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Correlation between mechanical damage behavior and electrical resistance change in CFRP composites as a health monitoring sensor
Materials Science and Engineering A 456 (2007) 286–291
Correlation between mechanical damage behavior and electrical resistance change in CFRP composites as a health monitoring sensor D.-Y. Song a,∗ , N. Takeda b , A. Kitano c a
Gunma Industrial Technology Center, 884-1 Kamesato-Cho, Maebashi-Shi 379-2147, Japan b The University of Tokyo, 7-3-1 Bunkyo-Ku, Hongo, Tokyo 113-8656, Japan c Composite Materials Research Laboratory, Toray Industries Inc., 1515, Matumae-Cho, Iyo-Gun, Ehime, Japan Received 7 August 2006; accepted 25 November 2006
Abstract Correlation between mechanical damage behavior and the change of electrical resistance for CFRP (carbon fiber reinforced plastic) composites was experimentally investigated and simulated using neural network approach. Electrical resistance was measured under simple tension and repeated loading–unloading, and observed to remain after removing load. The value of residual electrical resistance was dependent on the maximum strain applied in the past. The failure processes of two types of CFRP were characterized under loading. Consequently, it was revealed that the behaviors of electrical resistance change of two specimens had a close relation with their failure mechanisms. Moreover, the relationship between the applied strain corresponding to the damage, and the electrical resistance change until the ultimate failure of composite was simulated using neural network. There was a fairly good agreement between the simulation and experimental results. It is suggested that this detecting technique is applicable to the health-monitoring of composite structures. © 2006 Elsevier B.V. All rights reserved. Keywords: CFRP; Mechanical damage behavior; Electrical resistance change; Neural network; Health monitoring
1. Introduction If the maximum value of stress or strain applied on the composite structures in service can be predicted, it is possible to foresee the damage before the catastrophic fracture, and consequently the improvement of stability and reliability of the structure can be achieved. A strain gage is often used to measure strain, but this method is not practical after load is removed because we cannot know the maximum value of strain or stress unless data are measured continuously up to the ultimate fracture. The measurement of electrical resistance in materials and structures during and after loading has been suggested to be a promising method for foreseeing damage and preventing fatal fracture. This method utilizing conductive carbon fibers or particles has been considered to have the advantage in cost and accuracy because it does not need additional sensors and can measure the damage directly. Recently, the relationship between the mechanical parameters
∗
Corresponding author. Tel.: +81 27 290 3030; fax: +81 27 290 3040. E-mail address: [email protected] (D.-Y. Song).
0921-5093/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.msea.2006.11.130
(i.e. stress–strain curves) and the electrical resistance changes in CFRP and CFGFRP (carbon fiber/glass fiber reinforced plastic) composites has been experimentally investigated [1–8]. However, most of researches are limited to the qualitative evaluation and the quantitative evaluation of correlation between the mechanical behavior and the electrical resistance change, taking into account of failure mechanism, is not yet fully made. In the present study, the mechanical damage behavior and the change of electrical resistance for unidirectional CFRP composites was experimentally investigated and simulated using neural network trained with back propagation learning. To this end, monotonic loading and repeated loading–unloading tensile tests were carried out using CFRP consisted of two types of carbon fibers with different electro-mechanical properties, and their failure progresses were observed under loading using an optical microscope. Moreover, the relationship between the applied strain, which corresponds to the damage, and the electrical resistance change until ultimate failure of composite was simulated using the neural network with back propagation learning algorithm, and its result was compared with the experimental results.
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
287
2. Experiment and simulation 2.1. Experimental procedure All the specimens were fabricated from unidirectional carbon fiber/epoxy(#2500 of bisphenol type) prepreg. Two types of carbon fibers used were PAN based T700S and M46J of diameter 7 and 5 m, tensile strength 4.9 and 4.2 GPa, Young’s modulus 230.3 and 436.1 GPa, and ultimate elongation 2.1 and 1%, respectively. The electrical resistivity of T700S and M46J carbon fibers was 16.2 and 9.7 m, respectively. Unidirectional CFRP laminates were cured using an autoclave at 130 ◦ C and 3.92 MPa for 2 h. The dimension of specimens was 110 mm long, 5 mm wide, and 1 mm thick. The volume fraction of carbon fibers in CFRP was about 60%. Electrical contact between carbon fibers and thin lead-wires was ensured with a silver adhesive paste. Tensile tests were conducted at the crosshead speed of 0.5 mm/min. A constant electrical current of 1 mA was introduced to the specimen with the aid of thin lead-wires (0.26 mm in diameter) attached on both the specimen ends. The measurement of the electrical resistance was carried out by using a two-probe dc method. The electrical resistance change was acquired by measuring the voltage change due to loading using a digital multimeter of an accuracy of 1 V. 2.2. Simulation by neural network trained with back propagation learning Neural networks (NNs) are typically organized in layers. Layers are made up of a number of interconnected nodes which contain an activation function. Patterns are presented to the network via the input layer, which communicates to one or more intermediate layers where the actual processing is done via a system of weighted connections. The intermediate layers then link to an output layer where the answer is output. The intermediate layer represents the non-linearity of the network systems. Among the different neural network structures, back propagation neural networks (BPNNs) are most popular because of their powerful applicability in many different fields of science and engineering [9–13]. In principle, a BPNN may have several intermediate layers, but in practice, only one or two layers are used. The number of nodes in the intermediate layers is determined mainly by trial and error. In this study, an artificial neural network approach was used to identify the relationship between the applied strain (i.e., the damage state) and the changes in electrical resistance. In particular, owing to the optimum function to learn a non-linear problem, this neural network approach is very effective for the simulation of non-linear relationship between the change in electrical resistance and the applied strain as will be described later. Such function is performed by learning the connection weights, ωij of each neuron. Fig. 1 shows the structure of input and output of neural network used for simulation. In this work, the input and output signals were the change in electrical resistance and the applied strain, respectively. The transfer of signal is conducted by the neural network composed of one layer, three intermediate layers and one out-
Fig. 1. Structure of input and output of neural network.
put layer. Here, the neurons (x0 and y0 ) for the threshold are added to each input and intermediate layers in order to adjust simultaneously the threshold, θ and connection weights ωij as shown in Fig. 1 (i.e., the threshold of neuron is acted as one of connection weights). The output of each neuron, yj are given by the summation of each input, xi and connection weight, ωij as follows: n yi = f ωij xi = f (sj ) (1) i=0
where f is the sigmoid non-linear function, f(s) = 1/(1 + e−αs ) to be applied to the neuron. α is the gain which represents the slope of this function. The network learning is performed based on back propagation algorithm [11–13]. Here, the values of connection weights are adjusted iteratively to minimize the mean square error, E between the output of the network, oi and the desired output, ti . These relations are given as follows: E=
2 1 (ti − oi ) 2
ωij = −η
∂E ∂ωij
ωijnew = ωijold + ωij
(2) (3) (4)
where ωijnew and ωijold are the newly updated weight and the adjusting weight, respectively, ωij the correction of connection weights, and η is the learning rate (i.e., step size), which affects the rate of convergence of the weights during learning. In this work, a so-called momentum term was also added to preserve the direction and magnitude of a trend in movement of the weights during optimization. Table 1 shows the learning parameters of neural network assigned for this simulation. Furthermore, the used input data and the desired data were scaled to provide the analogue values ranging from 0 to 1 for learning
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Fig. 2. Stress/strain and change rate in electrical resistance/strain curves for CFRP specimens with T700S and M46J fibers. Table 1 Learning parameters of neural network assigned for this simulation
3. Experimental results and discussion
Parameters
Assigned values
Learning rate Momentum Convergence error Initial width of weights Gain of sigmoid function
0.99 0.9 0.0001 −0.5–0.5 0.9
3.1. Relations of stress/strain and change in electrical resistance/strain
of neural network as follows: M=
X + Xmin Xmax + Xmin
(5)
where M is the scaled data ranging from 0 to 1. Xmax and Xmin are the maximum and minimum values of data before scaling, respectively.
Fig. 3. Electrical resistance/strain curves of the specimen with T700S fiber under repeated loading and unloading removed.
Fig. 2(a) and (b) show the stress/strain and change in electrical resistance/strain curves obtained from tensile tests of CFRP. The stresses in both specimens increased almost linearly with increasing strain until the ultimate fracture of the specimen. The change in electrical resistance for specimen with T700S fiber increases almost linearly up to about 0.7% strain. But after this strain, the resistance increased more steeply in a non-linear behavior, and increases abruptly near 1.6% strain just before the specimen failure. The resistance change after the onset of non-linearity was observed to be irreversible (i.e., the resistance
Fig. 4. Relative change in residual electrical with applied maximum strain obtained after the load was removed.
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
change did not return to zero after the applied load was removed). These changes in electrical resistance occurring during static loading are known to be attributed to two separate effects [2,4–6] as follows: (i) the changes in resistance due to the changes in dimensions of the fibers owing to elastic strain, this corresponds to the initial stage of loading showing the linear increase in resistance, (ii) the changes in resistance caused by fiber fracture or other changes in the network of touching fibers, this corresponds
289
to the rapid increase in a non-linear behavior to the final failure as described in Fig. 2. In the case of specimen with M46J fiber, the entire trend of resistance change was similar to the case of T700S fiber specimen, but after about 0.2% strain indicating the linear increase of resistance, its change was more rapid than that of T700S fiber. These behaviors of electrical resistance changes are related to the failure mechanisms of specimens with T700S and M46J carbon fibers.
Fig. 5. (a) Failure processes observed on the surfaces of CFRP specimen with T700S carbon fiber under loading. (b) Failure processes observed on the surfaces of CFRP specimen with M46J carbon fiber under loading.
290
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
Fig. 3 shows the electrical resistance/strain curve of the specimen with T700S fiber under repeated loading and unloading test. When the strain was removed, the electrical resistance decreased, but did not return along the initial line and left some permanent change in electrical resistance. The residual resistance appeared after the applied stain of 0.5%, and then increased with increasing maximum applied strain. The existence of residual electrical resistance is considered to be related to the number of carbon fibers fractured on loading, and the contacting state between the fractured carbon fibers after the load was removed. Thus, this result suggests that the prediction of strain applied to CFRP composite is possible by electrical resistance change during loading. Fig. 4 shows the relative change in residual electrical resistance with the applied maximum strain for CFRP specimens with different carbon fibers after the load was removed. The residual electrical resistance was dependent on the maximum strain applied in the past. This means that CFRP has the ability to memorize the maximum strain applied in the past as a residual electrical resistance. Therefore, if the relationship between the strain and the residual electrical resistance until fracture were known, it is possible to foresee the fracture of CFRP composites by measuring their residual electrical resistance after straining. This resistance change can be also used as a signal which indicates the possibility of damage in composite materials. 3.2. Optical observation of failure process Fig. 5(a) and (b) show the photos of failure processes observed on the specimen surfaces under loading. For specimen with T700S carbon fiber, the initial fracture was observed to occur at the stress level of about 0.62σ max . As the load was further increased to the stress levels of 0.74σ max and 0.86σ max , fiber breakage occurred randomly on the whole surface, and fiber/matrix debondings were easily observed at the broken fiber ends. This is due to the shear stress concentrations generated by the fiber breakage. Pull-out fibers were significantly observed on the fractured section of the specimen and their lengths were relatively large. On the other hand, for specimen with M46J carbon fiber, the initial crack occurred at the stress level of about 0.65σ max , nearly equal to that of T700S, but this crack rapidly propagated across the specimen in connection with the breakage of neighboring fibers by subsequent loading. Furthermore, fiber/matrix debonding was hardly observed. The overall crack propagation path is almost straight and perpendicular to the load direction. This is due to the relatively strong adhesion of the fiber/matrix interface. Considering these results, it is revealed that the different behavior of resistance change of two specimens should provide a close relation with their failure mechanisms. 4. Comparison between simulation and experimental results As mentioned in the above sections, if the relationship between the applied strain and the electrical resistance change in the CFRP composites until fracture is known, it is possible
Fig. 6. Comparison between experimental results and simulation results obtained by neural network.
to predict the extent of damage by monitoring the changes in electrical resistance during and after loading. Thus this relationship was simulated using neural network approach (where, the electrical resistance change and the applied strain were chosen as input and output data, respectively). Fig. 6 shows the comparison between the experimental results and simulation results obtained using neural network trained with back propagation learning. It can be seen that simulation results agrees well with the experimental results. That is, a satisfactory learning was found to be accomplished at a relatively small learning operation (in this work, the number of learning times were about 26465 for T700S carbon fiber and about 2024 for M46J carbon fiber). This result is due to the establishment of optimal parameters through trial and error method. Therefore, the applied strain corresponding to the damage state in the overall region until ultimate failure can be predicted and identified by monitoring the changes in electrical resistance using neural network approach. 5. Conclusions Mechanical damage and the change in electrical resistance in CFRP composites was experimentally characterized and simulated using neural network approach. Electrical resistance was observed to remain after removing load. The value of residual electrical resistance was dependent on the maximum strain applied in the past. These results suggested that the maximum strain and the damage state could be predicted by monitoring the electrical resistance during and after loading. That is, the resistance change could be used as a signal indicating the possibility of damage in composites. Moreover, it was revealed that the behavior of electrical resistance change had a close rela-
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
tion with failure mechanisms of composites. The relationship between the applied strain (i.e., damage) and the electrical resistance change until ultimate failure of composites was simulated using the neural network trained with back propagation learning. The predicted results using neural network showed fairly good agreement with experimental results. It is also suggested that this detecting technique is applicable to the health-monitoring of composite structures. References [1] J.C. Abry, S. Bochard, A. Chateauminois, M. Salvia, G. Giraudm, Comp. Sci. Tech. 59 (1999) 925–935. [2] P.E. Irving, C. Thiagarajan, Smart Mater. Sruct. 7 (1998) 456–466. [3] M. Sugita, H. Yanagida, N. Muto, Smart Mater. Struct. 4 (1995) A52–A57.
291
[4] K. Schulte, H. Wittich, The Electrical Response of Strained and/or Damaged Polymer Matrix Composite, in: Proc. ICCM, 10, 1995, pp. 349–356. [5] A.S. Kaddour, F.A.R. Al-Salehi, M.J. Hinton, Comp. Sci. Tech. 51 (1994) 377–385. [6] X. Wang, D.D.L. Chung, Eletromechanical Behavior of Carbon Fiber, in: SPIE Conf., 3324, 1998, pp. 242–250. [7] N. Muto, H. Yanagida, H. Nakatsuji, M. Sugita, Y. Ohtsuka, J. Jpn. Soc. Compos. Mater. 18 (3) (1992) 144–150. [8] R. Prabhakaran, Exp. Tech. 14 (1990) 16–20. [9] P. Tsou, M.H. Shen, AIAA J. 32 (1) (1994) 176–183. [10] J.N. Kudva, N. Munir, P.W. Tan, Smart Mater. Struct. 1 (1992) 108–112. [11] S. Chinchalkar, J. Sound Vibr. 247 (2000) 259–276. [12] M.T. Hagan, H.B. Demuth, M. Beale, Neural Network Design, PWS Publishing Company, Boston, 1996. [13] R.P. Lipmann, in: V. Vemuri (Ed.), An Introduction to Computing With Neural Net, Artifical Neural Networks: Theoretical Concepts, vol. 1, Computer Soc. Press, IEEE, Washington, 1987, pp. 36–53.
Correlation between mechanical damage behavior and electrical resistance change in CFRP composites as a health monitoring sensor D.-Y. Song a,∗ , N. Takeda b , A. Kitano c a
Gunma Industrial Technology Center, 884-1 Kamesato-Cho, Maebashi-Shi 379-2147, Japan b The University of Tokyo, 7-3-1 Bunkyo-Ku, Hongo, Tokyo 113-8656, Japan c Composite Materials Research Laboratory, Toray Industries Inc., 1515, Matumae-Cho, Iyo-Gun, Ehime, Japan Received 7 August 2006; accepted 25 November 2006
Abstract Correlation between mechanical damage behavior and the change of electrical resistance for CFRP (carbon fiber reinforced plastic) composites was experimentally investigated and simulated using neural network approach. Electrical resistance was measured under simple tension and repeated loading–unloading, and observed to remain after removing load. The value of residual electrical resistance was dependent on the maximum strain applied in the past. The failure processes of two types of CFRP were characterized under loading. Consequently, it was revealed that the behaviors of electrical resistance change of two specimens had a close relation with their failure mechanisms. Moreover, the relationship between the applied strain corresponding to the damage, and the electrical resistance change until the ultimate failure of composite was simulated using neural network. There was a fairly good agreement between the simulation and experimental results. It is suggested that this detecting technique is applicable to the health-monitoring of composite structures. © 2006 Elsevier B.V. All rights reserved. Keywords: CFRP; Mechanical damage behavior; Electrical resistance change; Neural network; Health monitoring
1. Introduction If the maximum value of stress or strain applied on the composite structures in service can be predicted, it is possible to foresee the damage before the catastrophic fracture, and consequently the improvement of stability and reliability of the structure can be achieved. A strain gage is often used to measure strain, but this method is not practical after load is removed because we cannot know the maximum value of strain or stress unless data are measured continuously up to the ultimate fracture. The measurement of electrical resistance in materials and structures during and after loading has been suggested to be a promising method for foreseeing damage and preventing fatal fracture. This method utilizing conductive carbon fibers or particles has been considered to have the advantage in cost and accuracy because it does not need additional sensors and can measure the damage directly. Recently, the relationship between the mechanical parameters
∗
Corresponding author. Tel.: +81 27 290 3030; fax: +81 27 290 3040. E-mail address: [email protected] (D.-Y. Song).
0921-5093/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.msea.2006.11.130
(i.e. stress–strain curves) and the electrical resistance changes in CFRP and CFGFRP (carbon fiber/glass fiber reinforced plastic) composites has been experimentally investigated [1–8]. However, most of researches are limited to the qualitative evaluation and the quantitative evaluation of correlation between the mechanical behavior and the electrical resistance change, taking into account of failure mechanism, is not yet fully made. In the present study, the mechanical damage behavior and the change of electrical resistance for unidirectional CFRP composites was experimentally investigated and simulated using neural network trained with back propagation learning. To this end, monotonic loading and repeated loading–unloading tensile tests were carried out using CFRP consisted of two types of carbon fibers with different electro-mechanical properties, and their failure progresses were observed under loading using an optical microscope. Moreover, the relationship between the applied strain, which corresponds to the damage, and the electrical resistance change until ultimate failure of composite was simulated using the neural network with back propagation learning algorithm, and its result was compared with the experimental results.
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
287
2. Experiment and simulation 2.1. Experimental procedure All the specimens were fabricated from unidirectional carbon fiber/epoxy(#2500 of bisphenol type) prepreg. Two types of carbon fibers used were PAN based T700S and M46J of diameter 7 and 5 m, tensile strength 4.9 and 4.2 GPa, Young’s modulus 230.3 and 436.1 GPa, and ultimate elongation 2.1 and 1%, respectively. The electrical resistivity of T700S and M46J carbon fibers was 16.2 and 9.7 m, respectively. Unidirectional CFRP laminates were cured using an autoclave at 130 ◦ C and 3.92 MPa for 2 h. The dimension of specimens was 110 mm long, 5 mm wide, and 1 mm thick. The volume fraction of carbon fibers in CFRP was about 60%. Electrical contact between carbon fibers and thin lead-wires was ensured with a silver adhesive paste. Tensile tests were conducted at the crosshead speed of 0.5 mm/min. A constant electrical current of 1 mA was introduced to the specimen with the aid of thin lead-wires (0.26 mm in diameter) attached on both the specimen ends. The measurement of the electrical resistance was carried out by using a two-probe dc method. The electrical resistance change was acquired by measuring the voltage change due to loading using a digital multimeter of an accuracy of 1 V. 2.2. Simulation by neural network trained with back propagation learning Neural networks (NNs) are typically organized in layers. Layers are made up of a number of interconnected nodes which contain an activation function. Patterns are presented to the network via the input layer, which communicates to one or more intermediate layers where the actual processing is done via a system of weighted connections. The intermediate layers then link to an output layer where the answer is output. The intermediate layer represents the non-linearity of the network systems. Among the different neural network structures, back propagation neural networks (BPNNs) are most popular because of their powerful applicability in many different fields of science and engineering [9–13]. In principle, a BPNN may have several intermediate layers, but in practice, only one or two layers are used. The number of nodes in the intermediate layers is determined mainly by trial and error. In this study, an artificial neural network approach was used to identify the relationship between the applied strain (i.e., the damage state) and the changes in electrical resistance. In particular, owing to the optimum function to learn a non-linear problem, this neural network approach is very effective for the simulation of non-linear relationship between the change in electrical resistance and the applied strain as will be described later. Such function is performed by learning the connection weights, ωij of each neuron. Fig. 1 shows the structure of input and output of neural network used for simulation. In this work, the input and output signals were the change in electrical resistance and the applied strain, respectively. The transfer of signal is conducted by the neural network composed of one layer, three intermediate layers and one out-
Fig. 1. Structure of input and output of neural network.
put layer. Here, the neurons (x0 and y0 ) for the threshold are added to each input and intermediate layers in order to adjust simultaneously the threshold, θ and connection weights ωij as shown in Fig. 1 (i.e., the threshold of neuron is acted as one of connection weights). The output of each neuron, yj are given by the summation of each input, xi and connection weight, ωij as follows: n yi = f ωij xi = f (sj ) (1) i=0
where f is the sigmoid non-linear function, f(s) = 1/(1 + e−αs ) to be applied to the neuron. α is the gain which represents the slope of this function. The network learning is performed based on back propagation algorithm [11–13]. Here, the values of connection weights are adjusted iteratively to minimize the mean square error, E between the output of the network, oi and the desired output, ti . These relations are given as follows: E=
2 1 (ti − oi ) 2
ωij = −η
∂E ∂ωij
ωijnew = ωijold + ωij
(2) (3) (4)
where ωijnew and ωijold are the newly updated weight and the adjusting weight, respectively, ωij the correction of connection weights, and η is the learning rate (i.e., step size), which affects the rate of convergence of the weights during learning. In this work, a so-called momentum term was also added to preserve the direction and magnitude of a trend in movement of the weights during optimization. Table 1 shows the learning parameters of neural network assigned for this simulation. Furthermore, the used input data and the desired data were scaled to provide the analogue values ranging from 0 to 1 for learning
288
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
Fig. 2. Stress/strain and change rate in electrical resistance/strain curves for CFRP specimens with T700S and M46J fibers. Table 1 Learning parameters of neural network assigned for this simulation
3. Experimental results and discussion
Parameters
Assigned values
Learning rate Momentum Convergence error Initial width of weights Gain of sigmoid function
0.99 0.9 0.0001 −0.5–0.5 0.9
3.1. Relations of stress/strain and change in electrical resistance/strain
of neural network as follows: M=
X + Xmin Xmax + Xmin
(5)
where M is the scaled data ranging from 0 to 1. Xmax and Xmin are the maximum and minimum values of data before scaling, respectively.
Fig. 3. Electrical resistance/strain curves of the specimen with T700S fiber under repeated loading and unloading removed.
Fig. 2(a) and (b) show the stress/strain and change in electrical resistance/strain curves obtained from tensile tests of CFRP. The stresses in both specimens increased almost linearly with increasing strain until the ultimate fracture of the specimen. The change in electrical resistance for specimen with T700S fiber increases almost linearly up to about 0.7% strain. But after this strain, the resistance increased more steeply in a non-linear behavior, and increases abruptly near 1.6% strain just before the specimen failure. The resistance change after the onset of non-linearity was observed to be irreversible (i.e., the resistance
Fig. 4. Relative change in residual electrical with applied maximum strain obtained after the load was removed.
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
change did not return to zero after the applied load was removed). These changes in electrical resistance occurring during static loading are known to be attributed to two separate effects [2,4–6] as follows: (i) the changes in resistance due to the changes in dimensions of the fibers owing to elastic strain, this corresponds to the initial stage of loading showing the linear increase in resistance, (ii) the changes in resistance caused by fiber fracture or other changes in the network of touching fibers, this corresponds
289
to the rapid increase in a non-linear behavior to the final failure as described in Fig. 2. In the case of specimen with M46J fiber, the entire trend of resistance change was similar to the case of T700S fiber specimen, but after about 0.2% strain indicating the linear increase of resistance, its change was more rapid than that of T700S fiber. These behaviors of electrical resistance changes are related to the failure mechanisms of specimens with T700S and M46J carbon fibers.
Fig. 5. (a) Failure processes observed on the surfaces of CFRP specimen with T700S carbon fiber under loading. (b) Failure processes observed on the surfaces of CFRP specimen with M46J carbon fiber under loading.
290
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
Fig. 3 shows the electrical resistance/strain curve of the specimen with T700S fiber under repeated loading and unloading test. When the strain was removed, the electrical resistance decreased, but did not return along the initial line and left some permanent change in electrical resistance. The residual resistance appeared after the applied stain of 0.5%, and then increased with increasing maximum applied strain. The existence of residual electrical resistance is considered to be related to the number of carbon fibers fractured on loading, and the contacting state between the fractured carbon fibers after the load was removed. Thus, this result suggests that the prediction of strain applied to CFRP composite is possible by electrical resistance change during loading. Fig. 4 shows the relative change in residual electrical resistance with the applied maximum strain for CFRP specimens with different carbon fibers after the load was removed. The residual electrical resistance was dependent on the maximum strain applied in the past. This means that CFRP has the ability to memorize the maximum strain applied in the past as a residual electrical resistance. Therefore, if the relationship between the strain and the residual electrical resistance until fracture were known, it is possible to foresee the fracture of CFRP composites by measuring their residual electrical resistance after straining. This resistance change can be also used as a signal which indicates the possibility of damage in composite materials. 3.2. Optical observation of failure process Fig. 5(a) and (b) show the photos of failure processes observed on the specimen surfaces under loading. For specimen with T700S carbon fiber, the initial fracture was observed to occur at the stress level of about 0.62σ max . As the load was further increased to the stress levels of 0.74σ max and 0.86σ max , fiber breakage occurred randomly on the whole surface, and fiber/matrix debondings were easily observed at the broken fiber ends. This is due to the shear stress concentrations generated by the fiber breakage. Pull-out fibers were significantly observed on the fractured section of the specimen and their lengths were relatively large. On the other hand, for specimen with M46J carbon fiber, the initial crack occurred at the stress level of about 0.65σ max , nearly equal to that of T700S, but this crack rapidly propagated across the specimen in connection with the breakage of neighboring fibers by subsequent loading. Furthermore, fiber/matrix debonding was hardly observed. The overall crack propagation path is almost straight and perpendicular to the load direction. This is due to the relatively strong adhesion of the fiber/matrix interface. Considering these results, it is revealed that the different behavior of resistance change of two specimens should provide a close relation with their failure mechanisms. 4. Comparison between simulation and experimental results As mentioned in the above sections, if the relationship between the applied strain and the electrical resistance change in the CFRP composites until fracture is known, it is possible
Fig. 6. Comparison between experimental results and simulation results obtained by neural network.
to predict the extent of damage by monitoring the changes in electrical resistance during and after loading. Thus this relationship was simulated using neural network approach (where, the electrical resistance change and the applied strain were chosen as input and output data, respectively). Fig. 6 shows the comparison between the experimental results and simulation results obtained using neural network trained with back propagation learning. It can be seen that simulation results agrees well with the experimental results. That is, a satisfactory learning was found to be accomplished at a relatively small learning operation (in this work, the number of learning times were about 26465 for T700S carbon fiber and about 2024 for M46J carbon fiber). This result is due to the establishment of optimal parameters through trial and error method. Therefore, the applied strain corresponding to the damage state in the overall region until ultimate failure can be predicted and identified by monitoring the changes in electrical resistance using neural network approach. 5. Conclusions Mechanical damage and the change in electrical resistance in CFRP composites was experimentally characterized and simulated using neural network approach. Electrical resistance was observed to remain after removing load. The value of residual electrical resistance was dependent on the maximum strain applied in the past. These results suggested that the maximum strain and the damage state could be predicted by monitoring the electrical resistance during and after loading. That is, the resistance change could be used as a signal indicating the possibility of damage in composites. Moreover, it was revealed that the behavior of electrical resistance change had a close rela-
D.-Y. Song et al. / Materials Science and Engineering A 456 (2007) 286–291
tion with failure mechanisms of composites. The relationship between the applied strain (i.e., damage) and the electrical resistance change until ultimate failure of composites was simulated using the neural network trained with back propagation learning. The predicted results using neural network showed fairly good agreement with experimental results. It is also suggested that this detecting technique is applicable to the health-monitoring of composite structures. References [1] J.C. Abry, S. Bochard, A. Chateauminois, M. Salvia, G. Giraudm, Comp. Sci. Tech. 59 (1999) 925–935. [2] P.E. Irving, C. Thiagarajan, Smart Mater. Sruct. 7 (1998) 456–466. [3] M. Sugita, H. Yanagida, N. Muto, Smart Mater. Struct. 4 (1995) A52–A57.
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