Morphing effect control is still a major challenge in 4D-printing of polylactic acid (PLA). In this work, the influence of extrusion-based 3D-printing parameters on PLA-based material morphing was studied. A design of experiments was performed, where 5 factors (printing temperature, bed temperature, printing speed, fan speed, and flow) were explored at 2 levels. Crystallinity and morphing properties of each 3D-printed structure were determined and discussed. The crystallinity rates of the PLA-based specimens ranged from ca. 14% up to ca. 71%. The interaction between bed temperature and printing speed showed a significant impact on PLA-based samples crystallinity, where using these two parameters at their higher levels contributed to producing PLA-based specimens with higher crystallinity. When exposed to an external thermal stimulus of 60 °C, all settings were capable of acquiring a temporary shape and recover between ca. 71% and ca. 99% of the original shape, depending on the configurations the recovery times ranged from 8 to 50 s. The configuration that resulted in the highest recovery rate was: printing temperature at 220 °C, bed temperature at 40 °C, printing speed at 80 mm/s, fan speed at 0%, and flow at 100%. Regarding recovery time, the configuration of 180 °C for printing temperature, 80 °C for bed temperature, 10 mm/s for printing speed, 100% for fan speed and 150% for flow resulted in the longest recovery time. Overall, the experimental results clearly showed that the parameters of extrusion-based 3D-printing influence the crystallinity and transformability of PLA-based materials.
Hinweise
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1 Introduction
Additive manufacturing (AM), also known as 3D-printing, is an advanced technology that allows the creation of three-dimensional objects through the addition of successive layers of a certain material. Over the last few years, the significant advances in materials, printers, and processes technology has enhanced the product design and engineering sector [1‐3]. An important development that emerged was the concept of 4D-printing, as this technology allowed the development of dynamic structures. While 3D-printing produces static structures by adjusting the geometry and printing parameters, 4D-printing allows to control the final shape of an adaptable structure with programmable configuration, with the ability of materials to respond to external stimuli, such as light, humidity, heat, pH, or magnetic field, over time [4‐7]. Various materials can be employed in 3D-printing; however, most of them cannot be applied to 4D-printing as these materials do not respond to external stimuli [5, 8, 9]. Shape memory polymers (SMP) were presented as a promising class of materials to reach the fourth dimension, as these materials exhibit an ability to recover from a temporary programmed shape to a permanent shape in response to an external stimulus [3, 10]. SMP in 4D-printing must have two important capabilities: intelligence and printability [5]. Polylactic acid (PLA) is a SPM widely used in AM and exhibits favorable SME thermally induced shape memory effect (SME) based on glass transition (\({T}_{g}\)) or melting (\({T}_{m}\)) temperatures [11, 12]. Despite progress in 4D-printing of PLA-based materials, there are gaps in literature that need to be explored, such as the effect of morphing in a controlled manner. Transformation capacity depends on the raw material microstructure, along with the processing methods and parameters. To control this effect, it is essential to accurately understand all of their dependencies. The essence of shape memory effect is driven by entropy [13], where the theory behind deformation of 4D-printing materials and structures depends on the release of internal stress/strain from two different steps: (i) processing step and (ii) programming step [14, 15]. Zhang et al. [16] found for the first time that PLA-based filaments exhibited significant heat- and speed-induced shrinkage during 3D-printing. Optimal 4D-printing parameters are the biggest concern for researchers [17]. In recent years, there has been an effort to study the influence of the main 3D-printing parameters, such as printing speed, density and infill pattern, raster angle, on the deformation of structures with different geometries [4, 15, 18‐24]. Despite the importance of studying the influence of morphing on printing strategies at a macroscopic level, the microscopic level of the polymeric chain should not be neglected. By compression molding, Cunha et al. [25] demonstrated that as high the PLA crystallinity degree, high the SME, both in fixation and shape recovery. Through changes in the molecular weight of PLA, the dependence between \({T}_{g}\) and the shape memory effect was reported [13]. The increase in molecular weight reflected in increased entanglement of molecular chains, thus increasing the fixed phase of the polymeric chain. As for the shape memory effect, the work concluded that the molecular weight had a significant effect on the recovery rate. The chemical structure and the modifications brought about by the addition of other polymeric structures or additives, play an important role in the shape-changing performance of a polymeric material. Although the authors did not relate their results to possible changes in the polymer chain Kačergis et al. [26], they showed that the bed temperature significantly influenced the deformation of the structures. Prints with a bed temperature of 25 °C and a printing speed of 80 mm/s resulted in higher deformations compared to structures printed on hotter surfaces (60 °C). They concluded that the colder the build plate, the greater the final deformation of the structures at any printing speed.
There is a large gap in literature with the lack of relationship between the influence of the polymeric structure, 3D-printing parameters settings and the response to shape changes [27]. Therefore, the aim of this work was to explore the relationship between the transformation of shape and microscopic properties of a fused filament fabricated PLA-based material. The effect of 3D-printing deposition parameters on PLA crystallinity was studied through a design of experiments (DoE). Printing temperature, bed temperature, printing speed, fan speed, and flow were the 3D-printing parameters considered. The relationship between these parameters and the shape transformation ability was established using the hot programming process.
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2 Materials and methods
2.1 Material and specimen processing
Everfil PLA-N.01 filament (diameter of 1.75 mm, green color, density of 1.24 g/cm3, tensile yield strength of 60 MPa, tensile modulus of 53 MPa, flexural strength of 83 MPa, and flexural modulus of 3.8 MPa), further referred as gPLA, was supplied by 3DKordo filament (Białystok, Poland) [28].
For the 3D-printing specimens, a cuboid (20 × 20 × 0.8 mm) was designed using Autodesk Fusion 360 software, saved in.STL format, and imported to the Ultimaker Cura 4.4 3D slicing software (Fig. 1).
Fig. 1
Schematic illustration of the samples under study in the Ultimaker Cura 4.4 software (cuboid with dimensions: 20 × 20 × 0.8 mm)
×
The (FFF) process was performed using a B2X300 3D printer BEEVERYCREATIVE (Aveiro, Portugal) equipped with a 0.4 mm nozzle. In the context of 4D-printing, there are numerous relevant processing parameters. However, the objective of this work was to address the deposition parameters, specifically the printing parameters that could influence polymer crystallization. Table 1 describes some printing parameters that remained constant throughout the work.
Table 1
FFF processing parameters
Parameter
Value
Layer height
0.2 mm
Initial layer height
0.3 mm
Wall thickness
1.2 mm
Wall line count
3
Top/Bottom pattern
zig zag
Infil density
100%
Infill layer thickness
0.2 mm
2.2 Design of experiments
To understand the relationship between the morphing and microscopic properties of gPLA, a semi-crystalline polymer, a design of experiment (DoE) approach was used. In this study, the first objective selected to investigate was the influence of 3D-printing parameters on PLA crystallinity. For this, a factorial design of 25 experiments was used, where 5 configurations were explored each one at 2 levels (Table 2).
Table 2
3D-printing parameters and levels from a full-factorial DoE
Factor
Low level (− 1)
High level (+ 1)
A Printing temperature (°C)
180
220
B Bed temperature (°C)
40
80
C Speed (mm/s)
10
80
D Fan speed (%)
0
100
E Flow (%)
100
150
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The 3D-printing temperature, bed temperature, printing speed, fan speed, and flow were the settings under study. Each level is represented by a low level (− 1) or high level (+ 1), according to the DoE theory. In the process of preparing the DoE, the full factorial design 25 was chosen in the JMP (SAS) software based on the total parameters selected. For this design, a total of 32 (25 = 32) experiments were carried out. Table 3 shows the order of specimens tested according to their configurations. In each experiment, 10 replicates were printed.
Table 3
Design matrix for 25 full factorial design
Sample
A
B
C
D
E
Sample
A
B
C
D
E
1
− 1
− 1
− 1
− 1
− 1
17
− 1
− 1
− 1
− 1
+ 1
2
+ 1
− 1
− 1
− 1
− 1
18
+ 1
− 1
− 1
− 1
+ 1
3
− 1
+ 1
− 1
− 1
− 1
19
− 1
+ 1
− 1
− 1
+ 1
4
+ 1
+ 1
− 1
− 1
− 1
20
+ 1
+ 1
− 1
− 1
+ 1
5
− 1
− 1
+ 1
− 1
− 1
21
− 1
− 1
+ 1
− 1
+ 1
6
+ 1
− 1
+ 1
− 1
− 1
22
+ 1
− 1
+ 1
− 1
+ 1
7
− 1
+ 1
+ 1
− 1
− 1
23
− 1
+ 1
+ 1
− 1
+ 1
8
+ 1
+ 1
+ 1
− 1
− 1
24
− 1
+ 1
+ 1
+ 1
+ 1
9
− 1
− 1
− 1
+ 1
− 1
25
− 1
− 1
− 1
+ 1
+ 1
10
+ 1
− 1
− 1
+ 1
− 1
26
+ 1
− 1
− 1
+ 1
+ 1
11
− 1
+ 1
− 1
+ 1
− 1
27
− 1
+ 1
− 1
+ 1
+ 1
12
+ 1
+ 1
− 1
+ 1
− 1
28
+ 1
+ 1
− 1
+ 1
+ 1
13
− 1
− 1
+ 1
+ 1
− 1
29
− 1
− 1
+ 1
+ 1
+ 1
14
+ 1
− 1
+ 1
+ 1
− 1
30
+ 1
− 1
+ 1
+ 1
+ 1
15
− 1
+ 1
+ 1
+ 1
− 1
31
− 1
+ 1
+ 1
+ 1
+ 1
16
+ 1
+ 1
+ 1
+ 1
− 1
32
+ 1
+ 1
+ 1
+ 1
+ 1
2.3 Characterization of 3D-printed specimens
2.3.1 Thermal properties
Thermal characterization was carried out by differential scanning calorimetry (DSC), using a Chip-DSC 10 calorimeter (Linseis Messgeraete GmbH, Selb, Germany). The samples were heated from 30 °C to 200 °C using 3 temperature gradients. First, an increase from 30 °C to 120 °C at a 10 °C/min heating rate was carried out, aiming to eliminate the samples’ thermal history; then, a temperature decrease from 120 °C to 30 °C at a 10 °C/min cooling rate was promoted; and, in the last stage, a temperature increase form 30 °C to 200 °C at a 5 °C/min heating rate was performed. Performing two consecutive cycles in DSC thermal analysis enhances the reliability, stability, and representativeness of sample thermal characteristics. This practice can facilitates precise identification and comprehension of thermal events, thus providing a comprehensive insight into the sample's thermal behavior.
The glass transition temperature (\({T}_{g})\), melting temperature (\({T}_{m})\), and enthalpy of melting (\(\Delta {H}_{m})\) were measured. The percentage of crystallinity (\({\chi }_{c})\) was estimated from the DSC results using the following equation:
where \(\Delta {H}_{m}\) is the enthalpy of melting and \(\Delta {H}_{m}^{o}\) is the theoretical melting heat of 100% crystalline PLA, taken from the literature as 93.7 J/g [29, 30].
2.3.2 Shape memory experiments
The deformation and recovery of PLA-based 3D structures were studied using the hot programming process (Fig. 2).
Fig. 2
Scheme of hot programming process
×
After 3D-printing (original shape), each PLA-based sample was immersed into water at 60 °C. With the aid of an experimental bending device, each sample was deformed to its temporary shape. The temporary shape remained unchanged in the device for 1 min. Afterwards, the sample were removed from the water bath and cooled down to room temperature (25 °C ± 0.5 °C). Within the shape recovery step, each sample was once heated to 60 °C until recover its original form. The shape recovery was recorded using a Canon EOS 1300D digital camera (Canon, Amstelveen, Netherlands); deformation and recovery correspond to the change in angle as a function of transformation time. Image analysis using ImageJ 1.53 k software (Rasband, W.S., U. S. National Institutes of Health, Bethesda, Maryland, USA) was applied to determine the change in transformation angle. The percent of shape recovery was determined using the following equation:
where the angle corresponds to the angle obtained by the ImageJ software at a certain moment. As the sample’s original shape was flat, the initial angle was 180° (Fig. 3). The measurements were carried out in triplicate for each shape recovery experiment under the same experimental conditions and device.
Fig. 3
Schematic illustration of measuring angles using ImageJ software. Representation of two states: a Original form, b Temporary form
×
2.4 Statistical analysis
The factors and significant interactions resulting from the design of experiences were identified through analysis of variance, ANOVA. An ANOVA is performed with the null hypothesis that the variance between two or more populations is equal and the result of the ANOVA is the f value. The f value indicates the ratio of the variance between the sample means to the variance between the samples. The higher the value of f value, the greater the significance of a parameter. A parameter is considered significant if the calculated f value is greater than the critical f value defined by the confidence interval. In addition, when the p value is less than the significance level (\({\upalpha }\)<0.05, 95% confidence interval), it was claimed to be statistically significant. A small p value means that the statistic is unlikely to be this extreme by chance. The p value is the probability of getting an even more extreme statistic, given that the true value being tested is at the hypothesis value, usually zero. Analysis of variance was performed using the JMP software version < 14.0.0 > , (SAS Institute Inc).
3 Results and discussion
3.1 Effect of 3D-printing parameters on PLA crystallinity
When 3D-printed using different extrusion temperature, bed temperature, printing speed, fan speed, and flow conditions, the PLA-based specimens achieved distinctive crystallinity rates, ranging from ca. 14% up to ca. 71% (Table 4).
Table 4
Crystallinity of 3D-printed PLA-based specimens at different extrusion temperature, bed temperature, printing speed, fan speed, and flow conditions
Sample
A
B
C
D
E
Xc (%)
Sample
A
B
C
D
E
Xc (%)
1
− 1
− 1
− 1
− 1
− 1
56.3
17
− 1
− 1
− 1
− 1
+ 1
24.1
2
+ 1
− 1
− 1
− 1
− 1
46.8
18
+ 1
− 1
− 1
− 1
+ 1
17.8
3
− 1
+ 1
− 1
− 1
− 1
25.8
19
− 1
+ 1
− 1
− 1
+ 1
41.5
4
+ 1
+ 1
− 1
− 1
− 1
50.9
20
+ 1
+ 1
− 1
− 1
+ 1
23.3
5
− 1
− 1
+ 1
− 1
− 1
35.4
21
− 1
− 1
+ 1
− 1
+ 1
17.0
6
+ 1
− 1
+ 1
− 1
− 1
23.7
22
+ 1
− 1
+ 1
− 1
+ 1
26.1
7
− 1
+ 1
+ 1
− 1
− 1
48.5
23
− 1
+ 1
+ 1
− 1
+ 1
57.9
8
+ 1
+ 1
+ 1
− 1
− 1
40.5
24
− 1
+ 1
+ 1
+ 1
+ 1
47.9
9
− 1
− 1
− 1
+ 1
− 1
42.2
25
− 1
− 1
− 1
+ 1
+ 1
39.7
10
+ 1
− 1
− 1
+ 1
− 1
25.4
26
+ 1
− 1
− 1
+ 1
+ 1
45.7
11
− 1
+ 1
− 1
+ 1
− 1
23.4
27
− 1
+ 1
− 1
+ 1
+ 1
22.9
12
+ 1
+ 1
− 1
+ 1
− 1
16.1
28
+ 1
+ 1
− 1
+ 1
+ 1
45.8
13
− 1
− 1
+ 1
+ 1
− 1
50.0
29
− 1
− 1
+ 1
+ 1
+ 1
45.8
14
+ 1
− 1
+ 1
+ 1
− 1
13.7
30
+ 1
− 1
+ 1
+ 1
+ 1
23.8
15
− 1
+ 1
+ 1
+ 1
− 1
34.0
31
− 1
+ 1
+ 1
+ 1
+ 1
44.8
16
+ 1
+ 1
+ 1
+ 1
− 1
48.4
32
+ 1
+ 1
+ 1
+ 1
+ 1
70.7
From ANOVA statistics (Table 5), the crystallinity of 3D-printed PLA specimens was highly influenced by the interaction between bed temperature and printing speed, showing an f value of 11.5024, contrary to the fan speed (f value of 0.0003). Moreover, it also changed significantly for the fan speed-printing flow (p value of 0.03297) and printing temperature-bed temperature (p value of 0.03968) interactions.
Table 5
ANOVA statistics for crystallinity of 3D-printed PLA-based samples
Source
DF
Sum of squares
f-Value
p-Value
Bed temperature (°C)*Speed (mm/s)
1
1451.2578
11.5024
0.00373
Fan speed (%)*Flow (%)
1
687.2778
5.4473
0.03297
Printing temperature (°C)*Bed temperature (°C)
1
632.7903
5.0154
0.03968
Bed temperature (°C)*Flow (%)
1
533.8278
4.2310
0.05638
Bed temperature (°C)
1
441.7878
3.5015
0.07971
Speed (mm/s)
1
255.9453
2.0286
0.17358
Bed temperature (°C)*Fan speed (%)
1
196.5153
1.5575
0.22998
Speed (mm/s)*Flow (%)
1
180.0253
1.4269
0.24969
Printing temperature (°C)*Flow (%)
1
142.3828
1.1285
0.30387
Speed (mm/s)*Fan speed (%)
1
76.5703
0.6069
0.44733
Printing temperature (°C)
1
33.4153
0.2648
0.61385
Printing temperature (°C)*Speed (mm/s)
1
18.7578
0.1487
0.70489
Flow (%)
1
17.5528
0.1391
0.71405
Printing temperature (°C)*Fan speed (%)
1
1.2403
0.0098
0.92225
Fan speed (%)
1
0.0378
0.0003
0.98640
PLA, due to the absence of a flexible methylene segment in its backbone, is a polymeric material with a very slow crystallization kinetics [31, 32]. Varying the crystallization temperature of PLA favors the formation of one crystalline structure of PLA rather than another [33]. To be crystallized into the α crystalline form, typically obtained during melting, PLA needs to be cooled down at a very low rate [34]. Therefore, by increasing the bed temperature, the cooling rate was slowed down, thus inducing the PLA crystallization. As for the printing speed, it has been considered an indirect parameter of inter-layer cooling time [34]. Thus, high printing speeds tends to accelerate the inter-layer cooling rate and lead to PLA structures with decreased crystallinity (Fig. 4).
Fig. 4
Distributions and prediction profiles of crystallinity values obtained in the JMP software in the interactions: a bed temperature and printing speed; b fan speed and flow; c printing temperature and bed temperature
×
Figure 4a shows that a more significant gap in crystallinity rate was indeed verified when the highest printing speed factor was used with the lowest bed temperature. Nevertheless, as shown by the results, this can be overcome by the increase of printing bed temperature. At the lowest print speed level, increasing bed temperature only slightly affects crystallinity degree and its value is kept relatively low. This means that, at lower speed the influence of other factors can overcome that of bed temperature. Fan speed-printing flow interactions (Fig. 4b), also affected the crystallinity of 3D-printed PLA specimens, which can also be related to the inter-layer cooling time. Indeed, if the fan speed is turned off, the layers will cool down more slowly, thus imparting a greater crystallinity to the final 3D structure. Flow, as a 3D-printing parameter that allows to control the amount of material extruded during the melt-filament deposition, also strongly influences the crystallization kinetics of polymeric materials [35], as highlighted in Fig. 4b shows that for 3D samples printed at a low flow (factor -1) and with the fan speed turned off (factor -1) demonstrated a higher crystallinity rate than the ones processed also at a low flow (factor -1) but with the fan speed turned on (factor + 1), showing, once again, the cooling rate effect on PLA crystallinity. Although no direct relationship between fan speed and flow during 3D-printing was evidenced, these parameters can affect the quality and accuracy of the 3D-printing, which can indirectly influence the part's crystallinity. Figure 4c shows that extruding PLA the lowest temperature (180 °C) resulted in 3D samples with similar crystallinity rates (ca. 33%), regardless of the bed temperature used, which did not occur for 3D specimens extruded at the highest temperature (220 °C). Furthermore, the cooling rate of the printed layers can also be influenced by the extrusion temperature. When the extrusion temperature is raised, the printed layers will stay above the crystallization temperature for longer, favoring the crystallinity of the 3D structure. On the other hand, when the extrusion temperature is low, the printed layers quickly cool down to a temperature that does not allow crystallization, compromising the crystallinity kinetics. With the extrusion temperature at the upper factor (+ 1) and using the printing bed at the lower value (factor -1), the PLA crystallinity decreased to ca. 25%. Thus, the low bed temperature combined with a high printing temperature may have cooled the printed layers more quickly, which caused a decrease in crystallinity. The opposite trend was verified when the samples were printed at the highest printing and bed temperature. The extrusion temperature is a critical parameter in 3D-printing, since it affects the filament ability to melt, its viscosity and posterior crystallinity of the 3D-printed part. An increased extrusion temperature can mean an excessive melting of the polymeric material, compromising the crystallinity rate of the 3D-printed structure. Contrarywise, for a low extrusion temperature, the filament may not completely melt, maintaining a high portion of amorphous zones [36, 37].
3.2 Influence of 3D-printing parameters on PLA’s morphing ability
When exposed to an external temperature stimulus, namely to a water bath at 60 °C, all the 3D-printed PLA samples were able to acquire a temporary shape as well as to recover part their original shape (Fig. 5).
Fig. 5
Shape change of 3D-printed PLA-based specimens within the hot programming process
×
Deformation and recovery correspond to the change in angle as a function of transformation time, as shown for Sample 6 (Fig. 6). The settings used in Sample 6 represented the highest recovery rate values in this study, where at 17.8 s with an external stimulus temperature of 60 °C the sample reached a transformation angle of 177.45°. Being the initial angle of 180°, in this case, the sample reached a recovery rate of 98.59%. As can be seen from the results obtained (Fig. 7), the printing parameters influence both the recovery rate and the time required for the temporary form to recover to its original form, that is, the relaxation of residual stresses present in each sample is different. It is important to emphasize that no sample managed to recover 100% of the original form, except Sample 9 that obtained a recovery rate above 100%, that could be an outlier, and, therefore, was not considered in the discussion. Ratios greater than 100% are attributed to the excess of the self-folded format compared to the straight-printed format. The samples under study were able to recover between 71.19% and 98.59% of their original shape, respectively Sample 27 and Sample 6. Considering the printing parameters used (Table 3) it is possible to see that these samples were printed with completely opposite parameters. Sample 6 was printed using the highest value of printing temperature and speed and the remaining parameters with the lowest value, bed temperature, fan speed and flow. Sample6 managed to get closer to its original shape and needed a shorter time compared to Sample 27. Therefore, Sample 6 showed a more effective and fact shape recovering. When observing the recovering, it was noted that was necessary between 8 and 50 s (Sample 15 and Sample 27, respectively) for the samples to reach their maximum shape recovery (94.42 and 71.19%, respectively). In these samples the printing conditions differ in printing speed and flow, where Sample 15 was processed with a higher printing speed and lower flow than Sample 27.
Fig. 6
Morphing behavior obtained from the recovery stage of one of the replicas of Sample 6. The graph represents the response curve obtained
Fig. 7
Recovery rate and recovery time obtained in all 3D-printed gPLA samples
×
×
3.2.1 Influence of 3D-printing parameters on shape recovery rate
When considering the effect of each 3D-printing parameter under study on the shape recovery rate and the prediction profiles determined by the JMP software (Fig. 8), it was noted that no linearity was obtained between them. With the objective of obtaining structures that present high recovery rates, it was possible to verify that the recovery rate increased with the decrease in bed temperature, flow and fan speed, Fig. 8a, c, and d, respectively. On the contrary, it was observed that the recovery rate increased with increasing printing speed and printing temperature, Fig. 8b, e. In 4D-printing, high recovery rates indicate that structures have the ability to recover their original shape faster and more efficiently. Thus, structures with higher recovery rates will be more robust and reliable over time, while structures with low recovery rates may be less effective in their intended function or may have a shorter life cycle.
Fig. 8
Distribution of results and prediction profiles obtained through the JMP software on the recovery rate: a bed temperature, b printing speed, c flow, d fan speed, e printing temperature
×
3.2.2 3D-printing parameters on shape recovery time
When comparing the recovery time with the original shape, no linearity was evidenced among themselves (Fig. 9). To obtain short recovery times, up to approximately 20 s, the configuration should be using high bed temperatures, printing speed, and fan speed, while maintaining low flow and printing temperature. Contrarywise, long recovery times can be achieved using high bed temperature, flow and fan speed, while using low printing speeds and printing temperature. The material extrusion rate, as shown in Fig. 9c, exhibited an increasing behavior profile. When a greater flow rate was utilized, this resulted in an observed prolongation of recovery time. A 3D-printed structure with a higher flow rate results in a denser and more resistant structure, thereby influencing recovery in 4D-printing.
Fig. 9
Distribution of results and prediction profiles obtained through the JMP software on the recovery time: a bed temperature, b printing speed, c flow, d fan speed, e printing temperature
×
Recovery time is an important property in many applications, particularly those involving materials subjected to cyclic loading or repeated deformation. In applications involving shock absorption or impact resistance, a short recovery time may be desirable, allowing the material to absorb and dissipate impact energy quickly. In other applications, such as those involving structural stability or load-bearing capacity, a longer recovery time may be more appropriate. Ultimately, the ideal recovery time will depend on the specific requirements of the application in question.
4 Conclusion
This work aimed to tackle one of the biggest challenges of 4D-printing: controlling the morphing effect in structures produced through in extrusion-based 3D-printing. A design of experiments (DoE) was employed to study the relationship between the transformation capability and the microstructural properties of a semicrystalline PLA polymer. Printing temperature, bed temperature, printing speed, fan speed and flow were the parameters under study. The morphing effect was explored using an external thermal stimulus. Within the 3D-printing parameters studied, the interaction between the bed temperature parameters and the printing speed showed a significant impact on the crystallinity of PLA-based samples. The crystallinity rates obtained varied from ca. 14% up to ca. 71%. All PLA-based samples under investigation were capable of acquiring their temporary shape and, through an external temperature stimulus, of recovering up to ca. 99% of their original shape, showing a recovery time ranging from 8 to 50 s. Sample 6 (printing temperature at 220 °C, bed temperature at 40 °C, printing speed at 80 mm/s, fan speed at 0% and flow at 100%) showed the highest recovery rate. Sample 27 (print temperature at 180 °C, bed temperature at 80 °C, print speed at 10 mm/s, fan speed at 100% and flow at 150%) resulted in the sample with the lowest recovery rate and with the longest recovery time. Samples printed at the higher print speed level achieved a fast shape recovery. The field of 4D-printing is challenging, and, despite the well-known FFF process, it remains a complex process to understand and control. This complexity is due to the large number of associated process parameters. The current work limits the investigation of the process parameters in two levels and therefore it is recommended to increase the number of levels in future studies for more accurate results.
Acknowledgements
Thanks are due to the University of Aveiro, FCT/MCTES for the financial support of TEMA research unit (FCT Ref. UIDB/00481/2020 & UIDP/00481/2020) and CICECO Aveiro Institute of Materials (FCT Ref. UIDB/50011/2020, UIDP/50011/2020, & LA/P/0006/2020), and CENTRO01-0145-FEDER-022083—Regional Operational Program of the Center (Centro2020), within the scope of the Portugal 2020 Partnership Agreement, through the European Regional Development Fund. The authors also acknowledge FCT—Fundação para a Ciência e a Tecnologia, I.P. for the Individual Call to Scientific Employment Stimulus (IG, https://doi.org/10.54499/CEECIND/00430/2017/CP1459/CT0032) and the funding of MSC and TEPG PhD grants (FCT ref. 2020.04681.BD and SFRH/BD/143429/2019, respectively).
Declarations
Conflicts of interest
The authors declare no conflict of interest.
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Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.