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PLA composite filaments and 3D printed parts
Accepted Manuscript Shape Recovery Characteristics of SiC/C/PLA Composite Filaments and 3D Printed Parts Wenbo Liu, Nan Wu, Kishore Pochiraju PII: DOI: Reference:
S1359-835X(18)30058-7 https://doi.org/10.1016/j.compositesa.2018.02.017 JCOMA 4931
To appear in:
Composites: Part A
Received Date: Revised Date: Accepted Date:
11 October 2017 6 February 2018 9 February 2018
Please cite this article as: Liu, W., Wu, N., Pochiraju, K., Shape Recovery Characteristics of SiC/C/PLA Composite Filaments and 3D Printed Parts, Composites: Part A (2018), doi: https://doi.org/10.1016/j.compositesa.2018.02.017
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Shape Recovery Characteristics of SiC/C/PLA Composite Filaments and 3D Printed Parts Wenbo Liu1, Nan Wu1, Kishore Pochiraju2 1
Graduate Research Assistant 2
Professor
Department of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA
Abstract The shape recovery characteristics of SiC and Carbon filled (poly) lactic acid (PLA) filaments extruded for use with Fused Deposition Modeling (FDM) and parts printed with FDM have been analyzed. The SiC/C /PLA composite filaments were made with particle loading up to a maximum weight fraction of 60%. The shape recovery characteristics of the filaments and printed parts were tested with bending and tensile loads. Two parameters, recovery rate and recovery time were defined and monitored during the shape recovery process. This study shows that the recovery time can be correlated to the thermal conductivity of the material. The results show a viable method for tailoring the recovery time. Furthermore, tensile specimens were 3D printed and the shape recovery behavior can be observed in the printed structures. This paper describes fabrication methods, SMP composite response results and a correlation of SMP response with the composite thermal conductivity.
Corresponding author. E-mail addresses: [email protected]
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1. Introduction Shape Memory Polymers (SMPs), when subjected to an appropriate stimulus that rearranges its internal structures, transform from a programmed temporary shape to an original permanent shape. Stimulus can be provided in the form of heat, light, electricity, moisture, pH and magnetic fields [1]. Having properties of moderate cost, low density, as well as potential biocompatibility and biodegradability, shape memory behavior has been sought for developing novel intelligent materials.
Polyurethane,
polylactic
acid (PLA),
poly (ethylene vinyl
acetate),
and
polycaprolactone (PCL) are some of the polymer systems that exhibit shape memory properties [2-5]. Several of these polymers are used in modern additive manufacturing systems such as the Fused Deposition Modeling [6-8] in which a filament is melted and extruded through a die and the deposition point in space is changed by a computer-controlled three axis machine. The shape memory response can be altered by adding fillers and the resultant polymer composites (SMPCs) may yield higher strength, stiffness and other physical properties. Adding carbon black to rubber improved the abrasion resistance and mechanical properties of the tires, enhancing its durability [9]. A variety of fiber and particle reinforced polymer matrix composites with novel fillers such as carbon nanofibers, inorganic whiskers, and nanoclay have been widely discussed [10-14]. Most particulate fillers have poor compatibility with polymers since their surfaces are inorganic and polar in nature, so problems like poor dispersion and high viscosities may hinder further manufacturing processes [15]. One solution to these problems is the use of some additives to beneficially modify the surface of fillers. A wide range of such surface modifiers including fatty acids have been commercially used [16]. Another solution is to improve the mixing process by applying powerful compounding machinery, especially when considering high filler loadings [17-19]. Silicon carbide (SiC) has been used as a filler and fibers in composites by its virtue such as high stiffness and temperature resistance. Erkliğ, et al. [20] examined the mechanical properties
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of the silicon carbide micro-particle filled polyester resin with various filler contents. They found that the SiC filler significantly enhanced the tensile and flexural modulus values of the polyester composites compared to pure polyester resin. However, with more SiC added, the strength of the composites showed a decreasing trend. Tests revealed the effects of the relation between the quality of the particle/matrix interfacial bonding strength and stress transfer between the matrix and particulate fillers. Bi, et al. [21] developed a polymer composite with high dielectric permittivity,
low dielectric loss, and improved mechanical properties by using embedded
SiC/SiO2-W core-shell structure filler in poly (vinylidene fluoride) (PVDF) matrix. Hulugappa, et al. [22] showed that the addition of SiC improved not only the tensile and flexural properties and impact strength, but also the mode-I fracture toughness. Zhang, et al. [23] studied the effect of graded aggregate on the erosion resistance by making use of SiC with two kinds of particle size and epoxy resin. Results from the tests indicated that the erosion rate dropped with increasing hard spots and density and composite interface was also obtained from the modifications. Addition of graphite and carbon fillers is known to enhance thermal and electrical conductivity of composites. Ariño, et al. [24] discussed the influence of extrusion parameters such as screw geometry, temperature and screw speed on the electrical properties of graphite nanoplatelet-filled poly(ethylene-butyl acrylate) composites. Senyk, et al. [25] explored electromagnetic radiation (EMR) shielding performance of carbon-polymer materials. Scocchi, et al. [26] developed a finite element (FE) method that could effectively calculate the electrical conductivity of compression molded polymer-graphite composites. They investigated the influence of model features with different representative volume elements (RVEs) to calculations and accurate results could be obtained by adjusting appropriate parameters. Vilaverde, et al. [27] studied the mixing characteristics of graphite nanoplates (GnP) in polypropylene melt with a aid of a small scale extensional mixer. Literature indicates substantial evidence that graphite can be dispersed into polymeric matrices and conductivity of the resultant composite materials can be tailored to suit the design needs.
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In this paper, we describe a study on the effect of adding stiffening and conductive fillers in PLA matrix on the shape memory behaviors of Fused Deposition Modeling (FDM) filaments and printed parts. Three parameters, a recovery ratio, a recovery rate and a recovery time, are defined to evaluate the shape recovery performance of the material. Parameterization of the shape recovery response is described in the next section. The fabrication methods for both filaments and 3D printed tensile coupons are discussed in the third section. Fourth section of the paper describes the shape memory response for composites with SiC, C and SiC+C hybrid materials. We present the conductivity measurement, correlation between the conductivity and shape recovery characteristics. A relationship between the composite stiffness and force exerted by the composite during shape recovery are presented in the last three sections of the paper.
2. Parameterizing Shape Recovery Performance. In order to measure the shape recovery performance of composites, we define three parameters [28] --- bend angle recovery ratio
r
, recovery rate
, which is the time
r
rate of change of the bend angle recovery ratio, and max recovery time (Tmax), defined at the time at which the recovery rate is maximum (i.e. Tmax = argmax(
. For each material
composition, these parameters have been measured with 3D printed parts and FDM filaments produced initially with a fixed bend angles (e.g. 90° or 0°) and programming them to temporarily deformed angles. The shape recovery is triggered by immersing the part into water heated to 90°C. The shape transformation is monitored using a high-resolution microscope. Figure 1 shows the experimental set up used to trigger the shape recovery and imaging system mounted on the top that produces a high-resolution sequence of images that were analyzed to determine the shape recovery parameters. The angle recovery ratio is calculated as below: r
r ra
e
le – ri te
rre t le
le
t
(1)
4
The recovery rate (
, which is the instantaneous time derivative of the recovery ratio (Rr),
can be calculated by processing the sequence of images obtained during the recovery process. A series of pictures are shown in Figure 2(a). During the recovery process the current bend angle (Ө(t)) can be evaluated through image processing and recovery rate can be numerically determined. Figure 2(b) shows the recovery rate with respect to time and the recovery time (Tmax) for a typical specimen.
3. Fabrication and Characterization of the Filled Polymer Filaments In this effort, silicon carbide and graphite powders were used as fillers in polylactic acid matrix (PLA). The reinforcement powders were mixed and 3D printer filaments were extruded. The filaments were then fed into modified 3D printers using fused deposition modeling techniques. Figure 3 shows the steps used to produce filaments and print the parts. The figure also lists the process variables that were controlled to produce filaments and parts with optimized quality.
The reinforcements and matrix powders were first dried and fed into a ball mill. All
powders were dried in a Barnstead oven at 70 ℃ for 4 hours before further processing. The constituent materials used in this study were 74 micron PLA (Polylactic acid) powder of Natureworks IngeoTM Biopolymer 4032D supplied by NatureWorks® Co. LLC, USA, 15 micron black SiC (Silicon carbide) powder supplied by Panadyne Inc., and 44 micron Graphite Dry Lubricant supplied by Loudworlf.. The particulate-filled feedstock was compounded using a Lortone 33B Rotary Rock Tumbler at a rotation speed of 64 rpm. The dry powder mixing was performed in two molded-rubber barrels. Chrome steel balls with different sizes were added into the mixture for better particle dispersion and low material agglomeration according to the best practices in ball milling [29]. The ball milling process was conducted for 4 hours at room temperature. The extrusion process parameters were optimized for consistent diameter and density throughout the length of the filament. The weight fraction of the reinforcements were
5
controlled at both the powder and the filament scales, using an electronic balance with a precision of 0.01g. Before extrusion, each composite was sieved to ensure particle size consistency and to remove the chrome steel balls from the mixture. Powder mixtures were hermetically sealed in plastic bags preventing moisture absorption. A single screw extruder was used to produce the FDM filaments from the balled milled mixtures. The filaments are extruded at a constant die temperature and screw rotation speeds. In order to obtain filaments with a consistent diameter that can be appropriate for feeding into FDM machine, several tailorable variables such as die diameter, temperature and screw speed were analyzed and optimized. All filaments were extruded from a die with bore diameter of 1.35mm, melt temperature of 180 ℃ and a screw rotation speed of 35 rpm. Figure 4 shows the cross-sections of pure PLA, PLA with SiC and PLA with graphite filaments as observed through an optical microscope. The filaments were also characterized to determine the diameter variation, density and particle dispersion within the cross-sections. As shown in Figure 5, the diameter of filament decreases as more SiC was added into PLA as a whole and PLA with 20 % by weight of SiC has the diameter that is most close to standard value of 1.75mm with a deviation of around 0.06mm. One important explanation of this trend is that the filler decreased the swelling of the polymer at the die exit. Larger deviations in diameter are observed when SiC content is too low or too high, indicating the need for a closed-loop control of the extruder to produce more consistent filament diameters. At high filler loading filament, the extrusion rate was decreased significantly to account for the increased viscosity and the surface of the filament had a coarse texture.
Figure 6 shows the diameter variation data for varying
proportions of SiC and graphite filaments. Data of all these composite filaments are roughly near the standard value 1.75mm with variations of no more than 0.06mm about the mean diameter. Figure 7 shows the comparison of the theoretical and measured densities of the composite filaments. The dashed line shows the theoretical density variation for a composite with SiC loading in a PLA matrix. The measured mean densities correlate well with the expected density of
6
a homogenized composite. However, the filaments have density variations in the order of 5% of the mean density. The higher weight fractions composites have measurably lower density than the theoretical density indicating the presence of voids. We found that Pure PLA and SiC-filled PLA filaments provide better cross-section roundness than graphite-filled PLA filament. The SiC particles in PLA matrix were observed to be more evenly dispersed than graphite. In order to examine the dispersion of SiC in PLA further, the microstructure of the cross-section is analyzed with a scanning electron microscopy (SEM). Figure 8 shows the SEM micrograph of PLA with 50 % by weight SiC/PLA filament crosssection. Images indicate that SiC particles are well dispersed in the PLA matrix. As the SiC feedstock used before ball mill had 15 micron particle size, the milling considerably reduce SiC particle size. SEM images show SIC particles with sizes around 5 m. For some experiments, we extruded straight fragments (without coiling on the spool winder) and specimens with a length of 80 mm were cut from the extruders. The temporary shape programming of the specimens was carried at the temperature of 90 ℃. The straight filaments were bent into a U shape and the temperature was reduced to the room temperature, thereby fixing the shape. Figure 9 shows the straight and bent specimens. A single parameter, (t), was defined to measure the shape recovery state. The two ends of the filament (points A and B) and mid-point of the filament (O) were identified from image processing and the angle(AOB), (t), was tracked during shape recovery process. The shape recovery back to the straight filament was triggered by placing the bent wires into a bath of hot water. A video camera was used for image capture and processing during the shape recovery phase.
4. Shape recovery behavior of FDM filaments The shape recovery behavior of the filaments and parts was studied using series of images from the video obtained during the shape recovery. Figure 10 shows the shape memory behavior
7
of the pure PLA and PLA composites with various percentages of SiC loading. The shape of the pure PLA filament significantly slows down between 1.5 - 2.5 seconds after triggering. The test results show that the SiC filled composites recover faster than pure PLA matrix. This drop of recovery time is remarkable only for high filler loading composites such as the PLA with 50 % by weight SiC and its recovery time drops 40% compared with pure PLA. Graphite filled composites exhibit a much faster recovery speed (low recovery times). Figure 11 shows the recovery behavior of the Graphite/PLA composites. The images indicate that the shape recovery for this composite is complete within about 0.5 s. The higher thermal conductivity of graphite is the expected cause for the dramatic change in shape memory behavior. Figure 12 and 13 show the average recovery rate behavior
r
for SiC and
graphite filled PLA filaments. The sample data is collected from three to ten specimens for each type of filament. The addition of SiC into PLA matrix increases the peak recovery rate (max (
)). The maximum recovery rate of 50% by weight SiC filled PLA filaments is about 120
degrees/sec, which is nearly twice as the pure PLA filaments. Besides, time used to reach the peak value
is 1s for 50% by weight SiC filled PLA filaments and this value reduces
significantly compared to pure PLA filaments whose recovery speed is around 2s. Filaments filled with 10%, 20%, and 30% by weight of SiC have similar trend of increasing recovery rate and the highest recovery speed is about 80 degree/sec at 1.5s. For graphite filled PLA filaments, the maximum recovery rate is far higher than SIC. The highest observed rate is about 500 degree/sec for 30% by weight of filler loading at 0.5s. For the 50% by weight of graphite filled PLA filaments, the peak recovery rate is around 400 degree/sec with minimal time used of 0.33s. Filaments filled with 20% by weight reaches the highest recovery speed at the same time with 30% but its value is about 390 degree/sec, which is the lowest among the three.
8
We then measured the thermal conductivities of the filaments based on the ASTM D5470 test method. The experimental arrangement is shown in Figure 14 with the sample sandwiched between the two heat input rods and six temperature sensors, which are located at points 1-3 and 5-7. A constant electrical power kept a constant heat flow rate from the heater. Temperature data from six sensors were recorded after the system reached a thermal equilibrium. Each sample was tested at three different steady-state temperature conditions. The thermal conductivity of the sample was calculated with the considerations of heat loss and thermal contact conductance. The whole system is divided by seven sections:
~ . The heat
flow of each section is calculated as: (1) Based on geometry, the temperature of sample surface can be calculated as: (2) (3) Thermal contact conductance across the contact surface isdefined as follows [30-32]: (4) Then the equations for the heat transition of heat input sections are developed as: (5) (6)
(7) The only unknown parameters convection coefficient h was calculated by Eq. (6). The sample conductivity
was estimated with Eq. (7).
Figure 15 shows typical temperature
readings from the sensors for a specimen. The data corresponds to the PLA composite with 40 wt% graphite and 20 wt% SiC fillers. The system reaches steady state within 30 minutes and the
9
thermal conductivity of the specimen and the effective heat loss coefficient (h) are determined simultaneously using Eqs. (5), (6) and (7). For the data shown in Figure 15, the heat loss coefficient h can be determined as 5.23 W/(m2.°C) and the thermal conductivity of the material as K=4.65 W/(m.°C). Figure 16 shows the measured thermal conductivity and the time
at which the peak
recovery rate was observed after triggering the shape recovery. The decrease in recovery time can be correlated with the higher conductivity associated with high filler loading. The recovery time drops 87% as the conductivity increases seven-fold from that of the pure PLA.
5. Shape Recovery of 3D Printed Samples In this section, the shape recovery behavior (tensile strain) for samples printed from the composite filaments is described. The 3D printed samples were trained under stretching strains and the shape recovery process reverses sample strain. The FDM (Fused deposition modeling) printers were used and printing parameters including input filament diameter, printing temperature, printi
spee a
res l ti
were a j ste via the pri ter’s fir ware. There are
three kinds of samples prepared, pure PLA, PLA with 20 wt.% SiC filler and PLA with 50 wt.% SiC filler. Figure 17 shows the dimensions of the tensile specimen. The microstructures of sample surface were observed by an optical microscope are as shown in Figure 18. Based on the fila e t’s ia eter, the ista ce betwee each pri t road was adjusted to prevent the gap. Print road is the term used for the extruded form of the material coming out of die in a 3D printer. Print roads are laid down side-by-side in each layer of the printed part. The sample had considerable voids between the layers and within roads of a single layer. A tensile fixture was designed as shown in Figure 19. The samples were heated to 90°C and stretched at 3% tensile strain (samples failed beyond that load) by tightening the grip clamps. After cooling, the gage length was measured. The shape memory of the samples were triggered
10
by immersing the specimens into hot water. The comparison of trained and recovered samples with initial ones is shown in Figure 20. The samples are almost recovered back to initial length. Figure 21 shows the applied training strain and the strain measured in the specimens after the shape recovery. The applied strain during the training phase was kept slightly below failure strain for each of the specimens. The pure PLA and PLA with 20%wt SiC were seen to have a residual compressive strain after the shape recovery. The printed tensile specimens with the three material compositions had approximately 3% strain recovered shape memory transition. The last evaluation was to measure the force during the shape recovery and influence of the fiber loading on the shape recovery force. A thin curved sheet was designed and printed. The curved sheet specimen was trained to be straight. This evaluation was meant to be a qualitative measure of how the stiffness of the composite translates to the force during the shape recovery process. The shape recovery bent the thin sheet back into a curve and the force produced against a gage was used as the measure. The shape recovery was trigged with a hot plate with temperature set to 100 ℃. A sensitive digital force gage (Wagner Instruments, Greenwich, CT 06836, USA) was used for measuring the magnitude of force. This force sensor has a capacity of 250 N, a resolution of 0.2 N and the ability to record the peak tensile. Figure 22 shows the schematic of the arrangement of the devices used, i.e. the force sensor, test sample, hot plate and clip. The force sensor is positioned such that its measuring tip is located at the center of the sample. The peak mode of the sensor provides the maximum force applied against the sensor during the transition. Figure 23 shows the maximum recovery force measured for various SiC/PLA composites. The composites show relatively larger recovery force compared with pure PLA sheets. An increase in the filler loading increases the force by 12.9% and 76% for 20 % and 50 % by weight SiC composites. This experiment, albeit qualitative, shows that the stiffening the composite translates to increased force during the shape transition.
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6. Summary and Conclusions In summary, the shape memory characteristics of 3D printed photo-cured polymer have been investigated for their long-term self-recovery and thermal trigger recovery characteristics. Three parameters (recovery ratio, recovery rate and recovery time) are defined to evaluate the shape recovery performance. The angle recovery ratio is calculated as the fraction of the initial angle that is recovered after training. The recovery rate is the instantaneous time derivative of the recovery ratio. The recovery time is defined as the time interval between the initiation of the thermal trigger and the instant at which the recovery rate attains a maximum value. The process used for extruding the printer filament and for 3D printing has been described in detail. SiC and graphite are selected as fillers for PLA typically for their mechanical and thermal properties. The dimensional quality of filaments and printed parts were examined. The relationship between thermal conductivity and shape memory performance was investigated with several samples. This drop in recovery time is remarkable for high filler loading composites. For a 50 wt% C+10wt% SiC/ PLA composite the recovery time decreases by 87% when compared with pure PLA. Table 1 presents a summary of all the composite composites, experiments and shape recovery behavior results presented in this paper. The gray boxes represents the combinations for which reliable 3D printed tensile specimens could not be produced. Therefore, the shape tensile specimen shape recovery data could not be obtained. The tables shows a definitive relationship between the conductivity and the recovery time. In this paper, we demonstrated that by designing the material composition, thus altering the thermal conductivity, the shape memory response rates can be controlled. The composition to shape-to-shape recovery rate relationship (though thermal conductivity estimation) can help design structures that can activate shape memory responses at various rates. With the use of multiple filaments and print heads, 3D printing technologies enable manufacturing structures with highly heterogeneous material compositions. This approach is different from methods that were
12
demonstrated in earlier such as temperature variation [33] or structural thickness variation [34] to alter the shape memory response activation timing in the structure.
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Figure 1: Experimental setup used to capture the shape recovery behavior. The shape recovery of the sample is visually recorded and analyzed.
(b) (a) Figure 2: (a) A sequence of images obtained during shape recovery process. (b) Typical recovery rate behavior and the recovery time are determined from the image sequences.
18
Figure 3: Fabrication steps used to produce the composite filaments for 3D printing.
19
(a)
(b)
(c) Figure 4: Filament and cross-section images. (a) Pure PLA, (b) PLA with SiC fillers, (c) PLA with graphite fillers
20
Figure 5: Measured filament diameter with different percentages of SiC
Figure 6: Measured filament diameter with different graphite/SiC ratios
21
Figure 7: Relationship between density and weight percentage of SiC.
Figure 8: SEM Images of filament cross-section shown for PLA fibers with 50 % by weight SiC.
22
O
(t)
A
B
Figure 9: (a) Original shape and the (b) temporary shape of filament and the bend angle parameter ( (t)) used to measure the shape recovery rate.
23
Figure 10: Image sequences obtained for various filaments showing the shape recovery of pure PLA and PLA with SiC filaments. Times shown above is the time at which each picture is taken after triggering the shape recovery in the filament.
24
Figure 11: Shape memory behavior of PLA with graphite filaments.
25
Figure 12: The recovery rate behavior of the SiC-filled PLA filaments. The recovery time is seen to reduce with the addition of the SiC fillers.
26
Figure 13: The recovery rate for graphite-filled PLA filaments, The shape recovery time is again seen to be shorter for more conductive filaments.
Figure 14. Schematic of the experimental setup used for thermal conductivity measurements. Six sensors are located at points 1-3 and 5-7.
27
Figure 15. Temperatures recorded by the six sensors during the conductivity measurement experiment.
Figure 16. Relationship between thermal conductivity and maximum shape recovery time (Tmax)
28
Top View All dimensions are in mm
Layering (Thickness) View
Figure 17. Design of the tensile specimen used for 3D printing and shape recovery experiments.
29
Roads
Figure 18. Images of the surface (Top view) and Layering View (Side view) of the 3D printed specimen. The printer extrusion paths (roads) are seen in both the views.
Figure 19. Loading fixture used to deform the specimens to an elongated temporary shape.
30
Figure 20. Comparison of trained and recovered samples with initial ones
Figure 21. Shape recovery behavior of printed composite tensile coupons
31
Figure 22. Recovery force measurement experiment with a force gage
Figure 23. Measured recovery forces for various SiC-filled composites.
32
Filament Recovery Time
Tmax (s)
3D Printed Specimen Training Strain (%)
Recovered Strain (%)
Recovery Force (N)
Thermal Conductivity (W/(m*K))
PLA 1.90 3.25 -0.11 1.75 0.69 PLA+10%SiC 1.45 0.95 PLA+20%SiC 1.40 2.90 -0.17 1.98 1.25 PLA+50%SiC 1.30 3.00 0.38 3.08 1.65 PLA+40%SiC 0.70 3.57 +10%C PLA+40%SiC 0.60 3.88 +20%C PLA+50%SiC 0.25 4.78 +10%C Table 1. A summary of all the presented. Gray shaded cells indicate configurations for which 3D printing could not be optimized and hence, no reliable data was obtained.
33
S1359-835X(18)30058-7 https://doi.org/10.1016/j.compositesa.2018.02.017 JCOMA 4931
To appear in:
Composites: Part A
Received Date: Revised Date: Accepted Date:
11 October 2017 6 February 2018 9 February 2018
Please cite this article as: Liu, W., Wu, N., Pochiraju, K., Shape Recovery Characteristics of SiC/C/PLA Composite Filaments and 3D Printed Parts, Composites: Part A (2018), doi: https://doi.org/10.1016/j.compositesa.2018.02.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Shape Recovery Characteristics of SiC/C/PLA Composite Filaments and 3D Printed Parts Wenbo Liu1, Nan Wu1, Kishore Pochiraju2 1
Graduate Research Assistant 2
Professor
Department of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA
Abstract The shape recovery characteristics of SiC and Carbon filled (poly) lactic acid (PLA) filaments extruded for use with Fused Deposition Modeling (FDM) and parts printed with FDM have been analyzed. The SiC/C /PLA composite filaments were made with particle loading up to a maximum weight fraction of 60%. The shape recovery characteristics of the filaments and printed parts were tested with bending and tensile loads. Two parameters, recovery rate and recovery time were defined and monitored during the shape recovery process. This study shows that the recovery time can be correlated to the thermal conductivity of the material. The results show a viable method for tailoring the recovery time. Furthermore, tensile specimens were 3D printed and the shape recovery behavior can be observed in the printed structures. This paper describes fabrication methods, SMP composite response results and a correlation of SMP response with the composite thermal conductivity.
Corresponding author. E-mail addresses: [email protected]
1
1. Introduction Shape Memory Polymers (SMPs), when subjected to an appropriate stimulus that rearranges its internal structures, transform from a programmed temporary shape to an original permanent shape. Stimulus can be provided in the form of heat, light, electricity, moisture, pH and magnetic fields [1]. Having properties of moderate cost, low density, as well as potential biocompatibility and biodegradability, shape memory behavior has been sought for developing novel intelligent materials.
Polyurethane,
polylactic
acid (PLA),
poly (ethylene vinyl
acetate),
and
polycaprolactone (PCL) are some of the polymer systems that exhibit shape memory properties [2-5]. Several of these polymers are used in modern additive manufacturing systems such as the Fused Deposition Modeling [6-8] in which a filament is melted and extruded through a die and the deposition point in space is changed by a computer-controlled three axis machine. The shape memory response can be altered by adding fillers and the resultant polymer composites (SMPCs) may yield higher strength, stiffness and other physical properties. Adding carbon black to rubber improved the abrasion resistance and mechanical properties of the tires, enhancing its durability [9]. A variety of fiber and particle reinforced polymer matrix composites with novel fillers such as carbon nanofibers, inorganic whiskers, and nanoclay have been widely discussed [10-14]. Most particulate fillers have poor compatibility with polymers since their surfaces are inorganic and polar in nature, so problems like poor dispersion and high viscosities may hinder further manufacturing processes [15]. One solution to these problems is the use of some additives to beneficially modify the surface of fillers. A wide range of such surface modifiers including fatty acids have been commercially used [16]. Another solution is to improve the mixing process by applying powerful compounding machinery, especially when considering high filler loadings [17-19]. Silicon carbide (SiC) has been used as a filler and fibers in composites by its virtue such as high stiffness and temperature resistance. Erkliğ, et al. [20] examined the mechanical properties
2
of the silicon carbide micro-particle filled polyester resin with various filler contents. They found that the SiC filler significantly enhanced the tensile and flexural modulus values of the polyester composites compared to pure polyester resin. However, with more SiC added, the strength of the composites showed a decreasing trend. Tests revealed the effects of the relation between the quality of the particle/matrix interfacial bonding strength and stress transfer between the matrix and particulate fillers. Bi, et al. [21] developed a polymer composite with high dielectric permittivity,
low dielectric loss, and improved mechanical properties by using embedded
SiC/SiO2-W core-shell structure filler in poly (vinylidene fluoride) (PVDF) matrix. Hulugappa, et al. [22] showed that the addition of SiC improved not only the tensile and flexural properties and impact strength, but also the mode-I fracture toughness. Zhang, et al. [23] studied the effect of graded aggregate on the erosion resistance by making use of SiC with two kinds of particle size and epoxy resin. Results from the tests indicated that the erosion rate dropped with increasing hard spots and density and composite interface was also obtained from the modifications. Addition of graphite and carbon fillers is known to enhance thermal and electrical conductivity of composites. Ariño, et al. [24] discussed the influence of extrusion parameters such as screw geometry, temperature and screw speed on the electrical properties of graphite nanoplatelet-filled poly(ethylene-butyl acrylate) composites. Senyk, et al. [25] explored electromagnetic radiation (EMR) shielding performance of carbon-polymer materials. Scocchi, et al. [26] developed a finite element (FE) method that could effectively calculate the electrical conductivity of compression molded polymer-graphite composites. They investigated the influence of model features with different representative volume elements (RVEs) to calculations and accurate results could be obtained by adjusting appropriate parameters. Vilaverde, et al. [27] studied the mixing characteristics of graphite nanoplates (GnP) in polypropylene melt with a aid of a small scale extensional mixer. Literature indicates substantial evidence that graphite can be dispersed into polymeric matrices and conductivity of the resultant composite materials can be tailored to suit the design needs.
3
In this paper, we describe a study on the effect of adding stiffening and conductive fillers in PLA matrix on the shape memory behaviors of Fused Deposition Modeling (FDM) filaments and printed parts. Three parameters, a recovery ratio, a recovery rate and a recovery time, are defined to evaluate the shape recovery performance of the material. Parameterization of the shape recovery response is described in the next section. The fabrication methods for both filaments and 3D printed tensile coupons are discussed in the third section. Fourth section of the paper describes the shape memory response for composites with SiC, C and SiC+C hybrid materials. We present the conductivity measurement, correlation between the conductivity and shape recovery characteristics. A relationship between the composite stiffness and force exerted by the composite during shape recovery are presented in the last three sections of the paper.
2. Parameterizing Shape Recovery Performance. In order to measure the shape recovery performance of composites, we define three parameters [28] --- bend angle recovery ratio
r
, recovery rate
, which is the time
r
rate of change of the bend angle recovery ratio, and max recovery time (Tmax), defined at the time at which the recovery rate is maximum (i.e. Tmax = argmax(
. For each material
composition, these parameters have been measured with 3D printed parts and FDM filaments produced initially with a fixed bend angles (e.g. 90° or 0°) and programming them to temporarily deformed angles. The shape recovery is triggered by immersing the part into water heated to 90°C. The shape transformation is monitored using a high-resolution microscope. Figure 1 shows the experimental set up used to trigger the shape recovery and imaging system mounted on the top that produces a high-resolution sequence of images that were analyzed to determine the shape recovery parameters. The angle recovery ratio is calculated as below: r
r ra
e
le – ri te
rre t le
le
t
(1)
4
The recovery rate (
, which is the instantaneous time derivative of the recovery ratio (Rr),
can be calculated by processing the sequence of images obtained during the recovery process. A series of pictures are shown in Figure 2(a). During the recovery process the current bend angle (Ө(t)) can be evaluated through image processing and recovery rate can be numerically determined. Figure 2(b) shows the recovery rate with respect to time and the recovery time (Tmax) for a typical specimen.
3. Fabrication and Characterization of the Filled Polymer Filaments In this effort, silicon carbide and graphite powders were used as fillers in polylactic acid matrix (PLA). The reinforcement powders were mixed and 3D printer filaments were extruded. The filaments were then fed into modified 3D printers using fused deposition modeling techniques. Figure 3 shows the steps used to produce filaments and print the parts. The figure also lists the process variables that were controlled to produce filaments and parts with optimized quality.
The reinforcements and matrix powders were first dried and fed into a ball mill. All
powders were dried in a Barnstead oven at 70 ℃ for 4 hours before further processing. The constituent materials used in this study were 74 micron PLA (Polylactic acid) powder of Natureworks IngeoTM Biopolymer 4032D supplied by NatureWorks® Co. LLC, USA, 15 micron black SiC (Silicon carbide) powder supplied by Panadyne Inc., and 44 micron Graphite Dry Lubricant supplied by Loudworlf.. The particulate-filled feedstock was compounded using a Lortone 33B Rotary Rock Tumbler at a rotation speed of 64 rpm. The dry powder mixing was performed in two molded-rubber barrels. Chrome steel balls with different sizes were added into the mixture for better particle dispersion and low material agglomeration according to the best practices in ball milling [29]. The ball milling process was conducted for 4 hours at room temperature. The extrusion process parameters were optimized for consistent diameter and density throughout the length of the filament. The weight fraction of the reinforcements were
5
controlled at both the powder and the filament scales, using an electronic balance with a precision of 0.01g. Before extrusion, each composite was sieved to ensure particle size consistency and to remove the chrome steel balls from the mixture. Powder mixtures were hermetically sealed in plastic bags preventing moisture absorption. A single screw extruder was used to produce the FDM filaments from the balled milled mixtures. The filaments are extruded at a constant die temperature and screw rotation speeds. In order to obtain filaments with a consistent diameter that can be appropriate for feeding into FDM machine, several tailorable variables such as die diameter, temperature and screw speed were analyzed and optimized. All filaments were extruded from a die with bore diameter of 1.35mm, melt temperature of 180 ℃ and a screw rotation speed of 35 rpm. Figure 4 shows the cross-sections of pure PLA, PLA with SiC and PLA with graphite filaments as observed through an optical microscope. The filaments were also characterized to determine the diameter variation, density and particle dispersion within the cross-sections. As shown in Figure 5, the diameter of filament decreases as more SiC was added into PLA as a whole and PLA with 20 % by weight of SiC has the diameter that is most close to standard value of 1.75mm with a deviation of around 0.06mm. One important explanation of this trend is that the filler decreased the swelling of the polymer at the die exit. Larger deviations in diameter are observed when SiC content is too low or too high, indicating the need for a closed-loop control of the extruder to produce more consistent filament diameters. At high filler loading filament, the extrusion rate was decreased significantly to account for the increased viscosity and the surface of the filament had a coarse texture.
Figure 6 shows the diameter variation data for varying
proportions of SiC and graphite filaments. Data of all these composite filaments are roughly near the standard value 1.75mm with variations of no more than 0.06mm about the mean diameter. Figure 7 shows the comparison of the theoretical and measured densities of the composite filaments. The dashed line shows the theoretical density variation for a composite with SiC loading in a PLA matrix. The measured mean densities correlate well with the expected density of
6
a homogenized composite. However, the filaments have density variations in the order of 5% of the mean density. The higher weight fractions composites have measurably lower density than the theoretical density indicating the presence of voids. We found that Pure PLA and SiC-filled PLA filaments provide better cross-section roundness than graphite-filled PLA filament. The SiC particles in PLA matrix were observed to be more evenly dispersed than graphite. In order to examine the dispersion of SiC in PLA further, the microstructure of the cross-section is analyzed with a scanning electron microscopy (SEM). Figure 8 shows the SEM micrograph of PLA with 50 % by weight SiC/PLA filament crosssection. Images indicate that SiC particles are well dispersed in the PLA matrix. As the SiC feedstock used before ball mill had 15 micron particle size, the milling considerably reduce SiC particle size. SEM images show SIC particles with sizes around 5 m. For some experiments, we extruded straight fragments (without coiling on the spool winder) and specimens with a length of 80 mm were cut from the extruders. The temporary shape programming of the specimens was carried at the temperature of 90 ℃. The straight filaments were bent into a U shape and the temperature was reduced to the room temperature, thereby fixing the shape. Figure 9 shows the straight and bent specimens. A single parameter, (t), was defined to measure the shape recovery state. The two ends of the filament (points A and B) and mid-point of the filament (O) were identified from image processing and the angle(AOB), (t), was tracked during shape recovery process. The shape recovery back to the straight filament was triggered by placing the bent wires into a bath of hot water. A video camera was used for image capture and processing during the shape recovery phase.
4. Shape recovery behavior of FDM filaments The shape recovery behavior of the filaments and parts was studied using series of images from the video obtained during the shape recovery. Figure 10 shows the shape memory behavior
7
of the pure PLA and PLA composites with various percentages of SiC loading. The shape of the pure PLA filament significantly slows down between 1.5 - 2.5 seconds after triggering. The test results show that the SiC filled composites recover faster than pure PLA matrix. This drop of recovery time is remarkable only for high filler loading composites such as the PLA with 50 % by weight SiC and its recovery time drops 40% compared with pure PLA. Graphite filled composites exhibit a much faster recovery speed (low recovery times). Figure 11 shows the recovery behavior of the Graphite/PLA composites. The images indicate that the shape recovery for this composite is complete within about 0.5 s. The higher thermal conductivity of graphite is the expected cause for the dramatic change in shape memory behavior. Figure 12 and 13 show the average recovery rate behavior
r
for SiC and
graphite filled PLA filaments. The sample data is collected from three to ten specimens for each type of filament. The addition of SiC into PLA matrix increases the peak recovery rate (max (
)). The maximum recovery rate of 50% by weight SiC filled PLA filaments is about 120
degrees/sec, which is nearly twice as the pure PLA filaments. Besides, time used to reach the peak value
is 1s for 50% by weight SiC filled PLA filaments and this value reduces
significantly compared to pure PLA filaments whose recovery speed is around 2s. Filaments filled with 10%, 20%, and 30% by weight of SiC have similar trend of increasing recovery rate and the highest recovery speed is about 80 degree/sec at 1.5s. For graphite filled PLA filaments, the maximum recovery rate is far higher than SIC. The highest observed rate is about 500 degree/sec for 30% by weight of filler loading at 0.5s. For the 50% by weight of graphite filled PLA filaments, the peak recovery rate is around 400 degree/sec with minimal time used of 0.33s. Filaments filled with 20% by weight reaches the highest recovery speed at the same time with 30% but its value is about 390 degree/sec, which is the lowest among the three.
8
We then measured the thermal conductivities of the filaments based on the ASTM D5470 test method. The experimental arrangement is shown in Figure 14 with the sample sandwiched between the two heat input rods and six temperature sensors, which are located at points 1-3 and 5-7. A constant electrical power kept a constant heat flow rate from the heater. Temperature data from six sensors were recorded after the system reached a thermal equilibrium. Each sample was tested at three different steady-state temperature conditions. The thermal conductivity of the sample was calculated with the considerations of heat loss and thermal contact conductance. The whole system is divided by seven sections:
~ . The heat
flow of each section is calculated as: (1) Based on geometry, the temperature of sample surface can be calculated as: (2) (3) Thermal contact conductance across the contact surface isdefined as follows [30-32]: (4) Then the equations for the heat transition of heat input sections are developed as: (5) (6)
(7) The only unknown parameters convection coefficient h was calculated by Eq. (6). The sample conductivity
was estimated with Eq. (7).
Figure 15 shows typical temperature
readings from the sensors for a specimen. The data corresponds to the PLA composite with 40 wt% graphite and 20 wt% SiC fillers. The system reaches steady state within 30 minutes and the
9
thermal conductivity of the specimen and the effective heat loss coefficient (h) are determined simultaneously using Eqs. (5), (6) and (7). For the data shown in Figure 15, the heat loss coefficient h can be determined as 5.23 W/(m2.°C) and the thermal conductivity of the material as K=4.65 W/(m.°C). Figure 16 shows the measured thermal conductivity and the time
at which the peak
recovery rate was observed after triggering the shape recovery. The decrease in recovery time can be correlated with the higher conductivity associated with high filler loading. The recovery time drops 87% as the conductivity increases seven-fold from that of the pure PLA.
5. Shape Recovery of 3D Printed Samples In this section, the shape recovery behavior (tensile strain) for samples printed from the composite filaments is described. The 3D printed samples were trained under stretching strains and the shape recovery process reverses sample strain. The FDM (Fused deposition modeling) printers were used and printing parameters including input filament diameter, printing temperature, printi
spee a
res l ti
were a j ste via the pri ter’s fir ware. There are
three kinds of samples prepared, pure PLA, PLA with 20 wt.% SiC filler and PLA with 50 wt.% SiC filler. Figure 17 shows the dimensions of the tensile specimen. The microstructures of sample surface were observed by an optical microscope are as shown in Figure 18. Based on the fila e t’s ia eter, the ista ce betwee each pri t road was adjusted to prevent the gap. Print road is the term used for the extruded form of the material coming out of die in a 3D printer. Print roads are laid down side-by-side in each layer of the printed part. The sample had considerable voids between the layers and within roads of a single layer. A tensile fixture was designed as shown in Figure 19. The samples were heated to 90°C and stretched at 3% tensile strain (samples failed beyond that load) by tightening the grip clamps. After cooling, the gage length was measured. The shape memory of the samples were triggered
10
by immersing the specimens into hot water. The comparison of trained and recovered samples with initial ones is shown in Figure 20. The samples are almost recovered back to initial length. Figure 21 shows the applied training strain and the strain measured in the specimens after the shape recovery. The applied strain during the training phase was kept slightly below failure strain for each of the specimens. The pure PLA and PLA with 20%wt SiC were seen to have a residual compressive strain after the shape recovery. The printed tensile specimens with the three material compositions had approximately 3% strain recovered shape memory transition. The last evaluation was to measure the force during the shape recovery and influence of the fiber loading on the shape recovery force. A thin curved sheet was designed and printed. The curved sheet specimen was trained to be straight. This evaluation was meant to be a qualitative measure of how the stiffness of the composite translates to the force during the shape recovery process. The shape recovery bent the thin sheet back into a curve and the force produced against a gage was used as the measure. The shape recovery was trigged with a hot plate with temperature set to 100 ℃. A sensitive digital force gage (Wagner Instruments, Greenwich, CT 06836, USA) was used for measuring the magnitude of force. This force sensor has a capacity of 250 N, a resolution of 0.2 N and the ability to record the peak tensile. Figure 22 shows the schematic of the arrangement of the devices used, i.e. the force sensor, test sample, hot plate and clip. The force sensor is positioned such that its measuring tip is located at the center of the sample. The peak mode of the sensor provides the maximum force applied against the sensor during the transition. Figure 23 shows the maximum recovery force measured for various SiC/PLA composites. The composites show relatively larger recovery force compared with pure PLA sheets. An increase in the filler loading increases the force by 12.9% and 76% for 20 % and 50 % by weight SiC composites. This experiment, albeit qualitative, shows that the stiffening the composite translates to increased force during the shape transition.
11
6. Summary and Conclusions In summary, the shape memory characteristics of 3D printed photo-cured polymer have been investigated for their long-term self-recovery and thermal trigger recovery characteristics. Three parameters (recovery ratio, recovery rate and recovery time) are defined to evaluate the shape recovery performance. The angle recovery ratio is calculated as the fraction of the initial angle that is recovered after training. The recovery rate is the instantaneous time derivative of the recovery ratio. The recovery time is defined as the time interval between the initiation of the thermal trigger and the instant at which the recovery rate attains a maximum value. The process used for extruding the printer filament and for 3D printing has been described in detail. SiC and graphite are selected as fillers for PLA typically for their mechanical and thermal properties. The dimensional quality of filaments and printed parts were examined. The relationship between thermal conductivity and shape memory performance was investigated with several samples. This drop in recovery time is remarkable for high filler loading composites. For a 50 wt% C+10wt% SiC/ PLA composite the recovery time decreases by 87% when compared with pure PLA. Table 1 presents a summary of all the composite composites, experiments and shape recovery behavior results presented in this paper. The gray boxes represents the combinations for which reliable 3D printed tensile specimens could not be produced. Therefore, the shape tensile specimen shape recovery data could not be obtained. The tables shows a definitive relationship between the conductivity and the recovery time. In this paper, we demonstrated that by designing the material composition, thus altering the thermal conductivity, the shape memory response rates can be controlled. The composition to shape-to-shape recovery rate relationship (though thermal conductivity estimation) can help design structures that can activate shape memory responses at various rates. With the use of multiple filaments and print heads, 3D printing technologies enable manufacturing structures with highly heterogeneous material compositions. This approach is different from methods that were
12
demonstrated in earlier such as temperature variation [33] or structural thickness variation [34] to alter the shape memory response activation timing in the structure.
13
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Figure 1: Experimental setup used to capture the shape recovery behavior. The shape recovery of the sample is visually recorded and analyzed.
(b) (a) Figure 2: (a) A sequence of images obtained during shape recovery process. (b) Typical recovery rate behavior and the recovery time are determined from the image sequences.
18
Figure 3: Fabrication steps used to produce the composite filaments for 3D printing.
19
(a)
(b)
(c) Figure 4: Filament and cross-section images. (a) Pure PLA, (b) PLA with SiC fillers, (c) PLA with graphite fillers
20
Figure 5: Measured filament diameter with different percentages of SiC
Figure 6: Measured filament diameter with different graphite/SiC ratios
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Figure 7: Relationship between density and weight percentage of SiC.
Figure 8: SEM Images of filament cross-section shown for PLA fibers with 50 % by weight SiC.
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O
(t)
A
B
Figure 9: (a) Original shape and the (b) temporary shape of filament and the bend angle parameter ( (t)) used to measure the shape recovery rate.
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Figure 10: Image sequences obtained for various filaments showing the shape recovery of pure PLA and PLA with SiC filaments. Times shown above is the time at which each picture is taken after triggering the shape recovery in the filament.
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Figure 11: Shape memory behavior of PLA with graphite filaments.
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Figure 12: The recovery rate behavior of the SiC-filled PLA filaments. The recovery time is seen to reduce with the addition of the SiC fillers.
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Figure 13: The recovery rate for graphite-filled PLA filaments, The shape recovery time is again seen to be shorter for more conductive filaments.
Figure 14. Schematic of the experimental setup used for thermal conductivity measurements. Six sensors are located at points 1-3 and 5-7.
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Figure 15. Temperatures recorded by the six sensors during the conductivity measurement experiment.
Figure 16. Relationship between thermal conductivity and maximum shape recovery time (Tmax)
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Top View All dimensions are in mm
Layering (Thickness) View
Figure 17. Design of the tensile specimen used for 3D printing and shape recovery experiments.
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Roads
Figure 18. Images of the surface (Top view) and Layering View (Side view) of the 3D printed specimen. The printer extrusion paths (roads) are seen in both the views.
Figure 19. Loading fixture used to deform the specimens to an elongated temporary shape.
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Figure 20. Comparison of trained and recovered samples with initial ones
Figure 21. Shape recovery behavior of printed composite tensile coupons
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Figure 22. Recovery force measurement experiment with a force gage
Figure 23. Measured recovery forces for various SiC-filled composites.
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Filament Recovery Time
Tmax (s)
3D Printed Specimen Training Strain (%)
Recovered Strain (%)
Recovery Force (N)
Thermal Conductivity (W/(m*K))
PLA 1.90 3.25 -0.11 1.75 0.69 PLA+10%SiC 1.45 0.95 PLA+20%SiC 1.40 2.90 -0.17 1.98 1.25 PLA+50%SiC 1.30 3.00 0.38 3.08 1.65 PLA+40%SiC 0.70 3.57 +10%C PLA+40%SiC 0.60 3.88 +20%C PLA+50%SiC 0.25 4.78 +10%C Table 1. A summary of all the presented. Gray shaded cells indicate configurations for which 3D printing could not be optimized and hence, no reliable data was obtained.
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