Table 2: Tensile Stress Test Results of PETG.
Table 2 shows that the temperature of 220 C is
zero because the temperature is too low for the PETG
filament so that a tensile test specimen cannot be
made.
Analysis of Tensile Test Results with Factorial
Design
After testing the tensile test on the PLA and PETG
specimens and getting the results of the tensile stress
and strain values as above. Then the data was
analyzed using Factorial Design with Minitab.
Because researchers wanted to see whether or not
there was an influence or not from 3 independent
variables, namely the type of material, namely PLA
and PETG, the temperature is 220 °C, 240 °C, 260 °C,
and the layer thickness is 0.1 mm; 0.2mm; 0.3mm.
The following results of the tensile stress analysis on
the tensile test specimen test with a 3D printer in
Figure 14 below:
Figure 14: Factorial Design Result of Tensile Strength .
From the results of the Factorial Design in Figure
4.15, the first variable, namely the type of PLA and
PETG material, has a p-value of 0.000, and the value
is less than = 0.05. can be concluded that the
hypothesis (H0a) is rejected, and the alternative
hypothesis (H1a) is accepted, namely "There is an
effect of material type on the tensile strength of 3D
printer products made of PLA and PETG materials".
For the second variable, temperature with a p-value
of 0.000, the value is less than = 0.05. can be
concluded that the hypothesis (H0b) is rejected, and
the alternative hypothesis (H1b) of the study is
accepted, namely "There is an effect of temperature
on the tensile strength of the 3D printer product, PLA
and PETG materials." The third variable is layer
thickness with a p-value of 0.049, the value is less
than = 0.05. It can be concluded that the hypothesis
(H0c) is rejected and the alternative hypothesis (H1c)
of the study is accepted, namely "There is an effect of
layer thickness on the tensile strength of 3D printer
products made of PLA and PETG materials"
In the summary model, the values are the
reference data and part of the Factorial Design
analysis. The standard deviation value (S) is the
average deviation of the data points with the average
data. can be seen in Figure 14 that the value of S in
the data processing results is 0.443647 which means
that the deviation value of the data point with the
average data is 0.443647. The coefficient of
determination (R-sq) is the percentage contribution of
the influence given by the independent variable to the
dependent variable. In Figure 14 the coefficient of
determination has a value of 94.22% which means
that the influence of the independent variables,
namely the type of material, temperature, and layer
thickness, affects 94.22% of the dependent variable,
namely tensile strength. makes the remaining 5.78%
an error in the form of other independent variables
outside the material type, temperature, and layer
thickness as well as errors in testing or other things
that affect tensile strength.
In Figure 14, it is found that the interaction
between the variables of material type and
temperature is mutually influential, this is evidenced
by the p-value of 0.000, the value is less than = 0.05.
The second interaction found that the interaction
between the material type variable and layer thickness
also had an effect, this was evidenced by the p-value
of 0.002, the value was less than = 0.05. In the third
interaction, temperature and layer thickness variables
have an effect, this is evidenced by the p-value of
0.002, the value is less than = 0.05. The fourth
interaction of the three variables. Namely, the type of
material, temperature, and layer thickness interact
and influence each other, this is evidenced by the p-
value of 0.000, the value is less than = 0.05.