Page 47 - MSAM-1-3

Optimization of chemical admixtures for 3DCP

Materials Science in Additive Manufacturing

Table 6. Coefficients of derived models
Factors Coefficient of models
Static yield stress (Pa) Dynamic yield stress (Pa) Plastic viscosity (Pa·s) Thixotropy (Pa/s)
Interception 829.1 166.82 20.91 17369
A −71.89 −18.56 −1.65 −2550
B 80.27 8.64 1.01 1357
C −125.5 −10.35 −0.3685 −725.9
AB −87.19 −11.83 −0.0360 −262.1
AC −48.89 1.40 1.70 1219
BC 17.24 3.56 −2.23 −244.5
R2 0.9765 0.9922 0.8354 0.9749
Adequate precision 23.26 41.08 11.20 17.90

A B

C D

Figure 8. The relationship between residuals and run orders: (A) Static yield stress; (B) dynamic yield stress; (C) plastic viscosity; and (D) thixotropy.

that the obtained model is statistically significant. The and material rheological properties with high R-squared
adequate precision is a signal-to-noise ratio. It compares values in Table 6.
the range of the predicted values at the design points
to the average prediction error . Ratios greater than 4 Figure 10 shows the 3D response surface, in which the
[26]
indicate adequate model discrimination. Figure 9 shows coded value of superplasticizer is set as 0. As can be seen
the predicted values (calculated from the model) versus from Figure 10, the dosages required for accelerators or
the actual values (obtained from experiments). It is retarders to achieve different rheological properties can
clear that the models were successful in capturing the be found from the contour figures, for example, maximum
correlation between the dosages of chemical admixtures static yield stress or lowest plastic viscosity.

Volume 1 Issue 3 (2022) 8 https://doi.org/10.18063/msam.v1i3.16