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Kevlar tubes
Composite Structures 243 (2020) 112247
Contents lists available at ScienceDirect
Composite Structures journal homepage: www.elsevier.com/locate/compstruct
Experimental investigation and artificial intelligence-based modeling of the residual impact damage effect on the crashworthiness of braided Carbon/Kevlar tubes
T
ABSTRACT
Fiber reinforced plastic composites are promising candidates for building the next generation of automotive and aircraft structures. However, these materials are sensitive to any potential impact, which may cause matrix micro-cracking or internal inter-laminar delamination damages. This study provides insights into the sensitivity of braided Carbon/Kevlar round tubes to external damages and neural network-based models that can predict the consequences of damages on the crushbehavior (load-bearing capability). This was investigated by subjecting the tube to transverse low-velocity impacts at different energy levels and locations. Then, these pre-damaged tubes were crushed using a quasi-static compression test. The results indicate that the pre-impact energy levels have a significant effect on the deterioration of both the structure strength and the crush behavior. The locations of the damages are mainly responsible for altering the collapse behavior of the structure rather than its performance. The crush force efficiency is not significantly affected by the pre-impact energy levels, but it is highly affected by the preimpact/damage locations. The undamaged tubes were collapsed in a progressive manner, whereas splitting and crack propagation were the dominant failure modes in the tubes with residual damages. The path of those cracks was governed by the damage location. Artificial neural network-based models were developed, compared and improved with the objective to model the highly non-linear behavior of the load carrying capacity of the pre-impacted tubes. The developed model successfully provides a quick and accurate assessment at all compression strokes with an MSE of 0.000191 KN.
1. Introduction Carbon fiber reinforced plastics (CFRP) are being commonly used in aerospace and automotive industries for construction of flight and car body structures [1]. They are very attractive candidates for such applications owing to their superior specific strength, energy absorption, and durability [1,2]. Another emerging application in this area is usage of tubular CFRP structures as the linkage members for advanced-performance chassis structures [3]. The addition of Kevlar fibers with the carbon fiber braid will enhance the overall toughness of the material and lower its sensitivity to localized defects, such as notches and indentations [4,5]. Nevertheless, it will improve the post crush force efficiency and thus the specific energy due to the added elasticity to the structure [4]. This is significantly important to improve its resistance against low velocity impacts that might arise from dropping of metallic tools or other vehicle components on the assembly lines [6]. Such accidents will most likely cause the static and dynamic mechanical properties for the material to deteriorate [7]. Assessment and Numerical modeling of these residual damages in the material is a challenging task that is attracting the attention of the research community [6,8,9]. The assessment of residual damages in composite panels have been studied extensively, while fewer investigations were conducted on the behavior of composite tubes [10]. Their performance is most likely to be different when subjected to the similar loading conditions as the panels [3]. Chen et al. [11] studied the consequences of lateral preimpact damage on the crashworthiness of CFRP square tubes subjected to axial crushing. Effect of multiple levels of pre-impact energies were compared in addition to their position. The undamaged tubes were collapsed in a progressive folding mode whereas the damaged tubes exhibited a brittle fracture which initiated from the damaged zone and progressed around the circumference. Fiber breakage and delamination were the dominant failure modes. Consequently, their peak load and energy absorption capability were reduced by 38% and 58.3%, https://doi.org/10.1016/j.compstruct.2020.112247 Received 13 March 2020; Accepted 18 March 2020 Available online 21 March 2020 0263-8223/ © 2020 Published by Elsevier Ltd.
respectively. They also concluded that the failure mode and performance are more dependent to the pre-impact energy level rather than its location. The same authors attempted to replicate those findings using the finite element method. However, this method required an additional multi-stage modeling to improve its prediction accuracy due to the complexity of the failure mechanics of CFRP material. Similar observations were reported by Kobayashi and Kawahara [12] where the surface damages, such as indentations or micro-cracks, were very critical failure triggers in quasi-static loading condition. Sebaey and Mahdi [10] have subjected pre-impacted composite round tubes to lateral crushing orientation to assess the reduction in their crashworthiness parameters. They found out that only the peak load, crush force efficiency, and crush load stability were influenced. The rest of the parameters, such as specific energy absorption, were independent of the those damage initiators. The effect of pre-impact damage on the burst strength fatigue life was studied by Uyaner et al. [13]. Their results indicated that both parameters were reduced by the increment of the reimpact energy level that caused matrix cracking and delamination in the inner layers only. From the structural point of view, presence of those defects within the fibers interfaces will significantly lower the inter-laminar fracture toughness in the opening and in-plane shear modes [14]. In recent years, ANN-based models have been extensively employed to model highly nonlinear relationships between inputs and targets in numerous engineering disciplines, such as condition-based maintenance [15], automation [16] and materials [8,17]. In this research, the neural network-based models utilize the backpropagation (BP) procedure. The backpropagation learning paradigm is developed to train a feed-forward network and it is considered as an effective technique. It is used to exploit the exceptions and regularities in training samples. The backpropagation neural network (BPNN) is considered as the most widely used method among other ANN techniques. The network contains multiple layers: an input layer, one or more hidden
Composite Structures 243 (2020) 112247
using Imatek IM10 drop-weight tester with energies of 2, 5, and 7 J. The specimens were rested on fixtures from both ends. A 12 mm spherical impactor head was used to punch them. After introducing the damages, the collapsing mechanics and load-carrying performance of those tubes were studied under axial compressive loading conditions, according to ASTM E9-09 standard. Instron 5585 UTM was used with 6-inch diameter flat platens to crush the specimens at a crosshead speed of 50 mm/min up to 80% of their initial height. A 4 mm deep groves were engraved on the center of the compression platen to allow the tubular specimens to be fitted to restrict the turning of the specimens to eliminate sudden shifts in the load–displacement curves. A set of three specimens from each configuration were tested for ensuring a good data repeatability. Three parameters were considered for performance assessment; initial peak force (IPF), mean load, and crush force efficiency (CFE). The former represents the first peak in the measured load–displacement curve and the average force accounts for all the exhibited loads by the structure until the start of material densification. The CFE represents the stability of the structure in carrying the applied load without excessive jerk in which may be harmful to personnel in occupant related applications [3]. It is expressed as the ratio of the average force to the IPF. After that, artificial neural network-based models were developed and compared using the Mean Squared Error (MSE) measure. Those models were used to predict the load-carrying capacity range at each displacement value, which implicitly considers the uncertainty of results.
Table 1 Technical specifications of the hybrid composite material, adapted from PYROFIL TR30S (ASTM D-3039). Material property
Value
Density ( ) Tensile strength ( TS ) Modulus of elasticity Failure Strain ( f )
1.79 g/cm3 4413 MPa 296 GPa 1.5%
Stacking sequence
CF [ ± 45F/0U]S, CF/K [ ± 45F]
Fiber to resin ratio (Wf )
≈ 50%
layers, and an output layer. These hidden layers consist of a large number of hidden neurons. Activation propagation is forwarded from the input to the output layer. After that, the algorithm compares the output values with known target values, as these techniques are one of the major supervised-based learning techniques. It then propagates the error backward for evaluation and correction. Biases and weights are updated based on the calculated errors with the objective to meet target values. Therefore, this paper investigates the influence of the presence of the residual damage in hybrid braided carbon/Kevlar round tube on its load-bearing capacity under axial quasi-static crushing conditions. This damage was induced by a combination of low-velocity impacts with various energy levels and at different locations. Due to the high nonlinearity of the bearing carrying capacity, ANN-based models are developed and evaluated for performance to implicitly predict the crushing behavior of the hybrid braided carbon/Kevlar round.
3. Results and discussion
2. Methodology
3.1. Effect of pre-impact energy level
2.1. Specimens preparation
The pre-impact intensity was increased to impose greater residual damage on the tubes. Fig. 2 presents the resulted contact force and absorbed energy curves with respect to time. The minimum and peak forces were 686 N and 915 N at 2 and 7 J, respectively. The influence of each energy level on the load-carrying capacity is graphically represented using load versus displacement curves and a bar chart presented (see Fig. 3). In comparison with the control specimen, the damaged tubes were significantly weakened, especially when the preimpact energy increases. In addition, the load curve started to decline at a higher rate after the IPF. This occurs due to the pre-impact damage that triggered micro-cracks within the matrix. This, in turn, weakened the structure through propagation and delamination of the braided fibers. The reduction in IPF and mean load were larger than 34.6% and 36.3% and for the energies were higher than two joules, respectively. The two joule level doesn’t seem to have any significant consequence on the performance of the tube. Moreover, the CFE was relatively not affected by the presence of the residual damage in the material, regardless of the increments in the energy levels. This damage was not reflected on the CFE ratio as both the IPF and the mean load values were similarly reduced [10].
In the experimental part of this study, the composite round tubes were fabricated using two braided carbon fiber layers with a sandwiched unidirectional ply. Then, an outer braided layer comprising a hybrid carbon/Kevlar blend was added to improve the material toughness against impacts. Those tube were engineered to withstand the side pre-impacts and resist longitudinal crack propagation and splitting, while maintaining superior stiffness and strength to weight ratio. The technical specifications and lamination stacking sequence code is given in Table 1. The entire specimens were taken from a single 3 m long tube to eliminate fabrication uncertainties from affecting the results. The specimen length, inner diameter, and wall thickness were 75 mm, 25 mm, and 1.2 mm, respectively. 2.2. Testing and performance parameters The behavior of composite sandwich panels is complicated due to the complexity, and the highly anisotropic property of the material. In this section, the tubular specimens were subject to pre-impacts at different locations, as indicated in Fig. 1. Each case was pre-impacted
Fig. 1. The testing matrix; (a) The tubular control specimen, (b-c) specimens with a single pre-impact, and (d-f) specimens subjected to a combination of pre-impacts. The red arrows represent the location of the pre-impact. 2
Composite Structures 243 (2020) 112247
Fig. 2. (a) The contact force and (b) absorbed energy curves versus time at pre-impacts at 2, 5, and 7 J.
Fig. 3. (a) Load versus displacement curves for different pre-impact energies applied once at the center of the specimen and (b) a bar charts summarizing their performance parameters.
thin-walled structure [7]. The CFE ratio, on the other hand, was slightly improved by 4.1% over the control specimen. Similar observation was report by Sebaey and Mahdi [10]. Crack initiation and then propagation was highly expected to occur when two pre-impacts were combines on the same level (Fig. 4d). Two cracks were promoted and progressed at about ± 45 degrees causing a non-uniform stress concentration areas. This, in turn, promoted local buckling in the shape of a diamond. When those pre-impacts were shifted to the upper side of the specimen (Fig. 4e), the diamond fracture was shifted accordingly and both cracks met at the middle rather than the bottom. However, when there is a separation distance between the two pre-impacts (Fig. 4f), the crack propagation was progressing diagonally which caused a shift between the upper and bottom portions. Subsequently, the former portion failed at the very top from the fit joint with the platen and progressed in twisting and moving sideways. This is the result of moment which was created by the two vertical loads with a moment arm in-between. The load curve is much smoother compared to the others because the damaged area is not concentrated at one level. Hence, it allowed for more gradual fracturing from the upper and bottom levels. Moreover, the second re-enforcing stage is not as significant as observed in the others since the whole structure has collapsed gradually. The IPF and mean load were further reduced by the combination of pre-impacts in comparison with the previous cases. However, the highest CFE ratio among all the cases was exhibited by case (e) with more than 20% increase over the undamaged tube. Fig. 7 provides insights into micro-level fracture details using scanning electron microscopy technique. Four failure modes were observed: a) delamination between the braided fibers, b) fiber pullout from matrix, c) crack propagation, and d) carbon fiber breakage. The delamination between the carbon and kevlar fibers is cause due to the difference in their elasticity under a given stress [4]. It also depends on
3.2. Effect of pre-impact location The locations and their combinations had a great influence on the failure modes among the specimens. Those variations were more noticeable when the pre-impact energy was the highest. Fig. 4 demonstrates the collapsing behavior for those specimens throughout the compression stroke. Accordingly, the exhibited load versus displacement trends and performance parameters were different as seen in Figs. 5 and 6, respectively. Progressive failure mode was observed in the control specimens at the upper crush zone (transverse fiber tearing). Moreover, two folding layers were created towards the end of the stroke since it was a thin-walled tube with high toughness Kevlar fibers. Consequently, a high load level was maintained throughout the crushing stroke with initial and secondary peaks at the beginning. Those were resulted from the initial failure of the upper and then bottom face surfaces that were in the platen grooves. The sudden splitting fracture due to crack propagation was the dominant initial failure mode in the pre-impacted specimens. This mode was triggered from the damaged spot and then propagated to the nearest weakest area. In case of the single pre-impact in the center of the tube (Fig. 4b), it has propagated through the circumference of the tube to reach back to the initiation spot and close the loop. On the other hand, when the trigger spot was shifted to the middle of the upper tube portion (Fig. 4c), it has progressed to the upper face edge where the stress was more concentrated and at a closer distance. Both specimens were then crushed in a progressive tearing with folding manner afterwards. This justifies the rise in their load curve towards the end of the stroke. Their IPF and mean load performance were significantly lowered more than 31.2% and 28.4% for both cases relative to the control specimen, respectively (Fig. 6). This indicates the material sensitivity to the residual damage, especially when it has high specific stiffness and 3
Composite Structures 243 (2020) 112247
Fig. 4. The crushing behavior, displayed at six-stroke progress steps, of (a) the control specimen, (b-c) single pre-impacted tube, and (d-f) a combination of two preimpacts on tubes. The red arrows represent the location of the pre-impact.
Fig. 5. Load versus displacement history curves for (a) with and without pre-impact, and (b) pre-impact combinations.
the inter-laminar fracture toughness of the interface between those fibers and the matrix. This can be strengthened by employing chemical and mechanical surface pre-treatments to improve surface wettability and introduce better mechanical interlocks [14]. Similarly, kevlar fibers
pullout from the matrix is related to the same cause. Brittle cracking of the matrix can be reduced by adding nano-filler agents, such as reactive diluents and liquid rubber, to it prior to infusion to improve its toughness [18]. Breakage of the carbon fibers is common due to their 4
Composite Structures 243 (2020) 112247
Fig. 6. Bar chart summarizing performance parameters of the pre-impacted composite tubes in terms of IPF, mean load, and CFE.
Fig. 7. Scanning electron microscopy images highlighting the structural failure mechanism within the fiber-reinforced epoxy composite.
3.3. Prediction using ANN
Table 2 Mean Squared Error (MSE) of a number of selected single layer shallow neural network configurations (the best Configuration in bold). Nodes
MSE (training)
MSE (retrained)
MSE (retrained)
5 15 25 35 45 55 65 Two-layer NN 5,5 15,15 25,25 35,35 45,45 55,55
0.007328 0.005613 0.000952 0.001033 0.001896 0.001806 0.000601
0.005427 0.004076 0.001061 0.001069 0.00188 0.001266 0.00062
0.004919 0.002564 0.001036 0.001064 0.000843 0.001289 0.00065
0.002135 0.000617 0.000427 0.000578 0.000352 0.000459
0.002214 0.000592 0.000413 0.000206 0.000351 0.000439
0.002042 0.000632 0.000416 0.000191 0.000384 0.000466
A large data sets of 45,905 samples were normalized and then utilized to train, validate and test the proposed neural networks; 70% of the data sets were utilized for training, while the rest were used for validation and testing. One-layer and two-layer neural networks were optimized for performance using the trial and error approach. The performances of a number of selected NN configurations are list in Table 2. The best performing single-layer NN was found to be a network with 65 nodes (see Fig. 8) and a normalized MSE of 0.000601 N (see Table 2). The training time is 49 s, while the prediction of a single load carrying capacity takes 259 ms on average using a DELL OPTIPLEX 7440 AIO machine (Core I7-6700 CPU @ 3.4 GHz (8CPUs) processor and 8 GB memory). The error histogram shown in Fig. 9 (a) illustrates the error values versus the number of instances for the single layered NN. However, among the large number of the investigated NN configurations, the best performing configuration is a two-layer neural network with 35 nodes each. The network produced a normalized MSE of 0.000191 KN (see Table 2). The error histogram in Fig. 9 (b) shows smaller error values than the values shown in Fig. 9 (a). Therefore, the two-layer NN outperforms the one-layer NN in terms of prediction accuracy. The training time of the two-layer network is 69 s, while the prediction of a single load carrying capacity takes 310 ms on average.
high stiffness. This is a sign of high energy absorption achievement by the structure and a good adhesion between the fibers and the matrix. Increasing the density of those filaments in the orientation where their breakage was observed will improve the load-bearing capacity of the structure [19].
Fig. 8. The architecture of the best performing NN. 5
Composite Structures 243 (2020) 112247
Fig. 9. Error histograms of the best performing (a) single-layer and (b) two-layer neural networks (65 nodes and 35, 35 nodes).
Fig. 10. Experimental versus predicted data using (a) a single layer NN and (b) a two-layer NN (sample SP2).
Fig. 10 shows the experimental versus the predicted data using single layer and two layer neural networks, where it can be seen that the twolayer network yields a better prediction accuracy.
outperformed the other addressed NNs with an MSE value of 0.000191 KN only. 5. Data availability
4. Conclusion
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.
This study experimentally investigates the effect of the pre-impact energy level and location on the collapse behavior and load carrying capacity of carbon/kevlar hybrid round tubes. A remarkable reduction of up to 48.2% and 56.4% are observed in the initial peak force and mean load, respectively. On the other hand, the crushing force efficiency was improved in some cases, with more than 20% improvement. The failure mode was significantly influenced by the location and combination of the pre-impacts on the structure. Progressive failure mode was dominant in the control specimens, whereas splitting and crack propagation were observed in the damaged tubes. The crack tends to initiate the damage zone and then progress to the weakest area, which could be either the location of another pre-impacted area or the face edge of the tube. Furthermore, the highly non-linear behavior of the crush is modeled using ANN. This allows the predication of the load carrying capacity as well as the energy absorption at any displacement value or time. A large number of neural network configurations were proposed and then evaluated for performance with a view to improving the prediction performance of the network, which was assessed using the Mean Squared Error (MSE) performance measure. Based on the results of the conducted comparative study, a two-layer network; with 35 nodes each,
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Sutherland L. A review of impact testing on marine composite materials: part IIIdamage tolerance and durability. Compos Struct 2018;188:512–8. [2] Alkhatib F, Mahdi E. Energy absorption capability of composite hexagonal array systems: numerical analysis. In applied mechanics and materials. 2018. Trans Tech Publ. [3] Laban O, Mahdi E. Energy absorption capability of cotton fiber/epoxy composite square and rectangular tubes. J Nat Fibers 2016;13(6):726–36. [4] Mahdi E, Sebaey T. Crushing behavior of hybrid hexagonal/octagonal cellular composite system: aramid/carbon hybrid composite. Mater Des 2014;63:6–13. [5] Sebaey T, Wagih A. Flexural properties of notched carbon–aramid hybrid composite laminates. J Compos Mater, 2019: p. 0021998319855773. [6] Hauffe A, Hähnel F, Wolf K. Comparison of algorithms to quantify the damaged area in CFRP ultrasonic scans. Compos Struct 2019:111791. [7] Jia W, Wen W, Fang L. Low-velocity impact and post-impact biaxial residual
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Composite Structures 243 (2020) 112247 strength tests and simulations of composite laminates. Compos Struct 2019:111758. [8] Gowid S, Mahdi E, Alabtah F. Modeling and optimization of the crushing behavior and energy absorption of plain weave composite hexagonal quadruple ring systems using artificial neural network. Compos Struct 2019;229:111473. [9] Safonov A, et al. Modeling of cracking during pultrusion of large-size profiles. Compos Struct 2019:111801. [10] Sebaey T, Mahdi E. Crashworthiness of pre-impacted glass/epoxy composite tubes. Int J Impact Eng 2016;92:18–25. [11] Chen D, et al. Residual crashworthiness of CFRP structures with pre-impact damage–an experimental and numerical study. Int J Mech Sci 2018;149:122–35. [12] Kobayashi S, Kawahara M. Effects of stacking thickness on the damage behavior in CFRP composite cylinders subjected to out-of-plane loading. Compos A Appl Sci Manuf 2012;43(1):231–7. [13] Uyaner M, Kara M, Şahin A. Fatigue behavior of filament wound E-glass/epoxy composite tubes damaged by low velocity impact. Compos B Eng 2014;61:358–64. [14] Laban O, Mahdi E. Enhancing mode I inter-laminar fracture toughness of aluminum/fiberglass fiber-metal laminates by combining surface pre-treatments. Int J Adhes Adhes 2017;78:234–9. [15] Gowid S, Dixon R, Ghani S. Performance comparison between fast fourier transform-based segmentation, feature selection, and fault identification algorithm and
⁎
[16] [17] [18] [19]
neural network for the condition monitoring of centrifugal equipment. J Dyn Syst Meas Contr 2017;139(6):061013. Haggag M, et al. An intelligent hybrid experimental-based deep learning algorithm for tomato-sorting controllers. IEEE Access 2019;7:106890–8. Shokry A, et al. Constitutive models for the prediction of the hot deformation behavior of the 10% Cr steel alloy. Materials 2019;12(18):2873. He Y, et al. Micro-crack behavior of carbon fiber reinforced Fe3O4/graphene oxide modified epoxy composites for cryogenic application. Compos A Appl Sci Manuf 2018;108:12–22. Xiong J, et al. Sandwich-walled cylindrical shells with lightweight metallic lattice truss cores and carbon fiber-reinforced composite face sheets. Compos A Appl Sci Manuf 2014;56:226–38.
Othman Laban, Samer Gowid , Elsadig Mahdi, Farayi Musharavati Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P. O. Box 2713, Doha, Qatar E-mail address: [email protected] (S. Gowid). ⁎
Corresponding author. 7
Contents lists available at ScienceDirect
Composite Structures journal homepage: www.elsevier.com/locate/compstruct
Experimental investigation and artificial intelligence-based modeling of the residual impact damage effect on the crashworthiness of braided Carbon/Kevlar tubes
T
ABSTRACT
Fiber reinforced plastic composites are promising candidates for building the next generation of automotive and aircraft structures. However, these materials are sensitive to any potential impact, which may cause matrix micro-cracking or internal inter-laminar delamination damages. This study provides insights into the sensitivity of braided Carbon/Kevlar round tubes to external damages and neural network-based models that can predict the consequences of damages on the crushbehavior (load-bearing capability). This was investigated by subjecting the tube to transverse low-velocity impacts at different energy levels and locations. Then, these pre-damaged tubes were crushed using a quasi-static compression test. The results indicate that the pre-impact energy levels have a significant effect on the deterioration of both the structure strength and the crush behavior. The locations of the damages are mainly responsible for altering the collapse behavior of the structure rather than its performance. The crush force efficiency is not significantly affected by the pre-impact energy levels, but it is highly affected by the preimpact/damage locations. The undamaged tubes were collapsed in a progressive manner, whereas splitting and crack propagation were the dominant failure modes in the tubes with residual damages. The path of those cracks was governed by the damage location. Artificial neural network-based models were developed, compared and improved with the objective to model the highly non-linear behavior of the load carrying capacity of the pre-impacted tubes. The developed model successfully provides a quick and accurate assessment at all compression strokes with an MSE of 0.000191 KN.
1. Introduction Carbon fiber reinforced plastics (CFRP) are being commonly used in aerospace and automotive industries for construction of flight and car body structures [1]. They are very attractive candidates for such applications owing to their superior specific strength, energy absorption, and durability [1,2]. Another emerging application in this area is usage of tubular CFRP structures as the linkage members for advanced-performance chassis structures [3]. The addition of Kevlar fibers with the carbon fiber braid will enhance the overall toughness of the material and lower its sensitivity to localized defects, such as notches and indentations [4,5]. Nevertheless, it will improve the post crush force efficiency and thus the specific energy due to the added elasticity to the structure [4]. This is significantly important to improve its resistance against low velocity impacts that might arise from dropping of metallic tools or other vehicle components on the assembly lines [6]. Such accidents will most likely cause the static and dynamic mechanical properties for the material to deteriorate [7]. Assessment and Numerical modeling of these residual damages in the material is a challenging task that is attracting the attention of the research community [6,8,9]. The assessment of residual damages in composite panels have been studied extensively, while fewer investigations were conducted on the behavior of composite tubes [10]. Their performance is most likely to be different when subjected to the similar loading conditions as the panels [3]. Chen et al. [11] studied the consequences of lateral preimpact damage on the crashworthiness of CFRP square tubes subjected to axial crushing. Effect of multiple levels of pre-impact energies were compared in addition to their position. The undamaged tubes were collapsed in a progressive folding mode whereas the damaged tubes exhibited a brittle fracture which initiated from the damaged zone and progressed around the circumference. Fiber breakage and delamination were the dominant failure modes. Consequently, their peak load and energy absorption capability were reduced by 38% and 58.3%, https://doi.org/10.1016/j.compstruct.2020.112247 Received 13 March 2020; Accepted 18 March 2020 Available online 21 March 2020 0263-8223/ © 2020 Published by Elsevier Ltd.
respectively. They also concluded that the failure mode and performance are more dependent to the pre-impact energy level rather than its location. The same authors attempted to replicate those findings using the finite element method. However, this method required an additional multi-stage modeling to improve its prediction accuracy due to the complexity of the failure mechanics of CFRP material. Similar observations were reported by Kobayashi and Kawahara [12] where the surface damages, such as indentations or micro-cracks, were very critical failure triggers in quasi-static loading condition. Sebaey and Mahdi [10] have subjected pre-impacted composite round tubes to lateral crushing orientation to assess the reduction in their crashworthiness parameters. They found out that only the peak load, crush force efficiency, and crush load stability were influenced. The rest of the parameters, such as specific energy absorption, were independent of the those damage initiators. The effect of pre-impact damage on the burst strength fatigue life was studied by Uyaner et al. [13]. Their results indicated that both parameters were reduced by the increment of the reimpact energy level that caused matrix cracking and delamination in the inner layers only. From the structural point of view, presence of those defects within the fibers interfaces will significantly lower the inter-laminar fracture toughness in the opening and in-plane shear modes [14]. In recent years, ANN-based models have been extensively employed to model highly nonlinear relationships between inputs and targets in numerous engineering disciplines, such as condition-based maintenance [15], automation [16] and materials [8,17]. In this research, the neural network-based models utilize the backpropagation (BP) procedure. The backpropagation learning paradigm is developed to train a feed-forward network and it is considered as an effective technique. It is used to exploit the exceptions and regularities in training samples. The backpropagation neural network (BPNN) is considered as the most widely used method among other ANN techniques. The network contains multiple layers: an input layer, one or more hidden
Composite Structures 243 (2020) 112247
using Imatek IM10 drop-weight tester with energies of 2, 5, and 7 J. The specimens were rested on fixtures from both ends. A 12 mm spherical impactor head was used to punch them. After introducing the damages, the collapsing mechanics and load-carrying performance of those tubes were studied under axial compressive loading conditions, according to ASTM E9-09 standard. Instron 5585 UTM was used with 6-inch diameter flat platens to crush the specimens at a crosshead speed of 50 mm/min up to 80% of their initial height. A 4 mm deep groves were engraved on the center of the compression platen to allow the tubular specimens to be fitted to restrict the turning of the specimens to eliminate sudden shifts in the load–displacement curves. A set of three specimens from each configuration were tested for ensuring a good data repeatability. Three parameters were considered for performance assessment; initial peak force (IPF), mean load, and crush force efficiency (CFE). The former represents the first peak in the measured load–displacement curve and the average force accounts for all the exhibited loads by the structure until the start of material densification. The CFE represents the stability of the structure in carrying the applied load without excessive jerk in which may be harmful to personnel in occupant related applications [3]. It is expressed as the ratio of the average force to the IPF. After that, artificial neural network-based models were developed and compared using the Mean Squared Error (MSE) measure. Those models were used to predict the load-carrying capacity range at each displacement value, which implicitly considers the uncertainty of results.
Table 1 Technical specifications of the hybrid composite material, adapted from PYROFIL TR30S (ASTM D-3039). Material property
Value
Density ( ) Tensile strength ( TS ) Modulus of elasticity Failure Strain ( f )
1.79 g/cm3 4413 MPa 296 GPa 1.5%
Stacking sequence
CF [ ± 45F/0U]S, CF/K [ ± 45F]
Fiber to resin ratio (Wf )
≈ 50%
layers, and an output layer. These hidden layers consist of a large number of hidden neurons. Activation propagation is forwarded from the input to the output layer. After that, the algorithm compares the output values with known target values, as these techniques are one of the major supervised-based learning techniques. It then propagates the error backward for evaluation and correction. Biases and weights are updated based on the calculated errors with the objective to meet target values. Therefore, this paper investigates the influence of the presence of the residual damage in hybrid braided carbon/Kevlar round tube on its load-bearing capacity under axial quasi-static crushing conditions. This damage was induced by a combination of low-velocity impacts with various energy levels and at different locations. Due to the high nonlinearity of the bearing carrying capacity, ANN-based models are developed and evaluated for performance to implicitly predict the crushing behavior of the hybrid braided carbon/Kevlar round.
3. Results and discussion
2. Methodology
3.1. Effect of pre-impact energy level
2.1. Specimens preparation
The pre-impact intensity was increased to impose greater residual damage on the tubes. Fig. 2 presents the resulted contact force and absorbed energy curves with respect to time. The minimum and peak forces were 686 N and 915 N at 2 and 7 J, respectively. The influence of each energy level on the load-carrying capacity is graphically represented using load versus displacement curves and a bar chart presented (see Fig. 3). In comparison with the control specimen, the damaged tubes were significantly weakened, especially when the preimpact energy increases. In addition, the load curve started to decline at a higher rate after the IPF. This occurs due to the pre-impact damage that triggered micro-cracks within the matrix. This, in turn, weakened the structure through propagation and delamination of the braided fibers. The reduction in IPF and mean load were larger than 34.6% and 36.3% and for the energies were higher than two joules, respectively. The two joule level doesn’t seem to have any significant consequence on the performance of the tube. Moreover, the CFE was relatively not affected by the presence of the residual damage in the material, regardless of the increments in the energy levels. This damage was not reflected on the CFE ratio as both the IPF and the mean load values were similarly reduced [10].
In the experimental part of this study, the composite round tubes were fabricated using two braided carbon fiber layers with a sandwiched unidirectional ply. Then, an outer braided layer comprising a hybrid carbon/Kevlar blend was added to improve the material toughness against impacts. Those tube were engineered to withstand the side pre-impacts and resist longitudinal crack propagation and splitting, while maintaining superior stiffness and strength to weight ratio. The technical specifications and lamination stacking sequence code is given in Table 1. The entire specimens were taken from a single 3 m long tube to eliminate fabrication uncertainties from affecting the results. The specimen length, inner diameter, and wall thickness were 75 mm, 25 mm, and 1.2 mm, respectively. 2.2. Testing and performance parameters The behavior of composite sandwich panels is complicated due to the complexity, and the highly anisotropic property of the material. In this section, the tubular specimens were subject to pre-impacts at different locations, as indicated in Fig. 1. Each case was pre-impacted
Fig. 1. The testing matrix; (a) The tubular control specimen, (b-c) specimens with a single pre-impact, and (d-f) specimens subjected to a combination of pre-impacts. The red arrows represent the location of the pre-impact. 2
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Fig. 2. (a) The contact force and (b) absorbed energy curves versus time at pre-impacts at 2, 5, and 7 J.
Fig. 3. (a) Load versus displacement curves for different pre-impact energies applied once at the center of the specimen and (b) a bar charts summarizing their performance parameters.
thin-walled structure [7]. The CFE ratio, on the other hand, was slightly improved by 4.1% over the control specimen. Similar observation was report by Sebaey and Mahdi [10]. Crack initiation and then propagation was highly expected to occur when two pre-impacts were combines on the same level (Fig. 4d). Two cracks were promoted and progressed at about ± 45 degrees causing a non-uniform stress concentration areas. This, in turn, promoted local buckling in the shape of a diamond. When those pre-impacts were shifted to the upper side of the specimen (Fig. 4e), the diamond fracture was shifted accordingly and both cracks met at the middle rather than the bottom. However, when there is a separation distance between the two pre-impacts (Fig. 4f), the crack propagation was progressing diagonally which caused a shift between the upper and bottom portions. Subsequently, the former portion failed at the very top from the fit joint with the platen and progressed in twisting and moving sideways. This is the result of moment which was created by the two vertical loads with a moment arm in-between. The load curve is much smoother compared to the others because the damaged area is not concentrated at one level. Hence, it allowed for more gradual fracturing from the upper and bottom levels. Moreover, the second re-enforcing stage is not as significant as observed in the others since the whole structure has collapsed gradually. The IPF and mean load were further reduced by the combination of pre-impacts in comparison with the previous cases. However, the highest CFE ratio among all the cases was exhibited by case (e) with more than 20% increase over the undamaged tube. Fig. 7 provides insights into micro-level fracture details using scanning electron microscopy technique. Four failure modes were observed: a) delamination between the braided fibers, b) fiber pullout from matrix, c) crack propagation, and d) carbon fiber breakage. The delamination between the carbon and kevlar fibers is cause due to the difference in their elasticity under a given stress [4]. It also depends on
3.2. Effect of pre-impact location The locations and their combinations had a great influence on the failure modes among the specimens. Those variations were more noticeable when the pre-impact energy was the highest. Fig. 4 demonstrates the collapsing behavior for those specimens throughout the compression stroke. Accordingly, the exhibited load versus displacement trends and performance parameters were different as seen in Figs. 5 and 6, respectively. Progressive failure mode was observed in the control specimens at the upper crush zone (transverse fiber tearing). Moreover, two folding layers were created towards the end of the stroke since it was a thin-walled tube with high toughness Kevlar fibers. Consequently, a high load level was maintained throughout the crushing stroke with initial and secondary peaks at the beginning. Those were resulted from the initial failure of the upper and then bottom face surfaces that were in the platen grooves. The sudden splitting fracture due to crack propagation was the dominant initial failure mode in the pre-impacted specimens. This mode was triggered from the damaged spot and then propagated to the nearest weakest area. In case of the single pre-impact in the center of the tube (Fig. 4b), it has propagated through the circumference of the tube to reach back to the initiation spot and close the loop. On the other hand, when the trigger spot was shifted to the middle of the upper tube portion (Fig. 4c), it has progressed to the upper face edge where the stress was more concentrated and at a closer distance. Both specimens were then crushed in a progressive tearing with folding manner afterwards. This justifies the rise in their load curve towards the end of the stroke. Their IPF and mean load performance were significantly lowered more than 31.2% and 28.4% for both cases relative to the control specimen, respectively (Fig. 6). This indicates the material sensitivity to the residual damage, especially when it has high specific stiffness and 3
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Fig. 4. The crushing behavior, displayed at six-stroke progress steps, of (a) the control specimen, (b-c) single pre-impacted tube, and (d-f) a combination of two preimpacts on tubes. The red arrows represent the location of the pre-impact.
Fig. 5. Load versus displacement history curves for (a) with and without pre-impact, and (b) pre-impact combinations.
the inter-laminar fracture toughness of the interface between those fibers and the matrix. This can be strengthened by employing chemical and mechanical surface pre-treatments to improve surface wettability and introduce better mechanical interlocks [14]. Similarly, kevlar fibers
pullout from the matrix is related to the same cause. Brittle cracking of the matrix can be reduced by adding nano-filler agents, such as reactive diluents and liquid rubber, to it prior to infusion to improve its toughness [18]. Breakage of the carbon fibers is common due to their 4
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Fig. 6. Bar chart summarizing performance parameters of the pre-impacted composite tubes in terms of IPF, mean load, and CFE.
Fig. 7. Scanning electron microscopy images highlighting the structural failure mechanism within the fiber-reinforced epoxy composite.
3.3. Prediction using ANN
Table 2 Mean Squared Error (MSE) of a number of selected single layer shallow neural network configurations (the best Configuration in bold). Nodes
MSE (training)
MSE (retrained)
MSE (retrained)
5 15 25 35 45 55 65 Two-layer NN 5,5 15,15 25,25 35,35 45,45 55,55
0.007328 0.005613 0.000952 0.001033 0.001896 0.001806 0.000601
0.005427 0.004076 0.001061 0.001069 0.00188 0.001266 0.00062
0.004919 0.002564 0.001036 0.001064 0.000843 0.001289 0.00065
0.002135 0.000617 0.000427 0.000578 0.000352 0.000459
0.002214 0.000592 0.000413 0.000206 0.000351 0.000439
0.002042 0.000632 0.000416 0.000191 0.000384 0.000466
A large data sets of 45,905 samples were normalized and then utilized to train, validate and test the proposed neural networks; 70% of the data sets were utilized for training, while the rest were used for validation and testing. One-layer and two-layer neural networks were optimized for performance using the trial and error approach. The performances of a number of selected NN configurations are list in Table 2. The best performing single-layer NN was found to be a network with 65 nodes (see Fig. 8) and a normalized MSE of 0.000601 N (see Table 2). The training time is 49 s, while the prediction of a single load carrying capacity takes 259 ms on average using a DELL OPTIPLEX 7440 AIO machine (Core I7-6700 CPU @ 3.4 GHz (8CPUs) processor and 8 GB memory). The error histogram shown in Fig. 9 (a) illustrates the error values versus the number of instances for the single layered NN. However, among the large number of the investigated NN configurations, the best performing configuration is a two-layer neural network with 35 nodes each. The network produced a normalized MSE of 0.000191 KN (see Table 2). The error histogram in Fig. 9 (b) shows smaller error values than the values shown in Fig. 9 (a). Therefore, the two-layer NN outperforms the one-layer NN in terms of prediction accuracy. The training time of the two-layer network is 69 s, while the prediction of a single load carrying capacity takes 310 ms on average.
high stiffness. This is a sign of high energy absorption achievement by the structure and a good adhesion between the fibers and the matrix. Increasing the density of those filaments in the orientation where their breakage was observed will improve the load-bearing capacity of the structure [19].
Fig. 8. The architecture of the best performing NN. 5
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Fig. 9. Error histograms of the best performing (a) single-layer and (b) two-layer neural networks (65 nodes and 35, 35 nodes).
Fig. 10. Experimental versus predicted data using (a) a single layer NN and (b) a two-layer NN (sample SP2).
Fig. 10 shows the experimental versus the predicted data using single layer and two layer neural networks, where it can be seen that the twolayer network yields a better prediction accuracy.
outperformed the other addressed NNs with an MSE value of 0.000191 KN only. 5. Data availability
4. Conclusion
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.
This study experimentally investigates the effect of the pre-impact energy level and location on the collapse behavior and load carrying capacity of carbon/kevlar hybrid round tubes. A remarkable reduction of up to 48.2% and 56.4% are observed in the initial peak force and mean load, respectively. On the other hand, the crushing force efficiency was improved in some cases, with more than 20% improvement. The failure mode was significantly influenced by the location and combination of the pre-impacts on the structure. Progressive failure mode was dominant in the control specimens, whereas splitting and crack propagation were observed in the damaged tubes. The crack tends to initiate the damage zone and then progress to the weakest area, which could be either the location of another pre-impacted area or the face edge of the tube. Furthermore, the highly non-linear behavior of the crush is modeled using ANN. This allows the predication of the load carrying capacity as well as the energy absorption at any displacement value or time. A large number of neural network configurations were proposed and then evaluated for performance with a view to improving the prediction performance of the network, which was assessed using the Mean Squared Error (MSE) performance measure. Based on the results of the conducted comparative study, a two-layer network; with 35 nodes each,
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Sutherland L. A review of impact testing on marine composite materials: part IIIdamage tolerance and durability. Compos Struct 2018;188:512–8. [2] Alkhatib F, Mahdi E. Energy absorption capability of composite hexagonal array systems: numerical analysis. In applied mechanics and materials. 2018. Trans Tech Publ. [3] Laban O, Mahdi E. Energy absorption capability of cotton fiber/epoxy composite square and rectangular tubes. J Nat Fibers 2016;13(6):726–36. [4] Mahdi E, Sebaey T. Crushing behavior of hybrid hexagonal/octagonal cellular composite system: aramid/carbon hybrid composite. Mater Des 2014;63:6–13. [5] Sebaey T, Wagih A. Flexural properties of notched carbon–aramid hybrid composite laminates. J Compos Mater, 2019: p. 0021998319855773. [6] Hauffe A, Hähnel F, Wolf K. Comparison of algorithms to quantify the damaged area in CFRP ultrasonic scans. Compos Struct 2019:111791. [7] Jia W, Wen W, Fang L. Low-velocity impact and post-impact biaxial residual
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Composite Structures 243 (2020) 112247 strength tests and simulations of composite laminates. Compos Struct 2019:111758. [8] Gowid S, Mahdi E, Alabtah F. Modeling and optimization of the crushing behavior and energy absorption of plain weave composite hexagonal quadruple ring systems using artificial neural network. Compos Struct 2019;229:111473. [9] Safonov A, et al. Modeling of cracking during pultrusion of large-size profiles. Compos Struct 2019:111801. [10] Sebaey T, Mahdi E. Crashworthiness of pre-impacted glass/epoxy composite tubes. Int J Impact Eng 2016;92:18–25. [11] Chen D, et al. Residual crashworthiness of CFRP structures with pre-impact damage–an experimental and numerical study. Int J Mech Sci 2018;149:122–35. [12] Kobayashi S, Kawahara M. Effects of stacking thickness on the damage behavior in CFRP composite cylinders subjected to out-of-plane loading. Compos A Appl Sci Manuf 2012;43(1):231–7. [13] Uyaner M, Kara M, Şahin A. Fatigue behavior of filament wound E-glass/epoxy composite tubes damaged by low velocity impact. Compos B Eng 2014;61:358–64. [14] Laban O, Mahdi E. Enhancing mode I inter-laminar fracture toughness of aluminum/fiberglass fiber-metal laminates by combining surface pre-treatments. Int J Adhes Adhes 2017;78:234–9. [15] Gowid S, Dixon R, Ghani S. Performance comparison between fast fourier transform-based segmentation, feature selection, and fault identification algorithm and
⁎
[16] [17] [18] [19]
neural network for the condition monitoring of centrifugal equipment. J Dyn Syst Meas Contr 2017;139(6):061013. Haggag M, et al. An intelligent hybrid experimental-based deep learning algorithm for tomato-sorting controllers. IEEE Access 2019;7:106890–8. Shokry A, et al. Constitutive models for the prediction of the hot deformation behavior of the 10% Cr steel alloy. Materials 2019;12(18):2873. He Y, et al. Micro-crack behavior of carbon fiber reinforced Fe3O4/graphene oxide modified epoxy composites for cryogenic application. Compos A Appl Sci Manuf 2018;108:12–22. Xiong J, et al. Sandwich-walled cylindrical shells with lightweight metallic lattice truss cores and carbon fiber-reinforced composite face sheets. Compos A Appl Sci Manuf 2014;56:226–38.
Othman Laban, Samer Gowid , Elsadig Mahdi, Farayi Musharavati Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P. O. Box 2713, Doha, Qatar E-mail address: [email protected] (S. Gowid). ⁎
Corresponding author. 7