Page 41 - MSAM-1-3

Optimization of chemical admixtures for 3DCP

Materials Science in Additive Manufacturing

constituents and chemical admixtures [12-16] . Weng et al. CCD is efficient to find the desirable formulation within a
[8]
explored the impact of material constituents on rheological given boundary.
properties of 3D printable materials and proposed
statistical models to predict rheological properties. Zhang 2. Methodology
et al. presented that the buildability could increase by 2.1. Response surface method and central
[17]
150% with the addition of a small quantity of nanoclay. composite design
Apart from material constituents, chemical admixtures
also serve vital roles on rheology of concrete [12,13] . Dressler As one of the most reliable statistical methodologies in
et al. studied the effect of accelerator on the material DoE, response surface methodology (RSM) includes
[18]
properties in shotcrete 3D printing. Tao et al. investigated optimization procedures for the settings of factorial
[19]
the stiffening control of material using an inline mixing variables, such that the response reaches a desired
[26]
process with chemical admixtures. Yu et al. [20,21] studied maximum or minimum value . The RSM includes various
[26]
the influence of mortar composition on the aggregate bed design structures, such as CCD and Box-Behnken . CCD
process by adjusting the sand/cement ratio and the water/ design structure was used in this work as it explores a larger
process space and provides a higher prediction quality over
cement ratio in aggregate-bed 3D concrete printing.
the entire space than that of Box-Behnken.
Many research works have been conducted to study
The structure of CCD design includes corner points,
the impact of chemical admixtures on rheological axial points, and center points (corresponding to ±1,
properties [22-24] . However, there are still certain limitations. ±1.68, and 0 as shown in Figure 1). Corner points are
First, conclusions from the previous works are mainly the parameters with boundary values. The axial points
qualitative. Few quantitative results have been established can make the model in quadratic terms, considering the
to explain the impact of chemical admixtures on the curvature effect. The experiments of center points were
rheological properties. Furthermore, research needs to be replicated for several times to provide information on
carried out to explore the impact of chemical admixtures process reproducibility.
on thixotropy, which measures the structural rebuilding
rate of materials. Therefore, more attention should be 2.2. Rheology and time-dependent effect
paid to explore the impact of chemical admixtures on 2.2.1. Rheology of cementitious materials
rheological properties and construct models to predict
rheological properties. More specifically, an efficient Rheological properties of cementitious materials are
approach should be adopted for experimental design, and described by Bingham plastic model and characterized
quantitative models should be built empirically through a by the static/dynamic yield stress and plastic viscosity.
series of experiments . The correlation between shear stress τ (Pa) and shear rate
[25]
γ (1/s) in the Bingham model is described in Equation I:
Design of experiments (DoE) is a class of scientific
methodology for experimental design and data analysis k (I)
to improve research efficiency based on fundamental 0
[26]
mathematical statistics . It has been successfully Where τ is yield stress, which includes static yield
0
used in various research fields as a powerful approach stress τ (Pa) and dynamic yield stress τ (Pa). τ and τ are
s
d
s
d
to exploring the relationship between factors and the minimum shear stress to initiate and maintain the
responses [27-29] . One of the useful DoE methods is called flow of materials, respectively. Plastic viscosity k (Pa·s)
central composite design (CCD), quantifying the impact describes the resistance of fluid to flow when it is agitated.
of variables on responses through constructing statistical All the rheological parameters can be obtained from the
models. Using CCD, the experimental process can be
simplified, and the experimental runs can be reduced,
while the sufficient information can be extracted from
the experiment for data analysis. In summary, the CCD
method is more efficient than traditional one-factor at
one-time experiment design.
In this study, CCD was adopted to efficiently construct
statistical models, expressing the rheological characteristics
as functions of different factors, that is, various chemical
admixtures. The constructed statistical models are not
universally applicable , while the results indicate that the Figure 1. Design structure of CCD.
[30]

Volume 1 Issue 3 (2022) 2 https://doi.org/10.18063/msam.v1i3.16