Jamal J. Dawood
| Hind B. Ali* | Rana Anaee | Farag M. Mohammed
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
This paper focuses on the study of the use of fused deposition modeling (FDM) in enhancing the process parameters of formed components. Three variables (fill density, layer height, and printing speed) are considered to have a significant and significant effect on the tensile strength of acrylonitrile butadiene styrene specimens. The methodology of this study is based on experiments using the Taguchi strategy. On the other hand, previous studies have mainly focused on analyzing individual process parameters and their effect on the mechanical properties of FDM-manufactured parts. The results of this study, using Taguchi techniques and analysis of variance, show that the largest and most significant effect on the tensile strength of FDM structures was the fill density among the three process parameters. ANOVA results for the average tensile strength with a confidence interval of 66.595%, while ANOVA results for the Young Modulus at a confidence interval of 36.236% and the ANOVA findings for the fractured strength at a confidence interval of 50.228%. A higher F-value indicates that adjusting a process parameter has a greater impact on performance characteristics. In addition, there is a limited effect of the other process variable with a smaller effect, but it was still effective. Finally, valuable insights could be drawn from the results about the correlation between process parameters and mechanical properties of components. The study confirms encouraging results using FDM technology for researchers and future studies in terms of enhancing the structural integrity of the produced components.
acrylonitrile butadiene styrene (ABS), ANOVA, fused deposition modelling (FDM), mechanical properties, Taguchi approach
To generate a hierarchical product directly from a computer-aided design (CAD) file, additive manufacturing (AM) is the first and primary choice to meet this. Additive manufacturing is a technology that has been widely used for hierarchical 3D design as an advanced technology in this field and is known as 3D Printing [1, 2]. CAD software is used to provide instructions and commands in the design of the dedicated layers in various geometric patterns, where CAD works in AM to benefit from it in 3D scanners [3, 4]. Recently, rapid growth has been observed in the development of AM as it has become part of various manufacturing processes for wide fields such as agriculture, healthcare, automobiles, etc., as AM is adaptable and effective in every field where it is needed [5, 6]. Therefore, it has been realized that there is an increasing need for its use on a wide scale, for example, its use in customized components and prosthetics [7]. AM encompasses a diverse array of methods, including the fabrication of three-dimensional objects by gradually adding material layer by layer. The different processes involved are vat photopolymerization, powder bed fusion, sheet lamination, binder jetting, directed energy deposition, extrusion, and jetting of material [1].
The FDM-compatible thermoplastic polymers and reinforced materials include fiber-reinforced thermoplastics, Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyether Ether Ketone (PEEK), and polycarbonate [8, 9]. Recent years have seen an upward trend in research on the properties of materials created with FFF, particularly in relation to resistance to wear which becomes essential when using 3D-printed materials that must slide over the surface of another material [6]. FDM-produced parts are gradually replacing traditional components that are used in various industries, such as the aviation, locomotive, and health sectors [10]. The variables of the process and their configurations retain a significant influence on the mechanical properties of components printed by FDM [1, 5, 10]. Therefore, it is essential to assess the effects of input factors and predict results by using suitable process parameters in order to recover the mechanical properties of printed components. The combination of these characteristics is frequently challenging to understand, making FDM a complex process with many variables that affect material qualities and product quality [2, 8]. The quality and functionality of FDM printed items and printing characteristics, such as the orientation of the build, layer height, angle of the raster, width of the raster, air gap, filling density, and pattern, as well as rate of feed, have a significant impact on the outcomes [5, 11-13].
The status of mechanical qualities for functional fragments makes it imperative to investigate how process parameters affect mechanical performance [2, 14]. Therefore, more investigation is needed to identify printer parameters like filling density, layer height, and feed rate, especially in light of the paucity of available literature on the mechanical characteristics of objects produced by inexpensive 3D printers. Additionally, the investigation of the FDM process's impacts [15] on the criteria for mechanical performance is crucial when manufacturing continuous reinforced fiber 3D printed structures [16]. Numerous research studies havenow demonstrated how Layer height affects factors like surface quality, geometric correctness, production time,and expenses [17]. Polymer is the material most frequently employed for 3D printing, both for experimental and practical uses. Furthermore, due to its low cost, high adaptability, and straightforward process, fused deposition modeling, or FDM [18]. Is a popular additive manufacturing technique [7].
Leite et al. [2] used ANOVA statistical analysis to examine the mechanical qualities that are influenced by the four processing parameters, which include filling density, extrusion temperature, raster angle, and layer height. PLA 3D printed parts, toughness, modulus of elasticity, yield tensile strength, Ultimate Tensile Strength (UTS), and elongation at break have been tested.
Twenty-four experiments were conducted to assess the findings for all possible combinations of the factor levels. In Sagias et al. [19] study, the Taguchi approach was employed as an optimization tool. Using FDM 3D printing, the study aimed to improve the mechanical properties of the resulting ABS material. The researchers considered four factors in their work: layer height, printing pattern, printing force, and position, where each factor was considered to have three levels. The response variable was represented by the UTS. In the same vein, but for a somewhat different scenario, Wankhede et al. [20] applied Taguchi's approach. Filler density, layer height, support method, and ABS polymer samples are among the experimental input parameters to be investigated. In this perspective, some researchers have relied on Taguchi's L8-Orthogonal Range to determine the process parameters such as part manufacturing time and surface roughness to be measured. In addition, the process input variables have been determined. Dev and Srivastava [9] conducted an important study focusing on material consumption and compressive strength to improve the efficiency of the FDM process for fabricating lightweight high-strength parts through multi-objective FDM parameters.
It is evident from reading through the prior studies that the FDM process variables have a big influence on the mechanical characteristics of the printed components since these factors may be optimized by carefully choosing and adjusting them [21, 22]. Utilizing the Taguchi approach for experimental design and the ANOVA approach to maximize the goal functions, this study intends to assess the impact of three variables (Fill density, Layer height, and Printing speed) on the tensile property of 3D printed samples using ABS as the building material [23].
This research aims to optimize the FDM parameters to improve the mechanical properties of printed objects made of polymers. Until now, most of the research in this field has relied on experiments, with minimal emphasis on utilizing Design of Experiments (DOE) techniques to enhance the mechanical qualities of the finished fragments. Additionally, very few studies have explored the use of meta-heuristic algorithms to investigate 3D printing settings. The paper provides valuable insights into the combined effect of these three variables on FDM-manufactured components.
Through this work, the multifunctional FDM process' functionality may be improved, which can be utilized to satisfy FDM print design requirements more effectively by enhancing compressive characteristics, reducing the number of raw materials used, and speeding up printing times.
2.1 Samples and materials
The examination samples are produced using the specified process parameters following the four procedures outlined in Figure 1. The sample preparation procedure was structured according to the previous methods [24-26].
Figure 1. Steps of sample production
Figure 2. (a) The geometry tensile specimen ASTM D638 standard, (b) The specimen is placed on the 3D printer bed using Cura 5.5.0 software, (c) The process involves cutting samples, (d) Producing samples using an Ultimaker 2+ printer, and preparing the tensile specimen for examination, (e) Final samples.
2.2 3D printing
The samples utilized in this study were shaped with an Ultimaker 2+ 3D printer. The printer has a building envelope with dimensions of 210 mm by 210 mm by 205 mm, as depicted in Figure 3. It is capable of producing components with a precision of 0.2 mm. The research used Acrylonitrile Butadiene Styrene (ABS), which is the most often employed polymer in 3D printing, to fabricate components. ABS is a widely used thermoplastic compound with a specific chemical composition (C8H8)X, (C4H6)Y, and (C3H3N)Z) [18].
Figure 3. The machine Ultimaker 2+ 3D printer
2.3 Experimental design
The present investigation examines four key settings of the Ultimaker 2+ 3D printer to enhance and evaluate the correlation between these constraints and the recommended response characteristics. The selected parameters were layer height, filling density, and velocity of printing. The selection of input factors was based on the machine's specification, and the other characteristics are listed in Table 1.
Table 1. Process parameters and their respective ranges
|
Fixed Factors |
|||||
|
Factors |
Amount |
Unit |
|||
|
Filling-Pattern |
Line |
- |
|||
|
Thickness of the Shell- |
0.2 |
mm |
|||
|
Orientation of the part- |
45 |
degree |
|||
|
Size of the Nozzle (diameter) |
0.6 |
mm |
|||
|
Temperature during Printing |
230-260 |
℃ |
|||
|
Build plate temperature levels |
60 |
℃ |
|||
|
Control Factors |
|||||
|
Factors |
Levels |
Unit |
|||
|
1 |
2 |
3 |
|||
|
Printing velocity |
35 |
65 |
95 |
mm/s |
|
|
Layer height |
0.15 |
0.2 |
0.25 |
mm |
|
|
Filling density |
30 |
50 |
70 |
% |
|
Layer height refers to the vertical spacing between each layer that has been deposited, measured along the Z-axis, as the material flows from the nozzle tip of an FDM machine through extrusion. The size is often smaller than the extruder nozzle tips' diameter [21]. Filling density refers to the amount of material used to fill the space within a layer. This configuration can render a component completely or partially solid [22]. The primary factor that will impact the speed of 3D prints is the printing velocity. It controls the speed at which the printer's motors operate. This encompasses the motors that govern the X- and Y-axes, together with the motor responsible for the extruder [23].
2.4 Taguchi methods optimization
The Taguchi technique is a method that uses orthogonal arrays to organize the parameters and levels of a process. Taguchi established this experimental design, and it uses orthogonal arrays to determine how the process parameters should be adjusted [22]. The experimental outcomes are altered into signal-to-noise ratios (S/N ratios) to ensure the superiority of the process of production. In Table 2, the projected L9 orthogonal array and the outputs obtained through standard tensile testing of samples. The mechanical properties being evaluated are of maximization type, where the criterion "the larger, the better" is preferred as the guiding principle for the method of Taguchi. Eq. (1) represents the calculation of the signal-to-noise ratio(S/N):
$\frac{S}{N}=-10 \log 1 / n \sum_{i=1}^n 1 / Y_i^2$ (1)
Let "n" be the orthogonal arrays' number, and "Yi" represent the response variable. When selecting material for this study, choosing one with the highest tensile strength is advisable. The proportion of signal-to-noise (S/N) is a quantitative measure used to evaluate the performance of products and processes sensitive to noise. Optimal process parameter choices that result in the maximum signal-to-noise ratio always return superior quality with minimal variability [27-29]. This paper employed Taguchi methods using Minitab 16 software to determine the optimal configuration.
An experimental design of the matrix was developed using a Taguchi L9 mixed orthogonal array, which was determined by the input components and their respective levels. The details of the matrix can be found in Table 2.
Table 2. Experimental design matrix of L9 orthogonal array Taguchi
|
Filling Density % |
Layer Height mm |
Printing Velocity mm/s |
No. of Experiment |
|
30 |
0.15 |
35 |
1 |
|
30 |
0.2 |
65 |
2 |
|
30 |
0.25 |
95 |
3 |
|
50 |
0.15 |
65 |
4 |
|
50 |
0.2 |
95 |
5 |
|
50 |
0.25 |
35 |
6 |
|
70 |
0.15 |
95 |
7 |
|
70 |
0.2 |
35 |
8 |
|
70 |
0.25 |
65 |
9 |
2.5 Experimental procedure
The 3D-printed items were created using Cura software to help with the design process. The resulting design was stored as a .stl file and then imported into Insight, a specialized software for 3D printing [6]. For the purpose of assessing the strength of the material, a tensile test was conducted by placing the specimen into the grips of a testing device. The experiment followed the ASTM D638-03 standard [27] and employed a universal testing machine to measure tensile strength, as shown in Figure 4. During the test, the specimen was subjected to a strain rate of 5 mm/min and a load of 5 kN until it broke.
Figure 4. (a) Tensile Testing Machine, (b) Tensile specimens after testing
2.6 Validation analysis of variance
By analyzing sample variances, the analysis of variance (ANOVA) is a hypothesis-testing method that may be used to test the hypothesis that the means of three or more populations (or treatments) are equal. This expands upon the two independent samples t-tests.
The basis of ANOVA is a comparison of the variance (or variation) inside each sample and the variance between the data samples. The means of many samples will not be equal if the variance is significantly greater than the within variation. There will not be a discernible change in the sample means if the within and between variability are around the same size. ANOVA is used to compare treatments, analyze factors' impact on a variable, or compare means across multiple groups. Types of ANOVA include one-way (for comparing means of groups) and two-way (for examining the effects of two independent variables on a dependent variable).
By employing Orthogonal Array (OA), the number of trials to be examined is greatly reduced, increasing the experimental efficiency. Table 3 displays the L9 Orthogonal Array and the tensile test data.
The best results of Ultimate strength (MPa), Young modulus (GPa), and Fracture strength (MPa) were observed with the increase of the Density of Infilling (%), with the increase of layer thickness (mm), there is consequently a decrease in printing velocity (mm/s). However, it plays a role in both very high and very low-speed values. It impacts the machine's vibration during manufacturing and the solidification time of the material as it passes through the nozzle, ultimately impacting the structural integrity of the component treated with FDM.
Table 3. Value of Mechanical properties and the orthogonal array
|
Exp Serial No. |
Printing Velocity (S) (mm/s) |
Height of Layer (LT) (mm) |
Fracture Strength (MPa) |
Young Modulus (GPa) |
Ultimate Strength (MPa) |
Density of Filling (ID) (%) |
|
1 |
35 |
0.15 |
21.79 |
2.9 |
37.74 |
30 |
|
2 |
65 |
0.2 |
23.08 |
2.755 |
27.44 |
30 |
|
3 |
95 |
0.25 |
19.23 |
2.792 |
31.28 |
30 |
|
4 |
65 |
0.15 |
26.92 |
2.888 |
38.62 |
50 |
|
5 |
95 |
0.2 |
24.36 |
2.95 |
35.95 |
50 |
|
6 |
35 |
0.25 |
20.51 |
2.857 |
38.33 |
50 |
|
7 |
95 |
0.15 |
21.79 |
2.957 |
42.28 |
70 |
|
8 |
35 |
0.2 |
23.38 |
2.827 |
41.03 |
70 |
|
9 |
65 |
0.25 |
24.36 |
2.837 |
41.69 |
70 |
Taguchi's analysis approach has been adopted to investigate the effects on tensile strength. Table 4 shows the comparative impact of different factors on the variations in relative tensile strength. Our research indicates that the density of the filler material exhibits the most substantial influence on the tensile strength, with layer height and printing velocity being secondary factors.
Table 4. The response of signal-to-noise ratios for tensile strength
|
Levels |
Filling Density % |
Layer Height mm |
Printing Velocity mm/s |
|
1 |
18.78 |
21.99 |
21.26 |
|
2 |
21.49 |
20.42 |
21.18 |
|
3 |
22.57 |
20.43 |
20.4 |
|
Delta |
3.79 |
1.57 |
0.86 |
|
Rank |
1 |
2 |
3 |
Figure 5 presents the results and conclusions of The Taguchi method is used to analyze elongation, stress, elastic modulus, strength, and toughness values comprehensively. The graphs show the variations in tensile properties at different levels of printing variables. The influence of "filling density" on all outcomes is clearly noticeable. Based on the data in Table 4 and Figure 5, it is evident that the printing speed has a minimal effect on the tensile strength. Moreover, the results suggest that, aside from filling density, layer height also significantly impacts the tensile values. The findings indicate that increasing the filling density while decreasing the layer height improves the tensile strength, modulus of elasticity, and fracture strength of the PLA 3D-printed tensile test specimen. These plots are derived from tensile tests conducted on 3D-printed PLA material using the Taguchi method.
Figure 5. The signal-to-noise ratio (S-to-N ratio) of Tensile Strength
As the density of filling material increases, the structure of the specimen becomes more solid, leading to enhanced tensile strength. Figure 4 clearly demonstrates that the highest tensile strength is achieved at an infill percentage of 70%. The optimal route limits for achieving the highest UTS, modulus of elasticity, and strength of fracture are a filling density of 70%, a layer height of 0.15 mm, and a printing velocity of 35 mm/s.
Based on Figure 5, the thinnest layer with a thickness of 0.15 mm produces the uppermost tensile strength of 21.99 MPa. The tensile strength decreases to a minimum of 20.43 MPa when the layer thickness is 0.25 mm. Excessive stress application is the primary cause of failure in a tensile test. A decrease in layer thickness results in a more tightly packed specimen, enhancing bonding between the layers and consequently increasing tensile strength. Conversely, altering the thickness of the layers affects the density of the specimen. When the layers are less closely stacked, there is a reduction in tensile strength.
The experiment found that the speed at which the 3D printer operates does not directly affect the strength of the specimen. From experience, it could be realized that the strength of the sample is not directly affected by the speed of the 3D printer. In addition, the density of the material used and the height of each printed layer have a more direct effect than the printing speed. Higher printing speeds can cause the printer to vibrate, causing misaligned layers to move and producing uneven samples in the process. Conversely, when the printer runs at a very slow speed, it takes time for each layer to harden, which leads to weak adhesion between layers due to the low tensile strength when the printing speed is very slow.
3.1 ANOVA results of tensile strength
To determine the presence of significant differences in means across two or more variables. The ANOVA test represents a promising computational technique to address this by examining the variance. Identifying the process characteristics that have a significant and significant effect on the tensile strength is the importance of using the analysis of ANOVA [30], by dividing the overall variance of the tensile process test. By summing the squared differences from the overall mean, the final scale value of the composite parameters is represented relative to the individual contributions of each parameter. The results of the variance analysis for the tensile strength are presented in Tables 5, 6, and 7.
The research shows that the density of the filling has the most significant impact on tensile strength, followed by layer height and printing velocity.
Analysis of variance (ANOVA) has been performed to examine the results of the UTS and identify the key parameters affecting performance. Table 5 presents the ANOVA results for the average UTS with a confidence interval of 66.595%. The F-values from the ANOVA table are used to determine significance. A higher F-value indicates that adjusting a process parameter has a greater impact on performance characteristics.
Table 6 contains the ANOVA results for the Young Modulus at a confidence interval of 36.236%. The F values from the ANOVA table are used to assess the significance. The F-test assumes that increasing a process parameter will have a greater impact on performance characteristics when its F-value is larger.
Table 7 presents the ANOVA findings for the fracture strength at a confidence interval of 50.228%. The F-values from the ANOVA table are used to determine the significance of the data. The F-test assumes that increasing a process parameter will have a greater impact on performance characteristics when its F-value is larger.
Table 5. ANOVA table for ultimate tensile strength
|
Parameter |
Squares Sum |
Freedom’ Degree |
Mean of Squares |
F-value |
Influence (%) |
|
Layer Height |
2.224 |
2 |
1.112 |
0.786 |
17.92 |
|
Printing Velocity |
1.923 |
4 |
0.481 |
0.340 |
15.49 |
|
Filling density |
8.266 |
2 |
4.133 |
2.922 |
66.59 |
|
Error |
0 |
2 |
0 |
0 |
0 |
|
Total |
12.412 |
10 |
100 |
Table 6. ANOVA table for young modulus
|
Parameter |
Squares Sum |
Freedoms’ Degree |
Mean of Squares |
F-value |
Influence (%) |
|
Layer Height |
0.117 |
2 |
0.059 |
0.041 |
34.26 |
|
Printing Velocity |
0.101 |
2 |
0.050 |
0.036 |
29.5 |
|
Filling density |
0.124 |
4 |
0.031 |
0.022 |
36.24 |
|
Error |
0.000 |
2 |
0 |
0 |
0 |
|
Total |
0.342 |
10 |
100 |
Table 7. ANOVA table for fracture strength
|
Parameter |
Squares Sum |
Freedoms’ Degree |
Mean of Squares |
F-value |
Influence (%) |
|
Layer Height |
1.613 |
2 |
0.806 |
0.570 |
25.89 |
|
Printing Velocity |
1.488 |
2 |
0.744 |
0.526 |
23.88 |
|
Filling density |
3.129 |
4 |
0.782 |
0.553 |
50.23 |
|
Error |
0 |
2 |
0 |
0 |
0 |
|
Total |
6.230 |
10 |
100 |
This investigation implemented Taguchi's analysis technique to optimize the process parameters that influence the tensile strength of ABS specimens generated via FDM. The process variables examined in this research have an effect on the tensile strength of the specimen. The research findings yielded the following conclusions:
These findings suggest that it is possible to increase the strength and reliability of components produced using FDM. This study also provides valuable insights into the statistical significance of these process parameters.
|
ABS |
Acrylonitrile Butadiene Styrene |
|
AM |
Additive manufacturing |
|
ANOVA |
Analysis of variance |
|
CAD |
Computer-aided design |
|
DOE |
Design of Experiments |
|
FDM |
fused deposition modeling |
|
OA |
Orthogonal Array |
|
PEEK |
Polyether Ether Ketone |
|
PLA |
Polylactic Acid |
|
S/N |
signal-to-noise |
|
UTS |
Ultimate tensile strength |
|
Greek symbols |
|
|
n |
orthogonal arrays' number |
|
Y_i |
response variable |
|
Subscripts |
|
|
ID |
Density of Filling |
|
LT |
Height of Layer |
|
S |
Printing velocity |
[1] Muhsan, A.A., Ali, H.B., Hamza, M.S., Anaee, R.A. (2023). Effect of nickel–Graphene addition on the 3D printed 17-4 PH stainless steel. Results in Engineering, 20: 101612. https://doi.org/10.1016/j.rineng.2023.101612
[2] Leite, M., Fernandes, J., Deus, A.M., Reis, L., Vaz, M.F. (2018). Study of the influence of 3D printing parameters on the mechanical properties of PLA. In Proceedings of the 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018), pp. 547-552. https://doi.org/10.25341/D4988C
[3] Chacón, J.M., Caminero, M.A., García-Plaza, E., Núnez, P.J. (2017). Additive manufacturing of PLA structures using fused deposition modelling: Effect of process parameters on mechanical properties and their optimal selection. Materials & Design, 124: 143-157. https://doi.org/10.1016/j.matdes.2017.03.065
[4] Lokesh, N., Praveena, B.A., Reddy, J.S., Vasu, V.K., Vijaykumar, S. (2022). Evaluation on effect of printing process parameter through Taguchi approach on mechanical properties of 3D printed PLA specimens using FDM at constant printing temperature. Materials Today: Proceedings, 52: 1288-1293. https://doi.org/10.1016/j.matpr.2021.11.054
[5] Camposeco-Negrete, C., Varela-Soriano, J., Rojas-Carreón, J.J. (2021). The effects of printing parameters on quality, strength, mass, and processing time of polylactic acid specimens produced by additive manufacturing. Progress in Additive Manufacturing, 6: 821-840. https://doi.org/10.1007/s40964-021-00198-y
[6] Ali, H.B., Shawish, M.H., Hasan, S.K. (2023). Experimental investigation and statistical modelling for assessing the sliding wear of Futilized Filament Fabrication (FFF) fabricated parts. Periodicals of Engineering and Natural Sciences, 11(2): 248-261. https://doi.org/10.21533/pen.v11.i2.116
[7] Neamaha, Z.H., Al-Kindia, L.A., Al-Kindib, G. (2024). Design and manufacturing of custom 3D printed bone implants. Engineering and Technology Journal, 42(6): 665-676. http://doi.org/10.30684/etj.2023.143788.1609
[8] Abbas, T.F., Ali, H.B., Mansor, K.K. (2022). Influence of fdm process variables'on tensile strength, weight, and actual printing time when using abs filament. International Journal of Modern Manufacturing Technologies (IJMMT), 14(1): 7-13. https://doi.org/10.54684/ijmmt.2022.14.1.7
[9] Dev, S., Srivastava, R. (2020). Experimental investigation and optimization of FDM process parameters for material and mechanical strength. Materials Today: Proceedings, 26: 1995-1999. https://doi.org/10.1016/j.matpr.2020.02.435
[10] Tura, A.D., Mamo, H.B. (2022). Characterization and parametric optimization of additive manufacturing process for enhancing mechanical properties. Heliyon, 8(7): e09832. https://doi.org/10.1016/j.heliyon.2022.e09832
[11] Sharma, M., Sharma, V., Kala, P. (2019). Optimization of process variables to improve the mechanical properties of FDM structures. In Journal of Physics: Conference Series, 1240(1): 012061. https://doi.org/10.1088/1742-6596/1240/1/012061
[12] Torres, J., Cotelo, J., Karl, J., Gordon, A.P. (2015). Mechanical property optimization of FDM PLA in shear with multiple objectives. Jom, 67: 1183-1193. https://doi.org/10.1007/s11837-015-1367-y
[13] Abouelmajd, M., Bahlaoui, A., Arroub, I., Zemzami, M., Hmina, N., Lagache, M., Belhouideg, S. (2021). Experimental analysis and optimization of mechanical properties of FDM-processed polylactic acid using Taguchi design of experiment. International journal for simulation and multidisciplinary design optimization, 12: 30. https://doi.org/10.1051/smdo/2021031
[14] Brischetto, S., Torre, R. (2020). Tensile and compressive behavior in the experimental tests for PLA specimens produced via fused deposition modelling technique. Journal of Composites Science, 4(3): 140. https://doi.org/10.3390/jcs4030140
[15] Samykano, M., Selvamani, S.K., Kadirgama, K., Ngui, W.K., Kanagaraj, G., Sudhakar, K. (2019). Mechanical property of FDM printed ABS: influence of printing parameters. The International Journal of Advanced Manufacturing Technology, 102: 2779-2796. https://doi.org/10.1007/s00170-019-03313-0
[16] Hibbert, K., Warner, G., Brown, C., Ajide, O., Owolabi, G., Azimi, A. (2019). The effects of build parameters and strain rate on the mechanical properties of FDM 3D-Printed acrylonitrile Butadiene Styrene. Open Journal of Organic Polymer Materials, 9(1): 1-27. https://doi.org/10.4236/ojopm.2019.91001
[17] Abdullah, M.A., Abbas, T.F. (2023). Investigation and prediction of the impact of FDM process parameters on mechanical properties of PLA prints. Engineering and Technology Journal, 41(12): 1465-1473. https://doi.org/10.30684/etj.2023.140389.1466
[18] Abdulridha, H., Abbas, T. (2023). Analyzing the impact of FDM parameters on compression strength and dimensional accuracy in 3D printed PLA parts. Engineering and Technology Journal, 41(12): 1611-1626. https://doi.org/10.30684/etj.2023.143615.1599
[19] Sagias, V.D., Giannakopoulos, K.I., Stergiou, C. (2018). Mechanical properties of 3D printed polymer specimens. Procedia Structural Integrity, 10: 85-90. https://doi.org/10.1016/j.prostr.2018.09.013
[20] Wankhede, V., Jagetiya, D., Joshi, A., Chaudhari, R. (2020). Experimental investigation of FDM process parameters using Taguchi analysis. Materials Today: Proceedings, 27: 2117-2120. https://doi.org/10.1016/j.matpr.2019.09.078
[21] Sood, A.K., Ohdar, R.K., Mahapatra, S.S. (2012). Experimental investigation and empirical modelling of FDM process for compressive strength improvement. Journal of Advanced Research, 3(1): 81-90. https://doi.org/10.1016/j.jare.2011.05.001
[22] Vinodh, S. (2020). Parametric optimization of fused deposition modelling process using Grey based Taguchi and TOPSIS methods for an automotive component. Rapid Prototyping Journal, 27(1): 155-175. https://doi.org/10.1108/RPJ-10-2019-0269
[23] Gebisa, A.W., Lemu, H.G. (2019). Influence of 3D printing FDM process parameters on tensile property of ULTEM 9085. Procedia Manufacturing, 30: 331-338. https://doi.org/10.1108/RPJ-10-2019-0269
[24] Alvarez C, K.L., Lagos C, R.F., Aizpun, M. (2016). Investigating the influence of infill percentage on the mechanical properties of fused deposition modelled ABS parts. Ingenieríae Investigación, 36(3): 110-116. https://doi.org/10.15446/ing.investig.v36n3.56610
[25] Christiyan, K.J., Chandrasekhar, U., Venkateswarlu, K. (2016). A study on the influence of process parameters on the Mechanical Properties of 3D printed ABS composite. In IOP Conference Series: Materials Science and Engineering, 114(1): 012109. https://doi.org/10.1088/1757-899X/114/1/012109
[26] Panda, S.K., Padhee, S., Sood, A.K., Mahapatra, S.S. (2009). Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique. Intelligent Information Management, 1(2): 89-97. https://doi.org/10.4236/iim.2009.12014
[27] ASTM, I. (2002). Standard Test Method for Tensile Properties of Plastics. https://www.astm.org/standards/d638, accessed on 30 Sep. 2024.
[28] Chung Wang, C., Lin, T.W., Hu, S.S. (2007). Optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysis. Rapid Prototyping Journal, 13(5): 304-315. https://doi.org/10.1108/13552540710824814
[29] Teng, H.M., Fan, S.K., Jiang, C.H., Yang, Q.W., Liu, F.H., Tsao, C.C. (2022). Study on the printing quality of fused deposition modeling using Taguchi method. InJournal of Physics: Conference Series, 2345(1): 012014. https://doi.org/10.1088/1742-6596/2345/1/012014
[30] Kafshgar, A.R., Rostami, S., Aliha, M.R.M., Berto, F. (2021). Optimization of properties for 3D printed PLA material using Taguchi, ANOVA and multi-objective methodologies. Procedia Structural Integrity, 34: 71-77. https://doi.org/10.1016/j.prostr.2021.12.011
