A Comprehensive Review of Rheological Dynamics and Process Parameters in 3D Concrete Printing

Abstract

Three-dimensional concrete printing (3DCP) represents a paradigm shift in construction technology, enabling the automated, formwork-free fabrication of intricate geometries. Despite its rapid growth, successful implementation remains dependent on the precise control of material rheology and printing parameters. This review critically analyzes the foundational rheological properties of static yield stress, dynamic yield stress, plastic viscosity, and thixotropy and their influence on three core printability attributes, i.e., pumpability, extrudability, and buildability. Furthermore, it explores the role of critical process parameters, such as print speed, nozzle dimensions, layer deposition intervals, and standoff distance, in shaping interlayer bonding and structural integrity. Special emphasis is given to modeling frameworks by Suiker, Roussel, and Kruger, which provide robust tools for evaluating structural stability under plastic yield and elastic buckling conditions. The integration of these rheological and process-based insights offers a comprehensive roadmap for optimizing the performance, quality, and scalability of 3DCP.

1. Introduction

Extrusion-based 3D concrete printing (3DCP) is rapidly advancing as a transformative digital construction technology, enabling the automated fabrication of complex structural geometries without traditional formwork [1,2,3]. Extrusion-based methods involve the layer-wise deposition of fresh cementitious materials through a nozzle under controlled pressure, distinct from other additive manufacturing techniques, such as powder-bed or binder jetting. The successful implementation of extrusion-based 3DCP relies heavily on the fresh-state rheological behavior of concrete, which governs material performance during the pumping, extrusion, and deposition processes [4]. Rheological parameters, such as static yield stress, dynamic yield stress, plastic viscosity, and thixotropy, critically influence the flowability under pressure, geometric stability after deposition, and the capacity to support subsequent layers.

The complexity of controlling these parameters arises from their time-dependent evolution, particularly during the transition from fluid-like behavior to a solid-like state. Recent research emphasizes the importance of accurately characterizing this dynamic behavior, highlighting the interdependency between material rheology, printing conditions, and structural performance outcomes [5,6,7]. Furthermore, detailed studies have demonstrated that printing parameters, including nozzle geometry, print speed, standoff distance, and layer deposition intervals, significantly affect interlayer adhesion and the mechanical integrity of printed elements [8,9,10,11].

This review synthesizes the latest theoretical models and experimental findings linking rheological properties, printing parameters, and structural reliability in extrusion-based 3DCP. By offering an integrated understanding of these interrelationships, it supports the development of optimized, high-performance material formulations and advanced printing strategies tailored for scalable additive manufacturing in concrete construction. Beyond the fundamental evaluation of pumpability, extrudability, and buildability, the review incorporates state-of-the-art models that improve the prediction of failure mechanisms and print outcomes. Unlike traditional construction methods, 3DCP presents unique challenges due to the dynamic coupling of material properties and process conditions, where minor variations in rheology or equipment settings can compromise print stability. This work bridges the divide between materials science and structural engineering by systematically integrating rheological models, process parameter analysis, and structural behavior predictions. It highlights the critical influence of static and dynamic yield stress, plastic viscosity, and thixotropy on printability, while also examining how parameters such as print speed, nozzle geometry, and interlayer deposition time impact bonding quality and mechanical anisotropy. Furthermore, the inclusion of modeling frameworks by Suiker, Roussel, and Kruger adds practical value for designers seeking to predict failure modes and optimize construction sequences. Ultimately, this review provides a consolidated foundation and strategic roadmap for researchers, engineers, and industry stakeholders aiming to enhance structural reliability, material sustainability, and the real-world scalability of 3DCP technologies.

2. Rheological Behavior in 3DCP

The rheological properties of 3D printable concrete play a critical role in the printing process and are primarily governed by rheological parameters, such as static yield stress, dynamic yield stress, plastic viscosity, and thixotropy [12,13,14,15].

Dynamic yield stress is defined as the minimum shear stress required to maintain the material in a flowing state under continuous shear, while plastic viscosity describes the internal frictional resistance among particles during flow. These two parameters directly influence the flow behavior of materials during pumping and extrusion processes [13,16]. Additionally, fresh concrete may exhibit shear-thickening or shear-thinning phenomena. Shear thickening refers to a significant increase in viscosity with rising shear rates, mainly due to intense particle collisions and friction. Conversely, shear thinning is characterized by a decrease in viscosity as shear rates increase, which is attributed to particle rearrangement and ordering, leading to reduced internal resistance [17,18]. The accurate assessment of shear conditions at different stages of the printing process is vital to prevent nozzle clogging, ensure continuous and uniform extrusion, and maintain interlayer stability.

To quantitatively describe the stress–deformation relationship under shear, rheological models, such as the Bingham model, modified Bingham model, and Herschel-Bulkley model, are commonly adopted [16,19,20,21]. The Bingham model, suitable for ideal plastic fluids, is expressed as follows:

$$\tau ={\tau }_0+{\eta }_{\mathrm{p}}\dot{\gamma }$$

(1)

where τ is the shear stress, τ₀ is the dynamic yield stress, η_p is the plastic viscosity, and $\dot{\gamma }$ is the shear rate. However, practical concrete mixtures typically exhibit more complex rheological behaviors due to interparticle interactions, particle gradation, hydration processes, and chemical admixtures, thereby showing pronounced nonlinear characteristics under various shear rates. Therefore, the modified Bingham model is introduced to better reflect this nonlinear behavior, expressed as follows:

$$\tau ={\tau }_0+{\eta }_{\mathrm{p}}\dot{\gamma }+c{\dot{\gamma }}^2$$

(2)

where c is a shear rate related parameter. When c > 0, the material displays shear-thickening behavior, while c < 0 indicates shear-thinning behavior. This model offers a more precise description of the rheological behavior under varying shear rates. Additionally, the more general Herschel–Bulkley model is widely utilized to characterize the non-Newtonian flow behavior of concrete, as follows:

$$\tau ={\tau }_0+K{\dot{\gamma }}^n$$

(3)

where K is the consistency coefficient, and n is the flow index (with n < 1 indicating shear-thinning behavior, and n > 1 indicating shear-thickening behavior). The curves of the three typical rheological models described above are illustrated in Figure 1a.

Static yield stress represents the minimum shear stress required for the material to transition from rest to flow, reflecting the initial internal structural strength of the material [22], as shown in Figure 1b. Combined with thixotropy, it determines the structural recovery and stability of the material at rest [23]. During the 3D printing process, static yield stress directly affects the material’s ability to withstand loads imposed by newly deposited layers, preventing deformation or collapse. Thixotropy further characterizes the dynamic structural recovery of materials during resting periods and is typically expressed by the following equation [24]:

$${\tau }_{\mathrm{s}}\left(t\right)={\tau }_{\mathrm{s},0}+A_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}\cdot t$$

(4)

where τ_s (t) is the static yield stress at time t, τ_s,0 is the initial static yield stress, and A_thix is the structural build-up rate. However, due to rapid physicochemical interactions among cement particles and the rapid development of initial flocculation structures, static yield stress commonly exhibits a nonlinear growth trend at early stages, making an exponential-type equation more suitable for accurate description [25], as follows:

$${\tau }_{\mathrm{s}}(t)={\tau }_{\mathrm{s},0}+A_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}\cdot t_{\mathrm{c}}(e^{{\displaystyle \frac{t}{t_{\mathrm{c}}}}}-1)$$

(5)

where t_c is the characteristic time, determined by fitting the model predictions to the experimental data. Further equations for characterizing thixotropy are detailed in the literature [19].

To ensure reliable rheological data and optimize the fresh properties of 3D printable concrete, established standards and recommendations provide valuable guidance. ASTM C1749-17a [26] offers a standardized methodology for measuring the rheological parameters of hydraulic cement pastes using rotational rheometers, while the RILEM TC 266-MRP committee [27] has developed comprehensive recommendations for evaluating yield stress, plastic viscosity, and thixotropic behavior in a variety of cementitious systems. Additionally, the ACI 238.1 R-08 [28] report contextualizes these tests within the broader framework of fresh concrete workability and in-field practices, offering critical insights into the challenges and adaptations needed for real-world applications. These standards and recommendations collectively serve as a robust framework for interpreting and tailoring rheological properties in 3D-printed concrete systems.

The rheological properties of concrete mixtures are significantly influenced by composition and mix proportions, including water-to-binder ratios, aggregate size and content, mineral admixtures, and chemical additives [12,29,30,31]. For instance, incorporating fly ash, slag, and silica fume effectively reduces the plastic viscosity, enhancing fluidity. Conversely, the addition of nano-clay or viscosity-modifying agents increases thixotropy and yield stress, improving shape retention and structural recovery rate during printing [6,12]. Furthermore, although reducing the water-to-binder ratio can increase yield stress, it may also lead to excessively high plastic viscosity, impairing the processability of the printable material [6,18]. Therefore, the rheological parameters of 3D printable concrete and their interrelationships are crucial for the material design and optimization of printing processes. The precise characterization and control of these parameters can effectively enhance the stability and predictability of the printing process.

3. Printability

The method of 3DCP is rapidly evolving, promising significant improvements in construction efficiency, flexibility, and sustainability. The effective control of printability requires the careful optimization of interrelated rheological parameters, such as static and dynamic yield stress, plastic viscosity, and thixotropy [6,7,17,18,32]. Specifically, optimal pumpability demands low plastic viscosity to facilitate smooth flow under pumping conditions, while maintaining adequate dynamic yield stress to prevent segregation. Extrudability primarily relies on achieving appropriate dynamic yield stress and rheological consistency to ensure uniform filament deposition and geometric accuracy. Buildability necessitates sufficiently high static yield stress to support subsequent layers without deformation or collapse, reflecting the material’s structural integrity during early-age curing. The evolution of material behavior throughout the printing process follows a transition from flow-dominated to strength-governed regimes, requiring distinct theoretical models that span from rheology to solid mechanics [5,7]. Figure 2 provides an overview of this transition, illustrating the staged nature of material responses and the associated shifts in governing mechanisms. In the following sections, theories and models related to pumpability, extrudability, and buildability are systematically presented and discussed as key components of printability evaluation and process optimization.

3.1. Pumpability

Pumpability is a fundamental requirement for the successful implementation of 3DCP, governed by complex flow behaviors during transport through pipelines. Fresh concrete typically exhibits the following three main flow regimes during pumping [33,34,35]: plug flow, shear flow, and laminar flow, as depicted in Figure 3. Plug flow refers to the bulk movement of concrete in the pipe core with a nearly uniform velocity profile; shear flow arises due to velocity gradients near the pipe wall, where the central core remains relatively static; and laminar flow displays a smooth velocity gradient decreasing from the center toward the pipe wall. Identifying the actual flow regime is essential for the accurate prediction of the pumping pressure, the mitigation of the blockage risk, and the optimization of the construction operations. These regimes depend largely on the rheological characteristics of the concrete and the properties of the lubrication layer and are further influenced by fluid mechanics parameters, such as the Reynolds number [20,34,35].

Under laminar flow conditions, the relationship between pumping pressure and rheological parameters can be expressed using the Buckingham–Reiner equation [36,37]:

$$Q=\pi {\displaystyle \frac{3\Delta p_{\mathrm{l}}^4{R}^4+16{\tau }_0^4{L}^4-8{\tau }_{0}L{R}^3\Delta p_{\mathrm{l}}^3}{24\Delta p_{\mathrm{l}}^3{\eta }_{\mathrm{p}}L}}$$

(6)

where P is the pumping pressure, L is the pipe length, R is the pipe radius, Q is the volumetric flow rate, and $\mathrm{\Delta }{p}_{\mathrm{l}}$ is the total pressure loss in the pipeline, which can be quantified through differential pressure measurements acquired from pressure transducers installed at the inlet and outlet sections of the pipeline. This formulation characterizes the flow of Bingham fluids, incorporating both yield stress and plastic viscosity, and assumes ideal conditions, including no slip between concrete and the pipe wall as well as uniform pressure distribution across the pipe cross-section. However, in practical pumping operations, the presence of a lubrication layer and particle migration may lead to substantial deviations, often causing the model to overestimate actual pressure losses [35]. Consequently, corrections considering the lubrication layer properties and associated flow dynamics are necessary to improve predictive accuracy.

To better reflect real pumping behavior, Kaplan et al. [38] developed modified expressions based on the interfacial rheology of the lubrication layer, applicable to both plug and shear flow regimes.

For plug flow, deformation is localized at the lubrication layer, shown as follows:

$$P={\displaystyle \frac{2L}{R}}\left[{\displaystyle \frac{Q}{\pi R^2}}{\eta }_{\mathrm{i}}+{\tau }_{0,\mathrm{L}\mathrm{L}}\right]$$

(7)

For shear flow, both the lubrication layer and concrete bulk deform, shown as follows:

$$P={\displaystyle \frac{2L}{R}}\left[{\displaystyle \frac{\frac{Q}{\pi R^2}-\frac{R}{4{\eta }_{\mathrm{p}}}{\tau }_{0,\mathrm{L}\mathrm{L}}+\frac{R}{3{\eta }_{\mathrm{p}}}{\tau }_0}{1+\frac{R}{4{\eta }_{\mathrm{p}}}{\eta }_{\mathrm{i}}}}\cdot {\eta }_{\mathrm{i}}+{\tau }_{0,\mathrm{L}\mathrm{L}}\right]$$

(8)

Here, ${\tau }_{0,\mathrm{L}\mathrm{L}}$ denotes the yield stress of the lubrication layer, and ${\eta }_{\mathrm{i}}$ is the viscosity coefficient of the lubrication layer (defined as the ratio of its plastic viscosity ${\eta }_{\mathrm{L}\mathrm{L}}$ to thickness T_LL (see Figure 4)). These parameters can be experimentally determined using a sliding pipe rheometer (SLIPER). While Kaplan’s method incorporates the lubrication layer into pressure loss estimation, it does not provide a clear criterion for differentiating between plug and shear flow conditions [39].

To address this, Feys proposed a regime discrimination approach based on comparing interfacial shear stress to the bulk concrete’s yield stress. When the interfacial shear stress is lower than the concrete’s yield stress, plug flow prevails; otherwise, the flow transitions into a shear regime [39,40]. The interfacial shear stress ${\tau }_{\mathrm{c}}$ can be estimated as follows:

$${\tau }_{\mathrm{c}}={\displaystyle \frac{\Delta p{R}_{\mathrm{c}}}{2}}$$

(9)

where $∆p$ is the pressure loss per unit pipe length, and R_c is the distance from the center of the pipe to the lubrication layer, calculated by R-T_LL. Feys highlighted that two ratios, namely the ratio of concrete viscosity to lubrication layer viscosity and the ratio of yield stress to viscosity, are critical in determining the flow regime. An increase in viscosity was shown to significantly raise the required pumping pressure [39].

Kwon et al. [41] further developed a model emphasizing the influence of lubrication layer thickness and rheological properties on the pumping flow rate of concrete. Their model computes the flow rate as follows:

$$Q={\displaystyle \frac{\pi }{24{\eta }_{\mathrm{L}\mathrm{L}}{\eta }_{\mathrm{p}}}}\left[3{\eta }_{\mathrm{p}}{\displaystyle \frac{\Delta p}{L}}\left(R^4-R_{\mathrm{c}}^4\right)-8{\eta }_{\mathrm{p}}{\tau }_{0,\mathrm{L}\mathrm{L}}\left(R^3-R_{\mathrm{c}}^3\right)+3{\eta }_{\mathrm{L}\mathrm{L}}{\displaystyle \frac{\Delta p}{L}}\left(R_{\mathrm{c}}^4-R_{\mathrm{p}}^4\right)-8{\eta }_{\mathrm{L}\mathrm{L}}{\tau }_0\left(R_{\mathrm{c}}^3-R_{\mathrm{p}}^3\right)\right]$$

(10)

where ${\eta }_{\mathrm{L}\mathrm{L}}$ is the plastic viscosity of the lubrication layer. The concrete plug radius R_p is given by Equation (11), as follows:

$$R_{\mathrm{p}}=2{\tau }_0\left({\displaystyle \frac{L}{P_{\mathrm{i}}}}\right)\le R_{\mathrm{c}}$$

(11)

P = P_i + P_g

(12)

where P_i is the net pressure at the inlet of the pipeline, and P_g is the hydrostatic pressure due to the weight of the concrete. Kwon’s model is particularly suitable for predicting pumping behavior under high pressure or long distance conditions but relies heavily on accurate measurement of the lubrication layer thickness, typically achieved through ultrasonic techniques or on-site cross-sectional analysis [42].

In summary, although all aforementioned models acknowledge the critical role of the lubrication layer in concrete pumping, each prioritizes different aspects. Kaplan emphasizes interfacial rheological properties, Feys provides a criterion for flow regime classification, and Kwon focuses on the direct influence of lubrication layer thickness. In practical engineering applications, model selection should be informed by specific project requirements, such as pumping distance, expected pressure range, and the availability of rheological data.

3.2. Extrudability

Extrudability is a critical performance parameter in extrusion-based 3DCP, characterizing the ability of fresh cementitious material to be continuously and uniformly extruded through a nozzle under a defined pressure, while preserving geometric stability and minimizing flow instabilities, such as blockage or phase separation. The accurate prediction and control of extrudability are fundamental to optimizing process efficiency, structural build-up, and interlayer bonding in 3DCP applications. Several extrusion techniques have been established to quantify extrudability in cementitious systems. Ram extrusion is the most prevalent approach in laboratory settings, wherein a piston applies pressure to force material through a die, as shown in Figure 5. It forms the basis for most extrusion models, such as those proposed by Benbow and Bridgwater [43,44,45] and Perrot et al. [46,47,48]. Screw-based extrusion systems are common in automated 3DCP equipment and enable continuous extrusion, though analytical modeling is more complex due to dynamic shear and pressure gradients [3,49]. Vibration-assisted extrusion, studied in recent works [50], introduces superimposed oscillatory forces to reduce yield stress and enhance flowability. Extrusion geometries vary from cylindrical and conical dies to tapered and slit configurations, all of which influence pressure loss, wall shear stress, and die entry effects [11,41,42].

3.2.1. Benbow–Bridgwater Model: Ram Extrusion in Two Zones

The Benbow–Bridgwater model [43] partitions the extrusion process into two principal regions, as follows: (i) the converging entry zone (barrel), characterized by elongational flow, and (ii) the die land zone, characterized by steady-state shear flow. The total extrusion pressure P_e is expressed as follows:

$$P_{\mathrm{e}}=\left({\sigma }_0+\alpha V^a\right)\mathit{ln}\left({\displaystyle \frac{D_0}{D}}\right)+{\displaystyle \frac{M{L}_{\mathrm{e}}}{D}}\left({\tau }_0+\beta V^b\right)$$

(13)

In this equation, ${\sigma }_0$ represents the elongational yield stress (extrusion yield stress), which is often approximated via extrapolated data from uniaxial compression tests or ram extrusion. The parameters $\alpha$ and $\beta$ denote velocity-dependent empirical coefficients reflecting rate sensitivity, while V is the extrudate velocity. D₀, D, M, and L_e are the diameters of the barrel, diameters of the die land, the perimeter of the die, and the length of the die, respectively. a and b are fitting parameters in extrusion pressure–velocity relations. This model has been widely applied to cement-based materials, but its semi-empirical nature and reliance on geometry-specific parameters reduce its generalizability.

3.2.2. Perrot Model: Incorporating Frictional and Filtration Effects

Perrot et al. [46,47,48] proposed a ram extrusion model that incorporates both wall friction force (F_pl) and plastic shaping force (F_fr) to predict the total extrusion force (F_e) as a function of the material’s flow behavior and ram geometry. The total extrusion force can be expressed as follows:

$$F_{\mathrm{e}}=F_{\mathrm{p}\mathrm{l}}+F_{\mathrm{f}\mathrm{r}}=K_{\mathrm{c}}\cdot A_{\mathrm{d}}+K_{\mathrm{w}}\cdot P_{\mathrm{w}}\cdot \pi D_0{L}_0$$

(14)

$$K_{\mathrm{c}}={\tau }_0, \mathrm{when} BN={\displaystyle \frac{{\tau }_0{D}_0}{8{\eta }_{\mathrm{p}}V}}>100$$

(15)

$$K_{\mathrm{c}}={\tau }_0+{\eta }_{\mathrm{p}}\dot{\gamma }, \mathrm{when} BN={\displaystyle \frac{{\tau }_0{D}_0}{8{\eta }_{\mathrm{p}}V}}<100$$

(16)

where K_c represents the equivalent yield stress, and BN denotes the Bingham number. When BN > 0, the material behavior is predominantly plastic; when BN < 0, it exhibits viscoplastic characteristics. A_d is the cross-sectional area of the die outlet, calculated as $A_{\mathrm{d}}={\displaystyle \frac{\pi }{4}}{D}^2$, and K_w is the wall friction coefficient, which can be experimentally determined using shear box tests or Casagrande-type apparatus. The normal stress on the wall P_w can be estimated as the average hydrostatic pressure or more accurately by employing the Janssen theory. L₀ is the length of the barrel.

Furthermore, Perrot et al. [46] highlighted that cement-based materials are prone to drainage during low-velocity extrusion processes, which may significantly affect the flow regime and material homogeneity. To address this, a fluid filtration and consolidation criterion was introduced. This criterion compares the extrusion velocity V to the liquid phase filtration velocity q:

• If V ≫ q, the process can be considered undrained, and the material remains homogeneous, validating the use of the extrusion force model.
• Otherwise, phase separation may occur due to fluid drainage, leading to alterations in the yield stress (K_c) and wall friction coefficient (K_w).

Under such conditions, a modified Darcy friction model must be adopted to account for the evolving rheological and frictional behavior during the extrusion process.

3.2.3. Basterfield Model: Rheological Theory and Die Entry Flow

Basterfield et al. [51] proposed a physical model based on the Herschel–Bulkley assumption and spherically converging flow into the die. The pressure drop is given as follows:

$$P_{\mathrm{e}}=2{\sigma }_0\mathit{ln}\left({\displaystyle \frac{D_0}{D}}\right)+{\displaystyle \frac{2}{3n}}K{\left(\mathrm{s}\mathrm{i}\mathrm{n}{\theta }_{\mathrm{m}\mathrm{a}\mathrm{x}}\left(1+\mathrm{c}\mathrm{o}\mathrm{s}{\theta }_{\mathrm{m}\mathrm{a}\mathrm{x}}\right)\right)}^n\left(1-{\left({\displaystyle \frac{D}{D_0}}\right)}^{3n}\right){\left({\displaystyle \frac{2V}{D}}\right)}^n$$

(17)

The elongational yield stress ${\sigma }_0$ is often approximated as $\sqrt{3}{\tau }_0$. K and n are derived from fitting the Herschel–Bulkley model to flow curves. The half-angle ${\theta }_{\mathrm{m}\mathrm{a}\mathrm{x}}$ corresponds to the conical die entry angle. Unlike previous models, all terms in this formulation are physically meaningful and directly measurable, enhancing transferability across systems. Zhou et al. [52] further revised the Basterfield model by incorporating a simplified analytical orifice extrusion approach based on the Herschel–Bulkley viscoplastic relationship and the von Mises yield criterion, providing improved rheological characterization accuracy and enhanced experimental convenience.

In summary, the Benbow–Bridgwater model remains widely used due to its simplicity and adaptability to various laboratory setups; although, its empirical parameters limit cross-material comparisons. The Perrot model offers enhanced realism by accounting for wall friction and filtration, both critical to accurately modeling extrusion in real-world scenarios involving fiber-reinforced or thixotropic mortars. The Basterfield model, though more complex, stands out by relying solely on intrinsic rheological parameters and die geometry, making it more transferable across different systems.

3.3. Buildability

In extrusion-based 3DCP, buildability describes the ability of freshly deposited layers to support subsequent layers without undergoing structural failure. The following two primary failure modes govern buildability (see Figure 6): (i) material failure due to plastic yielding and (ii) structural failure from elastic buckling. A series of analytical and numerical models have been developed to predict and assess buildability based on rheological and mechanical parameters. This section reviews the major models that simultaneously account for both plastic yield failure and elastic buckling failure, emphasizing their underlying assumptions, related rheological inputs, and comparative advantages.

3.3.1. Suiker’s Model: Coupled Plastic Collapse and Elastic Buckling

Suiker [53,54] developed a comprehensive mechanistic model that distinguishes between elastic buckling and plastic collapse in 3D-printed wall-type structures. The material failure criterion is based on the Mohr–Coulomb model, requiring parameters such as cohesion c(t), internal friction angle ϕ(t), and dilatancy angle ψ(t), which are obtained from direct shear testing (DST) or triaxial compression testing (TCT). The elastic response incorporates a time-dependent modulus E(t), typically measured via ultrasonic wave transmission or oscillatory rheometer, contributing to flexural rigidity D_fr(t), with h the wall thickness and ν Poisson’s ratio.

Instead of directly comparing dimensional failure heights, Suiker proposed a dimensionless failure criterion that compares characteristic failure lengths through normalized scaling. The governing failure mechanism is selected using the following:

$$\begin{array}{c}{\displaystyle \frac{{\overline{l}}_{\mathrm{c}\mathrm{r}}}{{\overline{l}}_{\mathrm{p}}}}<\overline{\Lambda }\Rightarrow \mathrm{elastic\; buckling}\\ {\displaystyle \frac{{\overline{l}}_{\mathrm{c}\mathrm{r}}}{{\overline{l}}_{\mathrm{p}}}}>\overline{\Lambda }\Rightarrow \mathrm{plastic\; collapse}\end{array}$$

(18)

Here, ${\overline{l}}_{\mathrm{c}\mathrm{r}}$ is the dimensionless buckling length, which depends on the following:

• The dimensionless stiffness gain rate ${\overline{\xi }}_{\mathrm{E}}$, derived by fitting the evolution curve of elastic modulus E(t);
• Wall geometry, particularly the wall width to thickness ratio ${\overline{b}}_{\mathrm{w}}$=$\delta$/h;

${\overline{l}}_{\mathrm{p}}$ is the dimensionless plastic collapse length, influenced by the following:

The dimensionless strength development rate ${\overline{\xi }}_{\sigma }$, fitted from compressive or shear strength growth ${\sigma }_{\mathrm{p}}\left(t\right)$.

The plastic strength index ${\displaystyle \overline{\bigwedge }}$, acting as the normalizing factor, is defined as follows:

$$\overline{\bigwedge }={\left({\displaystyle \frac{h}{D_{\mathrm{f}\mathrm{t},0}}}\right)}^{1/3}{\displaystyle \frac{\left|{\sigma }_{\mathrm{p},0}\right|}{{\left(\rho g\right)}^{2/3}}}$$

(19)

where $D_{\mathrm{f}\mathrm{t},0}$ is the initial flexural rigidity, ${\sigma }_{\mathrm{p},0}$ is the initial compressive or shear strength from mechanical testing, $\rho$ is the material density, and g is gravitational acceleration.

This dimensionless formulation offers a robust, transferable means of assessing failure mode dominance under varied material and process conditions in 3D concrete printing. Subsequently, Wolfs et al. [55,56] extended Suiker’s model by employing finite element analysis and experimental validation to verify the dimensionless failure criterion. They refined parameter measurements (e.g., c(t), ϕ(t), E(t), ν and $\rho$) via triaxial compression testing, enhancing the model’s accuracy and practical applicability for predicting buildability limits in 3DCP.

3.3.2. Roussel’s Mixed Criterion: Rheological Yield and Stability

Roussel [4] proposed a hybrid framework to assess the printability of fresh concrete based on the following two complementary failure criteria:

• Strength-based failure, governed by the material’s yield stress;
• Stability-based failure, governed by the material’s elastic modulus and its ability to resist buckling.

These two mechanisms may dominate under different structural scales or printing conditions. This framework provides a comprehensive rheo-mechanical foundation to define printability in terms of material behavior, process constraints, and geometrical stability. When a freshly deposited layer must support the gravitational load of the layers above, the material needs to yield plastically. The critical condition is reached when the shear or compressive stress induced by gravity exceeds the current static yield stress τ_s(t). For a growing vertical structure of height H, the criterion reads:

$${\tau }_{\mathrm{s}}(t)\ge {\displaystyle \frac{\rho gH}{\sqrt{3}}}$$

(20)

If the material develops static yield stress over time at a constant structural build-up rate A_thix and has an initial static yield stress ${\tau }_{\mathrm{s},0}$, by substituting the static yield stress evolution model given previously in Equation (4) into Equation (20), the maximum buildable height H_m governed by the strength-based criterion is obtained as follows:

$$H_{\mathrm{m}}={\displaystyle \frac{\sqrt{3}}{\rho g}}·\left({\tau }_{\mathrm{s},0}+A_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}·t\right)$$

(21)

Similarly, Equation (5), which describes the nonlinear evolution of the static yield stress, can also be substituted into Equation (20) to estimate the maximum buildable height in cases of nonlinear structuration behavior.

As the number of stacked layers increases, the printed element forms a slender vertical structure prone to elastic buckling. The critical buckling height H_c is derived from Euler’s theory, as follows:

$$H_{\mathrm{c}}={\left({\displaystyle \frac{2E{\delta }^2}{3\rho g}}\right)}^{1/3}$$

(22)

where E is the current elastic modulus of the material, and the minimum elastic modulus E_c required to avoid buckling can be expressed as follows:

$$E_{\mathrm{c}}={\displaystyle \frac{3\rho gH}{2{\delta }^2}}$$

(23)

The transition between strength-dominated and buckling-dominated failure modes can be characterized by a critical transition height H_T. This threshold height indicates which failure mechanism is likely to occur first. Roussel provides the following analytical expression:

$$H_{\mathrm{T}}=2\delta ·\sqrt{{\displaystyle \frac{1+v}{3\sqrt{3}{\gamma }_{\mathrm{c}}}}}$$

(24)

where ${\gamma }_{\mathrm{c}}$ is critical shear strain at the onset of yielding (typically 0.01–0.05).

To prevent cold joints between deposited layers, the allowable time interval before placing the next layer must be limited. Roussel also gives an estimate of the maximum interlayer time T_max, as follows:

$$T_{\mathrm{m}\mathrm{a}\mathrm{x}}={\displaystyle \frac{\sqrt{\frac{{\left(\rho g{h}_0\right)}^2}{12}+{\left(\frac{2{\eta }_{\mathrm{p}}{V}_{\mathrm{n}}}{h_0}\right)}^2}}{A_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}}}$$

(25)

where h₀ is layer thickness, and V_n is nozzle speed.

3.3.3. Kruger’s Lower Bound Analytical Model: Plastic Yield Failure

Kruger et al. [57] proposed a lower bound analytical model exclusively focusing on plastic yield failure, explicitly excluding elastic buckling considerations. The model employs the Mohr–Coulomb criterion combined with Tresca and Rankine limit functions, accounting for physical nonlinearity in fresh concrete through rheological properties.

Kruger’s analytical framework provides two equations to calculate the maximum number of printable layers N_L, depending on whether re-flocculation or structuration dominates the material behavior. The maximum printable layers under re-flocculation-dominated conditions are calculated as follows:

$$N_{\mathrm{L}}=-\left[{\displaystyle \frac{{\tau }_{\mathrm{0,0}}}{\left(\frac{R_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}·l}{V_{\mathrm{p}}}\right)-\left(\frac{\rho g{h}_0}{2·{10}^3·F_{\mathrm{A}\mathrm{R}}}\right)}}\right]$$

(26)

Here, R_thix is the re-flocculation rate, obtained from the slope of shear stress build-up during 0–120 s rest intervals, l is the printing path length, determined from CAD models or G-code data, $V_{\mathrm{p}}$ is the printing speed, and F_AR is an aspect ratio correction factor, estimated by table lookup or experimental calibration, typically ranging from 1.3 to 1.7.

For structuration-dominated conditions, the maximum printable layers are given by Equation (27), as follows:

$$N_{\mathrm{L}}=-\left[{\displaystyle \frac{{\tau }_{\mathrm{s},0}+\left(\frac{A_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}·\left({\tau }_{\mathrm{0,0}}-{\tau }_{\mathrm{s},0}\right)}{R_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}}\right)}{\left(\frac{A_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}·l}{V_{\mathrm{p}}}\right)-\left(\frac{\rho g{h}_0}{2·{10}^3·F_{\mathrm{A}\mathrm{R}}}\right)}}\right]$$

(27)

In this equation, A_thix is derived from shear stress build-up during longer rest intervals (1200–3600 s).

The failure mechanism dominance is assessed by comparing the structure building rate (Equation (28)) and the material strengthening rate (Equation (29)), as follows:

$${\sigma }_{\mathrm{b}}\left(t\right)={\displaystyle \frac{\rho g{h}_0{V}_{\mathrm{p}}}{2·{10}^3·l·F_{\mathrm{A}\mathrm{R}}}}·t$$

(28)

$$m_{\mathrm{M}\mathrm{a}\mathrm{t}}={\displaystyle \frac{{\tau }_{\mathrm{s},0}{·R}_{\mathrm{t}\mathrm{h}\mathrm{i}\mathrm{x}}}{{\tau }_{s,0}-{\tau }_{\mathrm{0,0}}}}$$

(29)

where ${\sigma }_{\mathrm{b}}\left(t\right)$ is the structure building stress, and m_Mat is the material strengthening rate. If the slope of the structure building stress over time is less than m_Mat, the material is considered to be strengthening faster, and Equation (27) is applicable; otherwise, Equation (26) should be used.

Since the model incorporates a strength correction factor (F_AR), it is highly accurate for structures with small height-to-width ratios, for which plastic yield failure typically dominates. However, Kruger’s approach also explicitly recommends employing Suiker’s elastic buckling model for thin-walled structures with larger height-to-width ratios. Kruger’s model, validated experimentally, generally predicts buildability accurately within a 10% margin, providing a reliable and practical tool for early-stage mix design and optimization.

3.4. Synthesis of Rheological Effects on Printability

The interplay between rheological parameters and printability attributes forms the foundation of effective extrusion-based 3DCP. Each rheological parameter distinctly influences specific printability metrics including pumpability, extrudability, and buildability, as highlighted by quantitative models and supported by empirical findings from recent studies.

Pumpability is critically governed by dynamic yield stress and plastic viscosity. According to the Buckingham–Reiner and Kaplan models, increased dynamic yield stress enhances stability against segregation but significantly increases pumping pressure and blockage risk. Optimal pumpability requires lower plastic viscosity to minimize pumping pressure and ensure smooth flow through pipelines. Lubrication layer properties, such as yield stress and viscosity, also critically affect pumping efficiency. Studies have shown that the addition of hydroxypropyl methylcellulose (HPMC) can enhance rheological stability by improving cohesion and water retention, but excessive thixotropy may reduce pumpability by increasing resistance over distance due to early structuration [58,59].

Extrudability primarily depends on dynamic yield stress, plastic viscosity, and wall friction. Higher dynamic yield stress helps stabilize filament geometry immediately after extrusion; however, excessive values introduce risks of nozzle clogging and irregular deposition. The Benbow–Bridgwater and Perrot models emphasize balancing extrusion yield stress with wall friction and filtration forces to maintain uniformity. Additives such as metakaolin and nano-clays have been shown to enhance extrudability by improving thixotropic properties, particularly shear recovery and flocculation strength [60,61]. Thixotropy plays a critical role in extrusion-based 3D printing by enabling rapid structural rebuilding after shear-induced breakdown, thereby preserving filament integrity and minimizing deformation during deposition. Sufficient thixotropy ensures the filament retains its shape upon exiting the nozzle, preventing collapse or spreading. As a result, high thixotropic response is essential for continuous, uniform extrusion and reliable layer formation.

Buildability is primarily governed by static yield stress and thixotropy. Static yield stress reflects the material’s capacity to resist deformation under self-weight and the load from successive layers, and its time-dependent evolution directly determines the printable height and structural stability. According to failure models, such as Mohr–Coulomb and Roussel’s framework, insufficient static yield stress can lead to plastic collapse or elastic buckling during printing. Thixotropy further influences buildability by enabling rapid structural rebuilding during resting intervals, promoting stiffness gain, and minimizing deformation [62,63]. A higher structural build-up rate and flocculation strength contribute to increased buildable height and improved interlayer stability. Moreover, enhanced thixotropy reduces porosity and improves bonding at layer interfaces, mitigating mechanical anisotropy and improving overall printing quality [61,64].

Table 1. Summary of printability models: parameters, methods, rheological relations, and model applicability.

Category	Model	Key Parameters	Measurement	Associated Rheological Parameters	Validity and Applicability	Limitations and Assumptions
Pumpability	Buckingham–Reiner Model	Dynamic yield stress, plastic viscosity	Rotational viscometer, pressure drop measurements	Dynamic yield stress, plastic viscosity	Basic theoretical model for Bingham fluids in laminar flow, neglects wall slip and complex shear profiles	Ideal Bingham flow assumption, may not reflect thixotropy and real inhomogeneities
	Kaplan Model	Yield stress and viscosity of lubrication layer, lubrication layer thickness	Sliding pipe rheometer (SLIPER), rotational tests	Lubrication layer yield stress, viscosity, thickness	Improves accuracy by accounting for lubrication layer effects, applicable to plug and shear flow	No explicit flow regime separation criterion, limited to certain flow conditions
	Feys’ Regime Discrimination	Interfacial shear stress, concrete yield stress, lubrication layer thickness	Differential pressure measurement, ultrasonic techniques for layer thickness	Interfacial shear stress, lubrication layer properties	Provides flow regime classification, useful for transition flow analysis	Assumes direct relationship between interfacial shear and bulk yield stress, requires calibration
	Kwon Model	Plastic viscosity of lubrication layer, plug radius, inlet and hydrostatic pressure	Ultrasonic measurement, cross-sectional analysis	Lubrication layer viscosity, plug geometry	Suitable for high-pressure, long-distance pumping; considers geometry effects	Requires precise measurement of lubrication layer thickness and pressure data, limited to high-pressure cases
Extrudability	Benbow-Bridgwater Model	Elongational yield stress, empirical velocity-dependent coefficients (a, b), extrudate velocity, die geometry (barrel and die land diameters, perimeter, length)	Ram extrusion tests, uniaxial compression tests, empirical fitting from velocity–pressure data	Dynamic yield stress, plastic viscosity, elongational yield stress	Widely used due to simplicity; suitable for laboratory tests; limited generalization due to empirical nature and geometry-specific parameters	Semi-empirical; requires extensive calibration; limited across different geometries or materials
	Perrot Model	Yield stress, Bingham number (BN), wall friction coefficient (Kw), filtration velocity, normal wall stress (Pw), ram geometry (barrel length, die cross-sectional area)	Ram extrusion tests, shear box tests or Casagrande-type apparatus, filtration tests, hydrostatic pressure estimation	Static and dynamic yield stress, wall friction, filtration properties, viscosity	Realistic for cementitious materials exhibiting frictional and filtration effects; suitable for fiber-reinforced and thixotropic materials; applicable in realistic extrusion conditions	Complex calibration due to friction and filtration parameters; significant deviations possible if filtration velocity underestimated
	Basterfield Model	Herschel–Bulkley parameters (yield stress, consistency coefficient K, flow index n), conical die entry angle	Rheometer, precise geometric measurement of conical dies	Static yield stress, plastic viscosity, consistency index K, flow index n	More transferable and precise, relies solely on intrinsic rheological parameters and die geometry; useful across different extrusion setups	Complex, computationally demanding; requires precise rheological characterization; sensitive to accurate geometry measurements
Buildability	Suiker’s Model	Static yield stress, elastic modulus, cohesion, internal friction angle, dilatancy angle	Direct shear testing (DST), triaxial compression testing (TCT), ultrasonic wave transmission, oscillatory rheometer	Static yield stress, elastic modulus	Comprehensive evaluation of plastic yield and elastic buckling; widely validated through finite element analysis and experimental tests; suitable for tall, slender structures	Requires homogeneous material assumption and accurate parameter measurements; complex to calibrate for heterogeneous or fiber-reinforced mixes
	Roussel’s Mixed Criterion	Static yield stress, elastic modulus, critical shear strain, structural build-up rate	Rheometer for yield stress, ultrasonic testing for modulus, empirical estimation of critical shear strain	Static yield stress, elastic modulus, thixotropy	Provides combined yield and buckling criteria; highly adaptable for various print geometries and printing conditions; effective in predicting maximum stable heights	Assumes constant elastic modulus across layers, simplified shear strain treatment; limited capture of local structural variations
	Kruger’s Lower Bound Analytical Model	Static yield stress, structural build-up rate, re-flocculation rate, aspect ratio correction factor	Shear stress build-up measurements over defined rest intervals (0–120 s, 1200–3600 s), printing path analysis (CAD/G-code)	Static yield stress, thixotropy	Reliable for short, wide structures predominantly subject to plastic yielding; accurate within a 10% margin; practical for early mix-design optimization	Excludes elastic buckling explicitly; heavily dependent on geometry-specific calibration factors; may underestimate height limits for slender walls

provides a concise summary of the key quantitative models addressing pumpability, extrudability, and buildability, clearly outlining their primary parameters, measurement techniques, associated rheological properties, and respective validity and limitations. These summarized models collectively highlight the crucial role of precisely determining rheological parameters including dynamic and static yield stress, plastic viscosity, and thixotropy to ensure reliable and consistent 3D printing performance. Despite their widespread application and verification, these models inherently rely on assumptions, such as homogeneous material behavior, idealized flow conditions, and simplified structural interactions.

Currently, there are no direct international standards specifically addressing rheological property testing for 3D printing cement-based materials; although, recommendations and general rheology testing standards (e.g., ASTM C1749, RILEM TC 266-MRP) offer valuable guidance for adapting established methods. This gap in standardized protocols underscores the importance of ongoing research and the development of robust, reproducible testing methodologies tailored to 3D printing processes. In this context, the precise optimization of rheological parameters, such as dynamic and static yield stress, plastic viscosity, and thixotropy is essential to achieve consistent and reliable performance in 3D concrete printing applications. Controlling rheological performance through tailored admixture combinations and processing conditions is essential to achieve desirable pumpability, extrudability, buildability, and interlayer bonding, ultimately leading to structurally robust and geometrically accurate printed concrete components.

4. Discussion on the Influence of Printing Parameters

The performance of 3D-printed concrete structures is strongly influenced by printing parameters, such as layer deposition time, nozzle size, and print speed, which critically affect interlayer adhesion and overall structural stability. Experimental and numerical studies indicate that these parameters directly influence the microstructural characteristics and mechanical properties of printed elements, making their optimization crucial for ensuring print quality and structural reliability [8,65].

The relationships among key printing parameters, intermediate interfacial effects, and final structural performance are illustrated in Figure 7. In this conceptual diagram, the outer ring presents the key process parameter, including layer deposition time, print speed, nozzle size, nozzle height, and layer thickness. These parameters affect core mechanisms, such as interlayer bonding strength, compaction and adhesion, and interface porosity, which in turn govern the structural integrity and mechanical performance of the printed concrete.

Layer deposition time, the interval between successive layers, significantly impacts interlayer bond strength. Prolonged intervals can lead to decreased interlayer adhesion due to surface drying and reduced interfacial bonding strength, resulting in structural weakness at layer interfaces. Conversely, optimized deposition intervals maintain adequate moisture at the interface, enhancing mechanical integrity and reducing anisotropic behavior in printed structures [66,67,68].

Print speed further influences the build quality and structural performance of printed structures. Higher printing speeds reduce interlayer bonding strength due to insufficient compaction and inadequate material adhesion between layers, thereby decreasing structural integrity. On the other hand, moderate print speeds ensure better rheological control and improved interfacial bonding, leading to enhanced stability and geometric accuracy [9,10,69,70].

Nozzle diameter is another critical factor, as it directly controls filament geometry and influences the interlayer contact area. A larger nozzle diameter generally enhances buildability by producing filaments with greater width and height, thus improving structural stability [71]. However, smaller nozzle diameters tend to enhance fiber alignment in fiber-reinforced concrete, significantly improving mechanical properties, such as tensile and flexural strength, due to better fiber orientation along the printing direction [69]. Therefore, selecting an optimal nozzle size involves balancing between buildability and mechanical performance.

Standoff distance (or nozzle height) also plays a vital role in defining interlayer adhesion. Excessively high standoff distances reduce the compaction pressure exerted by the nozzle, weakening interlayer bonding and increasing porosity at the interface. Conversely, appropriately reduced nozzle heights enhance filament interpenetration and interface densification, thus improving overall structural performance to enhance interlayer bonding and reduce defects at interfaces [68,70,72].

Furthermore, novel nozzle designs, incorporating interface shaping and side trowels, have been developed to enhance interlayer bonding and reduce defects at interfaces [8,73]. Such nozzles optimize the interfacial geometry, significantly reducing notch-induced stress concentrations and improving interlayer shear strength. Overall, these parameters collectively govern filament geometry, compaction pressure, and interfacial moisture, thereby directly affecting interlayer bonding and global structural performance. As illustrated in Figure 7, the coordinated adjustment of printing parameters and their influence on interface quality is essential for achieving high structural integrity and consistent mechanical performance in 3D-printed concrete. Systematic optimization based on rheological and mechanical evaluation is, thus, key to ensuring printing reliability and overall build quality.

5. Conclusions and Future Outlook

This review systematically examined the critical relationships between rheological parameters and key printability attributes, including pumpability, extrudability, and buildability in extrusion-based 3D concrete printing (3DCP). The analysis highlighted the essential roles of dynamic and static yield stress, plastic viscosity, and thixotropy, supported by quantitative models and experimental validations. Several predictive models, including those by Buckingham–Reiner, Kaplan, Benbow Bridgwater, Perrot, Suiker, Roussel, and Kruger, were discussed to assess printability and guide material–process selection. In addition, process parameters, such as print speed, nozzle geometry, standoff distance, and layer deposition time, were shown to significantly affect interlayer bonding and structural performance. The following conclusions are drawn:

• Dynamic and static yield stress, plastic viscosity, and thixotropy collectively control all phases of printing from flow through the pump to deposition and interlayer stability.
• Pumpability is primarily governed by dynamic yield stress and plastic viscosity. Identifying plug, shear, and laminar flow conditions is crucial for minimizing pressure loss and avoiding blockages. Models by Kaplan, Feys, and Kwon provide differentiated approaches to quantify flow based on lubrication layer and pipe–wall interactions.
• Extrudability depends on dynamic yield stress, wall friction, and filtration effects. Models by Benbow Bridgwater and Perrot highlight the importance of balancing these parameters to achieve consistent filament formation. Ram extrusion tests remain a reliable laboratory-scale method for evaluating extrudability, while advanced models, such as those by Perrot and Basterfield, integrate friction and filtration effects to enhance predictability under realistic printing conditions.
• Buildability is controlled by static yield stress and thixotropy, as highlighted by models from Suiker, Roussel, and Kruger, demonstrating how these parameters determine layer stability and resistance to collapse during deposition.
• Print speed, nozzle size, layer interval, and standoff distance significantly influence the buildability of the structure and the quality of interlayer bonding. The novel nozzle designs and optimized deposition timing have been shown to enhance structural integrity and reduce interfacial weaknesses.

The findings underscore the necessity of integrated material–process design, multi-parameter testing, and the continued refinement of predictive models to ensure reliable and high-performance 3D concrete printing. In the future, the advancement of sustainable and high-performance materials will be central to the evolution of 3DCP. Low-carbon alternatives to ordinary Portland cement, such as fly ash, ground granulated blast-furnace slag (GGBS), calcined clays, and geopolymers, offer substantial reductions in CO₂ emissions, while contributing unique rheological profiles suitable for digital fabrication. Geopolymer-based mixes, for instance, provide rapid strength development and chemical durability; whereas, slag and fly ash blends enhance flowability by reducing plastic viscosity. However, these materials often present challenges, such as delayed setting and increased water demand, which must be addressed through admixtures and optimized printing parameters. In parallel, fiber reinforcement particularly with steel, polypropylene (PP), basalt, or hybrid combinations has become a key strategy for improving structural integrity in 3D-printed elements. Nonetheless, fiber integration must be carefully managed, as it affects rheological behavior, filament geometry, and interlayer bonding. The orientation of fibers, influenced by nozzle design and print speed, also impacts anisotropic mechanical properties. To fully realize the potential of both low-carbon and fiber-reinforced systems, future research must develop mix designs that balance sustainability, rheological performance, and structural reliability, complemented by advanced monitoring, adaptive printing strategies, and performance-based testing frameworks.

Additionally, the continued advancement of 3DCP centers on closing the gap between theoretical models, laboratory experimentation, and real-world construction applications. A key priority is the development of comprehensive, multiscale validation methods that can correlate rheological properties measured under controlled conditions with structural behavior observed in full-scale printing. Digital twin technology, paired with advanced simulation tools, offers promising opportunities for real-time process optimization through adaptive control systems. This will be especially valuable for handling variations in mix consistency or environmental conditions during printing. Concurrently, research must accelerate in the area of alternative cementitious systems, including recycled and bio-based materials, to meet growing sustainability demands without sacrificing print performance. To facilitate cross-comparison between materials and technologies, a standardized printability index grounded in measurable rheological, mechanical, and process-based indicators is essential. Durability-focused investigations, particularly those exploring performance under temperature fluctuations, chemical exposure, and long-term loading, will support assurance in structural reliability. Equally important is the integration of in-line rheological monitoring systems, enabling continuous quality assurance throughout the print process. Multiphysics modeling approaches should be further refined to simulate the interaction of flow, setting kinetics, and structural deformation in complex geometries. Dynamic printing protocols, capable of adjusting parameters, such as speed, layer height, or nozzle pressure in response to sensor feedback, could unlock greater precision and consistency. Finally, establishing industry-wide standards, in collaboration with regulators and stakeholders, will be vital for ensuring the safety, reproducibility, and market readiness of 3DCP systems on a global scale.