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Parametric study of a fibrous energy absorbing material under impact shear loading
Composite Structures 232 (2020) 111583
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Parametric study of a fibrous energy absorbing material under impact shear loading
T
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Jared Correiaa, Vijaya Chalivendraa, , Yong Kimb a b
Department of Mechanical Engineering, University of Massachusetts Dartmouth, 285 Old Westport Road, North Dartmouth, MA 02747, United States Department of Bioengineering, University of Massachusetts Dartmouth, 285 Old Westport Road, North Dartmouth, MA 02747, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Impact energy absorption Electro-flocking Medium strain rate Fiber based padding materials Shear energy absorption
A comprehensive experimental impact characterization study of novel impact energy absorbing (IEA) materials under impact shear loading is conducted. Electro-flocking process is employed to fabricate the novel fiber based padding materials, also known as flocked energy absorbing materials (FEAM). FEAM IEA panels are prepared by flocking 1–3 mm long, 6–60 denier nylon fibers onto a planar polyester fabric sheet. The impact energy absorption under combined pre-compression loading and shear impact loading is investigated using a custom built guided weight drop tower along with a specially designed pre-compression and shear loading fixture for this study. A parametric study is performed where the effect of fiber material properties such as flock fiber length, diameter and flock density (number of flock fibers per area) on IEA is investigated and they are later compared with that of Vinyl Nitrile (VN) foam materials. Padding material based on FEAM configurations showed remarkable improvement when compared directly to VN foam with a 135% increase in shear strain energy density for the high impact velocity loading condition. Additionally, for low velocity impact conditions, the FEAM based padding materials out performed with a 49% increase in shear strain energy density as compared to VN foam.
1. Introduction Recently there has been an increase in the interest of protecting athletes from potential neuropathic brain injury. As humans come to better understand the mechanisms of energy transfer to the brain due to traumatic events, and how that energy transfer leads to damaged brain matter, a need for novel materials to help mitigate concussive blows to the head will arise. Although improvements in football helmets have helped to prevent skull fractures and subdural hematoma, many players still suffer from concussive and sub-concussive impacts [1]. Viscoelastic materials, such as elastomeric foams, have been used extensively in sports head gear to help absorb energy from impacts and attempt to protect wearers from mild traumatic brain injury (mTBI) [2,3]. These foams will undergo large deformations under relatively low stresses effectively decreasing the applied forces on the head by increasing the time during which the impactor velocity is decreased [4,5]. Past researchers have mainly investigated these foams and other materials under 1D compressive impact loading [6–11]. Landro et al. [6] determined deformation mechanisms and energy absorption capability of expanded polystyrene (EPS) foams and polycarbonate shells for protective helmets using a combination of experimental and finite element tools. It is demonstrated that the energy absorption capability
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of foam material can be controlled at two different stages: at the macroscopic scale, by selecting the foam density able to minimize the transmitted load and the acceleration; at the microscopic scale, by adjusting EPS internal structure in terms of hollow bead dimensions and wall thicknesses. Ouellet et al. [7] investigated three different types of foams (expanded polystyrene, high-density polyethylene, and polyurethane) under a wide range of strain rates (0.008/s–2500/s). They employed a standard compression test device for low strain rates, a drop tower apparatus for medium strain rates, and a polymeric split Hopkinson pressure bar apparatus for high strain rates. It was discovered from their results that strain rate effects become more pronounced at rates above approximately 1000/s. Gimble and Hoshizaki [8] investigated attenuation of impact energy of vinyl nitrile foam in an air chamber. They conducted a parametric study of using three densities of foam, two hardness values of chambers, three drop weights and three drop masses using a free drop rig. They identified a significant difference in peak linear acceleration between the vinyl nitrile and air chambers. Cui et al. [9] developed model graded foam materials using numerical tools to investigate the effect of various gradient functions on the energy absorption under impact loads. They reported that the functionally graded foam showed superior energy absorption compared to the uniform foam and moreover convex gradients performed better
Corresponding author. E-mail address: [email protected] (V. Chalivendra).
https://doi.org/10.1016/j.compstruct.2019.111583 Received 31 May 2019; Received in revised form 29 August 2019; Accepted 21 October 2019 Available online 22 October 2019 0263-8223/ © 2019 Elsevier Ltd. All rights reserved.
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together to form a relatively uniform sample. The flock densities are calculated from the differing weights of the substrates at each step in the process to get an accurate amount of fibers on the substrate. The following equation is then used to calculate the flock density
than concave gradients. Ramirez et al. [11] studied plateau stress and energy absorption of low density (≤300 kg/m3) polyurea (PU) foams and expanded polystyrene (EPS) for a broad strain rate regime (0.04/s – 5000/s). The plateau stress and energy absorption of low density PU foams exhibited a strong rate dependence. The strain rate sensitivity of PU foams was found to have strong influence on cell size for low strain rates and on cell wall aperture size for intermediate and high strain rates. However for EPS foam, it remained nearly indifferent to strain rate. At low and intermediate strain rates, the plastic crushing in the EPS and the high plateau stress produced a much higher energy absorption than the viscoelastic dissipation in the PU foams. Regarding fiber based materials, recently, Lewis et al. [12] reported impact force loss behavior of flocked energy absorbing materials (FEAM) under compression using ball drop tests. They demonstrated a promising improvement in force-loss% properties by sandwiching either foam or spacer fabric between two FEAM layers. These three-layer structures showed higher force-loss% values than individual foam or spacer fabric components. Very recently, Fodor et al. [13] determined loss tangent values of FEAM based padding materials under compressional loading using a custom designed dynamic mechanical analyzer. A parametric study was performed to study the effect of short fiber diameter, length, density, frequency, and temperature on loss tangent values. Loss tangent of FEAM materials increases as the measuring frequency and flocked layer’s flock density (fibers per area) increases. Even though no significant trend is noticed as a function of temperature the highest loss tangent is achieved at room temperature. As discussed above, foam and fiber based materials are excellent in absorbing impact energy under compressive loads, however their performance under impact shear loading conditions has not yet been studied. Moreover, the rotational acceleration that induces mTBI are due to shear deformation of brain tissues [14–20] and padding materials that can absorb energy under shear loads have a higher possibility of preventing mTBI in players. Towards this goal, for the first time, an effort is made to characterize impact energy absorption of FEAM based padding materials under a combined dynamic shear loading condition with a static pre-compression load. A custom designed pre-compression fixture along with a custom built guided drop weight tower is used to determine the shear impact energy absorption. The effect of fiber diameter, fiber length, their density and impact velocity on shear energy absorption is determined and later compared with padding materials made from Vinyl Nitrile (VN) foam.
FD = 9 × 106
m AdL
(1)
where FD is the flock density in fibers/mm2, m is the mass of the flock applied in grams, A is the substrate area in mm2, d is the flock denier (which is proportional to the flock diameter) or grams of fiber per 9000 meters (g/9000 m), and L is the flock fiber length in mm. The 9 × 106 comes about due to the denier measurement being defined as number of grams per 9000 meters of a given fiber. This experiment maintains a constant number of layers (three flocked and one non-flocked sheet) for every sample. The sample thickness is then measured using a micrometer penetrometer. The thickness of samples that have similar parametric design (flock density, denier, and flock fiber length) are then averaged to be used in determining the sample shear strain during testing. The width and length of the samples are taken using a digital caliper at three different locations and similarly averaged for determining shear strain during testing. The measured thickness changes depending on the fiber length and fiber denier because we keep the number of substrates constant. Table 1 demonstrates the thickness values for each group. The sample length and width are maintained constant and measured to be 101.76 ± 1.46 mm and 101.42 ± 1.59 mm respectively. 2.2. Experimental methods A guided weight drop (GWD) tester is used in conjunction with a double lap shear jig to test the FEAM based padding materials and Vinyl Nitrile (VN) Foam samples under dynamic shear loading combined with static pre-load compression. VN foam is considered the control material for this study because current football helmet padding uses this material to help protect players from head injuries. The densities of VN foam and different FEAM configurations have been included in Table 2. The GWD has a mass of 5 kilograms that can be dropped from varying heights of 25 and 75 centimeters. The shear jig (as shown in Fig. 3) sits below the hanging mass and consists of a frame with two compression plates, and a central impact plate. The compression plates sit on each side of the central impact plate and are used to secure one sample to each side of the central plate. A threaded rod with strain gauge is used to compress the sample to a specified value of 1500 Newtons (1500 Newtons is used as a pre-compression value since it prevents slipping of pads between the shear jig compression plates). The central plate contains a linear variable displacement transducer (LVDT) manufactured by P3 America, Inc (San Diego, CA) capable of infinite resolution with proper noise control and a 30 mm stroke length as well as an ICP® quartz force sensor manufactured by PCB Piezotronics (Depew, NY) with broadband resolution (1 to 10000 Hz) of 1.3 N-rms and a compression range of 88.96kN. The sensors are then routed to two data acquisition (DAQ) units, one purchased from Measurement Computing (MCC of Norton, MA) and one from National Instruments (NI of Austin, TX). The MCC DAQ has a sample rate of 100 kS/s and records the displacement over time of the central plate upon impact, while the NI DAQ has a sample rate of 51.2 kS/s and records the force time curves applied to the central plate upon impact. All data is acquired via the LABVIEW development suite. The data is then imported to an in house-built MATLAB program for postprocessing (zero-phase low pass FIR Kaiser window filter at 500 Hz) and analyzation. Fig. 4 shows an example of a raw and filtered impulse. Notice the high frequency strain wave variations present in the raw data which requires filtration to gain any useful information from these curves. Finally, Fig. 5 shows the actual test setup used in this study. A characteristic pulse collected from the LVDT and force sensor for a typical sample is shown in Fig. 6(a). A viscoelastic response appears due
2. Materials and methods 2.1. Materials and manufacturing FEAM samples are constructed using the up-flocking manufacturing process [12,13]. A representation of this process is shown in Fig. 1. First, a thin film of adhesive (Key Polymer FF3822 of Lawrence, MA) is applied to the surface of a polyester substrate (black polyester gabardine purchased from www.fabric.com). This is accomplished via a draw down technique. This fabric is then attached to the negative electrode of the flocking chamber with adhesive side face down. Nylon flock fibers (Cellusuede Products, Inc of Rockford, IL), short fibers 1–3 mm in length, are then spread uniformly onto the positive electrode. A 300 μA current is applied for five second intervals (a total of three times) across the electrodes creating an 80 kV electric field that levitates the flocked fibers up into the adhesive layer of the substrate. The samples are then hung dry for 24 hours and then oven cured at 255 °F for ten minutes followed by 325 °F for ten minutes. After the required number of substrates are created, the opposite sides of the fabric are coated with a thin film of adhesive using a similar technique. The substrates are then laminated together forming a thick pad as shown in Fig. 2. Finally, the pad is cured once more at 255 °F for ten minutes followed by 325 °F for ten minutes. Only samples with relatively similar flock densities (flock fibers per unit area) are laminated 2
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Fig. 1. Electrostatic flocking operation principle.
to the phase shift of displacement. The two black vertical lines represent the first half cycle of impact assuming a free vibration. The propagation of strain waves in the system results in a noisy signal that is filtered and cropped in MATLAB. This force–time curve is used to find the shear stress of the sample material by dividing the force by the sample inplane area. Since two test specimens are sharing the impact load, load is divided equally for each sample. Shear strain is calculated by taking the tangent of the displacement over the sample thickness. Finally shear stress-shear strain curves are generated and used to calculate the strain energy density of each sample by integrating the area under the curve. Fig. 6(b) shows a characteristic shear stress strain curve for a typical sample. It is noted here that the below assumptions are made while determining the shear strain energy density.
Table 1 Average thickness values of vinyl nitrile foam and FEAM specimens. ID
Denier
Fiber Length (mm)
Mean Thickness (mm)
VN Foam 0610 0620 2020 2030 4520 4530 6020 6030
– 6 6 20 20 45 45 60 60
– 1 2 2 3 2 3 2 3
10.97 ± 0.16 6.43 ± 0.31 9.17 ± 1.45 8.85 ± 0.49 10.83 ± 1.77 9.34 ± 0.62 12.98 ± 0.53 9.78 ± 0.31 12.29 ± 0.37
diameter in denier, flock fiber length (FL), and flock density on the energy absorption characteristics of FEAM based padding samples and compare them to a common VN foam at two impact velocity levels. The design incorporated four different deniers, two different lengths per denier, two different flock densities per denier and two different drop heights. Each configuration was tested a total of five times. The levels of each factor are summarized here
• The maximum displacement occurs at the time the force impulse returns to zero. • The force applied is divided evenly between the two samples. • The samples return to their original configuration over time while zero load is applied therefore closing the hysteresis loop, hence the area under the curve is the energy absorbed by the material.
• Denier (6, 20, 45, 60) • Fiber Length (Low, High)
2.3. Experimental design This study is designed to investigate the effect of flock fiber
Fig. 2. Manufacturing process for creating FEAM padding materials. 3
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information regarding peak shear and peak strain values for each sample group. The average shear energy density of each group is then plotted with 95% confidence interval error bars calculated using the student t-distribution and a sample size of N = 5. This metric is used instead of standard deviation because it allows comparing the means of each group with a predetermined probabilistic accuracy while standard deviation simply shows the spread of the data. The 6 denier, short fiber, low density samples provided the best shear strain energy density as compared with foam for both the low and high impact velocity tests.
Table 2 Average densities of VN foam and FEAM pads. ID
Denier
Fiber Length (mm)
Average Density (kg/m3)
VN Foam 0610 0620 2020 2030 4520 4530 6020 6030
– 6 6 20 20 45 45 60 60
– 1 2 2 3 2 3 2 3
98.82 ± 2.89 280.96 ± 19.11 209.48 ± 12.21 218.01 ± 7.51 162.50 ± 18.34 201.66 ± 21.40 166.65 ± 17.66 236.86 ± 10.85 182.47 ± 31.90
3.2. Low impact velocity Fig. 7 shows the average stress strain relationship of each sample group for the 25 cm drop height tests. The legend contains the code for each sample group in the format XXY0Z where XX is the denier, Y is the flock fiber length in mm, and Z is the high (H) or low (L) flock density. This plot shows that foam is a much stiffer material than the FEAM samples. The peak stress is larger for the foam samples however the maximum strain is less than that of FEAM. This is a good indicator that FEAM should provide better energy absorption than foam since it is decreasing the applied load while increasing the amount of deformation. Fig. 8 shows the average shear strain energy density (area under the shear stress-shear strain diagram of Fig. 7) of each group for the 25 cm drop height. The results of the FEAM padding are not affected much by the differing denier for this drop height. The density also has little significance at this impact velocity except for two cases, one being the 45 denier pad with long fibers and the other the 6 denier pad with long fibers. The impairment due to increased flock density within these denier groups is most likely attributed to agglomerations of fibers during the flocking process when trying to achieve higher densities. These agglomerations in turn create non-homogeneity of the flock fibers lowering the ability of the pad to absorb energy. The fiber length however drastically changes the shear energy absorption abilities of the pads for all denier groups effectively increasing the shear strain energy density by decreasing the lengths of the fibers. In theory, a longer fiber will buckle more easily as compared to a shorter fiber. Thus, prebuckling of the fibers when a static pre-compression load is applied could explain this phenomenon. Pre-buckling here is defined as the point at which most of the fibers are buckled due to the static precompression load. Excessive pre-buckling degrades the ability of the material to perform as intended which is to allow buckling of fibers upon impact to dissipate energy. It is expected that an optimal pre-load compression level exists that allows fibers to approach their buckling
• Flock Density (Low, High) • Drop Height (25 cm, 75 cm) also called Impact Velocity (Low, High) The low and high level values for a given denier are summarized in Table 3. Due to the expected interaction between fiber denier (diameter) and density, the groups for the two different densities had to be categorized as low and high for a given denier since each denier has a different maximum possible density (which was set as the high density configuration) due to the differing diameters. As an example, at the 6 denier level, the max density was 202.32 fibers/mm2 while for the 60 denier level, the max density was 28.98 fibers/mm2. Also due to the availability of flock fibers, fiber lengths had to also be categorized as low and high for a given denier since for two separate deniers manufacturers generally do not make the same fiber lengths, for example it is almost impossible to find a 20 denier flock fiber with a 1 mm length while for a 6 denier fiber the 3 mm long fibers had mechanical characteristics which were not ideal for flocking (poor sift-ability and poor flock activity). In general, it is advised by the manufacturer to avoid high aspect ratio (Length/Diameter) fibers for flocking processes. The difference between the length groups is held constant at 1 mm difference in length between the short and long groups. The low flock density group is held at 30–70% of the high group and has more variance since flock density cannot be perfectly controlled due to hard to control factors such as flock moisture content and electrical conductivity. 3. Results and discussion 3.1. FEAM and foam comparison The average shear stress and strain curves are plotted for each sample group at two differing impact velocity levels. These plots show
Fig. 3. Shear jig CAD assembly with (a) frontal view and (b) cross sectional view. 4
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Fig. 4. Typical raw force and displacement data and their filtered curves.
however, due to variances this is a negligible change.
threshold. If this value is exceeded then the material will perform less effectively however, this was not investigated in this study. Percentage increase/decrease in shear strain energy density of each group of samples as compared directly to foam is summarized in Table 4 for both impact velocities. The 6 denier, short fiber, low density samples saw a 49% increase in shear strain energy density on the average when compared with VN foam at the low impact velocity test condition. The high-density samples of that group also show a 42% increase on the average. When looking at the other sample groups the samples manufactured with shorter fibers had generous increases in strain energy density from 25-41% on the average. The worst performing result was a 45 denier, long fiber, high density sample group with a decrease in strain energy density of about 7% on the average
3.3. High impact velocity Fig. 9 shows the average stress strain relationship of each sample group for the 75 cm drop height tests. The legend contains the code for each sample group in the format XXY0Z where XX is the denier, Y is the flock fiber length in mm, and Z is the high (H) or low (L) flock density. This plot proves again that foam is a much stiffer material than the FEAM samples. The peak stress is largest for the 6 denier FEAM samples although it is not significantly different than that of the foam. Again the 6 denier FEAM samples have highly improved shear strain capabilities when compared with the both the foam and other FEAM samples. This
Fig. 5. Actual lab setup highlighting the force sensor, displacement sensor, and drop weight. 5
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Fig. 6. (a) Characteristic displacement and force pulse of typical impacted sample filtered at 500 Hz (b) characteristic shear stress–strain diagram for a typical impacted sample.
fibers to be flocked on the substrates which increases the number of buckling fibers and frictional fiber interactions. The higher impact velocity also seems to negate the effects of non-homogeneity discussed in Section 3.2 since there are relatively there is no changes due to density with exception of the 45 denier group. Again, the shorter fibers especially for the lower denier groups show increased shear strain energy density most likely due to the pre-buckling phenomenon discussed in the previous Section 3.2. As shown in Table 4 6 denier, short fiber, low density samples saw a 135% increase in shear strain energy density on the average when compared with VN foam at the high impact velocity test condition. The next best configuration of FEAM was the 6 denier, short fiber, high density samples which saw a 105% increase in the shear strain energy density on the average as compared to foam at the high impact velocity test condition. The other 6 denier configurations also performed exceptionally well with the worst one still achieving a 43% increase in strain energy density on the average. The remaining configurations performed generally around the same as the foam samples with deviations of about ± 11% change on the average. There was one exception which is the 45 denier long fiber group which performed much worse than the foam at about a 24% decrease in strain energy density on the average. In order to examine how the current testing conditions relate to
Table 3 Experimental design parameter levels with the values for each denier group. Denier
Independent Variable
Low Level
High Level
6
Fiber Length (mm) Level Flock Density (Fibers/mm2) Fiber Length (mm) Level Flock Density (Fibers/mm2) Fiber Length (mm) Level Flock Density (Fibers/mm2) Fiber Length (mm) Level Flock Density (Fibers/mm2) Drop Height (cm)[Impact Velocity (m/ s)]
1 L H 63.76 202.32 2 L H 29.06 40.81 2 L H 8.56 20.97 2 L H 19.00 28.98 25[2.22]
2 L H 29.19 85.64 3 L H 8.48 18.16 3 L H 9.1 23.24 3 L H 7.4 18.87 75[3.84]
20
45
60
ALL
indicates that the FEAM samples should provide better energy absorption under pre-compression and shear impact loading conditions. Fig. 10 shows the bar graphs of the average shear strain energy density for the high impact velocity (75 cm drop height). At this higher impact velocity, denier has almost no effect except at the 6 denier level. The lower denier (smaller diameter) allows for an increased number of
Fig. 7. Average shear stress strain curves for each sample group at the 25 cm (low impact velocity) drop height. 6
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Fig. 8. Average shear strain energy density for 25 cm drop height (low impact velocity).
on field impacts, not to mention the complexity of an on-field impact as compared to a 1-D laboratory test. The range of specific impact energies for mTBIs occurs across a wide spectrum. Fig. 11 shows the specific impact energy levels of these tests compared with specific impact energy of mTBIs at different levels of play. In youth football specific impact energies for mTBIs can occur as low as 7.03 J/kg and as high as 15.13 J/kg (although concussion risk probability for youth football needs more investigation) [21]. At the professional and collegiate levels however, concussions tend to occur between 27.38 J/kg to 43.25 J/kg [21]. These tests relate most directly to youth level impacts however there is a consideration to be made that in an actual impact only a percentage of the velocity will produce shearing forces (which cannot be determined directly) should, in general, lower each range of specific impact energies for all levels of play (since the studies in this manuscript are essentially mimicking a non-normal impact).
Table 4 Percent change on average of FEAM configurations as compared with VN foam. FL
Denier
Density
Drop Height
Fitted Mean
Percent Difference with Foam
L L H H L L L L L H L H H H H H L L L L L L L L H H H H H H H H
6 6 6 6 20 45 20 60 45 20 60 20 60 60 45 45 6 6 20 20 45 60 45 60 6 20 60 45 20 60 6 45 FOAM FOAM
L H L H L L H L H H H L L H L H L H H L L L H H L L L L H H H H
75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 75
196.12 171.03 132.45 119.29 90.28 89.51 88.3 82.27 79.25 77.34 75.94 74.58 73.89 73.7 63.65 63.31 41.82 39.94 39.51 39.3 39.18 37.54 35.56 35.1 31.08 29.99 29.96 29.49 28.05 27.65 27.06 26.21 28.09 83.5
134.87 104.82 58.62 42.86 8.11 7.19 5.74 −1.47 −5.08 −7.37 −9.05 −10.68 −11.50 −11.73 −23.77 −24.17 48.87 42.18 40.65 39.90 39.48 33.64 26.59 24.95 10.64 6.76 6.65 4.98 −0.15 −1.56 −3.66 −6.69 0 0
3.4. Analysis of variance A general linear ANOVA model is applied to the data acquired from the drop tests to determine which parameters have the most significant effect on the strain energy density of FEAM samples. Table 5 shows the results of the ANOVA performed using Minitab 18 software with a significance level of 0.05. Fiber length, denier, density, and drop height are all significant factors at this level. Additionally, the two term interactions of fiber length with denier, fiber length with drop height, denier with drop height, and the three-way interaction of fiber length with denier and drop height were all found to be statistically significant. These significant interactions indicate that the main effects alone cannot predict the shear strain energy density response, hence, to properly predict the shear strain energy density of the material we must have information of all the dependent variables (fiber length, denier, and drop height). The interaction of density and denier although not found to be statistically significant does have a relatively lower p value as compared with the other non-significant factors and interactions and therefore should be taken into consideration in future studies. The model also had an R2 value of 95.66% which indicates that most of the variance is accounted for in the model. It should also be mentioned that the residuals showed no evidence of nonnormality, outliers, or
impacts experienced during mTBIs, specific impact energy, defined as energy per unit mass, is plotted against strain energy density for the FEAM and Foam. This normalization of impact energy is necessary because of the vast difference in impact mass between laboratory and 7
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Fig. 9. Average shear stress strain curves for sample groups at the 75 cm (high impact velocity) drop height.
the 95% confidence level. Most interactions were found to be insignificant with the exception of the two factor interactions of fiber length with denier, fiber length with drop height, denier with drop height and finally the three-factor interaction of fiber length with denier and drop height. Although the two-factor interaction of denier with density was found to be insignificant statistically its p-value is relatively low compared to the other insignificant factors and this should be taken into consideration in future studies. Certain FEAM configurations (6 denier, short fiber, low density) showed tremendous improvement when compared directly to VN foam with a 134.8% increase in shear strain energy density for the high impact velocity loading condition. The same configuration also out performed all other configurations for the low impact velocity loading
undefined variables. There was some slight fanning hinting at nonconstant variance however this can be attributed to the different impact velocities. Tests conducted at greater impact velocities generally have higher variability. There was also no evidence of trends in the residuals indicating that all the data are independent. 4. Conclusion The experiment presented here demonstrates the energy absorption capabilities of FEAM based padding materials under dynamic shear load with static pre-compression loading. Each factor investigated (fiber length, fiber denier, flock density, and drop height) was found to be significant on the shear strain energy density of FEAM materials at
Fig. 10. Average shear strain energy density for 75 cm drop height (high impact velocity). 8
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Fig. 11. Specific impact energy plotted against strain energy density of FEAM and VN Foam and comparison with youth football specific impact energy ranges for mTBIs.
influence the work reported in this paper.
Table 5 Results of the general linear model ANOVA for the FEAM sample factors. Source
DF
Adj SS
Adj MS
F-Value
P-Value
FL Denier Density Drop Height FL * Denier FL * Density FL * Drop Height Denier * Density Denier * Drop Height Density * Drop Height FL * Denier * Density FL * Denier * Drop Height FL * Density * Drop Height Denier * Density * Drop Height FL * Denier * Density * Drop Height Error Total
1 3 1 1 3 1 1 3 3 1 3 3 1 3 3
11,231 46,672 823 154,842 4489 126 2029 628 42,155 188 23 3462 202 468 24
11,231 15,557 823 154,842 1496 126 2029 209 14,052 188 8 1154 202 156 8
119.45 165.47 8.75 1646.90 15.91 1.34 21.58 2.22 149.45 2.00 0.08 12.27 2.14 1.66 0.09
0.000 0.000 0.004 0.000 0.000 0.249 0.000 0.088 0.000 0.160 0.970 0.000 0.146 0.179 0.968
127 158
11,941 274,839
94
Acknowledgments The authors acknowledge the financial support of the Science and Technology (S&T) Grant of University of Massachusetts Dartmouth’sPresident’s Office. Data availability The raw/processed data required to reproduce these findings cannot be shared at this time due to technical or time limitations. References [1] Johnston JM, Ning H, Kim JE, Kim YH, Soni B, Reynolds R, et al. Simulation, fabrication and impact testing of a novel football helmet padding system that decreases rotational acceleration. Sport Eng 2015;18:11–20. https://doi.org/10.1007/ s12283-014-0160-4. [2] Ramirez BJ, Gupta V. Evaluation of novel temperature-stable viscoelastic polyurea foams as helmet liner materials. Mater Des 2018;137:298–304. https://doi.org/10. 1016/j.matdes.2017.10.037. [3] Bird ET, Bowden AE, Seeley MK, Fullwood DT. Materials selection of flexible opencell foams in energy absorption applications. Mater Des 2018;137:414–21. https:// doi.org/10.1016/j.matdes.2017.10.054. [4] Obispo SL, Fulfillment IP, Tan BT. Exploring the Relationship Between Cellular Structure and Mechanical Properties of Polymer Foams; 2018. [5] Belingardi G, Montanini R, Avalle M. Characterization of polymeric structural foams under compressive impact loading by means of energy-absorption diagram. Int J Impact Eng 2001;25:455–72. https://doi.org/10.1016/S0734-743X(00)00060-9. [6] Olivieri D, Di Landro L, Sala G. Deformation mechanisms and energy absorption of polystyrene foams for protective helmets. Polym Test 2002;21:217–28. https://doi. org/10.1016/S0142-9418(01)00073-3. [7] Ouellet S, Cronin D, Worswick M. Compressive response of polymeric foams under quasi-static, medium and high strain rate conditions. Polym Test 2006;25:731–43. https://doi.org/10.1016/j.polymertesting.2006.05.005. [8] Gimbel Genille, Hoshizaki Blaine. A comparison between vinyl nitrile foam and new air chamber technology on attenuating impact energy for ice hockey helmets. Int J Sport Sci Eng 2008;2:154–61. [9] Cui L, Kiernan S, Gilchrist MD. Designing the energy absorption capacity of functionally graded foam materials. Mater Sci Eng, A 2009;507:215–25. https://doi.org/ 10.1016/j.msea.2008.12.011. [10] Zhou J, Hassan MZ, Guan Z, Cantwell WJ. The low velocity impact response of foam-based sandwich panels. Compos Sci Technol 2012;72:1781–90. https://doi. org/10.1016/j.compscitech.2012.07.006. [11] Ramirez BJ, Kingstedt OT, Crum R, Gamez C. Gupta V. Tailoring the rate-sensitivity of low density polyurea foams through cell wall aperture size. J Appl Phys
condition with a 48.9% increase in shear strain energy density as compared to VN foam. This configuration is the best because a small denier allows for higher max flock densities to be achieved. The higher max flock densities mean more buckling fibers on impact which increase the energy absorption of the material. The impairment due to increased flock density within each denier group is most likely attributed to agglomerations of fibers during the flocking process when trying to achieve higher densities. Shorter fibers also had the largest effect on increasing the strain energy density; since it is harder to buckle a shorter fiber the static compression pre-load does not prebuckle the fibers before the impact. It is expected there is an optimal pre-load compression level for each fiber length, denier and flock density configuration however it was not investigated in this experiment. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to 9
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[17] Zhang L, Yang KH, King AI. A proposed injury threshold for mild traumatic brain injury. J Biomech Eng 2004;126:226. https://doi.org/10.1115/1.1691446. [18] Kimpara H, Iwamoto M. Mild traumatic brain injury predictors based on angular accelerations during impacts. Ann Biomed Eng 2012;40:114–26. https://doi.org/ 10.1007/s10439-011-0414-2. [19] Morrison B, Sundstrom LE, Ateshian GA, Thomas FC, Cater HL, Hung CT, et al. A tissue level tolerance criterion for living brain developed with an in vitro model of traumatic mechanical loading. SAE Tech Pap Ser 2018;1:93–105. https://doi.org/ 10.4271/2003-22-0006. [20] Elkin BS, Morrison B. Region-specific tolerance criteria for the living brain. Stapp Car Crash J 2007;51:127–38. 2007-22-0005 [pii]. [21] Campolettano ET, Gellner RA, Rowson S. Relationship between impact velocity and resulting head accelerations during head impacts in youth football. Conf Proc Int Res Council Biomech Inj IRCOBI 2018;2018:326–33.
2017;121. https://doi.org/10.1063/1.4985280. [12] Lewis A, Matos H, Rice MJ, Kim Y. Impact force loss behavior of flocked surfaces. Text Res J 2016;88. https://doi.org/10.1177/0040517516679149. [13] Fodor FK, Chalivendra V, Kim Y, Lewis A. Dynamic mechanical behavior of flocked layer composite materials. Compos Struct 2018;207. https://doi.org/10.1016/j. compstruct.2018.09.083. [14] Weaver AA, Danelson KA, Stitzel JD. Modeling brain injury response for rotational velocities of varying directions and magnitudes. Ann Biomed Eng 2012. https://doi. org/10.1007/s10439-012-0553-0. [15] Kleiven S. Why most traumatic brain injuries are not caused by linear acceleration but skull fractures are. Front Bioeng Biotechnol 2013;1:253–6. https://doi.org/10. 3389/fbioe.2013.00015. [16] Post A, Blaine Hoshizaki T. Rotational Acceleration, Brain Tissue Strain, and the Relationship to Concussion. J Biomech Eng 2015;137:030801https://doi.org/10. 1115/1.4028983.
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Contents lists available at ScienceDirect
Composite Structures journal homepage: www.elsevier.com/locate/compstruct
Parametric study of a fibrous energy absorbing material under impact shear loading
T
⁎
Jared Correiaa, Vijaya Chalivendraa, , Yong Kimb a b
Department of Mechanical Engineering, University of Massachusetts Dartmouth, 285 Old Westport Road, North Dartmouth, MA 02747, United States Department of Bioengineering, University of Massachusetts Dartmouth, 285 Old Westport Road, North Dartmouth, MA 02747, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Impact energy absorption Electro-flocking Medium strain rate Fiber based padding materials Shear energy absorption
A comprehensive experimental impact characterization study of novel impact energy absorbing (IEA) materials under impact shear loading is conducted. Electro-flocking process is employed to fabricate the novel fiber based padding materials, also known as flocked energy absorbing materials (FEAM). FEAM IEA panels are prepared by flocking 1–3 mm long, 6–60 denier nylon fibers onto a planar polyester fabric sheet. The impact energy absorption under combined pre-compression loading and shear impact loading is investigated using a custom built guided weight drop tower along with a specially designed pre-compression and shear loading fixture for this study. A parametric study is performed where the effect of fiber material properties such as flock fiber length, diameter and flock density (number of flock fibers per area) on IEA is investigated and they are later compared with that of Vinyl Nitrile (VN) foam materials. Padding material based on FEAM configurations showed remarkable improvement when compared directly to VN foam with a 135% increase in shear strain energy density for the high impact velocity loading condition. Additionally, for low velocity impact conditions, the FEAM based padding materials out performed with a 49% increase in shear strain energy density as compared to VN foam.
1. Introduction Recently there has been an increase in the interest of protecting athletes from potential neuropathic brain injury. As humans come to better understand the mechanisms of energy transfer to the brain due to traumatic events, and how that energy transfer leads to damaged brain matter, a need for novel materials to help mitigate concussive blows to the head will arise. Although improvements in football helmets have helped to prevent skull fractures and subdural hematoma, many players still suffer from concussive and sub-concussive impacts [1]. Viscoelastic materials, such as elastomeric foams, have been used extensively in sports head gear to help absorb energy from impacts and attempt to protect wearers from mild traumatic brain injury (mTBI) [2,3]. These foams will undergo large deformations under relatively low stresses effectively decreasing the applied forces on the head by increasing the time during which the impactor velocity is decreased [4,5]. Past researchers have mainly investigated these foams and other materials under 1D compressive impact loading [6–11]. Landro et al. [6] determined deformation mechanisms and energy absorption capability of expanded polystyrene (EPS) foams and polycarbonate shells for protective helmets using a combination of experimental and finite element tools. It is demonstrated that the energy absorption capability
⁎
of foam material can be controlled at two different stages: at the macroscopic scale, by selecting the foam density able to minimize the transmitted load and the acceleration; at the microscopic scale, by adjusting EPS internal structure in terms of hollow bead dimensions and wall thicknesses. Ouellet et al. [7] investigated three different types of foams (expanded polystyrene, high-density polyethylene, and polyurethane) under a wide range of strain rates (0.008/s–2500/s). They employed a standard compression test device for low strain rates, a drop tower apparatus for medium strain rates, and a polymeric split Hopkinson pressure bar apparatus for high strain rates. It was discovered from their results that strain rate effects become more pronounced at rates above approximately 1000/s. Gimble and Hoshizaki [8] investigated attenuation of impact energy of vinyl nitrile foam in an air chamber. They conducted a parametric study of using three densities of foam, two hardness values of chambers, three drop weights and three drop masses using a free drop rig. They identified a significant difference in peak linear acceleration between the vinyl nitrile and air chambers. Cui et al. [9] developed model graded foam materials using numerical tools to investigate the effect of various gradient functions on the energy absorption under impact loads. They reported that the functionally graded foam showed superior energy absorption compared to the uniform foam and moreover convex gradients performed better
Corresponding author. E-mail address: [email protected] (V. Chalivendra).
https://doi.org/10.1016/j.compstruct.2019.111583 Received 31 May 2019; Received in revised form 29 August 2019; Accepted 21 October 2019 Available online 22 October 2019 0263-8223/ © 2019 Elsevier Ltd. All rights reserved.
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together to form a relatively uniform sample. The flock densities are calculated from the differing weights of the substrates at each step in the process to get an accurate amount of fibers on the substrate. The following equation is then used to calculate the flock density
than concave gradients. Ramirez et al. [11] studied plateau stress and energy absorption of low density (≤300 kg/m3) polyurea (PU) foams and expanded polystyrene (EPS) for a broad strain rate regime (0.04/s – 5000/s). The plateau stress and energy absorption of low density PU foams exhibited a strong rate dependence. The strain rate sensitivity of PU foams was found to have strong influence on cell size for low strain rates and on cell wall aperture size for intermediate and high strain rates. However for EPS foam, it remained nearly indifferent to strain rate. At low and intermediate strain rates, the plastic crushing in the EPS and the high plateau stress produced a much higher energy absorption than the viscoelastic dissipation in the PU foams. Regarding fiber based materials, recently, Lewis et al. [12] reported impact force loss behavior of flocked energy absorbing materials (FEAM) under compression using ball drop tests. They demonstrated a promising improvement in force-loss% properties by sandwiching either foam or spacer fabric between two FEAM layers. These three-layer structures showed higher force-loss% values than individual foam or spacer fabric components. Very recently, Fodor et al. [13] determined loss tangent values of FEAM based padding materials under compressional loading using a custom designed dynamic mechanical analyzer. A parametric study was performed to study the effect of short fiber diameter, length, density, frequency, and temperature on loss tangent values. Loss tangent of FEAM materials increases as the measuring frequency and flocked layer’s flock density (fibers per area) increases. Even though no significant trend is noticed as a function of temperature the highest loss tangent is achieved at room temperature. As discussed above, foam and fiber based materials are excellent in absorbing impact energy under compressive loads, however their performance under impact shear loading conditions has not yet been studied. Moreover, the rotational acceleration that induces mTBI are due to shear deformation of brain tissues [14–20] and padding materials that can absorb energy under shear loads have a higher possibility of preventing mTBI in players. Towards this goal, for the first time, an effort is made to characterize impact energy absorption of FEAM based padding materials under a combined dynamic shear loading condition with a static pre-compression load. A custom designed pre-compression fixture along with a custom built guided drop weight tower is used to determine the shear impact energy absorption. The effect of fiber diameter, fiber length, their density and impact velocity on shear energy absorption is determined and later compared with padding materials made from Vinyl Nitrile (VN) foam.
FD = 9 × 106
m AdL
(1)
where FD is the flock density in fibers/mm2, m is the mass of the flock applied in grams, A is the substrate area in mm2, d is the flock denier (which is proportional to the flock diameter) or grams of fiber per 9000 meters (g/9000 m), and L is the flock fiber length in mm. The 9 × 106 comes about due to the denier measurement being defined as number of grams per 9000 meters of a given fiber. This experiment maintains a constant number of layers (three flocked and one non-flocked sheet) for every sample. The sample thickness is then measured using a micrometer penetrometer. The thickness of samples that have similar parametric design (flock density, denier, and flock fiber length) are then averaged to be used in determining the sample shear strain during testing. The width and length of the samples are taken using a digital caliper at three different locations and similarly averaged for determining shear strain during testing. The measured thickness changes depending on the fiber length and fiber denier because we keep the number of substrates constant. Table 1 demonstrates the thickness values for each group. The sample length and width are maintained constant and measured to be 101.76 ± 1.46 mm and 101.42 ± 1.59 mm respectively. 2.2. Experimental methods A guided weight drop (GWD) tester is used in conjunction with a double lap shear jig to test the FEAM based padding materials and Vinyl Nitrile (VN) Foam samples under dynamic shear loading combined with static pre-load compression. VN foam is considered the control material for this study because current football helmet padding uses this material to help protect players from head injuries. The densities of VN foam and different FEAM configurations have been included in Table 2. The GWD has a mass of 5 kilograms that can be dropped from varying heights of 25 and 75 centimeters. The shear jig (as shown in Fig. 3) sits below the hanging mass and consists of a frame with two compression plates, and a central impact plate. The compression plates sit on each side of the central impact plate and are used to secure one sample to each side of the central plate. A threaded rod with strain gauge is used to compress the sample to a specified value of 1500 Newtons (1500 Newtons is used as a pre-compression value since it prevents slipping of pads between the shear jig compression plates). The central plate contains a linear variable displacement transducer (LVDT) manufactured by P3 America, Inc (San Diego, CA) capable of infinite resolution with proper noise control and a 30 mm stroke length as well as an ICP® quartz force sensor manufactured by PCB Piezotronics (Depew, NY) with broadband resolution (1 to 10000 Hz) of 1.3 N-rms and a compression range of 88.96kN. The sensors are then routed to two data acquisition (DAQ) units, one purchased from Measurement Computing (MCC of Norton, MA) and one from National Instruments (NI of Austin, TX). The MCC DAQ has a sample rate of 100 kS/s and records the displacement over time of the central plate upon impact, while the NI DAQ has a sample rate of 51.2 kS/s and records the force time curves applied to the central plate upon impact. All data is acquired via the LABVIEW development suite. The data is then imported to an in house-built MATLAB program for postprocessing (zero-phase low pass FIR Kaiser window filter at 500 Hz) and analyzation. Fig. 4 shows an example of a raw and filtered impulse. Notice the high frequency strain wave variations present in the raw data which requires filtration to gain any useful information from these curves. Finally, Fig. 5 shows the actual test setup used in this study. A characteristic pulse collected from the LVDT and force sensor for a typical sample is shown in Fig. 6(a). A viscoelastic response appears due
2. Materials and methods 2.1. Materials and manufacturing FEAM samples are constructed using the up-flocking manufacturing process [12,13]. A representation of this process is shown in Fig. 1. First, a thin film of adhesive (Key Polymer FF3822 of Lawrence, MA) is applied to the surface of a polyester substrate (black polyester gabardine purchased from www.fabric.com). This is accomplished via a draw down technique. This fabric is then attached to the negative electrode of the flocking chamber with adhesive side face down. Nylon flock fibers (Cellusuede Products, Inc of Rockford, IL), short fibers 1–3 mm in length, are then spread uniformly onto the positive electrode. A 300 μA current is applied for five second intervals (a total of three times) across the electrodes creating an 80 kV electric field that levitates the flocked fibers up into the adhesive layer of the substrate. The samples are then hung dry for 24 hours and then oven cured at 255 °F for ten minutes followed by 325 °F for ten minutes. After the required number of substrates are created, the opposite sides of the fabric are coated with a thin film of adhesive using a similar technique. The substrates are then laminated together forming a thick pad as shown in Fig. 2. Finally, the pad is cured once more at 255 °F for ten minutes followed by 325 °F for ten minutes. Only samples with relatively similar flock densities (flock fibers per unit area) are laminated 2
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Fig. 1. Electrostatic flocking operation principle.
to the phase shift of displacement. The two black vertical lines represent the first half cycle of impact assuming a free vibration. The propagation of strain waves in the system results in a noisy signal that is filtered and cropped in MATLAB. This force–time curve is used to find the shear stress of the sample material by dividing the force by the sample inplane area. Since two test specimens are sharing the impact load, load is divided equally for each sample. Shear strain is calculated by taking the tangent of the displacement over the sample thickness. Finally shear stress-shear strain curves are generated and used to calculate the strain energy density of each sample by integrating the area under the curve. Fig. 6(b) shows a characteristic shear stress strain curve for a typical sample. It is noted here that the below assumptions are made while determining the shear strain energy density.
Table 1 Average thickness values of vinyl nitrile foam and FEAM specimens. ID
Denier
Fiber Length (mm)
Mean Thickness (mm)
VN Foam 0610 0620 2020 2030 4520 4530 6020 6030
– 6 6 20 20 45 45 60 60
– 1 2 2 3 2 3 2 3
10.97 ± 0.16 6.43 ± 0.31 9.17 ± 1.45 8.85 ± 0.49 10.83 ± 1.77 9.34 ± 0.62 12.98 ± 0.53 9.78 ± 0.31 12.29 ± 0.37
diameter in denier, flock fiber length (FL), and flock density on the energy absorption characteristics of FEAM based padding samples and compare them to a common VN foam at two impact velocity levels. The design incorporated four different deniers, two different lengths per denier, two different flock densities per denier and two different drop heights. Each configuration was tested a total of five times. The levels of each factor are summarized here
• The maximum displacement occurs at the time the force impulse returns to zero. • The force applied is divided evenly between the two samples. • The samples return to their original configuration over time while zero load is applied therefore closing the hysteresis loop, hence the area under the curve is the energy absorbed by the material.
• Denier (6, 20, 45, 60) • Fiber Length (Low, High)
2.3. Experimental design This study is designed to investigate the effect of flock fiber
Fig. 2. Manufacturing process for creating FEAM padding materials. 3
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information regarding peak shear and peak strain values for each sample group. The average shear energy density of each group is then plotted with 95% confidence interval error bars calculated using the student t-distribution and a sample size of N = 5. This metric is used instead of standard deviation because it allows comparing the means of each group with a predetermined probabilistic accuracy while standard deviation simply shows the spread of the data. The 6 denier, short fiber, low density samples provided the best shear strain energy density as compared with foam for both the low and high impact velocity tests.
Table 2 Average densities of VN foam and FEAM pads. ID
Denier
Fiber Length (mm)
Average Density (kg/m3)
VN Foam 0610 0620 2020 2030 4520 4530 6020 6030
– 6 6 20 20 45 45 60 60
– 1 2 2 3 2 3 2 3
98.82 ± 2.89 280.96 ± 19.11 209.48 ± 12.21 218.01 ± 7.51 162.50 ± 18.34 201.66 ± 21.40 166.65 ± 17.66 236.86 ± 10.85 182.47 ± 31.90
3.2. Low impact velocity Fig. 7 shows the average stress strain relationship of each sample group for the 25 cm drop height tests. The legend contains the code for each sample group in the format XXY0Z where XX is the denier, Y is the flock fiber length in mm, and Z is the high (H) or low (L) flock density. This plot shows that foam is a much stiffer material than the FEAM samples. The peak stress is larger for the foam samples however the maximum strain is less than that of FEAM. This is a good indicator that FEAM should provide better energy absorption than foam since it is decreasing the applied load while increasing the amount of deformation. Fig. 8 shows the average shear strain energy density (area under the shear stress-shear strain diagram of Fig. 7) of each group for the 25 cm drop height. The results of the FEAM padding are not affected much by the differing denier for this drop height. The density also has little significance at this impact velocity except for two cases, one being the 45 denier pad with long fibers and the other the 6 denier pad with long fibers. The impairment due to increased flock density within these denier groups is most likely attributed to agglomerations of fibers during the flocking process when trying to achieve higher densities. These agglomerations in turn create non-homogeneity of the flock fibers lowering the ability of the pad to absorb energy. The fiber length however drastically changes the shear energy absorption abilities of the pads for all denier groups effectively increasing the shear strain energy density by decreasing the lengths of the fibers. In theory, a longer fiber will buckle more easily as compared to a shorter fiber. Thus, prebuckling of the fibers when a static pre-compression load is applied could explain this phenomenon. Pre-buckling here is defined as the point at which most of the fibers are buckled due to the static precompression load. Excessive pre-buckling degrades the ability of the material to perform as intended which is to allow buckling of fibers upon impact to dissipate energy. It is expected that an optimal pre-load compression level exists that allows fibers to approach their buckling
• Flock Density (Low, High) • Drop Height (25 cm, 75 cm) also called Impact Velocity (Low, High) The low and high level values for a given denier are summarized in Table 3. Due to the expected interaction between fiber denier (diameter) and density, the groups for the two different densities had to be categorized as low and high for a given denier since each denier has a different maximum possible density (which was set as the high density configuration) due to the differing diameters. As an example, at the 6 denier level, the max density was 202.32 fibers/mm2 while for the 60 denier level, the max density was 28.98 fibers/mm2. Also due to the availability of flock fibers, fiber lengths had to also be categorized as low and high for a given denier since for two separate deniers manufacturers generally do not make the same fiber lengths, for example it is almost impossible to find a 20 denier flock fiber with a 1 mm length while for a 6 denier fiber the 3 mm long fibers had mechanical characteristics which were not ideal for flocking (poor sift-ability and poor flock activity). In general, it is advised by the manufacturer to avoid high aspect ratio (Length/Diameter) fibers for flocking processes. The difference between the length groups is held constant at 1 mm difference in length between the short and long groups. The low flock density group is held at 30–70% of the high group and has more variance since flock density cannot be perfectly controlled due to hard to control factors such as flock moisture content and electrical conductivity. 3. Results and discussion 3.1. FEAM and foam comparison The average shear stress and strain curves are plotted for each sample group at two differing impact velocity levels. These plots show
Fig. 3. Shear jig CAD assembly with (a) frontal view and (b) cross sectional view. 4
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Fig. 4. Typical raw force and displacement data and their filtered curves.
however, due to variances this is a negligible change.
threshold. If this value is exceeded then the material will perform less effectively however, this was not investigated in this study. Percentage increase/decrease in shear strain energy density of each group of samples as compared directly to foam is summarized in Table 4 for both impact velocities. The 6 denier, short fiber, low density samples saw a 49% increase in shear strain energy density on the average when compared with VN foam at the low impact velocity test condition. The high-density samples of that group also show a 42% increase on the average. When looking at the other sample groups the samples manufactured with shorter fibers had generous increases in strain energy density from 25-41% on the average. The worst performing result was a 45 denier, long fiber, high density sample group with a decrease in strain energy density of about 7% on the average
3.3. High impact velocity Fig. 9 shows the average stress strain relationship of each sample group for the 75 cm drop height tests. The legend contains the code for each sample group in the format XXY0Z where XX is the denier, Y is the flock fiber length in mm, and Z is the high (H) or low (L) flock density. This plot proves again that foam is a much stiffer material than the FEAM samples. The peak stress is largest for the 6 denier FEAM samples although it is not significantly different than that of the foam. Again the 6 denier FEAM samples have highly improved shear strain capabilities when compared with the both the foam and other FEAM samples. This
Fig. 5. Actual lab setup highlighting the force sensor, displacement sensor, and drop weight. 5
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Fig. 6. (a) Characteristic displacement and force pulse of typical impacted sample filtered at 500 Hz (b) characteristic shear stress–strain diagram for a typical impacted sample.
fibers to be flocked on the substrates which increases the number of buckling fibers and frictional fiber interactions. The higher impact velocity also seems to negate the effects of non-homogeneity discussed in Section 3.2 since there are relatively there is no changes due to density with exception of the 45 denier group. Again, the shorter fibers especially for the lower denier groups show increased shear strain energy density most likely due to the pre-buckling phenomenon discussed in the previous Section 3.2. As shown in Table 4 6 denier, short fiber, low density samples saw a 135% increase in shear strain energy density on the average when compared with VN foam at the high impact velocity test condition. The next best configuration of FEAM was the 6 denier, short fiber, high density samples which saw a 105% increase in the shear strain energy density on the average as compared to foam at the high impact velocity test condition. The other 6 denier configurations also performed exceptionally well with the worst one still achieving a 43% increase in strain energy density on the average. The remaining configurations performed generally around the same as the foam samples with deviations of about ± 11% change on the average. There was one exception which is the 45 denier long fiber group which performed much worse than the foam at about a 24% decrease in strain energy density on the average. In order to examine how the current testing conditions relate to
Table 3 Experimental design parameter levels with the values for each denier group. Denier
Independent Variable
Low Level
High Level
6
Fiber Length (mm) Level Flock Density (Fibers/mm2) Fiber Length (mm) Level Flock Density (Fibers/mm2) Fiber Length (mm) Level Flock Density (Fibers/mm2) Fiber Length (mm) Level Flock Density (Fibers/mm2) Drop Height (cm)[Impact Velocity (m/ s)]
1 L H 63.76 202.32 2 L H 29.06 40.81 2 L H 8.56 20.97 2 L H 19.00 28.98 25[2.22]
2 L H 29.19 85.64 3 L H 8.48 18.16 3 L H 9.1 23.24 3 L H 7.4 18.87 75[3.84]
20
45
60
ALL
indicates that the FEAM samples should provide better energy absorption under pre-compression and shear impact loading conditions. Fig. 10 shows the bar graphs of the average shear strain energy density for the high impact velocity (75 cm drop height). At this higher impact velocity, denier has almost no effect except at the 6 denier level. The lower denier (smaller diameter) allows for an increased number of
Fig. 7. Average shear stress strain curves for each sample group at the 25 cm (low impact velocity) drop height. 6
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Fig. 8. Average shear strain energy density for 25 cm drop height (low impact velocity).
on field impacts, not to mention the complexity of an on-field impact as compared to a 1-D laboratory test. The range of specific impact energies for mTBIs occurs across a wide spectrum. Fig. 11 shows the specific impact energy levels of these tests compared with specific impact energy of mTBIs at different levels of play. In youth football specific impact energies for mTBIs can occur as low as 7.03 J/kg and as high as 15.13 J/kg (although concussion risk probability for youth football needs more investigation) [21]. At the professional and collegiate levels however, concussions tend to occur between 27.38 J/kg to 43.25 J/kg [21]. These tests relate most directly to youth level impacts however there is a consideration to be made that in an actual impact only a percentage of the velocity will produce shearing forces (which cannot be determined directly) should, in general, lower each range of specific impact energies for all levels of play (since the studies in this manuscript are essentially mimicking a non-normal impact).
Table 4 Percent change on average of FEAM configurations as compared with VN foam. FL
Denier
Density
Drop Height
Fitted Mean
Percent Difference with Foam
L L H H L L L L L H L H H H H H L L L L L L L L H H H H H H H H
6 6 6 6 20 45 20 60 45 20 60 20 60 60 45 45 6 6 20 20 45 60 45 60 6 20 60 45 20 60 6 45 FOAM FOAM
L H L H L L H L H H H L L H L H L H H L L L H H L L L L H H H H
75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 75
196.12 171.03 132.45 119.29 90.28 89.51 88.3 82.27 79.25 77.34 75.94 74.58 73.89 73.7 63.65 63.31 41.82 39.94 39.51 39.3 39.18 37.54 35.56 35.1 31.08 29.99 29.96 29.49 28.05 27.65 27.06 26.21 28.09 83.5
134.87 104.82 58.62 42.86 8.11 7.19 5.74 −1.47 −5.08 −7.37 −9.05 −10.68 −11.50 −11.73 −23.77 −24.17 48.87 42.18 40.65 39.90 39.48 33.64 26.59 24.95 10.64 6.76 6.65 4.98 −0.15 −1.56 −3.66 −6.69 0 0
3.4. Analysis of variance A general linear ANOVA model is applied to the data acquired from the drop tests to determine which parameters have the most significant effect on the strain energy density of FEAM samples. Table 5 shows the results of the ANOVA performed using Minitab 18 software with a significance level of 0.05. Fiber length, denier, density, and drop height are all significant factors at this level. Additionally, the two term interactions of fiber length with denier, fiber length with drop height, denier with drop height, and the three-way interaction of fiber length with denier and drop height were all found to be statistically significant. These significant interactions indicate that the main effects alone cannot predict the shear strain energy density response, hence, to properly predict the shear strain energy density of the material we must have information of all the dependent variables (fiber length, denier, and drop height). The interaction of density and denier although not found to be statistically significant does have a relatively lower p value as compared with the other non-significant factors and interactions and therefore should be taken into consideration in future studies. The model also had an R2 value of 95.66% which indicates that most of the variance is accounted for in the model. It should also be mentioned that the residuals showed no evidence of nonnormality, outliers, or
impacts experienced during mTBIs, specific impact energy, defined as energy per unit mass, is plotted against strain energy density for the FEAM and Foam. This normalization of impact energy is necessary because of the vast difference in impact mass between laboratory and 7
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Fig. 9. Average shear stress strain curves for sample groups at the 75 cm (high impact velocity) drop height.
the 95% confidence level. Most interactions were found to be insignificant with the exception of the two factor interactions of fiber length with denier, fiber length with drop height, denier with drop height and finally the three-factor interaction of fiber length with denier and drop height. Although the two-factor interaction of denier with density was found to be insignificant statistically its p-value is relatively low compared to the other insignificant factors and this should be taken into consideration in future studies. Certain FEAM configurations (6 denier, short fiber, low density) showed tremendous improvement when compared directly to VN foam with a 134.8% increase in shear strain energy density for the high impact velocity loading condition. The same configuration also out performed all other configurations for the low impact velocity loading
undefined variables. There was some slight fanning hinting at nonconstant variance however this can be attributed to the different impact velocities. Tests conducted at greater impact velocities generally have higher variability. There was also no evidence of trends in the residuals indicating that all the data are independent. 4. Conclusion The experiment presented here demonstrates the energy absorption capabilities of FEAM based padding materials under dynamic shear load with static pre-compression loading. Each factor investigated (fiber length, fiber denier, flock density, and drop height) was found to be significant on the shear strain energy density of FEAM materials at
Fig. 10. Average shear strain energy density for 75 cm drop height (high impact velocity). 8
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Fig. 11. Specific impact energy plotted against strain energy density of FEAM and VN Foam and comparison with youth football specific impact energy ranges for mTBIs.
influence the work reported in this paper.
Table 5 Results of the general linear model ANOVA for the FEAM sample factors. Source
DF
Adj SS
Adj MS
F-Value
P-Value
FL Denier Density Drop Height FL * Denier FL * Density FL * Drop Height Denier * Density Denier * Drop Height Density * Drop Height FL * Denier * Density FL * Denier * Drop Height FL * Density * Drop Height Denier * Density * Drop Height FL * Denier * Density * Drop Height Error Total
1 3 1 1 3 1 1 3 3 1 3 3 1 3 3
11,231 46,672 823 154,842 4489 126 2029 628 42,155 188 23 3462 202 468 24
11,231 15,557 823 154,842 1496 126 2029 209 14,052 188 8 1154 202 156 8
119.45 165.47 8.75 1646.90 15.91 1.34 21.58 2.22 149.45 2.00 0.08 12.27 2.14 1.66 0.09
0.000 0.000 0.004 0.000 0.000 0.249 0.000 0.088 0.000 0.160 0.970 0.000 0.146 0.179 0.968
127 158
11,941 274,839
94
Acknowledgments The authors acknowledge the financial support of the Science and Technology (S&T) Grant of University of Massachusetts Dartmouth’sPresident’s Office. Data availability The raw/processed data required to reproduce these findings cannot be shared at this time due to technical or time limitations. References [1] Johnston JM, Ning H, Kim JE, Kim YH, Soni B, Reynolds R, et al. Simulation, fabrication and impact testing of a novel football helmet padding system that decreases rotational acceleration. Sport Eng 2015;18:11–20. https://doi.org/10.1007/ s12283-014-0160-4. [2] Ramirez BJ, Gupta V. Evaluation of novel temperature-stable viscoelastic polyurea foams as helmet liner materials. Mater Des 2018;137:298–304. https://doi.org/10. 1016/j.matdes.2017.10.037. [3] Bird ET, Bowden AE, Seeley MK, Fullwood DT. Materials selection of flexible opencell foams in energy absorption applications. Mater Des 2018;137:414–21. https:// doi.org/10.1016/j.matdes.2017.10.054. [4] Obispo SL, Fulfillment IP, Tan BT. Exploring the Relationship Between Cellular Structure and Mechanical Properties of Polymer Foams; 2018. [5] Belingardi G, Montanini R, Avalle M. Characterization of polymeric structural foams under compressive impact loading by means of energy-absorption diagram. Int J Impact Eng 2001;25:455–72. https://doi.org/10.1016/S0734-743X(00)00060-9. [6] Olivieri D, Di Landro L, Sala G. Deformation mechanisms and energy absorption of polystyrene foams for protective helmets. Polym Test 2002;21:217–28. https://doi. org/10.1016/S0142-9418(01)00073-3. [7] Ouellet S, Cronin D, Worswick M. Compressive response of polymeric foams under quasi-static, medium and high strain rate conditions. Polym Test 2006;25:731–43. https://doi.org/10.1016/j.polymertesting.2006.05.005. [8] Gimbel Genille, Hoshizaki Blaine. A comparison between vinyl nitrile foam and new air chamber technology on attenuating impact energy for ice hockey helmets. Int J Sport Sci Eng 2008;2:154–61. [9] Cui L, Kiernan S, Gilchrist MD. Designing the energy absorption capacity of functionally graded foam materials. Mater Sci Eng, A 2009;507:215–25. https://doi.org/ 10.1016/j.msea.2008.12.011. [10] Zhou J, Hassan MZ, Guan Z, Cantwell WJ. The low velocity impact response of foam-based sandwich panels. Compos Sci Technol 2012;72:1781–90. https://doi. org/10.1016/j.compscitech.2012.07.006. [11] Ramirez BJ, Kingstedt OT, Crum R, Gamez C. Gupta V. Tailoring the rate-sensitivity of low density polyurea foams through cell wall aperture size. J Appl Phys
condition with a 48.9% increase in shear strain energy density as compared to VN foam. This configuration is the best because a small denier allows for higher max flock densities to be achieved. The higher max flock densities mean more buckling fibers on impact which increase the energy absorption of the material. The impairment due to increased flock density within each denier group is most likely attributed to agglomerations of fibers during the flocking process when trying to achieve higher densities. Shorter fibers also had the largest effect on increasing the strain energy density; since it is harder to buckle a shorter fiber the static compression pre-load does not prebuckle the fibers before the impact. It is expected there is an optimal pre-load compression level for each fiber length, denier and flock density configuration however it was not investigated in this experiment. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to 9
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