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 can be found in the Introduction section.

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