Analyzing the Impact of FDM Parameters on Compression Strength and Dimensional Accuracy in 3D printed PLA Parts

calculated using the ASTM D695 compression test. The results illustrated how printing parameters affected samples' mechanical and physical properties, which were proven by the ultimate compression stress UCS and the percentage of compression average deviation. The analysis of variance shows the significance of infill density (100%) for UCS, while layer thickness (0.15 mm) is significant for compression average percentage deviation. For instance, the increase in the infill density from 20% to 100% shows that the strength climbed from 4 MPa to 56.5 MPa. Similarly, reducing layer thickness from 0.3 mm to 0.15 mm results in a diminished dimensional accuracy deviation from 1.65% to 0.446%, approximately three times less than that of the specimen with a 0.3 mm layer thickness.


Introduction
Three-dimensional printing technology, called fused deposition modeling (FDM), has created new opportunities for producing parts that would be difficult or impossible to manufacture using conventional methods [1,2].This technology selectively joins materials layer by layer to build the required component based on a 3D Computer-Aided Design (CAD) model [3].Non-metallic 3D printing frequently employs materials such as Polylactic Acid (PLA) and Acrylonitrile Butadiene Styrene (ABS) [4].PLA, in particular, has gained popularity due to its affordability, wide availability, and lightweight properties [5].Additionally, 3D-printed parts made from PLA tend to exhibit superior mechanical characteristics compared to those made from ABS [6].In FDM systems, a filament is typically melted and extruded through a nozzle.The extruded polymer is deposited layer by layer on the build plate by moving the nozzle head in three degrees of freedom (DOFs) based on G-code instructions.Continuous filament feed is achieved using two rollers spinning in opposite directions.Consequently, the part's shape and size are gradually constructed by depositing layers of material on the build plate [7].During the layering process, the printer nozzle follows the spatial coordinates of the CAD model specified in the G-code files to create the part's size and structure.A threedimensional CAD model is created at the outset of the FDM process.The STL format, commonly used in FDM Cura software, simplifies this model's geometry and transfers it to slicing software.This software segments the part into several fundamental triangular components.Subsequently, the slicing software generates a hardware process plan for the FDM machine using this data [8,9].Compressive tests based on ASTM D695 have assessed material characteristics based on printing parameters [10].
Numerous works have been performed to optimize the production parameters for the FDM process to produce high-quality printed parts.For instance.Kumar et al. [11] explored the evolution of processing defects and the correlation of mechanical behavior with process parameters in 3D printing.It uses the L27 orthogonal array and Criteria Importance through Intercriteria Correlation (CRITIC) embedded Weighted Aggregated Sum Product Assessment (WASPAS) to optimize parts' mechanical attributes.The study found that layer thickness, print speed, and temperature significantly control part quality and strength.The results showed a maximum flexural strength of 78.52 MPa, an ultimate tensile strength of 45.52 MPa, and an impact strength of 6.21 kJ/m 2 .Ambade et al. [12] investigated the effect of infill pattern and density on compressive strength, determining ultimate compressive strength, Young's modulus, and strength-to-weight ratio.Bedan et al. [13] explored the interactive influence of process parameters (infill density and shell thickness) on ABS prints, assessing relative strength, weight, and compressive strength through compression tests.Sivaraos et al. [14] developed an Artificial Neural Network (ANN) model to optimize dimensional properties in 3D printing (FDM) using control factors like layer thickness, orientation, raster angle, raster width, and air gap.The model, with 15 neurons and 2 layers, demonstrated accurate predictions with percentage errors ranging from 0.01% to 25.49% for length, less than 10% for weight, and less than 4% for thickness.This model can be extended to optimize other additive manufacturing process parameters.Farooq et al. [15] examined the impact of process variables on stainless steel grade SS 316L, manufactured through the laser-powder bed fusion process (L-PBF), during high-speed turning.The analysis includes cutting speed, depth of cut, and feed rate.Parametric optimization was performed to achieve the desired response characteristics, reducing machining cost, carbon emissions, specific energy, tool life, and surface roughness.Popović et al. [16] investigated optimal parameters for PLA polymer FDM parts, focusing on nozzle temperature and printing speed.Results show that 190°C and 40 mm/min are most effective, but 80 mm/min speed may be considered for higher FDM productivity.Harris et al. [17] explored the impact of volume distributions (10-90%) on the tensile, flexure, and compressive characterization of FFF and epoxy systems.It uses scanning electron microscopy (SEM) and a high-quality camera for mechanical characterization.The study reveals high tensile strain and compressive strength for lower FDM percentages, suggesting potential for future materialbased innovations in HDM.Abbas et al. [18] examined the effects of FDM parameters (outer shell width, infill density, layer thickness, and infill pattern) on PLA compressive properties, revealing that while layer thickness has a minor impact on compressive resistance, infill density plays a more significant role.Abas et al. [19] examined the impact of 3D printing parameters on dimensional deviations in polylactic acid-printed parts.The study found that infill density significantly affects length and width deviation, while layer height significantly affects angle and height deviation.The optimal results were obtained using an integrated approach of desirability and WASPAS, providing a guideline for fabricating assistive devices with highdimensional accuracy.Kumar et al. [20] conducted an experimental investigation of the influence of FDM parameters (raster width, infill density, and raster angle) on the mechanical characteristics of PLA parts under compressive and flexural loading, observing the considerable impact of infill density on compressive strength and modulus.Begum et al. [21] developed a novel structure for evaluating flexible porosity in bone scaffolds made of polyamide (PA 2200).A CAD model was created using specific input parameters, and the porosity was controlled by varying input parameters.The model showed 29% to 30% reductions in experimental porosity compared to theoretical.Structural analysis and computational fluid dynamics analysis confirmed the model's potential for successful bone implant applications, with a maximum pressure of 1.799 Pa.Abbas et al. [22] studied the main parameters influencing printing time and product weight in the FDM process for ABS thermoplastic products, highlighting the significant impact of infill pattern and density on specimen strength.Ali et al. [23] presented an experimental approach to study the effects of structural factors on the mechanical characteristics of PLA hollow-sphere structures produced with FDM.Parab et al. [24] examined the optimal PLA infill pattern for 3D printing based on compressive strength and found that triangular infill surpassed the default line infill pattern in various test conditions.Syed et al. [25] optimized FDM process parameters for tensile strength, flexural strength, and longitudinal shrinkage using the Grey-Taguchi approach.Input parameters include layer thickness, raster angle, fill density, number of contours, printing temperature, and speed.The Taguchi L27 orthogonal array is used for the statistical design of the experiment, and Grey relational analysis is used for optimization.The optimal parameters minimize longitudinal shrinkage while maximizing tensile and flexural strengths.The qualities of 3Dprinted specimens under varied processing circumstances and materials were investigated by Abeykoon et al., [26].Thermal gravimetric analysis, SEM, differential scanning calorimetry, tensile, compression, bending, and compression testing methods were employed.Young's modulus improved with infill density, according to the results, with pure PLA having the greatest Young's modulus.According to the study, a linear fill pattern, 90°C infill speed, 215°C nozzle temperature, and 100% infill density are the ideal process parameters.Tanga et al. [27] used digital image correlation (DIC) to investigate the mechanical characteristics of 3D-printed PLA lattice constructions.FDM was utilized to create lattice structures and tensile samples.According to the study, tensile strength and elastic modulus increase first and subsequently drop with increasing printing temperature.There is a declining trend in yield strength, densification strain, and plastic platform stress.The rate of printing is likewise increasing.Mauryaa et al. [28] investigated the impact of process variables (infill pattern, layer thickness, build orientation, and infill density) on dimensional accuracy, flatness, and cylindricity in a prototype connecting rod made of polylactic acid, identifying the best parameters for each category.
These studies have collectively played a crucial role in advancing our understanding of the critical parameters influencing the quality and performance of 3D-printed objects using FDM technology.These studies have explored various variables and shed light on their individual and interactive effects on mechanical and physical properties.While these studies have substantially contributed to the field, our work aims to enhance this knowledge base by comprehensively examining the intricate relationships between the selected parameters.The physical and mechanical properties of parts produced with the FDM system vary depending on the specified printing parameters.Some printed samples exhibit poor mechanical and physical properties due to the values of selected printing parameters, which significantly impact the properties of the produced samples.Therefore, this study aims to determine how infill density, infill pattern, overlap percentage, layer thickness, shell thickness, and top/bottom layer number affect the physical and mechanical properties of printed samples fabricated using the FDM method.Based on test results, the ultimate compression strength (UCS) and average compression deviation percentage of the specimens are evaluated and analyzed to identify variable values that influence the properties of printed specimens.

Experimental Work
Polylactic Acid (PLA) material, specifically TORWELL PLA filament, was used to fabricate samples.The samples were produced using a Creality Ender-5 Pro FDM printer equipped with a 0.4 mm diameter nozzle, as depicted in Figure 1.The specifications of the FDM setup are provided in Table 1.PLA, derived from lactic acid building blocks, is known for its biodegradability and bioactivity.It is a versatile, cost-effective material suitable for a wide range of applications and is considered one of the most environmentally friendly fibers currently available, despite its inherent brittleness [29].Tables 2 and 3 present the specifications and properties of the PLA material, respectively.The specimen was initially designed as a CAD model according to ASTM standard D695, and then converted into STL format for further processing, as shown in Figure 2 (a, b).The workflow of this study is illustrated in Figure 3. Test specimens were designed to investigate the impact of six input factorsinfill density, pattern, layer thickness, shell thickness, top/bottom layer number, and infill overlap-across five levels, as outlined in Table 4. Based on the previous research, the most effective parameters in 3D printing research are driven by the need to achieve optimal outcomes in the printed objects: infill density, layer thickness, shell thickness, infill overlap, and top/bottom layer number.The strong point of selecting these parameters lies in their collective influence on critical aspects of the printing process, including strength, surface finish, and dimensional accuracy.By strategically adjusting these parameters, researchers gain comprehensive control over the printing process, allowing for a fine-tuned optimization based on specific objectives.For instance, infill density impacts the internal structure and strength of the object, layer thickness affects resolution and printing speed, shell thickness influences structural integrity, infill overlap determines adhesion between layers, and top/bottom layer number affects surface quality and overall strength.An influential, straightforward, and systematic technique is developed by designing experiments using the Taguchi method to identify the ideal machining conditions in the production process.The Taguchi approach was employed to set up the experiment.Six process parameters were examined to understand how FDM parameters affected compression strength: infill density percentage, infill pattern, layer thickness, number of top/bottom layers, shell thickness, and infill overlap percentage.
Each of these parameters had five levels of variation.The Taguchi method evaluated the performance characteristic that deviates from the desired values using the signal-to-noise (S/N) ratio [30][31][32].The Taguchi method is favored for determining optimal machining conditions because it emphasizes robust design and efficiency.Its key strengths include focusing on finding conditions less sensitive to variations, using fractional factorial design for efficient study of multiple factors, employing the Signal-to-Noise ratio for comprehensive assessment, utilizing orthogonal arrays for balanced exploration of factor space and is known for costeffectiveness in industrial settings.To maximize compression strength and minimize compression average deviation percentage, the higher-the-better compression strength and the lower-the-better dimensional accuracy should be selected.Equations 1 and 2 can represent the S/N ratio for the higher-the-better and smaller-the-better performance characteristics, respectively: Larger is bette: Smaller is better: where n = total number of measurements, yi = value of the measured characteristics.At room temperature, the specimens were tested in accordance with ASTM D695 standards on a WDW-200E computercontrolled electronic universal testing machine, as illustrated in Figure 4, with a crosshead speed of 1.5 mm/min to evaluate the mechanical properties of the fabricated specimens.Load, deformation, stroke, and time data were recorded during the experiments.The ultimate compression strength was determined using the recorded data.The stresses and mechanical properties were determined using the actual dimensions of each specimen rather than the CAD model.The compression strength of each PLA test sample was estimated using Equation 3. The various PLA filament test specimens are shown in Figure 5.
where: σ = Compression stress (N/mm²), F = Applied force (N), A = Cross-sectional area of the fabricated part (mm²)  All the molded specimens were compared to the CAD model to assess the influence of the processing parameters on the compression average percentage deviation (dimensional accuracy).Digital vernier calipers were used to measure each specimen's dimensions.For each run, every piece of geometry was measured three times.The average percentage deviation for the geometry was then determined using Equations (3-5) [33].

Results and Discussion
The results in Table 5 illustrate the impact of six printing parameters on the Ultimate Compression Strength (UCS) and compression average percentage deviation (dimensional accuracy) for 25 specimens.

Results From The Compression Test
The Ultimate Compression Strengths (UCS) of the specimens are presented in Table 5. Figure 6 displays the main-effects graph for the response parameter, i.e., the compression strength of FDM parts.According to Figure 6, this study explored the influence of FDM parameters on the ultimate compression strengths of the specimens.As evident from the trend lines, infill density is the most significant parameter affecting compression strength.Higher infill density means more material inside the part, providing better support and reinforcement to the part's interior, thus making it more resistant to compressive forces.The experimental results presented in this study demonstrate that increasing infill density leads to higher compression strength.For instance, a specimen with 100% infill density exhibits a compression strength of 56.5 MPa, approximately ten times higher than that of a specimen with 20% infill density (4 MPa).However, it's worth noting that the expected maximum compressive strength based on reference [34] is 44.64 MPa for the specimen, whereas the strength at 100% is equal to the 65.9 MPa claimed in literature [17].The increase in infill density is associated with increased material usage.While maximum strength can be achieved with 100% infill density, it's important to consider cost, printing time, and material consumption when determining the necessary infill density based on the product's type and application.In addition, as seen in Figure 7, the line infill pattern outperforms other patterns by providing a maximum compression strength of 56.5 MPa among the five chosen patterns.This superiority can be attributed to the line infill pattern's ability to evenly distribute loads during compression, reducing voids or areas with less material that could weaken the structure.Additionally, the line pattern aligns with the primary stress direction, enhancing resistance to compression forces in that specific direction.A layer thickness of 0.2 mm yields the best compression strength.This may be because a thinner layer allows for more controlled extrusion, providing the filament with adequate melting time.Conversely, compared to a 0.1 mm layer thickness, a 0.2 mm layer thickness has fewer layers, reducing the risk of interlayer failure and improving overall mechanical properties.Figure 6 shows that a 2 mm shell thickness results in the highest UCS value among shell thickness variations.Similarly, specimens with 6 top/bottom layers and 20% infill overlap exhibit the highest UCS values.Conversely, specimens with a 0.4 mm shell thickness generally demonstrate lower UCS values.The UCS shows a linear increase with more top/bottom layers.
An analysis of variance (ANOVA) was performed to determine the parameter significance of output responses.The impact of the parameters was compared across all parameter levels and experiment repeats.The p-value is evaluated in comparison to the α = 0.05 confidence interval.By showing a 95% confidence interval, the α aids in interpreting the parametric significance.The process parameter with a low p-value relative to α is classified as a significant variable in the response.In addition, all input control parameters are compared, and the significance is evaluated using the previously stated criteria [35][36][37].The Taguchi L25 orthogonal array and the signal-to-noise (SN) ratio of leading and trailing observations of compression strength and average percentage deviation have been calculated.Table 6 presents the ANOVA test results for compression strength.Analysis of Variance (ANOVA) was employed to assess the P-value and identify the printing parameter with the most significant impact on compression strength.The compression strength results were obtained, and Table 6 displays the percentage contribution of each parameter along with individual P-values and F-values.From the P-values in the ANOVA table, it can be deduced that infill density % and shell thickness are the most significant parameters affecting compression strength, with a P-value of 0.000 at a 95% confidence level.Infill density refers to the quantity of material utilized to fill the interior space of a 3D-printed object, and higher infill densities typically result in stronger parts due to the increased material content needed to withstand compressive forces.Similarly, a thicker shell provides better resistance to compression forces by creating a more robust exterior structure.In contrast, infill overlap % with a P-value of 0.052, infill pattern with a P-value of 0.218, and layer thickness with a P-value of 0.285 do not show significant influence.In addition to the p-value, which determines the significance of the parameter, the influence of the analyzed parameters can also be estimated based on the percentage contribution of each parameter to the total variation of the experimental results [38][39][40].This analysis confirms that the percentage of infill density has the greatest influence on compression strength, contributing 78.856% at a 95% confidence level, as shown in Figure 8.

Results of Compression Average Percentage Deviation
On the other hand, the compression average percentage deviation of the specimens is presented in Table 5. Figure 9 illustrates the main-effects graph for the response parameter, namely, the compression average percentage deviation of FDM parts.As shown in Figure 9, this study investigated the impact of FDM parameters on the compression average percentage deviation of the specimens.As evident from the trend lines, layer thickness is the most influential parameter affecting the compression average percentage deviation.Layer thickness directly impacts the vertical resolution of a 3D print.A smaller layer thickness results in finer layers, enabling a more accurate representation of intricate details and curved surfaces.Conversely, thicker layers may lead to a loss of detail and a less accurate representation of the original design.
As demonstrated, the specimen with a 0.15mm layer thickness exhibited the minimum deviation in dimensional accuracy for 3D printed parts (0.44633%), approximately three times less than that of the specimen with a 0.3 mm layer thickness Smaller than 1

Effects Pareto
Isolates the most important effects.
mean minus the smallest mean.
Effect is defined as the largest Main Effects Screener for ultimate compression strength (UCS) MPa Summary Report (1.65367%).However, it's worth noting that the expected minimum percentage deviation based on reference [33] is 2.8%, while the expected maximum value is 9.07% for specimens.Thinner layers can promote better adhesion and bonding between successive layers, which can help prevent delamination or warping, ultimately enhancing dimensional accuracy.The increase in layer thickness has been associated with an increase in the deviation observed in PLA parts.A similar study [28] on PLA parts reported a smaller deviation in dimensional accuracy at 100 μm layer thickness.Additionally, as shown in Figure 10, the cubic pattern resulted in a minimum deviation of (0.446%) among the five selected infill patterns.This can be attributed to the cubic infill pattern's composition of evenly spaced, interlocking cubes, providing a highly uniform and stable internal structure.This uniformity improves dimensional accuracy by reducing the likelihood of warping, distortion, or inconsistent layer adhesion.
Unlike some infill patterns that may have larger voids or open spaces, the cubic pattern densely fills the interior of the part.Fewer voids mean less potential for structural deformation or deviation from the intended dimensions.The cubic pattern's regular and predictable geometric layout makes it easier to anticipate how the infill will interact with the shell of the part during printing, minimizing the chances of unexpected dimensional variations.The Taguchi L25 orthogonal array and the signal-to-noise (SN) ratio of leading and trailing observations of compression strength and average percentage deviation have been computed.Table 7 presents the ANOVA test results for the compression average percentage deviation.It can be inferred from the P-values in the ANOVA table that layer thickness is the most significant parameter affecting compression average percentage deviation (dimensional accuracy), with a P-value of 0.002 at the 95% confidence level.Layer thickness plays a pivotal role because it directly impacts the resolution and precision of the printed object.Thinner layers lead to finer details and smoother surfaces, thus enhancing overall dimensional accuracy.The influence of the analyzed parameters can also be estimated by considering the percentage contribution of each parameter to the total variation in the experimental results.This analysis confirms that the percentage of layer thickness exerts the greatest influence on compression average percentage deviation, contributing 37.013% at the 95% confidence level, as depicted in Figure 11.

Data Means
Look for panels with large differences between levels.
standard deviations: Colors based on effect size, in Larger than 2 Between 1 and 2 Smaller than 1

Effects Pareto
Isolates the most important effects.
mean minus the smallest mean.
Effect is defined as the largest Surface topography is crucial for understanding the quality of the printed surface.Therefore, critical experimental observations were conducted using a Stereo microscope, and these results are presented in Figure 12. Figure (12-a) depicts that specimens formed with 100% infill density, a line pattern, 0.2mm layer thickness, 0.8mm shell thickness, 2 top/bottom layer numbers, and 20% overlap exhibit rough fractured surfaces.These specimens feature material failure during compressive loading, resulting in higher compressive strength.In contrast, Figure (12-b) illustrates specimens that ruptured along the layers, forming smooth fractured surfaces.This suggests that specimens with 20% infill density, a grid pattern, 0.1mm layer thickness, 0.4 mm shell thickness, 2 top/bottom layer numbers, and 0% overlap did not allow the material to resist the compressive load effectively.The failure occurred due to poor interfacial adhesion between the layers, leading to lower compressive strength.The specimens formed with these parameters were ineffective in transferring compressive load from one layer to another, resulting in lower strength.On the other hand, Figure (12-c) demonstrates minimal deviation in dimensional accuracy, achieved with a low layer thickness and a higher top/bottom layer number.This is attributed to the direct impact of layer thickness on vertical resolution, while the top/bottom layer number influences the surface quality of a 3D print.In contrast, Figure (12-d) illustrates a higher deviation in dimensional accuracy.The changes in mechanical and physical properties of test specimens fabricated using different process parameters are illustrated in the interaction graphs in Figures 13 and 14.These interaction plots demonstrate how the value of the second categorical parameter influences the relationship between one categorical parameter and a continuous response.On the x-axis of these plots are the means for the levels of one parameter, and separate lines are shown for each level of another parameter.

Main Effects Screener for compression average deviation % Summary Report
Notably, the lines in these interaction plots are not parallel, indicating that the value of FDM parameters has a discernible impact on both compression strength and dimensional accuracy.Figure 13 illustrates that the strength levels are notably higher for a line infill pattern with 100% infill density, a layer thickness of 0.2 mm, a shell thickness of 0.8 mm, 2 top and bottom layers, and a 20% infill overlap.Figure 14 shows that the minimum average compression deviation percentage occurs with 100% infill density, a cubic pattern, a layer thickness of 0.15 mm, a shell thickness of 0.4 mm, 6 top and bottom layers, and a 15% infill overlap.Cubic mathematical models were formulated based on the experimental results listed in Table 5, using Minitab 17 and employing regression analysis.This process involved fitting a model to the experimental data to establish a functional relationship between FDM parameters and response properties.Equations 7-11, presented in Appendix A, represent the mathematical models for the relationship between infill pattern and compression strength.Considering various process parameters, they serve as a valuable tool for predicting compression strength in our 3D printing process.Conversely, Equations 12-16, as illustrated in Appendix A, depict the mathematical models for the relationship between infill pattern and compression average percentage deviation, which is a valuable tool for predicting minimum deviation in dimensional accuracy while considering the same process parameters.
The percentage error between the measured and predicted ultimate compression strength (UCS) and compression average percentage deviation of PLA parts was calculated according to Equation 6.Table 8 presents the results, showing that the maximum percentage error values between the measured and predicted Ultimate Compression Strength (UCS) and compression average deviation% of PLA parts were 0.44% and 0.948%, respectively.Conversely, the minimum percentage error values between the measured and predicted Ultimate Compression Strength (UCS) and compression average deviation of PLA parts were 0.10% and 0.041%, respectively, as depicted in Figures 15 and 16.These percentage error results are deemed acceptable, indicating that the model has performed satisfactorily predicting the responses.

Conclusion
The impact of FDM parameters on the compression test mechanical characteristics and dimensional accuracy of FDM specimens was investigated using Taguchi's L25 DOE.Before compression testing, linear dimension measurements were performed to assess the specimens' dimensional accuracy, following the ASTM D695 standard.Based on the experimental results, the following conclusions could be drawn: Based on the findings regarding the mechanical characteristics of the fabricated specimens, it was determined that a combination of parameters, including 100% infill density, a line infill pattern, 0.2 mm layer thickness, 0.8 mm shell thickness, two top and bottom layers, and 20% infill overlap optimized the compression strength of parts, achieving a value of 56.5 MPa.Regarding the physical characteristics of the FDM specimens, it was found that a cubic pattern with 100% infill density, 0.15 mm layer thickness, 0.4 mm shell thickness, 6 top/bottom layers, and 15% infill overlap exhibited the lowest average percentage deviation among the selected parameters, with a value of 0.446%.The ANOVA analysis concluded that infill density had the most significant impact on compression strength, contributing 78.856%.On the other hand, layer thickness had the greatest impact on compression average percentage deviation, with a contribution of 37.013%.A comparison between the predicted and measured results has been recorded, and the maximum percentage error of the model that fits the data well was 0.44% and 0.948% for ultimate compression strength (UCS) and compression average percentage deviation, respectively.In conclusion, it is evident that optimizing mechanical and physical properties simultaneously through FDM parameter selection is challenging.To enhance the strength of printed parts, it is recommended to use a line infill pattern, 100% infill density, 0.2 mm layer thickness, 0.8 mm shell thickness, two top and bottom layers, and 20% infill overlap as FDM parameters.Conversely, to minimize dimensional accuracy deviation, choosing a cubic infill pattern, 100% infill density, 0.15 mm layer thickness, 0.

Figure 5 :
Figure 5: PLA filament testing specimens after the test

Figure 6 :
Figure 6: Main effect plot for ultimate compression strength (UCS) MPa

Figure 7 :
Figure 7: Ultimate compression strength (UCS) variation with FDM parameters for printed parts

Figure 8 :
Figure 8: Process parameters contributions of compressive strength

Figure 9 :
Figure 9: Main effect plot for compression average percentage deviation

Figure 10 :Figure 11 :
Figure 10: Compression average percentage deviation variation with FDM parameters for printed parts

Figure 12 :
Figure 12: Surface topography of fractured samples using Stereo Scope (20x magnification) for (a) maximum ultimate compression strength UCS MPa, (b) minimum ultimate compression strength UCS MPa, (c) minimum average compression percentage deviation, and (d) maximum average compression percentage deviation

Figure 13 :
Figure 13: Plot for compression strength interaction

Figure 14 :
Figure 14: Plot for compression average percentage deviation interaction

Figure 15 :
Figure 15: Relationship between error (percentage error between measured and predicted (UCS) MPa) and number of experiments

Table 4 :
Process parameters of PLA filament

Table 5 :
Ultimate compression strength (UCS) and compression average percentage deviation of printed parts

Table 6 :
Results of Analysis of Variance Tests for Compression Strength

Table 7 :
Results of Analysis of Variance Tests for Compression Average percentage deviation

Table 8 :
Percentage error for ultimate compression strength (UCS) of PLA parts