here, the FSMs were applied to monitor the temperature distribution of the stiffeners and stiffened panel during a cool down cycle from 80°C to 20°C, as shown in Figure 7, to simulate a harsh operational environment. Evident temperatures differences are visible between the bottom panel and two top stiffeners, due to a low thermal conductivity of the composite. A thermal gradient of larger than 10 °C was observed within the bottom panel or the two top stiffeners. As a high thermal gradient can induce stress within composites, temperature measurements can help to estimate the structural health of the composites. On the other hand, dielectric sensor are useful for monitoring the hydrothermal ageing of the polymers due to environmental effects.35-36

Figure 7. Distributed temperature measurements of the composites during a cool down cycle from 80 to 20°C.

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ACS Sensors CONCLUSIONS Future polymer composites should be tailored with autonomous sensing properties to provide real-time feedback to the end-users on the condition of the materials. Here, we report on an easily scalable approach to achieve sensory polymer composites with versatile and customizable functionalities by the design and integration of PCB technology-inspired flexible sensor matrix in polymer composites. The integrated FSM, capable of simultaneous capacitance and temperature measurements, was applied for in situ production process monitoring of a stiffened panel. These sensor matrices successfully monitored the vacuum seal, the resin flow front during infusion, the degree of cure, the evolution of internal temperature, and the vitrification of the resin distributedly. Moreover, the sensor matrices were able to diagnose structural health, like delamination, and thermal gradient of the structure during service. Furthermore, PCB technology inspired flexible sensor matrix fabrication permits further extended functionalities of the sensory composites with additional computational and communication chips, enabling technologies for the next generation of Internet of Things. In today’s era of the connected world, this PCB technology inspired flexible sensor matrix will likely attract considerable attention for its application to sensory polymer composites. EXPERIMENTAL SECTION Materials. The epoxy system (Hexion, Columbus, OH, USA) used in this study is a two-part system comprising the resin (EPIKOTE MGS RIMR 135) and the hardener (EPIKURE MGS RIMH 137). 500 g of the resin and 150 g of the hardener were thoroughly mixed and degassed. Then, the blended and degassed resin was infused into the vacuum bag through the resin inlet. The resin outlet was connected with a tube to the vacuum line, and once the resin reaches the outlet, both tubes are clamped to stop the resin flow and keep the bag under vacuum. The stiffened panel was cured at 70 °C on a hotplate for 12 hours. Fabrication of the FSMs. The fabrication process starts by structuring a copper pattern on a flexible PI-Cu (UPISEL-N BE1410, UBE Inc., Japan) substrate via photolithography and wet etching. Afterwards, an organic solderability preservative was applied to the copper surface. The lead-free solder paste (DP5505, Interflux, Belgium, material: Sn96.5Ag3Cu0.5) was dispensed manually using a dispensing system (UltraSaver, EFD Inc, USA) and the commercial temperature measurement ICs (DS18B20, Maxim Integrated, USA) were assembled manually and soldered in a reflow oven (IBL SLC300, Siemens). The polyimide surface was laser cut to create the in-plane meander-shaped interconnections for perforated sensor matrix using a CO2 laser system. An HP 4284A Precision LCR meter performed the impedance measurement of the IDS at 1 kHz as a reference. Integration and measurements of the flexible sensor matrix. Three FSMs were integrated into the stiffened panel during a vacuum assisted infusion process. One FSM was placed on top of a glass mold and below the stiffened panel. Four layers of unidirectional glass fibers (UDO ES 500, SGL Technologies GmbH, Germany), in a [0, 90]2s layup, were stacked on top of the FSM as the stiffened panel. Two foam blocks were placed on top of the stiffened panel. Two perforated FSMs were glued on top of each foam block. Four layers of the same glass fibers, in a [0, 90]2s layup, were stacked on top of the foam and the FSM. Finally, the complete stack was sealed using a vacuum bag, and prepared for resin infusion. The stiffened panel was placed on a hotplate set to 70 °C to accelerate the curing of the epoxy resin. The FSMs were connected to a custom designed readout circuit

built around a microcontroller (ATmega1280, Atmel, San Jose, USA) for data collection and processing. The microcontroller consumes 10 mA, with an operating voltage of 5V and an operating frequency of 8 MHz, as stated in the datasheet.

AUTHOR INFORMATION Corresponding Author *[email protected] *[email protected]

ACKNOWLEDGMENTS The research leading to these results received funding from the Flemish Agency for Innovation by Science and Technology (IWT) – through the program for Strategic Basic Research (SBO) under grant agreement n° 120024 (Self Sensing Composites). Yang was supported by JSPS Postdoctoral Fellowship for Foreign Researchers (ID No: PE17020). Yang acknowledges help from Xiao-Meng Wu.

REFERENCES (1) Stankovich, S.; Dikin, D. A.; Dommett, G. H.; Kohlhaas, K. M.; Zimney, E. J.; Stach, E. A.; Piner, R. D.; Nguyen, S. T.; Ruoff, R. S. Graphene-based composite materials. Nature 2006, 442 (7100), 282-6, DOI: 10.1038/nature04969. (2) Ajayan, P. M.; Tour, J. M. Materials science: nanotube composites. Nature 2007, 447 (7148), 1066-8, DOI: 10.1038/4471066a. (3) Kim, J. H.; Han, M. J.; Seo, S. Flexible, Stretchable, and Patchable Organic Devices Integrated on Freestanding Polymeric Substrates. J. Polym. Sci. B 2015, 53 (6), 453-460, DOI: 10.1002/polb.23662. (4) Chawla, K. K. Composite materials: science and engineering, Springer Science & Business Media: 2012. (5) Hardis, R.; Jessop, J. L. P.; Peters, F. E.; Kessler, M. R. Cure kinetics characterization and monitoring of an epoxy resin using DSC, Raman spectroscopy, and DEA. Compos. Part A Appl. Sci. Manuf. 2013, 49, 100-108, DOI: 10.1016/j.compositesa.2013.01.021. (6) Qiao, W.; Lu, D. A survey on wind turbine condition monitoring and fault diagnosis—Part I: Components and subsystems. IEEE Trans. Ind. Electron. 2015, 62 (10), 6536-6545. (7) Tsai, S. W.; Melo, J. D. D. A unit circle failure criterion for carbon fiber reinforced polymer composites. Compos. Sci. Technol. 2016, 123, 71-78, DOI: 10.1016/j.compscitech.2015.12.011. (8) Minakuchi, S.; Takeda, N.; Takeda, S.-i.; Nagao, Y.; Franceschetti, A.; Liu, X. Life cycle monitoring of large-scale CFRP VARTM structure by fiber-optic-based distributed sensing. Composites Part A: Applied Science and Manufacturing 2011, 42 (6), 669-676. (9) Picot, O. T.; Rocha, V. G.; Ferraro, C.; Ni, N.; D'Elia, E.; Meille, S.; Chevalier, J.; Saunders, T.; Peijs, T.; Reece, M. J.; Saiz, E. Using graphene networks to build bioinspired selfmonitoring ceramics. Nat Commun 2017, 8, 14425, DOI: 10.1038/ncomms14425. (10) Abot, J. L.; Song, Y.; Vatsavaya, M. S.; Medikonda, S.; Kier, Z.; Jayasinghe, C.; Rooy, N.; Shanov, V. N.; Schulz, M. J. Delamination detection with carbon nanotube thread in selfsensing composite materials. Compos. Sci. Technol. 2010, 70 (7), 1113-1119, DOI: 10.1016/j.compscitech.2010.02.022. (11) Tuncol, G.; Danisman, M.; Kaynar, A.; Sozer, E. M. Constraints on monitoring resin flow in the resin transfer molding

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