Optimization of 3D Printing Parameters for Enhanced Mechanical Strength: Effects of Glass Fiber Reinforcement and Fill Ratio Using RSM and ANOVA

Abstract

This research aimed to provide valuable insights for future studies and enhance manufacturing processes by investigating the effect of incorporating fibers into 3D printing to improve the mechanical properties of fabricated components. The experimental design was carried out using Design-Expert software, employing the Central Composite Design (CCD) methodology. Seventeen experiments were conducted, with predefined input parameters, layer height, filler ratio, and printing speed, analyzed through the Response Surface Methodology (RSM) using Design-Expert version 12. An Analysis of Variance (ANOVA) revealed that the filler ratio had the most significant effect on fracture strength. The influence of different printing parameters printing speed, layer height, and filler ratio on the mechanical properties and print quality was systematically investigated. The results indicated that the filler ratio was the most critical factor, with a 100% fill ratio yielding the highest tensile strength. Conversely, a 50% fill ratio significantly reduced production costs, but at the expense of mechanical performance. Thus, if strength is the primary requirement, a higher fill ratio is recommended. The effect of printing speed was found to be less significant compared to layer height and filler ratio. The maximum recorded tensile strength was 540.65 N, achieved with a layer height of 0.5 mm, a 100% fill ratio, and a printing speed of 8 mm/s. In contrast, the lowest recorded tensile strength was 389.93 N, observed with a layer height of 0.4 mm, a 50% fill ratio, and a printing speed of 4 mm/s. After applying a transformation function, the data showed good alignment with the normal distribution on the probability plot, indicating that the assumption of normality was satisfied. Additionally, the incorporation of glass fibers significantly enhanced the mechanical strength of the printed samples.

1. Introduction

Three-dimensional printing technologies first emerged in the late 1980s, initially referred to as rapid prototyping (RP) technology. This process facilitated the creation of prototypes at a relatively low cost and gradually evolved through extensive research and experimentation to produce industrial-grade prototypes [1,2]. The first patent application for RP technology was submitted by the Japanese inventor Dr. Hideo Kodama. However, due to his failure to meet the patent submission requirements within the designated timeframe, Dr. Kodama sought legal assistance to address the issue [3,4]. In 1986, the field of 3D printing advanced significantly with the granting of the world’s first patent for a device called the Stereolithography Apparatus (SLA), developed by Charles (Chuck) Hull. This innovation enabled the production of rapid prototypes with high precision. Hull went on to establish 3D Systems Corporation, which remains one of the leading global companies in the 3D printing industry. After extensive testing and experimentation, the first commercial 3D rapid prototyping system, the SLA-1 printer, was introduced in 1987, marking a significant milestone in the industry [5]. Another major advancement came in 1989, when American scientist Carl Deckard at the University of Texas patented Selective Laser Sintering (SLS) technology, complementing Hull’s innovation and enabling efficient rapid prototyping. Shortly thereafter, in 1992, Scott Crump patented Fused Deposition Modeling (FDM), a technology widely adopted for both rapid prototyping and entry-level 3D printers. In the same year, Hans Langer founded EOS GmbH in Europe, focusing on the development of Laser Sintering Process technology. Under his leadership, EOS GmbH became a leading company known for manufacturing high-quality prototypes. The company further advanced the field by introducing Direct Metal Laser Sintering (DMLS) technology [6,7]. Additional patents followed, including Ballistic Particle Manufacturing (BPM) by William Masters, Laminated Object Manufacturing (LOM) by Michael Feygin, and Solid Ground Curing (SGC) by Itzchak Pomerantz. Emanuel Sachs is also credited with significant contributions to the development of 3D printing technology. During the early 2000s, new technologies emerged alongside the continuous refinement of existing ones. Researchers and companies shifted their focus to industrial applications, prototype development, and specialized technological advancements, leading to the widespread adoption of the term Additive Manufacturing (AM). Companies such as Arcam and Z Corporation in Western Europe played a crucial role in advancing the technology [8]. Studies have investigated the delamination behavior of PLA-TPU bi-material composites, combining polylactic acid (PLA) and thermoplastic polyurethane (TPU) [9]. By 2007, 3D printing underwent a major transformation, evolving through its second, third, and even fourth generations by 2013. These advancements demonstrated significant success and impact in global markets [10].

Glass fibers are widely used to reinforce composites in aerospace, automotive, and construction industries due to their high strength and lightweight properties. PLA 3D-printed components are commonly used in prototyping, biomedical implants, and eco-friendly packaging due to their biodegradability and ease of printing. Several studies have explored ways to enhance the mechanical properties of thermoplastic materials by incorporating fibers or particles into the matrix [11]. Gray Iv et al. [12] introduced thermotropic liquid crystalline polymer fibrils into polypropylene (PP) to develop a composite filament for FFF. They used a capillary rheometer to simulate the FFF process, leading to an improvement in the tensile strength of the extruded strands. Similarly, Zhong et al. [13] investigated short fiberglass-reinforced acrylonitrile butadiene styrene (ABS) in the FFF process. The addition of plasticizers and compatibilizers enhanced filament processability, and their experiments demonstrated that varying fiber content in fiberglass-reinforced ABS composites significantly increased tensile strength and surface hardness.

Shofner et al. [14] examined the impact of vapor-grown carbon fibers in ABS filaments used for FFF, observing a 39% increase in tensile strength at a 10 wt% nanofiber loading. Tekinalp et al. [15] reinforced ABS with short carbon fibers to assess its processability, microstructure, and mechanical behavior. Their results showed an 115% increase in tensile strength and a 700% rise in modulus, with carbon fiber alignment reaching 91.5% along the printing direction. Additionally, glass fiber reinforcement has been suggested as a method to enhance the strength, rigidity, and toughness of PLA [16]. Prior research also indicates that incorporating nanomaterials such as carbon nanotubes, nanowires, and nanoparticles into thermoplastics via FFF could further enhance the mechanical performance of printed parts [17]. The effectiveness of glass fiber-reinforced thermoplastics depends on multiple factors, including the properties of the matrix and fibers, fiber content, orientation, aspect ratio, distribution, and fiber–matrix adhesion. Among these, FFF process parameters influence the mechanical behavior of fiber-reinforced composites [18].

Since now, the effects of FFF process parameters on the mechanical properties of printed samples are not fully understood, which is the aim of this study. The Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) play crucial roles in optimizing FFF of glass fiber-reinforced PLA components. The RSM helps in modeling and analyzing the relationships between printing parameters (e.g., infill, layer height, and printing speed) to maximize mechanical performance, such as tensile strength and surface finish. By using a systematic experimental design, the RSM enables efficient identification of optimal process conditions, reducing material waste and production time. ANOVA, on the other hand, is essential for statistically validating the significance of each parameter and their interactions, ensuring that observed improvements are not due to random variations. Together, the RSM and ANOVA facilitate precise control over the FFF process, leading to enhanced strength, durability, and reliability of glass fiber-reinforced PLA components in industrial and biomedical applications.

2. Materials and Methods

2.1. Filament and Glass Fibers

The base material selected in this research is polylactic acid (PLA) filament (Yuso Company, Xi’an, China), which is a biodegradable thermoplastic made from renewable sources such as corn flour, sugar cane, and starch [19]. After printing, this material is very hard and strong, yet somewhat fragile. Although it is not necessary to heat the 3D printer workbench, heating it can improve the final quality of the part. Additionally, PLA plastic takes 48 months to break down in water. Glass fibers are produced from molten glass as continuous strands and are the most widely used reinforcement in the composite industry [20]. These fibers offer suitable strength and hardness and maintain their mechanical properties at high temperatures. They also exhibit good resistance to moisture and corrosion, and they are relatively inexpensive. Made from special compounds resistant to weather conditions, glass fibers are used extensively across various industries, including aviation, space, and automotive, transportation, sports equipment, and machinery parts, chemical, medical, and household appliances [21]. They are employed as reinforcement in a wide range of matrices, such as plaster, cement, thermoset, thermoplastic plastics, resin, and bitumen. The production of fiberglass and FRP (Fiber Reinforced Polymer) parts represents the most common applications of glass fiber products [22]. In this research, the diameter of the glass fibers is 0.4 mm.

2.2. Printing Process

The SIZAN ECO EXTEND 3D printer (SIZAN ECO EXTEND, Kashan, Iran) was used for printing. Using precise engineering design and advanced features such as a filament stop detection sensor, continuing printing after a power failure, and the work table dimensions 40 × 40 × 70 cm, with a printing accuracy of 0.1 mm, this printer was a suitable option for printing precise parts. The device provided a professional 3D printing process with a powerful 32-bit board, filament stop detection sensor, and full access to professional printer settings. The sturdy work table with four-sided restraints was completely fixed, guaranteeing printing quality. During printing, glass fibers were continuously injected to increase the strength of the final piece. Figure 1 depicts the printing process by the SIZAN ECO EXTEND 3D printer.

2.3. Characterization

Tensile testing of the samples was performed using the Santam STM-50 (Santam, Tehran, Iran) model tensile testing machine. This device works with a stretching speed of 5 mm/min. In this test, 15 mm of the sample was placed in the clamps of both machine jaws and 50 mm was subjected to tension. To extract the failure mechanisms of printed parts, the fracture surfaces of the samples were examined using a Field Emission Scanning Electron Microscope (FESEM) (Zeiss, Berlin, Germany). This was the Zeiss Sigma 300 HV model. It can provide very high-resolution images, essential for precisely analyzing fracture mechanisms.

3. Experiment Design

Different methods were used to design the experiments in this research. According to the background of the study, the response surface model (RSM) method was selected using Design Expert version 12 software. The parameters of the tests are determined as described in

Table 1. Parameters examined in this research.

	Layer Height [mm]	Infill [%]	Speed [mm/s]
1	0.4	50	4
2	0.5	75	8
3	0.6	100	12

.

Due to the costs required for the purchase and manufacture of parts and the limitation of financial resources, the main goal was to obtain an acceptable model to review and analyze the results with the minimum possible number of tests. In this regard, the design of the experiment was carried out using Design Expert software and the design method (C.C.D). Based on the results, 17 experiments were proposed for review, the details of which are specified in

Table 2. The proposed parameters of the experiment design to conduct the experiment.

Run	Layer Height (mm)	Infill (%)	Speed (mm/s)	Tensile Strength (Mpa)
1	0.5	75	8	466.6
2	0.5	75	12	490.1
3	0.4	50	12	426.3
4	0.6	100	12	532.8
5	0.4	75	8	426.33
6	0.4	100	12	476.2
7	0.5	75	8	490.1
8	0.4	50	4	389.93
9	0.5	75	12	497.43
10	0.6	50	12	424.3
11	0.6	75	8	499.06
12	0.4	100	4	479.86
13	0.6	50	4	459.4
14	0.6	100	4	503.35
15	0.5	100	8	540.65
16	0.5	50	8	438.6
17	0.5	75	8	490.1

.

Print parameters include factors that affect printed samples’ quality and mechanical properties. These parameters include nozzle temperature, print pattern, and bed temperature. Each of these parameters is precisely specified in

Table 3. Printing parameters.

Run	Process Variable	Operating Condition
1	Nozzle Diameter	1 mm
2	Filament Diameter	1.75 mm
3	Layer Height	0.4–0.5–0.6 mm
4	Speed	4–8–12 mm/s
5	Solid Layers	Top—1, Bottom—1, Shell—2
6	Infill	50–75–100%
7	Fill Density	1.25 gr/cm3
8	Internal Fill Pattern	Rectilinear
9	Perimeter Speed	50 mm/s
10	Infill Speed	60 mm/s
11	Travel Speed	80 mm/s
12	Nozzle Temperature	PLA–210 °C
13	Bed Temperature	PLA–55 °C
14	Print	Horizontal
15	Sample length	mm (according to the map) Figure 1

and Figure 2. Additionally, the geometry of the samples is very important for achieving reliable and reproducible results. In this research, all samples were designed based on ASTM D638 with standard dimensions and shapes for tensile and mechanical tests.

4. Results and Discussion

4.1. Regression Analysis

According to the experimental design described before, the practical experimental steps were carefully implemented. In this experiment, the samples were subjected to tensile testing, and the results related to the fracture strength were collected. The Design Expert program was used to analyze the obtained data, which allows for accurate analysis of the variance of the data. The study of variance (ANOVA) showed that the greatest influence on the fracture strength was related to the filler.

The filler, or the sample’s internal structure, was identified as a major factor in the cubic model.

Table 4. Analysis of Variance table related to the experiment.

Source	Sum of Squares	Degrees of Freedom (df)	Mean Square	F-Value	p-Value
Model	26,264.75	11	2387.70	220.12	<0.0001	Significant
A-Layer Height	2644.83	1	2644.83	243.83	<0.0001
B-Infill	5207.10	1	5207.10	480.05	<0.0001
C-speed	475.24	1	475.24	43.81	0.0012
AC	183.94	1	183.94	16.96	0.0092
BC	75.15	1	75.15	6.93	0.0464
A2	1696.26	1	1696.26	156.38	<0.0001
C2	57.21	1	57.21	5.27	0.0701
ABC	1367.12	1	1367.12	126.04	<0.0001
A2B	335.94	1	335.94	30.97	0.0026
A2C	231.65	1	231.65	21.36	0.0057
AB2	513.80	1	513.80	47.37	0.0010
Residual	54.24	5	10.85
Lack of Fit	54.24	3	18.08			Not Significant
Pure Error	0.0000	2	0.0000
Cor Total	26,318.98	16

contains the details of the variance analysis and shows that this model is statistically significant and acceptable. These results highlight the filler’s importance in the samples’ tensile strength and confirm the validity of the cubic model used. Therefore, it can be concluded that the experimental design and statistical analysis methods used in this study were able to identify and model the effect of different factors on the fracture strength.

This can provide valuable guidance for future research and improvement of production processes.

To examine the impact of various factors in experimental design programs, the following symbols are often used in the Analysis of Variance (ANOVA):

• Source: Identify the factor or interaction being studied (e.g., individual factors and interactions).
• Squared sums: Calculates the total variance due to each source. The total of the squared discrepancies between observed values and the mean is calculated for each source.
• Define degrees of freedom (df): The number of independent variables in the analysis is indicated. It is determined by the number of elements and interactions under test.
• The Mean Square: Represents the average variance (variance) for each source, obtained by dividing the sum of squares by degrees.

Table 5. Statistical characteristics of the predicted model for surface roughness.

Std. Dev.	3.29
Mean	472.42
C.V. %	0.6972
R2	0.9979
Adjusted R2	0.9934
Predicted R2	0.9250
Adeq Precision	55.0046

shows the values of statistical indices: the coefficient of determination (R²), the adjusted coefficient of determination (Adjusted R²), and the predicted coefficient of determination (Predicted R²) for the developed model and the force response. These indices indicate the model’s goodness of fit to the data, the improvement in fit considering the number of variables used, and the model’s ability to predict new data. As observed, the values of these indices are close to 1, indicating high accuracy and precision of the statistical relationships. High values of these indices suggest that the model has successfully explained the variations in fracture force and has a high ability to predict results. This high accuracy and precision in the statistical indices confirm the model’s validity and demonstrate that the model can be effectively used for analyzing and predicting results under similar conditions. Therefore, the results obtained from this model are acceptable and reliable and can serve as a foundation for further research and improvements in this field.

Considering the mentioned points and the analyses performed using the Design Expert software, two other important outputs should be noted. The first output is the analysis of the input and output data conducted by the software, based on which the proposed formula for predicting the fracture force in sample tests was provided. This formula is presented in Table 5 and includes the effects of each experimental parameter on the final model result. The second output is the examination of the effects of various parameters on the fracture force, showing the impact of each parameter based on precise statistical analysis. This formula results from comprehensive and accurate analyses by the software using experimental data and statistical modeling. Therefore, it can be said that the proposed formula has high accuracy in predicting the fracture force and can be used as a reliable tool for analyzing and improving sample test processes. These outputs help the research better understand the relationships between different parameters and the fracture force and develop more accurate predictive models for future studies. Considering the mentioned points and the analyses performed using the Design Expert software, two other important outputs should be noted. By analyzing the outputs based on the inputs conducted by the software, the proposed formula for predicting the force in sample tests is provided in the formula.

• This formula examines the effect of each parameter based on its impact on the final model result.

$$\begin{gathered} Force=488.90+36.36A+51.02B+15.41C-4.80AC+3.06BC \\ -23.67A^2-4.35C^2+13.07ABC-14.49A^{2}B \\ -12.03A^{2}C-17.92A{B}^2 \end{gathered}$$

(1)

Figure 3 shows the normal probability distribution of the data and the residuals plot (internally studentized) of the force values, respectively. The normal probability plot of the data is displayed in Figure 3, covering a range from 389.93 to 540.65 after applying the transformation function. As shown in this plot, the data approximately align with the corresponding normal line, indicating that the data follow a normal distribution. This alignment suggests that the residuals are normally distributed and that the statistical model describes the data well.

The residuals plot (internal students) in Figure 4 also illustrates the distribution and dispersion of the data relative to the normal line, confirming the model’s validity and accuracy. These results demonstrate that the model effectively predicts the tested forces and that the data follow a normal distribution, further reinforcing the validity and accuracy of the statistical model. Consequently, it can be concluded that the statistical model used for data analysis is both reliable and accurate and can be effectively used to predict results under similar conditions.

The analyses conducted on the results from the Design Expert software regarding the fracture force in tensile testing demonstrate the impact of various parameters on this force. The three-dimensional response surface and two-dimensional color spectrum charts related to the effects of significant parameters are presented in Figure 5a,b. These charts clearly show the impact of layer height and infill, which have been identified as the most influential parameters. The three-dimensional chart shows a complex and nonlinear relationship between layer height, infill, and fracture force. As both layer height and infill percentage increase, the fracture force also increases, indicating the importance of these two parameters in determining the final strength of the samples. The two-dimensional color spectrum chart clearly illustrates the effect of each parameter across different ranges, providing a graphical means for more precise and transparent analysis. These charts assist researchers in optimizing 3D printing parameters and achieving better results in tensile testing.

The three-dimensional response surface and two-dimensional color spectrum charts related to the effects of layer height changes–infill on force are presented in Figure 6a,b.

The three-dimensional response surface and two-dimensional color spectrum charts related to the effects of layer height variation ratio to speed on the force are presented in Figure 7a,b.

4.2. Optimization Derived from Experimental Design

Optimization means finding the most ideal response considering the constraints and requirements of the problem. The details of this process are shown in Figure 8. This figure presents the optimal values for the lowest settings and the chart depicting how the designed variable parameters affect the braking force. To examine the effect of three input factors, the optimal condition includes a layer height of 0.449 mm, infill of 98.91%, and speed of 6.46 mm/s, designed and analyzed using the Design Expert software. The results indicate that these optimal values provide a desirable braking force. The charts in Figure 7 clearly show the impact of each of these parameters, allowing researchers to understand the effects of various changes on the breaking force. This optimization, considering these parameters, helps researchers adjust the 3D printing process to achieve the best results in tensile testing. These analyses significantly improve the quality and efficiency of production processes and enhance precision and effectiveness in similar projects.

4.3. Fracture Surface Analysis

Using SEM images, one can examine the detailed distribution of glass fibers within the PLA matrix, assess the adhesion between fibers and the matrix, and identify any defects or microcracks in the sample structures. Fracture surface analysis was conducted on tensile samples with varying printing parameters, using both reinforced and non-reinforced PLA, to evaluate potential differences in failure mechanisms between the samples. As illustrated in Figure 9, the fracture surfaces of the non-reinforced PLA sample show severe plastic deformation in some areas. This deformation highlights the high flexibility of PLA in the absence of reinforcing fibers. Under high stress, the material exhibits deformation rather than brittle fracture. This mechanical behavior can be attributed to the intrinsic properties of PLA and how forces are distributed throughout its structure.

The second sample, with a speed of 8 mm/s, a layer height of 0.5 mm, and an infill percentage of 75%, was the first choice for printing without fiber. Three models of the same sample were printed, and the final tensile value was 430.6 N. In this sample without fiber, although the filling percentage was high, 75%, the energy expended to deform the sample was 0.200 J, which was less compared to samples reinforced with fiber, which have a similar filling percentage. The energy value of the reinforced sample was 0.250 joules, and this increase in energy was the result of adding fiber to the reinforced sample [23]. As shown in Figure 10, microscopic images of the sample were printed with a 0.6 mm nozzle at a speed of 4 mm/s and 100% infill. These images show that using a larger nozzle diameter leads to layer separation in the sample. This layer separation may be due to insufficient adhesion between layers during the printing process, exacerbated by the increased nozzle diameter and reduced printing precision. Additionally, in higher magnification images, the fibers protruding out are visible. This phenomenon indicates a lack of continuity and proper adhesion between the fibers and the PLA matrix, which can weaken the mechanical properties of the samples [24].

The energy required to deform the sample increased, reaching 0.250 J as the filling ratio rose to 100%. This increase was due to the higher fill density which eliminated interstitial gaps between the layers of the three-dimensional sample, making it more cohesive and uniform [25]. Consequently, greater force is needed to fracture the sample. Figure 11 is shows microscopic images of the sample printed with a 0.5 mm nozzle at a speed of 8 mm/s and 100% infill. These images reveal the phenomenon of fibrillation, which begins with the appearance of porosities and microcracks that eventually lead to the formation of fibers. In the early stages, these porosities and microcracks act as stress concentration points, gradually expanding. As deformation increases and the fiber walls thin, the fibers begin to separate, leading to the formation of larger cracks. The fibrillation phenomenon may be attributed to the high printing speed, which does not allow sufficient time for proper adhesion between layers [26].

From the above graph, it is clear that there is a significant increase in the energy value, which is represented by the area under the force-deformation curve [27]. The energy consumed to deform the fiber-reinforced sample was 0.290 J, while the energy expended to deform the fiber-free sample under the same printing conditions was 0.175 J. This highlights the importance of adding fibers to enhance the material’s mechanical properties. Figure 12 shows the microscopic image of the sample printed with 75% infill, a 0.5 mm nozzle, and an 8 mm/s speed. As shown, the diameter of the reinforcing fibers in this sample has been measured. Comparing the fracture surfaces of samples printed with 100% and 75% infill shows minimal differences, indicating that the infill percentage does not significantly affect the failure mechanisms. Both types of samples exhibit similar fracture surfaces characterized by fiber breakage and plastic deformation. These results suggest that other factors, such as layer adhesion, temperature, and printing speed, may impact fracture behavior more than the infill percentage [28].

The energy consumed to deform the fiber-reinforced sample was 0.245 J, while the energy expended to deform the fiber-free sample under the same printing conditions was 0.175 J. This highlights the importance of adding fibers to enhance the material’s mechanical properties [29]. Thus, the analysis of SEM images revealed that the distribution and adhesion of glass fibers in the PLA matrix, along with the presence of microcracks and defects, significantly impact the fracture behavior of the samples. Accordingly, adjusting printing parameters can substantially affect the final quality of the samples. Optimizing parameters, such as nozzle diameter and printing speed, can help reduce porosities and microcracks, thereby improving the mechanical strength and durability of the printed samples. Specifically, reducing the printing speed and using nozzles with more suitable diameters can enhance layer adhesion and decrease microscopic defects, reducing fibrillation and improving final sample quality [30]. These results can guide the improvement of 3D printing processes and improve the efficiency and quality of final products [31]. This study uses the Response Surface Methodology (RSM) to extract the model and determine the most significant impacts. This method designs the experimental matrix by considering the number of variables and the maximum and minimum limits set for each variable. Thus, the number of tests and the levels of each variable in each test are determined. In the RSM, the range selected for each factor is crucial [32]. The range for each factor should be coded and fall within the interval of −1 to 1 to ensure proper regression analysis, as the units of independent variables are not the same, and even if they were, their selected ranges might differ. When the number of variables is large, this method is preferred over more cumbersome methods such as full factorial design [27].

5. Conclusions

To obtain a composite with high physical and chemical properties suitable for use in 3D printing, fibers were combined with a polymer using the Fused Deposition Modeling technique to print 3D samples with varying printing parameters such as (printing speed, layer height, and infill percentage). The impact of these parameters on the properties and print quality of the samples was then studied. As observed, the infill percentage has the greatest impact. A closer look at the data reveals that infill at 100% achieves maximum strength.

• Naturally, an infill of 50% results in lower production costs. However, using a higher infill percentage is better if the required criterion is the part’s strength. The impact of printing speed is significantly less compared to the other two variables.
• The highest force recorded was 540.65 N with a layer height of 0.5 mm, 100% infill, and a speed of 8 mm/s.
• The lowest force recorded was 389.93 N with a layer height of 0.4 mm, 50% infill, and a speed of 4 mm/s.
• After applying the transformation function, it is observed that the data align closely with the normal distribution line on the normal probability plot, indicating that the normal distribution is satisfied.
• Glass fibers have a significant impact on the strength of the samples in the printing process.

Fused Deposition Modeling of glass fiber-reinforced PLA components faces limitations such as poor interlayer adhesion and fiber breakage during extrusion, which can reduce mechanical strength and uniformity. Additionally, the high viscosity of fiber-filled PLA can cause nozzle clogging and restrict printability, limiting the achievable fiber content and overall material performance.