3D Objects, Optimizing 3D Prints, Sustainability

Optimizing 3D Print- Conclusion

The study investigated the surface roughness of 3D printed objects through a statistical experiment and X-ray computed tomography of their shell and internal structure, to determine the optimal configurations for print quality.

A wide range of optimal configurations were determined. The study confirms the existing literature that infill levels do not play a major role in the surface quality of the printed objects. Pigmentation of the material does not influence the final surface quality at the chosen temperature. However, natural PLA is consistently present in all the sets of ideal configurations. The material, however, does shrink, adding to the unevenness of the surface and overall dimensions. Shape plays the most important role in deciding the surface quality of these objects.

When an appropriate configuration is used, it is possible to minimize the number of rejected prints and avoid the wastage of filament. The study also shows that tapered objects such as cones will show more unevenness on their external surface when compared to non-deformed objects like cylinders. This confirmation is extremely useful when it comes to performing appropriate design choices while printing, and making additive manufacturing more sustainable.

The future direction is to investigate the surface quality of the objects when printed with and without support structures while also considering polyhedral objects.

 

 

References

  1. Adam G A O, Zimmer D (2015) On design for additive manufacturing: evaluating geometrical limitations, Rapid Prototyping journal, 21/6:662-670. DOI 10.1108/RPJ-06-2013-0060
  2. Afrose F M, Masood S H, Iovenitti P, Nikzad M, Sbarski I (2015) Effects of part build orientations on fatigue behavior of FDM-processed PLA material, Progress in Additive Manufacturing 1: 21. DOI: 10.1007/s40964-015-0002-3
  3. Alfaghani A, Qattawi A, Alrawi B, Guzman A (2017) Experimental Optimization of Fused Deposition Modelling Processing Parameters: a Design-for-Manufacturing Approach, Procedia Manufacturing, Open Journal of Applied Sciences, 7, 291-318. DOI 10.4236/ojapps.2017.76024
  4. Armillotta A (2006) Assessment of surface quality on textured FDM prototypes, Rapid Prototyping Journal 12/1:35-41. DOI 10.1108/13552540610637255
  5. Babout L (2006) X-Ray Tomography Imaging: A Necessary Tool for Material Science. Automatyka 10:117–124
  6. Bill V, Fayard A (2017) Building an Entrepreneurial and Innovative Culture in a University Makerspace. URL https://peer.asee.org/27985, accessed 17 July 2017
  7. Boschetto A, Veniali F (2010) Intricate Shape Prototypes Obtained by FDM, International Journal of Material Forming 3/1:1099-1102. DOI 10.1007/s12289-010-0963-1
  8. Cruz Sanchez F A, Lanza S, Boudaoud H, Hoppe S, Camargo M (2015) Polymer Recycling and Additive Manufacturing in an Open Source Context: Optimization of Processes and Methods. pp 1591–1600
  9. Cuiffo M, Snyder J, Elliott A, Romero N, Kannan S, Halada G P (2017) Impact of The Fused Deposition (Fdm) Printing Process on Polylactic Acid (PLA). Chemistry and Structure Appl Sci 7:579. DOI 10.3390/app7060579
  10. Di Angelo L, Di Stefano P, Marzola A (2017) Surface quality prediction in FDM additive manufacturing, International Journal of Advanced Manufacturing Technology 93: 3655. DOI 10.1007/s00170-017-0763-6
  11. Freitas D, Almeida H A, Bártolo H, Bártolo P J (2016) Sustainability in extrusion-based additive manufacturing technologies. Progress in Additive Manufacturing 1:65–78. DOI 10.1007/s40964-016-0007-6
  12. Gajdoš I, Slota J (2013) Influence of Printing Conditions on Structure in FDM Prototypes. Tehnički vjesnik 20:231–236.
  13. Garlotta D (2001) A Literature Review of Poly(Lactic Acid). Journal of Polymers and the Environment 9:63–84. DOI 10.1023/A:102020082
  14. Galantucci L M, Bodi I, Kacani J, Lavecchia F (2015) Analysis of dimensional performance for a 3D open-source printer based on fused deposition modeling technique, Procedia CIRP 28:82-87. DOI 10.1016/j.procir.2015.04.014
  15. Huang T, Wang S, He K (2015) Quality Control for Fused Deposition Modeling Based Additive Manufacturing: Current Research and Future Trends, The First International Conference on Reliability Systems Engineering. DOI 10.1109/ICRSE.2015.7366500
  16. Jensen M, Wilhjelm J E (2007) X-Ray Imaging: Fundamentals and Planar Imaging. URL-http://www2.compute.dtu.dk/courses/02511/docs/X-RayAndCT.pdf, accessed 17 July 2017
  1. Lindermann C, Jahnke U, Moi M, Koch R (2012) Analyzing Product Lifecycle Costs for a Better Understanding of Cost Drivers in Additive Manufacturing, 23rd Annual International Solid Freeform Fabrication Symposium. pp 177-188
  2. Mitra A (2012) Fundamentals of Quality Control and Improvement, seventh edn. John Wiley & Sons, Inc., Hoboken, New Jersey
  3. Montgomery DC (2013) Design and Analysis of Experiments, eighth edn. JohnWiley & Sons, Inc., Hoboken, New Jersey
  4. Polak R, Sedlacek F, Raz K (2017) Determination of FDM Printer Settings with Regard to Geometrical Accuracy, Proceedings of the 28th DAAAM International Symposium. pp 561-566
  5. Pérez M, Medina-Sánchez G, Garcia-Collado A, Gupta M, Carou D 2018 Surfaace Quality Enhancement of Fused Deposition Modeling (FDM) Printed Samples Based on the Selection of Critical Printing Parameters, Materials 11:1382
  6. Rahmati S, Vahabli E (2015) Evaluation of analytical modeling for improvement of surface roughness of FDM test part using measurement results, International Journal of Advanced Manufacturing Technology 79:823-829. DOI 10.1007/s00170-015-6879-7
  7. Redwood B, Schöffer F, Garret B (2017) The 3D Printing Handbook: Technologies, Design and Applications, first edn. 3D Hubs, Amsterdam
  8. Valerga AP, Batista M, Puyana R, Sambruno A, Wendt C, Marcos M (2017) Preliminary Study of PLA Wire Colour Effects on Geometric Characteristics of Parts Manufactured by FDM. Procedia Manufacturing 13:924–931. DOI 10.1016/j.promfg.2017.09.161
  9. Wittbrodt B, Pearce J M (2015) The Effects of PLA Color on Material Properties of 3-D Printed Components. Additive Manufacturing 8:110–116. DOI 10.1016/j.addma.2015.09.006
3D Objects, Engineering, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- Results: Tomography and Morphological Variations (Part 2)

Results: Tomography and Morphological Variations

Using an analysis software available with the scanner called CT-Analyser, it was possible to make measurements to determine the smallest of anomalies in the scanned objects. At first, the thickness of the layers of the scanned objects were measured. The results showed that the thickness was closer to the theoretical value. However, it also showed that each successive layer causes the print material to shrink [3], causing some layers to protrude outside the expected region, affecting the overall dimensions [14], and hence the surface quality, as seen in Fig. 8. The horizontal cross sections of 20% and 80% infill levels show that the infill doesn’t completely meet the wall of the object [12]. As each successive layer is printed, the points where any two paths intersect show a higher amount of PLA deposition.

Fig 8 Uneven surface of a scanned hollow pink cone

The box plots of the average layer thickness in all the scanned objects and the average distance between consecutive edges in cones are compared as shown in Fig. 9. The horizontal lines in the middle of the plots indicate the median value. The layer thickness is a critical factor which directly affects the surface quality [14]. The analysis shows that the layer thickness is close to the mean value, but always lesser than the expected value, indicating shrinkage; this is true for all scanned objects.

For a cone, each successive layer printed must be smaller than the previous layer under it, i.e., as they taper, their size gets consecutively smaller [21], hence the shape tends to worsen. The distances between the edges of two consecutive layers should be constant, since they are right circular cones. However, when this distance is measured using CT-Analyser, the values are highly inconsistent at all infill levels. This is especially visible in the upper layers of the cone in the scans, which can be seen in Fig. 8.

 

Fig. 9 Box plots of various measurements done on the scanned objects.

 

The tomographic images show irregularities in the final few layers of the cones, regardless of the infill. The pigmentation may influence certain properties [23], but they affect the surface quality the least. When the tomographic images from the natural PLA cylinder and pink PLA cylinder were compared, there was little to no difference in the surface evenness of their shell. However, it is to be noted that the insides of the hollow cylinders show the final layers sagging, in turn, leaving unnecessary frizzy material inside the shell, as seen in Fig. 5 and Fig. 6 in both pink and natural cylinders.

The infill doesn’t affect the surface quality of the object by much [3,21], as confirmed from the results in Table 2-4. The cylinder has an even surface. Even when it is hollow, the final layers are printed uniformly. The cones are uneven regardless of the infill, and their final layers tend to misalign. This is indicated by the tomographic scans, and the analysis from measuring the distances between two consecutive edges, which shows a high variability. The accompanied video shows the individual layers of a 20% infill pink cone. The transition of each layer (shown in light blue color) reveals unevenness in their edges since they are not perfectly circular, indicating surface roughness. The statistical experiment also repeatedly puts cones in the least ideal configuration, supporting the argument that tapered objects tend to have poorer surface quality.

 

References

References can be found in the Introduction section.

3D Objects, Engineering, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- Results: Tomography and Morphological Variations (Part 1)

Results: Tomography and Morphological Variations

The X-ray computed tomography shows the differences in the external shell of the prints as well as the internal structure of the infill. Each layer of PLA can be easily observed in the images. For simplicity, the most prominent image from among the hundreds of images in the dataset are represented in the Fig. 5-7. The number on their top right corner is the corresponding image taken from the captured dataset. The blue objects were not scanned due to the insufficiency of time and lengthy scanning process. Fig. 5-7 show one of the hundreds of images captured during the CT scan. The difference in the background colors is because of the adjustments made to get a good contrast wherever necessary.

Fig. 5 X-ray CT scanned images of the outer shell of all objects.

 

Fig. 6 X-ray CT scanned images of the vertical cross section of all objects

 

Fig. 7 X-ray CT scanned images of the horizontal cross section of all objects

 

There are very few differences in the morphological structures of the cylinders of either color with the same infill level. There are, however, variation in the way the infill is printed inside the shell of the objects. It is to be noted that in Fig. 6 and Fig. 7, the cross-sectional images with a darker background have a different contrast level than that of the images with lighter background. This happened during image adjustment using the software NRecon, which is used to reconstruct the shadow projections. It does not affect the analysis.

When performing the scans, it might not be possible to capture the entire object as the field of view of the receiver is limited (less than 30 mm). Hence only the top portion of the cone was captured. In the case of the cylinders, again, the top was captured, but since the diameter was around 30mm, the detector could only receive a little over 4/5th of it. So, only a part of the wall can be seen. Again, this does not affect the measurement, as the objects are symmetric about the vertical planes.

 

References

References can be found in the Introduction section.

Engineering, Optimizing 3D Prints

Stalling Footage: Specimen inside the CT Scanner

Nothing too exciting this month…

Unfortunately, I have been busy with some other work the past few weeks and didn’t get the chance to write the Part D of my X-Ray Tomography mini-series. To compensate for that I have some footage of how the insides of the CT scanner used looks like with the mechanisms initiated. It is just a small approximately 52 seconds of an animated gif format of the video.

I would like to imagine the scan is going on or I’m performing the analyses while this is happening:

Optimizing 3d Prints Gif that shows the specimen inside the CT scanner.

See you next month with the actual post.

3D Objects, Engineering, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints: Results: Optimum Configurations for 3D Printing (Part 2)

Results: Optimum Configurations for 3D Printing

The summary of optimal settings obtained in the case of Experiment-1 and Experiment-2 can be found in Table 2 and Table 3 respectively. It shows the most ideal and least ideal configurations to use when it comes to choosing between the factors. It also shows which factors, or the factorial interactions contribute to the printed object in a statistically significant manner. This indicates the user to keep an eye on them.

Infill (Factor B) Most Ideal Configuration Least Ideal Configuration Statistically Significant Factors
Low High Height Base Area Height Base Area Height Base Area
Hollow 20% Natural

20%

None

Natural

20%

None

Natural

20%

Tapered

Natural

Hollow

Tapered

C No

Factors

Hollow 80% Natural

80%

None

Natural

80%

None

Pink

80%

Tapered

Natural

80%

Tapered

C B
Hollow Solid Natural

Solid

None

Pink

Solid

None

Pink

Solid

Tapered

Natural

Solid

Tapered

No

Factors

No

Factors

20% 80% Pink

80%

None

Pink

80%

Tapered

Pink

80%

Tapered

Natural

80%

Tapered

A, C

A*C

A*B*C

A

A*B, A*C

A*B*C

20% Solid Natural

Solid

None

Pink

20%

None

Pink

20%

Tapered

Natural

Solid

Tapered

A, C

B*C

B
80% Solid Natural

80%

None

Natural

80%

None

Pink

80%

Tapered

Pink

80%

Tapered

A, C B, C

A*C

Table 2 Summary of the results of the optimal configurations of Experiment-1

 

Infill (Factor B)

Most Ideal Configuration Least Ideal Configuration

Statistically Significant Factors

Low High Height Base Area Height Base Area Height Base Area
Hollow 20% Natural

Hollow

None

Natural

20%

None

Natural

Hollow

Tapered

Blue

20%

Tapered

C

A*C

B
Hollow 80% Natural

80%

None

Natural

80%

None

Natural

Hollow

Tapered

Natural

Hollow

None

C B, C

B*C

Hollow Solid Natural

Solid

None

Natural

Solid

None

Natural

Hollow

Tapered

Blue

Solid

Tapered

C

A*C

A

A*B, B*C

20% 80% Natural

80%

None

Natural

80%

None

Natural

20%

Tapered

Blue

20%

Tapered

C

A*C

A*B*C

C

A*B

20% Solid Natural

Solid

None

Natural

20%

None

Natural

20%

Tapered

Blue

Solid

Tapered

C

A*C

A, B, C
80% Solid Natural

80%

None

Natural

80%

None

Blue

80%

Tapered

Blue

Solid

Tapered

A, C

A*B, A*C

A*B*C

B, C

A*B

Table 3 Summary of the results of the optimal configurations of Experiment-2

 

When color pigments are added to natural PLA, some of its properties such as crystallinity is affected [25], leading to variation in the way the material prints. Hence, a similar experiment was performed with two different pigmentations of PLA. Factor A’s levels were changed to pink and blue. However, other factors and the settings were kept the same. The expected values also remained the same. Table 4 shows the optimal configurations for the Experiment-3.

 

Infill (Factor B)

Most Ideal Configuration Least Ideal Configuration

Statistically Significant Factors

Low High Height Base Area Height Base Area Height Base Area
Hollow 20% Blue

20%

None

Pink

20%

None

Blue

Hollow

Tapered

Blue

20%

Tapered

No Factors No

Factors

Hollow 80% Blue

80%

None

Pink

80%

Tapered

Pink

80%

Tapered

Blue

Hollow

Tapered

No Factors B
Hollow Solid Blue

Hollow

None

Pink

Solid

None

Blue

Solid

Tapered

Blue

Solid

Tapered

No Factors No

Factors

20% 80% Blue

80%

None

Pink

80%

Tapered

Pink

80%

Tapered

Blue

20%

Tapered

A, C

A* C

A, B, C

A* C

20% Solid Blue

Solid

None

Pink

20%

None

Pink

20%

Tapered

Pink

Solid

Tapered

A, C

A* C

A, B, C

 

80% Solid Blue

Solid

None

Pink

80%

Tapered

Pink

80%

Tapered

Blue

Solid

Tapered

A, B, C

A*C, B*C

A, B, C

A*C

A*B*C

Table 4 Summary of the results of the optimal configurations of Experiment-3

 

The data from Table 2-4 show that tapered objects should have a lower priority while 3D printing. The bigger the shape, the more accurate the overall geometry [20], hence the cones can clearly be seen having the least ideal configuration in all cases, because the size of each successive layer reduces since they taper. Also, objects printed using natural PLA are consistently seen in the most ideal configuration columns in Table 2-3, making it preferable when compared to its colored counterparts. Choosing the settings available in Tables 2-4 could prove beneficial to reduce filament wastage while printing using PLA.

 

References

References can be found in the Introduction section.

3D Objects, Engineering, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints: Results: Optimum Configurations for 3D Printing (Part 1)

Results: Optimum Configurations for 3D Printing

When the factorial analysis was performed, each of the three experiments had six sub-experiments for every combination of the infill level. Each of these sub-experiments produced graphs and charts for further analyses to obtain the sets of optimal configurations. Fig. 3 shows one example with the pareto chart and cube plot in the case of the second experiment with an infill setting of 20% and 80%. Fig. 4 shows the main effect and interaction among factors with an infill setting of 20% and 80% in the case of Experiment-1. All the relevant graphs and charts can be found in the supplementary material.

The ideal configuration was determined by comparing the fitted means from the cube plot to the expected value. The expected value for the height was 30 mm and for base area was 706.8583 mm2. The pareto chart shows the statistically significant factors in Fig. 3. The statistically significant factors indicate that the differences between these groups are not simply due to a chance and are real, this can be said with a confidence level of 95%, since all the obtained data was normally distributed [18,19].

 

Fig. 3 Pareto chart and cube plot of 20% and 80% infill for height (top) and base area (bottom) in Experiment-2.

 

The experimental factors can show effects such as main effect and interaction effects. The main effect is the effect of one factor on the experiment while ignoring the effects by all other factors. It shows how much the average performance of one level differs from the average performance of another level [18,19].

In different settings, different factors show main effect. For example, the plot in Fig. 3 indicates that the infill factor doesn’t show main effect, because the plot is nearly parallel to the central line of average. However, the color and the shape of the object show a significant level of main effect.

In the case of the interaction plot, which shows whether one factor affects another [18,19], the graphs make it very clear that the tendency to interact is high when the lines are intersecting. Henceforth, if two or more factors interact, they are indicated using an asterisk between them.

 

Fig. 4 Main effects and interaction of 20% and 80% infill for height (top) and base area (bottom) in Experiment-1.

 

References

References can be found in the Introduction section.

3D Objects, Engineering, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- Experiments: X-ray Tomography

Experiments: X-ray Tomography

A powerful technique in determining the internal structure of closed objects is X-ray computed tomography (CT) or simply, tomography or CT scan. A beam of X-rays is projected on the desired specimen. The radiation transmitted by the specimen is captured by an optical receiver. Images are captured for each discrete rotation of the specimen, and they are reconstructed into a 3-dimensional density map. This map can be used to observe the internal structure and determine its flaws and structural inhomogeneity [5,12,16]. It can also be used to determine minute morphological variations. A Bruker Skyscan 1172 Micro-CT scanner was used to perform this experiment. The specimen here is the 3D printed PLA object using the FFF printer. This is placed using a mount and a paraffin film, so that there is no movement. This film is transparent to X-rays.

It was important to choose an appropriate voltage to keep the power supplied very close to 10 W, so that the X-ray source and the transmitted X-rays from the specimen remains high. A low voltage would result in an inefficient capture of images by the receiver [5,16]. The voltage chosen here was 44 kV. The corresponding current was 222 mA.

When objects were scanned, the captured dataset had 631 horizontal cross section and 1000 vertical cross section slices of images. When it came to the scanning the outer shell, 641 images were captured for pink cones and cylinders, whereas 901 images were captured for the transparent cylinders. The resolution of the obtained images was set to 1K (1000 x 666 ppi for the outer shell, 1000 x 632 ppi for the vertical cross section, and 1000 x 1000 ppi for the horizontal cross sections). The images were captured for all 4 infill levels for natural cylinders, and for both pink cones and cylinders.

 

References

References can be found in the Introduction section.

3D Objects, Engineering, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- Experiments: Statistical Conformity

Experiments: Statistical Conformity

While determining the quality of the 3D printed objects, it was important to make sure that all the prints conformed to the specification. The two measurements of interests were the height of the object and the diameter, as they are required to determine surface area of the objects. When the right-circular cone and right-circular cylinder were modeled, they were designed to have a height and base diameter of 30 mm each. The statistical software, Minitab was used to analyze the data. Three factors were considered.

The first was pigmentation. It is important because it has an influence on the final print-shape. The user always has an option to use colored PLA filament. Adding colored pigments to natural PLA will give it different properties [25]. For an extruder temperature above 493 K (219.85 °C), the pigmentation becomes relevant as it affects the roughness of the final print [23], hence a lower optimal temperature was chosen. The temperature can also alter the printing material’s crystallinity, depending on its color [25], which will, in turn, affect the appearance of the printed object. In this experiment, the colors used were natural (translucent), pink, and blue.

The second factor was the amount of infill. The amount of infill has a range of 0 to 100%. An infill level of 20% and 80% are commonly used [23], and on top of that, the two extreme levels of 0 (hollow) and 100% (solid) were chosen along with them. For a shorter print duration, a lower infill is chosen, for better stability, a higher infill is chosen [23].

Finally, the third was the type of shape/deformation of the object, i.e., whether it gradually tapered (cone), or didn’t (cylinder). There are several other factors which can contribute to the quality of print such as the temperature of the nozzle, size of the nozzle, rate of filament retraction and many more [9,20,21,26]. They are not considered in this experiment.

Once the prints were complete, they were measured using a digital Vernier caliper for accurate measurements, and the grand averages of the values of the height and base diameter were obtained. The area of the base was also calculated. Each experiment was replicated to check for bias. The test used to perform the analysis was Anderson-Darling test using Minitab. The collected data was confirmed to be normal with a 95% confidence interval, as shown in Fig. 2 for cones of 80% infill. Similar data for other infill levels of all objects also exists and is made available in the supplementary material.

Fig. 2 Graphs showing normality of the measurement data in the case of 80% infill level.

 

In Fig. 2, the factor that determines the significance of the result of the normality test is p-value. Since the p-values were higher than 0.05, the data collected is normal and does not have false-positives. The closer the p-value to 1, the more normally distributed is the data [18,19]. It also shows the standard deviation of the data.

Experiment-1 was a 2k factorial design, where k is the number of factors; each factor has two levels. The pigmentation factors were selected to be natural (translucent) and pink, and the shape was tapered or none. Each time the experiment was performed, only a pair of the infill factors was chosen to keep the levels consistent. i.e., a combination of two among hollow, 20%, 80%, and solid were chosen. Depending upon the combination, an appropriate level was chosen to be the lower infill and higher infill level. This was done to determine the optimum levels of infill if the choice were among the combinations. The shape/deformation factor were tapered and none.

Experiment-2 was the same as the first, except the pigmentation was changed from pink to blue. Finally, Experiment-3 factored the colored PLAs (pink and blue) for analysis. Table 1 shows the factors and their associated levels. For simplicity, they are labelled as factors A, B, and C in the table and henceforth.

 

Factor Level 1 Level 2
Factor A (Pigmentation) Color 1 Color 2
Factor B (Infill) Lower Infill Higher Infill
Factor C (Shape) None Tapered

Table 1. Factors and their levels

 

References

References can be found in the Introduction section.

3D Objects, Engineering, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- Experiments

Experiments

To determine the optimal configuration, two kinds of analyses were performed on 3D printed objects of select shapes or deformation. The deformation here indicates whether the overall shape of the object is tapering or not. The shapes chosen were right circular cylinder and right circular cones. These objects were printed at different levels of infill and with different colors. The colors are referred to as pigmentation, especially in the figures.

The first was a statistical experiment, performed to determine whether the shapes conformed to the specifications. The second was a tomographic scan to determine the variation of structure and the layers of the printed objects.

The printed layer thickness was set to be approximately 200 µm. The printer used was Ultimaker 2+ Extended. It was set to the following settings: nozzle size of 0.4 mm, nozzle temperature set at 210 °C, default build plate temperature of 60 °C, and PLA filament thickness of 2.85 mm. Fig. 1 shows a selection of print samples of different colors, shapes, and infill.

Fig. 1 Print samples from one set of experiment

3D Objects, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- An Introduction

Introduction

Many 3D printers provide a high print resolution, suitable for developing high-fidelity prototypes from a computer aided design model. One of the most widely available printing processes is Fused Filament Fabrication (FFF) type, also known by its trademarked term, Fused Deposition Modeling (FDM) in common literature.  Intricate shapes can be printed through FDM printers such as airfoil and Moebius strips [7]. FFF prototype surfaces can be enhanced on a millimeter scale even when they have geometric textures [4]. However, it is common practice among engineers and designers to build the low-fidelity versions first, as a proof of concept. There are several low-fidelity FFF printers available in the market. They can be used with a wide range of materials. But the most frequently used materials are spools of polylactic acid (PLA) as they are less toxic [8,9,13,23]. Due to the ease in their operation, portability, and abundant materials, 3D printers are designed to have fairly good environmental features, making them practical in educational institutions [6]. However, they can be made more sustainable [11] and economical [17] through material waste reduction.

Experimental studies have showcased the properties of different materials or different colors [23] while investigating effects of individual factors on the printed object. Each study focuses on at least one parameter and one material to show its impact on the quality of the final product. Poor surface finish is often caused by tessellation of the computer aided design file and slicing processes. However, the surface roughness can be reduced by modeling a design through optimizing the parameters before fabrication [14,22,26].

Printing of the first layer is crucial, as uneven material deposition on the first layer can change the specimen height of other layers [14]. Surfaces of the printed objects, especially ones which are textured, tend to show the staircase effect, where each printed layer is distinctly visible and looks like a staircase [10]. It is an undesired side effect in low fidelity 3D printers.

Parameters such as build direction, temperature of the extruder, and layer height play a major role in showing dimensional accuracy when compared with infill pattern [3]. The quality of geometry of the product also depends on print speed and layer height [20]. The surface roughness is also affected by the wall thickness of the printed object [21]. Although part build orientation affects mechanical properties such as tensile fatigue of the PLA material [2], this study focuses on the surface quality and dimensions of the objects.

Using appropriate design rules while building prototypes can save the hassle of wasted material, time, and costs associated with them. Current design rules exist only for certain boundary conditions and does not include all types of printing processes [1]. Statistical and engineering process control can be used to detect and correct the variation in the fabrication process [15]. The cost benefits of 3D printing are industry specific. However, material costs make up to 12% of the total costs in additive manufacturing. On top of that quality assurance costs need to be considered [17].

PLA is inexpensive, but wasting it should not be encouraged. Because of poor design choices, material type, amount of infill, and several other factors, many prints fail, and many do not appear as expected by the user. In other words, they do not have good quality of print. Hence, hundreds of printed objects are discarded and can easily affect the environment, making the process less sustainable, unless properly recycled [8]. But recycling can affect the material, which could, in turn, affect the print quality made using the recycled material [8,25]. So, this study shows a way for carefully planning the 3D printing process by using the most favorable settings, to obtain the best possible results without unnecessarily wasting filaments.

Existing approaches use Analytical modeling [22], Taguchi method [3] and factorial designs [4,14,21] to determine dimensional flaws, and X-ray tomography [5,12] or scanning electron microscopy [24] to determine internal and morphological flaws. In the current investigation, the print material was chosen as PLA because it has consistently been proven to print with ease [13] and is not toxic. To reduce the waste from rejected prints, this study uses a 2k factorial design to obtain a range of optimal print settings. An X-ray tomography is also performed to determine and analyze the unevenness of the print layers and surface quality.

 

Continue reading “Optimizing 3D Prints- An Introduction”