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 can be found in the Introduction section.

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

Optimizing 3D Prints- 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


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”

3D Objects, Blurbs, Optimizing 3D Prints, Sustainability

Optimizing 3D Prints- Brief

Due to the plethora of things made using 3D printers, a large amount of waste is produced in the form of failed prints and wasted filaments to obtain prints of the best quality. It is important to ensure that the printing material wastage is minimal, even when it is inexpensive, for a more sustainable additive manufacturing. To keep a printed object the closest in appearance to its computer aided design, it is ideal to test the parameters that make for its surface quality. With the appropriate settings for these parameters, it is possible to reduce material waste and print failures. This paper shows that, it is possible to determine the optimal settings for different levels of infill, so that the user specifications are met. It also presents the statistical experiments performed on the printed objects of specific shapes, color and infill level, the tomographic images of the outer shell and the internal structure of their infill, to obtain the favorable configurations for optimal print quality.


This was supposed to be a journal paper titled Determining Favorable Configurations for Low-fidelity Filament Freeform Fabrication 3D Printers to Attain Optimal Print Quality and Reduce Wastage, but I think I will post it in my blog instead.

Why? Because this is the best course of action. Enjoy my months of research which I will post occasionally.

2D designs, Engineering, Interactive Design, Optimizing 3D Prints

Heat Maps

It’s 3.14! Happy Pi Day!

A heat map is a graphical representation of collected data, where large data points are plotted in such a way that it represents the concentration of those points through colors. The color scheme depends upon the choice of the user. Normally, a darker color represents higher density and a lighter color, lower density of the data points.

Using heat maps often help identify the flaws within physical objects (if one knows what to do and how to use it), and movements of mouse cursor, or density of visual concentration while eye tracking in interactive displays.

This makes them very useful in user experience and usability studies to understand why people choose certain parts of a website or a software, and where they have their eyes fixed while using it.

Below is an blurred image of a website (left) and its heat map generated (right) while I was testing it to improve its usability.

Below is a time lapse video of heat maps generated by scanning hundreds of layers of a 3D printed object using an X-ray CT scanner for one of my projects, which has something to do with optimizing 3D prints. More on this another time.