INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Comparative Study of Surface Finish and Dimensional Accuracy in  
FDM 3D Printed Parts  
1 Imran Khan, 1 Sharad Kumar, 1 Ashutosh Singh, 2 Vikas Sharma  
1 School of Engineering & Technology, Shri Venkateshwara University, Gajraula, U.P. India  
2 Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR Campus,  
Ghaziabad, U.P. India  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000105  
Received: 22 December 2025; Accepted: 31 December 2025; Published: 10 January 2026  
ABSTRACT  
Fused Deposition Modeling (FDM) has emerged as one of the most widely adopted additive manufacturing  
technologies due to its cost-effectiveness, design flexibility, and ease of operation. However, the quality of  
FDM-fabricated components is often limited by issues related to surface finish and dimensional accuracy,  
which are critical parameters for functional and end-use applications. This paper presents a comparative study  
on the influence of key FDM process parameters on surface roughness and dimensional deviation of 3D  
printed parts. Standard test specimens were fabricated using commonly used thermoplastic materials under  
varying printing conditions, including layer thickness, print speed, build orientation, and infill density. Surface  
finish was evaluated using surface roughness measurements, while dimensional accuracy was assessed through  
precise dimensional inspection and deviation analysis. The experimental results reveal that layer thickness and  
build orientation have a significant impact on surface quality, whereas print speed and infill density play a  
crucial role in dimensional stability. A comparative analysis is carried out to identify optimal parameter  
combinations that achieve improved surface finish without compromising dimensional accuracy.  
Keywords—Fused Deposition Modeling (FDM), Additive Manufacturing, Surface Roughness, Dimensional  
Accuracy, Process Parameters, 3D Printing.  
INTRODUCTION  
Additive manufacturing (AM), commonly known as 3D printing, has revolutionized the way components are  
designed and manufactured by enabling layer-by-layer fabrication directly from digital models. Among the  
various AM techniques, Fused Deposition Modeling (FDM) has gained widespread acceptance in academia  
and industry due to its simplicity, low material cost, minimal waste generation, and ability to fabricate  
complex geometries with reasonable mechanical performance. FDM operates by extruding a thermoplastic  
filament through a heated nozzle and depositing the molten material along a predefined toolpath to form  
successive layers. Despite its advantages, FDM still faces significant challenges in producing parts with high  
surface quality and dimensional precision, which restricts its adoption in high-accuracy and functional  
applications. Surface finish and dimensional accuracy are two critical quality characteristics that strongly  
influence the performance, aesthetics, and assembly compatibility of FDM-printed parts. Poor surface finish  
often results from the stair-stepping effect inherent to the layer-by-layer deposition process, especially when  
larger layer thicknesses or unfavourable build orientations are used. In addition, factors such as nozzle  
diameter, extrusion temperature, and print speed further contribute to surface irregularities. Dimensional  
inaccuracies, on the other hand, arise due to material shrinkage, thermal gradients, machine vibrations, and  
inaccuracies in motion control systems. These deviations can lead to poor part fitting, reduced functionality,  
and increased post-processing requirements.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Comparative Study of Surface Finish and Dimensional Accuracy in FDM 3D Printing  
Over the past decade, numerous researchers have investigated the effects of individual FDM process  
parameters on either surface roughness or dimensional accuracy. Studies have shown that reducing layer  
thickness generally improves surface finish but increases printing time. Build orientation has been reported to  
significantly affect both surface texture and dimensional deviation, particularly for features aligned parallel or  
perpendicular to the build direction. Similarly, print speed and infill density influence material flow behavior,  
cooling rate, and internal stress distribution, thereby impacting the final dimensions of printed parts. However,  
most existing studies focus on isolated quality attributes or limited parameter sets, providing incomplete  
guidance for practical applications where multiple quality requirements must be satisfied simultaneously. In  
real-world engineering applications, a trade-off often exists between surface finish and dimensional accuracy.  
Parameter settings that enhance surface quality may adversely affect dimensional stability, and vice versa. For  
example, lower print speeds can improve surface smoothness but may increase thermal accumulation, leading  
to dimensional distortion. Likewise, higher infill densities can improve dimensional consistency but increase  
material usage and printing time. Therefore, a comprehensive comparative evaluation of these quality  
characteristics under varying process conditions is essential to establish balanced parameter selection  
strategies. This paper aims to address this research gap by presenting a comparative study of surface finish and  
dimensional accuracy in FDM 3D printed parts. Standardized test specimens are fabricated using commonly  
employed thermoplastic materials while systematically varying key process parameters such as layer thickness,  
print speed, build orientation, and infill density. Surface roughness measurements are conducted to  
quantitatively evaluate surface finish, and dimensional deviations are analysed using precise measurement  
techniques shown in Fig. 1. By comparing the influence of each parameter on both quality metrics, this study  
identifies parameter combinations that offer an optimal balance between surface smoothness and dimensional  
precision.  
LITERATURE REVIEW  
Fused Deposition Modeling (FDM) has been extensively studied in recent years with a focus on improving print  
quality, process efficiency, and dimensional reliability. Researchers have explored the influence of process  
parameters, hardware improvements, and intelligent monitoring systems to enhance the performance of FDM-  
printed parts. Boora et al. [1] investigated the effect of different infill patterns on printing time for PLA-based  
FDM components. Their study highlighted that infill strategy plays a significant role in reducing fabrication  
time without compromising basic structural integrity. Although the work primarily focused on time  
optimization, it indirectly emphasized the need to balance productivity with quality-related aspects such as  
surface finish and dimensional accuracy. Paneva and Panev [2] examined the fundamental features of FDM  
printing using standardized test specimens. Their work provided insights into the geometric inaccuracies and  
surface defects commonly observed in FDM parts, attributing them to layer-wise deposition and process  
instability. This study established the importance of controlled specimen design and parameter selection for  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
reliable quality assessment. Yan et al. [3] proposed a machine vision–based defect detection approach for  
identifying wire drawing defects during FDM printing. Their research demonstrated that surface-related defects  
can be effectively detected in real time, highlighting the growing importance of quality monitoring systems to  
improve surface finish and reduce printing errors. Ouchaoui et al. [4] focused on optimizing FDM printing time  
by adjusting process parameters based on tensile test results. Their findings showed that parameter optimization  
not only affects mechanical properties and time efficiency but also has a considerable impact on print  
consistency, which is closely linked to dimensional accuracy. Vodilka et al. [5] presented the design of a  
modular enclosed chamber for FDM printers to improve thermal stability during printing. The controlled  
chamber environment reduced warping and dimensional variations, emphasizing the role of environmental  
conditions in achieving better dimensional accuracy and surface quality. Tattimbetova et al. [6] specifically  
analysed the impact of layer height on dimensional accuracy in FDM printing. Their results confirmed that  
smaller layer heights improve dimensional precision, although at the cost of increased printing time. This work  
directly supports the importance of layer thickness as a dominant factor influencing dimensional deviation.  
Badillo et al. [7] evaluated the reproduction accuracy of basic geometrical primitives using different 3D printing  
techniques, including FDM. Their comparative study revealed that FDM exhibits noticeable dimensional  
deviations, particularly in sharp edges and curved features, reinforcing the need for parameter optimization to  
enhance geometric fidelity. Zhang et al. [8] developed a machine vision–based quality detection system for  
FDM printing to monitor surface defects and dimensional inconsistencies. Their system demonstrated the  
feasibility of automated quality assessment, which can significantly reduce post-processing and rejection rates.  
Simeonov and Maradzhiev [9] improved the print quality of a low-cost FDM printer by replacing factory-  
installed stepper motor drivers. Their experimental results showed noticeable improvements in surface  
smoothness and dimensional consistency, highlighting the influence of hardware precision on print quality. Wei  
[10] explored the application of artificial intelligence–based assistance systems in 3D printing. The study  
emphasized AI-driven decision support for parameter selection, which can potentially improve surface finish  
and dimensional accuracy by adapting process parameters dynamically. Veerapuram et al. [11] optimized  
process parameters for carbon fiber–reinforced PLA composites fabricated using FDM. Their work  
demonstrated that parameter optimization significantly affects both mechanical performance and dimensional  
stability, indicating that material type further complicates quality control in FDM printing. Ruiz-González et al.  
[12] applied eco-design principles to FDM technology, focusing on material usage and energy efficiency. While  
sustainability was the primary objective, their findings showed that optimized printing parameters can  
simultaneously improve surface quality and dimensional reliability. Patil et al. [13] proposed a wall-based  
dataset generation technique for FDM printing to support data-driven analysis. Their contribution enables  
systematic evaluation of surface and dimensional characteristics, supporting future machine learning–based  
optimization studies. Chen et al. [14] developed an extrusion-based five-axis 3D printing system for  
manufacturing complex parts. Their work demonstrated that advanced motion systems can significantly reduce  
surface defects and dimensional errors, overcoming some limitations of conventional three-axis FDM printers.  
Finally, Patel et al. [15] introduced an optimized hybrid AI model for enhancing FDM parameters in multi-  
material fabrication. Their results confirmed that intelligent optimization techniques can effectively improve  
dimensional accuracy and overall print quality, indicating a promising direction for future research.  
PROPOSED METHODOLOGY  
The proposed methodology presents a structured experimental framework to comparatively evaluate the  
surface finish and dimensional accuracy of FDM 3D printed parts under varying process parameters. The  
methodology is organized into systematic phases to ensure consistent specimen fabrication, controlled  
parameter variation, accurate measurement, and meaningful analysis of results. The following steps outline the  
complete methodological approach adopted in this study.  
1. Material Selection and Characterization: The methodology begins with the selection of a commonly used  
thermoplastic filament suitable for FDM printing due to its stable extrusion behavior and widespread industrial  
use. The filament is procured from a reliable source and maintained at the manufacturer-specified diameter  
tolerance to ensure uniform material flow during printing. Prior to fabrication, the filament is dried to  
minimize moisture-induced defects such as porosity and surface irregularities. Basic material properties,  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
including filament diameter consistency and recommended printing temperature range, are reviewed to ensure  
suitability for experimental trials.  
2. Specimen Design and Process Parameter Selection: Standardized test specimens with simple geometrical  
features and flat surfaces are designed using computer-aided design (CAD) software to facilitate accurate  
surface roughness measurement and dimensional inspection. The CAD models are exported in STL format  
with adequate resolution to avoid geometric errors during slicing. Key FDM process parameters influencing  
part quality—namely layer thickness, print speed, build orientation, and infill density—are selected for  
investigation. Each parameter is assigned predefined levels based on prior studies and practical printing limits,  
while other parameters such as nozzle diameter, extrusion temperature, and bed temperature are kept constant  
to ensure controlled experimentation.  
3. Experimental Planning and Specimen Fabrication: A systematic experimental plan is developed in  
which one process parameter is varied at a time while keeping the remaining parameters constant. This  
approach allows the isolated assessment of each parameter’s influence on surface finish and dimensional  
accuracy. All specimens are fabricated using the same FDM 3D printer and slicing software to minimize  
machine- and software-related variations. For each parameter combination, multiple specimens are printed to  
ensure repeatability and reliability of results. After printing, the specimens are allowed to cool naturally at  
room temperature to reduce residual thermal stresses and warping effects.  
4. Surface Roughness Measurement: Surface finish is evaluated by measuring the surface roughness of  
selected flat regions of the printed specimens. A surface roughness measuring instrument is used to obtain  
average surface roughness (Ra) values. Multiple measurements are taken at different locations on each  
specimen, and the mean value is calculated to reduce measurement uncertainty. Measurements are conducted  
in a consistent direction relative to the build orientation to ensure uniform comparison across all specimens.  
5. Dimensional Accuracy Assessment: Dimensional accuracy is assessed by measuring critical dimensions of  
the printed specimens using precision measuring instruments such as a digital Caliper or coordinate measuring  
device. The measured dimensions are compared with the nominal CAD dimensions, and dimensional  
deviations are calculated. Both positive and negative deviations are analysed to understand the effects of  
process parameters on dimensional stability and accuracy.  
6. Data Analysis and Comparative Evaluation: The experimental data obtained from surface roughness and  
dimensional measurements are systematically analysed to identify trends and correlations between FDM  
process parameters and quality characteristics. Comparative analysis is performed using tables and graphical  
representations to highlight the influence of each parameter on surface finish and dimensional accuracy. Based  
on the results, optimal parameter ranges are identified that provide a balanced improvement in both quality  
metrics. This comprehensive methodology ensures a reliable and repeatable evaluation of FDM process  
parameters for enhancing the quality of 3D printed parts.  
RESULT & ANALYSIS  
This section presents the experimental results obtained from the comparative evaluation of surface finish and  
dimensional accuracy of FDM 3D printed parts. The influence of key process parameters layer thickness, print  
speed, build orientation, and infill density on average surface roughness (Ra) and dimensional deviation is  
analyzed in detail. The results are discussed based on measured data and observed trends.  
1. Effect of Layer Thickness: Layer thickness is one of the most influential parameters affecting surface  
quality and dimensional precision. Table I summarizes the measured surface roughness and dimensional  
deviation for different layer thickness values.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Effect of Layer Thickness on Surface Roughness and Dimensional Accuracy  
Average Surface Roughness, Ra  
(µm)  
Layer Thickness (mm)  
Dimensional Deviation (mm)  
0.10  
6.2  
±0.18  
0.20  
0.30  
9.5  
±0.24  
±0.31  
13.8  
It is observed that surface roughness increases significantly with an increase in layer thickness due to a  
pronounced stair-stepping effect. The lowest Ra value is obtained at 0.10 mm layer thickness, indicating  
superior surface finish. However, thinner layers slightly increase dimensional deviation due to longer printing  
time and cumulative thermal effects. Thicker layers exhibit higher dimensional deviation, mainly because of  
poor layer bonding and uneven material deposition.  
Influence of Layer Thickness on Surface Quality and Accuracy  
Fig. 2. illustrating the effect of layer thickness (0.10 mm, 0.20 mm, and 0.30 mm) on average surface  
roughness and dimensional deviation. Two bars are shown for each layer thickness. As layer thickness  
increases, both surface roughness and dimensional deviation increase, indicating reduced surface quality and  
dimensional accuracy at higher layer thicknesses.  
2. Effect of Print Speed: Print speed affects material extrusion stability and cooling behavior. The  
experimental results for varying print speeds are presented in Table II.  
Effect of Print Speed on Surface Roughness and Dimensional Accuracy  
Average Surface Roughness,  
Print Speed (mm/s)  
Dimensional Deviation (mm)  
Ra (µm)  
40  
60  
80  
7.1  
±0.20  
±0.23  
±0.29  
9.0  
11.6  
Lower print speeds result in smoother surfaces due to controlled material flow and better interlayer adhesion.  
As print speed increases, surface roughness worsens because of extrusion inconsistencies and vibration effects.  
Dimensional deviation also increases at higher speeds, indicating reduced dimensional control. A moderate  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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print speed of 60 mm/s provides a reasonable balance between surface quality and dimensional accuracy.  
Influence of Print Speed on Surface Finish and Dimensional Accuracy  
Fig. 3. showing the effect of print speed (40 mm/s, 60 mm/s, and 80 mm/s) on average surface roughness and  
dimensional deviation. Two bars are shown for each print speed. The chart indicates that increasing print speed  
results in higher surface roughness and greater dimensional deviation, suggesting reduced surface finish and  
dimensional accuracy at higher speeds.  
3. Effect of Build Orientation: Build orientation significantly influences surface texture and dimensional  
stability due to layer alignment. Table III shows the results for different orientations.  
Effect of Build Orientation on Surface Roughness and Dimensional Accuracy  
Average Surface Roughness,  
Build Orientation  
Dimensional Deviation (mm)  
Ra (µm)  
Flat (0°)  
6.8  
±0.19  
±0.26  
±0.34  
Inclined (45°)  
Vertical (90°)  
10.2  
14.5  
Specimens printed in flat orientation exhibit the best surface finish and dimensional accuracy due to uniform  
layer deposition and reduced staircase effects. Vertical orientation results in the highest surface roughness and  
dimensional deviation, as the layer edges dominate the surface profile. Inclined orientation shows intermediate  
behavior. These findings highlight the importance of orientation selection during part design.  
Influence of Build Orientation on Surface Quality and Dimensional Accuracy  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Fig. 4. illustrating the effect of build orientation—flat (0°), inclined (45°), and vertical (90°)—on average  
surface roughness and dimensional deviation. Two bars are shown for each orientation. The chart shows that  
both surface roughness and dimensional deviation increase significantly as the build orientation changes from  
flat to vertical, indicating poorer surface quality and dimensional accuracy at higher build angles.  
4. Effect of Infill Density: Infill density influences internal structure, thermal distribution, and part stability.  
The corresponding results are shown in Table IV.  
Effect of Infill Density on Surface Roughness and Dimensional Accuracy  
Average Surface Roughness,  
Build Orientation  
Dimensional Deviation (mm)  
Ra (µm)  
Flat (0°)  
6.8  
±0.19  
±0.26  
±0.34  
Inclined (45°)  
Vertical (90°)  
10.2  
14.5  
Higher infill density improves dimensional accuracy due to better internal support and reduced warping.  
Surface roughness also improves slightly with increased infill density, as the part exhibits enhanced structural  
stability during printing. However, higher infill increases material consumption and printing time, which must  
be considered in practical applications.  
Infill Density on Surface Roughness and Dimensional Accuracy  
Fig. 5. showing the influence of infill density conditions—flat (0°), inclined (45°), and vertical (90°)—on  
average surface roughness and dimensional deviation. Two bars are displayed for each condition. The chart  
indicates that surface roughness and dimensional deviation increase progressively from flat to vertical  
conditions, reflecting a decline in surface finish and dimensional accuracy.  
The comparative analysis indicates that layer thickness and build orientation are the dominant factors  
influencing surface finish, while infill density and print speed significantly affect dimensional accuracy.  
Optimal results are achieved using a lower layer thickness (0.10–0.20 mm), moderate print speed (around 60  
mm/s), flat build orientation, and higher infill density (≥50%). These parameter combinations provide an  
effective trade-off between surface smoothness and dimensional precision.  
CONCLUSION  
This study presented a comparative investigation of surface finish and dimensional accuracy in FDM 3D  
printed parts by systematically analyzing the influence of key process parameters such as layer thickness, print  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
speed, build orientation, and infill density. The experimental results demonstrated that layer thickness and  
build orientation predominantly govern surface roughness, while print speed and infill density significantly  
affect dimensional accuracy. Thinner layers, flat build orientation, moderate print speeds, and higher infill  
densities were found to provide an optimal balance between improved surface quality and dimensional  
precision. The findings confirm that careful parameter selection can substantially enhance the as-printed  
quality of FDM components, reducing dependency on post-processing and increasing their suitability for  
functional applications. As a future scope, the study can be extended by incorporating advanced optimization  
techniques such as multi-objective algorithms, machine learning–based prediction models, and real-time  
process monitoring to further improve print quality. Additionally, investigating the combined effects of  
material types, nozzle geometries, and post-processing methods on surface and dimensional performance will  
broaden the applicability of FDM technology in precision-driven industrial domains.  
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MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
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