Method for optimizing 3D printing cement-based material mix proportion based on response surface method

Abstract

The invention provides a method for optimizing the mix proportion of a 3D printing cement-based material based on a response surface method, and belongs to the technical field of 3D printing. The method for optimizing the mix proportion of the 3D printing cement-based material based on the response surface method comprises the following steps: determining level values of test factors including an accelerator, a hydroxypropyl methyl cellulose ether solution and a polycarboxylate superplasticizer, then determining a basic mix proportion of the 3D printing cement-based material, and then determining test groups; testing according to the test groups to obtain test results; performing variance analysis and polynomial fitting regression on the test result, and establishing a regression equation for the fluidity and the dynamic yield stress according to the test result; setting the fluidity and the dynamic yield stress of the required 3D printing cement-based material, and then determining the optimal mix proportion of the accelerator, the hydroxypropyl methyl cellulose ether solution and the polycarboxylate superplasticizer according to the regression equation.

Description

Technical Field

[0001] This invention belongs to the field of 3D printing technology, specifically relating to a method for optimizing the mix proportions of cement-based materials for 3D printing based on response surface methodology. Background Technology

[0002] 3D concrete printing technology (hereinafter referred to as 3DPC) has injected new vitality into the construction industry with its advantages of "flexibility, low carbon footprint, and speed." This technology not only overcomes the problems of high labor requirements, large resource consumption, and heavy environmental burden in traditional concrete structure construction, but also enables personalized customization of building structures, greatly improving construction efficiency and controlling related costs. Numerous researchers have developed a series of new materials for 3DPC, whose rheological properties meet the specific requirements of 3D printing cement-based materials.

[0003] Although the three admixtures—accelerators, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizers—constitute small proportions in the material composition, they can significantly improve the material's performance. This requires not only excellent flowability during transport to ensure a smooth printing process, but also rapid solidification after printing and exhibiting high strength in the early stages. Yield stress and flowability are key indicators for measuring the ability of 3D-printed cementitious materials to resist plastic deformation and recover flow characteristics under shear strain. These two parameters can effectively describe the working performance and buildability of 3D-printed cementitious materials at the mechanistic level. However, the effects of these three admixtures on the yield stress and flowability of 3D-printed cementitious materials are quite complex, and the influence of these three admixtures on the dynamic yield stress and flowability of 3D-printed cementitious materials has not yet been studied. Therefore, determining the optimal proportions of accelerators, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizers based on the required dynamic yield stress and flowability of the 3D-printed cementitious materials has become a pressing technical challenge in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the mix proportions of 3D-printed cementitious materials based on response surface methodology. The method provided by this invention can determine the optimal mix proportions of accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer based on the dynamic yield stress and flowability of the desired 3D-printed cementitious material.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a method for optimizing the mix proportions of 3D printed cement-based materials based on response surface methodology, comprising the following steps: (1) Determine the levels of the test factors, such as quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer, then determine the basic mix proportion of 3D printed cement-based materials, and then determine the test group; (2) Conduct experiments according to the experimental groups obtained in step (1) and obtain experimental results; (3) Perform variance analysis and polynomial fitting regression on the test results obtained in step (2), and establish regression equations for flowability and dynamic yield stress based on the test results; (4) Set the required flowability and dynamic yield stress of the 3D printed cement-based material, and then determine the optimal mixing ratio of the quick-setting agent, hydroxypropyl methyl cellulose ether solution and polycarboxylate superplasticizer according to the regression equation obtained in step (3); The basic mix proportions of the 3D printing cement-based material in step (1) are as follows: water-cement ratio of 0.3, mass ratio of cement:silica fume:mineral powder of 1:0.3:0.15, mortar ratio of 1.45:1.5, and mass of polypropylene fiber of 2% of the total mass of cement, silica fume and mineral powder.

[0006] Preferably, the quick-setting agent in step (1) is an alkali-free quick-setting agent.

[0007] Preferably, in step (1), the mass concentration of the hydroxypropyl methylcellulose ether solution is 1-3%, and the viscosity of the hydroxypropyl methylcellulose ether solution is 50-150 Pa·s.

[0008] Preferably, the water reduction rate of the polycarboxylate superplasticizer in step (1) is 35-45%.

[0009] Preferably, the experimental grouping in step (1) is determined using Design Expert 13, Origin, or Matlab software.

[0010] Preferably, in step (3), analysis of variance and multinomial fitting regression are performed using Design Expert 13, Origin or Matlab software.

[0011] Preferably, the regression equations in step (3) are shown in Equations I and II: Y1=156.20-3.12A-0.9375B+13.31C+0.125AB+1.13AC+1.00BC+2.02A 2 -5.60B 2 -4.60C 2 Formula I; In Formula I, Y1 represents fluidity in mm; A represents accelerator in wt%; B represents hydroxypropyl methylcellulose ether solution in wt%; and C represents polycarboxylate superplasticizer in wt%. Y2=626.40+45.58A-32.47B-358.30C-2.65AB+11.75AC+21.35BC+73.97A 2 +68.82B 2 -16.77C 2 Formula II; In Formula II, Y2 is the dynamic yield stress in Pa; A is the quick-setting agent in wt%; B is the hydroxypropyl methylcellulose ether solution in wt%; and C is the polycarboxylate superplasticizer in wt%.

[0012] Preferably, the flowability of the 3D printed cement-based material required in step (4) is 150~180mm.

[0013] Preferably, the dynamic yield stress of the 3D printed cement-based material required in step (4) is 200~800 Pa.

[0014] This invention provides a method for optimizing the mix proportion of 3D printed cement-based materials based on response surface methodology, comprising the following steps: determining the level values ​​of experimental factors—accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer—then determining the basic mix proportion of the 3D printed cement-based material, and subsequently determining experimental groups; conducting experiments according to the experimental groups and obtaining experimental results; performing variance analysis and polynomial regression on the experimental results, and establishing regression equations for flowability and dynamic yield stress based on the experimental results; setting the required flowability and dynamic yield stress of the 3D printed cement-based material, and then determining the optimal mix proportion of accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer according to the regression equations; the basic mix proportion of the 3D printed cement-based material is: water-cement ratio of 0.3, cement:silica fume:mineral powder mass ratio of 1:0.3:0.15, mortar ratio of 1.45:1.5, and polypropylene fiber mass of 2% of the total mass of cement, silica fume, and mineral powder. This invention, based on response surface methodology, first determines the level values ​​of experimental factors, then determines the basic mix proportion, and subsequently determines the experimental groups for testing. Based on the experimental results, a regression equation is obtained with the flowability and dynamic yield stress of the 3D-printed cement-based material as response values, and the accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer as response variables. Finally, the optimal mix proportion of the accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer is determined according to the required flowability and dynamic yield stress of the 3D-printed cement-based material. Experimental results show that the optimal mix proportion obtained by the method provided in this invention is: 0.321% accelerator, 0.241% hydroxypropyl methylcellulose ether solution, and 0.231% polycarboxylate superplasticizer. The predicted values ​​for the obtained 3D-printed cement-based material are: flowability of 153.763 mm and dynamic yield stress of 768.031 Pa, which are close to the predicted values. Attached Figure Description

[0015] Figure 1 This is a contour plot showing the effect of the interaction between A and B on Y1 when C is at the center level. Figure 2 This is a response surface plot showing the effect of the interaction between A and B on Y1 when C is at the center level. Figure 3 This is a contour plot showing the effect of the interaction between A and C on Y1 when B is at the center level. Figure 4 This is a response surface plot showing the effect of the interaction between A and C on Y1 when B is at the center level. Figure 5 This is a contour plot showing the effect of the interaction between B and C on Y1 when A is at the center level. Figure 6 This is a response surface plot showing the effect of the interaction between B and C on Y1 when A is at the center level. Figure 7 This is a contour plot showing the effect of the interaction between A and B on Y2 when C is at the center level. Figure 8 This is a response surface plot showing the effect of the interaction between A and B on Y2 when C is at the central level. Figure 9 This is a contour plot showing the effect of the interaction between A and C on Y2 when B is at the center level. Figure 10 This is a response surface plot showing the effect of the interaction between A and C on Y2 when B is at the center level. Figure 11 This is a contour plot showing the effect of the interaction between B and C on Y2 when A is at the center level. Figure 12 This is a response surface plot showing the effect of the interaction between B and C on Y2 when A is at the center level. Figure 13 This is a superimposed graph of multiple response values ​​obtained after balancing the regression equations of Y1 and Y2 in Example 1. Detailed Implementation

[0016] This invention provides a method for optimizing the mix proportions of 3D printed cement-based materials based on response surface methodology, comprising the following steps: (1) Determine the levels of the test factors, such as quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer, then determine the basic mix proportion of 3D printed cement-based materials, and then determine the test group; (2) Conduct experiments according to the experimental groups obtained in step (1) and obtain experimental results; (3) Perform variance analysis and polynomial fitting regression on the test results obtained in step (2), and establish regression equations for flowability and dynamic yield stress based on the test results; (4) Set the required flowability and dynamic yield stress of the 3D printed cement-based material, and then determine the optimal mixing ratio of the quick-setting agent, hydroxypropyl methyl cellulose ether solution and polycarboxylate superplasticizer according to the regression equation obtained in step (3); The basic mix proportions of the 3D printing cement-based material in step (1) are as follows: water-cement ratio of 0.3, mass ratio of cement:silica fume:mineral powder of 1:0.3:0.15, mortar ratio of 1.45:1.5, and mass of polypropylene fiber of 2% of the total mass of cement, silica fume and mineral powder.

[0017] This invention does not impose any special restrictions on the source of the raw materials; commercially available products familiar to those skilled in the art can be used.

[0018] This invention determines the levels of the test factors accelerator, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer, then determines the basic mix proportion of the 3D printed cement-based material, and finally determines the test grouping.

[0019] In this invention, the quick-setting agent is preferably an alkali-free quick-setting agent.

[0020] The present invention does not have any particular limitation on the specific type of alkali-free quick-setting agent, and any alkali-free quick-setting agent well known to those skilled in the art can be used.

[0021] As one implementation method, the alkali-free quick-setting agent can be Kezhijie alkali-free quick-setting agent.

[0022] In this invention, the mass concentration of the hydroxypropyl methylcellulose ether solution is preferably 1-3%, more preferably 2%; the viscosity of the hydroxypropyl methylcellulose ether solution is preferably 50-150 Pa·s, more preferably 100 Pa·s.

[0023] In this invention, the water reduction rate of the polycarboxylate superplasticizer is preferably 35-45%, more preferably 40%.

[0024] The present invention does not have any special limitations on the operation of determining the level values ​​of the test factors quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer, and can use methods well known to those skilled in the art, such as Box-Behnken Design (BBD).

[0025] In this invention, the upper, middle and lower levels of the quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer are preferably independently coded with 1, 0 and -1; -1 is preferably a low level; 1 is preferably a high level; and 0 is preferably a center point.

[0026] In this invention, the basic mix proportions of the 3D printing cement-based material are as follows: water-cement ratio of 0.3, mass ratio of cement:silica fume:mineral powder of 1:0.3:0.15, mortar ratio of 1.45:1.5, and polypropylene fiber of 2% of the total mass of cement, silica fume and mineral powder.

[0027] In this invention, the cement is preferably P·O 42.5 ordinary Portland cement; the silica content in the silica fume is preferably ≥88wt%; the loss on ignition of the silica fume is preferably 0.41wt%; the average particle size of the silica fume is preferably 1.33µm; the mineral powder is preferably S95 grade mineral powder; the fineness of the mineral powder is preferably ≥800 mesh; the sand is preferably natural sand; the particle size of the sand is preferably 0.5~1mm; the length of the polypropylene fiber is preferably 6mm; the diameter of the polypropylene fiber is preferably 0.02mm.

[0028] The present invention does not impose any special limitation on the preparation method of the 3D printed cement-based material; any preparation method well known to those skilled in the art can be used.

[0029] In this invention, the determination of experimental groups is preferably achieved using Design Expert 13, Origin, or Matlab software.

[0030] The present invention does not have any special limitations on the operation of determining the experimental groups using Design Expert 13, Origin or Matlab software; any operation known to those skilled in the art can be used.

[0031] After obtaining the test groups, the present invention conducts tests according to the test groups and obtains test results.

[0032] The present invention does not have any special limitations on the operation of conducting experiments according to the test groups and obtaining test results; any operation known to those skilled in the art can be used.

[0033] After obtaining the experimental results, the present invention performs variance analysis and polynomial fitting regression on the experimental results, and establishes regression equations for flowability and dynamic yield stress based on the experimental results.

[0034] In this invention, it is preferred to use Design Expert 13, Origin, or Matlab software for analysis of variance and multinomial fitting regression.

[0035] This invention does not impose any special limitations on the operation of using Design Expert 13, Origin or Matlab software for analysis of variance and multinomial fitting regression; any operation familiar to those skilled in the art can be used.

[0036] This invention does not impose any special limitations on the operation of establishing regression equations for flowability and dynamic yield stress based on experimental results; any operation familiar to those skilled in the art can be used.

[0037] In this invention, the regression equation is preferably as shown in Equations I and II: Y1=156.20-3.12A-0.9375B+13.31C+0.125AB+1.13AC+1.00BC+2.02A 2 -5.60B 2 -4.60C 2 Formula I; In Formula I, Y1 represents fluidity in mm; A represents accelerator in wt%; B represents hydroxypropyl methylcellulose ether solution in wt%; and C represents polycarboxylate superplasticizer in wt%. Y2=626.40+45.58A-32.47B-358.30C-2.65AB+11.75AC+21.35BC+73.97A 2 +68.82B 2 -16.77C 2 Formula II; In Formula II, Y2 is the dynamic yield stress in Pa; A is the quick-setting agent in wt%; B is the hydroxypropyl methylcellulose ether solution in wt%; and C is the polycarboxylate superplasticizer in wt%.

[0038] In this invention, AB, AC and BC are preferably the interaction between the quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer.

[0039] After obtaining the regression equation, the present invention sets the flowability and dynamic yield stress of the required 3D printing cement-based material, and then determines the optimal mixing ratio of the accelerator, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer according to the regression equation.

[0040] In this invention, the flowability of the desired 3D printed cement-based material is preferably 150~180mm; the dynamic yield stress of the desired 3D printed cement-based material is preferably 200~800Pa.

[0041] In this invention, the fluidity is preferably measured in accordance with GB / T2419-2005 "Method for Determining the Flowability of Cement Mortar"; the dynamic yield stress is preferably tested using an ICAR Plus concrete rheometer.

[0042] The present invention does not impose any special limitations on the operation of determining the optimal mixing ratio of quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer according to the regression equation, and any operation known to those skilled in the art can be used.

[0043] This invention is based on the response surface methodology. First, the level values ​​of the experimental factors are determined. Then, the basic mix proportion is determined. After that, the experimental groups are determined and the experiments are conducted. Based on the experimental results, a regression equation is obtained with the flowability and dynamic yield stress of the 3D printed cement-based material as the response values ​​and the accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer as the response variables. Finally, the optimal mix proportion of the accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer is determined based on the required flowability and dynamic yield stress of the 3D printed cement-based material.

[0044] The method provided in this invention is based on the Box-Behnken central composite principle. Using the flowability and dynamic yield stress of 3D-printed cementitious materials as response values, and selecting three factors (accelerator, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer) as response variables, the influence of admixtures on the flowability and dynamic yield stress of 3D-printed cementitious materials was investigated. A quadratic regression equation was used to analyze each response value, obtaining the regression equation and the optimal mix proportion. The results are close to the measured values, meeting the printability requirements, demonstrating that the response surface methodology can be used for mix proportion optimization of 3D-printed cementitious materials.

[0045] The technical solutions of this invention will be clearly and completely described below with reference to the embodiments thereof. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0046] Example 1 A method for optimizing the mix proportions of 3D printed cement-based materials based on response surface methodology comprises the following steps: (1) Determine the levels of the test factors: quick-setting agent, hydroxypropyl methylcellulose ether solution with a mass concentration of 2% and a viscosity of 100 Pa·s, and polycarboxylate superplasticizer with a water reduction rate of 40%. Then determine the basic mix proportion of the 3D printed cement-based material. After that, use Design Expert 13 software to determine the test group. Among them, the quick-setting agent is Kezhijie alkali-free quick-setting agent; The upper, middle, and lower water levels of the quick-setting agent, hydroxypropyl methylcellulose ether solution, and polycarboxylate superplasticizer are all coded with 1, 0, and -1; -1 represents a low level; 1 represents a high level; and 0 represents the center point. The basic mix proportions of the 3D printing cement-based material are as follows: water-cement ratio of 0.3, cement:silica fume:mineral powder mass ratio of 1:0.3:0.15, mortar-mortar ratio of 1.45:1.5, and polypropylene fiber mass of 2% of the total mass of cement, silica fume, and mineral powder. The cement is P·O 42.5 ordinary Portland cement; The silica fume contains 88.30 wt% silica, has a loss on ignition of 0.41 wt%, and an average particle size of 1.33 µm. The mineral powder is S95 grade mineral powder with a fineness ≥800 mesh; The sand is natural sand with a particle size of 0.5~1mm; The polypropylene fiber has a length of 6 mm and a diameter of 0.02 mm; The 3D-printed cement-based material is prepared as follows: 1) Weigh out the cement, silica fume, mineral powder and sand and pour them into the mixing pot. Dry mix for 180 seconds until the raw materials are evenly mixed to obtain solid powder. 2) Add quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer and 3 / 4 water to the solid powder obtained in step 1) and stir for 120s. Then add the remaining 1 / 4 water and stir for 60s. Then add polypropylene fiber and stir for 120s to obtain 3D printing cement-based material. (2) Conduct experiments according to the experimental groups obtained in step (1) and obtain experimental results; (3) The test results obtained in step (2) were analyzed by variance and polynomial fitting regression using Design Expert 13 software, and regression equations for flowability and dynamic yield stress were established based on the test results. The regression equations are shown in Equations I and II: Y1=156.20-3.12A-0.9375B+13.31C+0.125AB+1.13AC+1.00BC+2.02A 2 -5.60B 2 -4.60C 2 Formula I; In Formula I, Y1 represents fluidity in mm; A represents accelerator in wt%; B represents hydroxypropyl methylcellulose ether solution in wt%; and C represents polycarboxylate superplasticizer in wt%. Y2=626.40+45.58A-32.47B-358.30C-2.65AB+11.75AC+21.35BC+73.97A 2 +68.82B 2 -16.77C 2 Formula II; In Formula II, Y2 is the dynamic yield stress in Pa; A is the quick-setting agent in wt%; B is the hydroxypropyl methylcellulose ether solution in wt%; and C is the polycarboxylate superplasticizer in wt%. (4) Set the required flowability of the 3D printed cement-based material to 150~180mm and the dynamic yield stress to 200~800Pa. Then, based on the regression equation obtained in step (3), obtain multiple value schemes and evaluate them using the desirability index. The optimal solution is shown in Table 1, with a desirability index of 1.

[0047] Table 1. Optimal solution after response surface optimization, along with predicted and actual values.

[0048] As can be seen from Table 1, the predicted values ​​of the present invention are close to the actual values, which meets the requirements for printability, indicating that the method provided by the present invention can be used for the mix proportion optimization of 3D printing cement-based materials.

[0049] The fluidity was determined in accordance with GB / T2419-2005 "Method for Determination of Flowability of Cement Mortar". (1) Fill the mortar into the truncated cone mold as specified, tamp it down, and gently lift it vertically upwards.

[0050] (2) Start the jumping table and complete 25 jumps within 25 seconds. Test the diffusion diameter and take the average value.

[0051] The dynamic yield stress test uses the ICAR Plus concrete rheometer, and the test procedure is as follows: (1) Pre-shear at a speed of 0.5 rps for 20 s; (2) The shearing is completed when the initial speed is 0.5 rps and the final speed is 0.05 rps.

[0052] Example 1 uses Design Expert 13 software to design response surface experiments according to the Box-Behnken Design (BBD) principle. The sample factors and level values ​​are shown in Table 2, the experimental groups and experimental results are shown in Table 3, and the variance analysis results are shown in Tables 4 and 5.

[0053] Table 2 Sample Factors and Level Values

[0054] Table 3 Experimental Groups and Results

[0055] Table 4. Results of the analysis of variance (Y1)

[0056] Table 5. Results of the analysis of variance (Y2)

[0057] Tables 4 and 5 show that the p-values ​​for both the Y1 and dynamic yield stress models are less than 0.0001, both less than 0.05, indicating that regression equations Y1 and Y2 are significant. The lack-of-fit values ​​are 0.0656 and 0.1770, respectively, both greater than 0.05, indicating that the regression equations have good fit. The F-values ​​show that the magnitudes of the influence of each factor on Y1 are C > A > B, and the magnitudes of the influence on dynamic yield stress are C > A > B. Specifically, in regression equation Y1, the p-values ​​for factors A, C, and BC are all less than 0.05, indicating that the interaction between A, C, and B×C has a significant impact on Y1, while the interaction between A×B has a smaller effect. In regression equation Y2, the p-value for factor C is less than 0.05, indicating that the interaction between C and dynamic yield stress is significant, while the interaction between A, B, and C has a small effect.

[0058] Based on the results of the analysis of variance, Design Expert 13 software was used to plot response surface plots and contour plots according to the regression equations, analyzing the effects of A, B, and C on the 3D printed cement-based materials Y1 and Y2, respectively. When one of the factors A, B, and C is fixed, the influence of the interaction between the other two factors on the response value can be represented by contour plots and response surface plots. The results are shown in [Figure number missing]. Figures 1-12 Among them, the response surface and contour plot can intuitively reflect the degree of influence of the interaction on the response value. The steeper the surface and the denser the contour lines, the more significant the influence. The closer the contour lines are to ellipses, the stronger the interaction between the two factors.

[0059] Figure 1 This is a contour plot showing the effect of the interaction between A and B on Y1 when C is at the center level. Figure 2 This is a response surface plot showing the effect of the interaction between A and B on Y1 when C is at the center level. Figure 3 This is a contour plot showing the effect of the interaction between A and C on Y1 when B is at the center level. Figure 4 This is a response surface plot showing the effect of the interaction between A and C on Y1 when B is at the center level. Figure 5 This is a contour plot showing the effect of the interaction between B and C on Y1 when A is at the center level. Figure 6 This is a response surface plot showing the effect of the interaction between B and C on Y1 when A is at the center level. Figure 7 This is a contour plot showing the effect of the interaction between A and B on Y2 when C is at the center level. Figure 8 This is a response surface plot showing the effect of the interaction between A and B on Y2 when C is at the central level. Figure 9 This is a contour plot showing the effect of the interaction between A and C on Y2 when B is at the center level. Figure 10 This is a response surface plot showing the effect of the interaction between A and C on Y2 when B is at the center level. Figure 11 This is a contour plot showing the effect of the interaction between B and C on Y2 when A is at the center level. Figure 12 This is a response surface plot showing the effect of the interaction between B and C on Y2 when A is at the central level.

[0060] from Figure 1 , 2 As can be seen from Figures 7 and 8, when the content of B is near the center of the coordinate axis, as A increases, Y1 gradually decreases and Y2 gradually increases. This is because the aluminum sulfate and other components in A react rapidly with the tricalcium silicate and tricalcium aluminate in the cement, accelerating the hydration process of the cement and causing the cement particles to solidify rapidly, thereby shortening the working time of the 3D printed cement-based material, resulting in a decrease in Y1 and an increase in Y2. When the content of A is at a higher level, Y1 first increases and then decreases as the content of B increases. This is because the incorporation of B can significantly improve the fluidity retention ability of the 3D printed cement-based material. This is partly due to the binding effect of B with water molecules; on the other hand, B can form a film-like network structure and encapsulate the cement, effectively reducing the evaporation of water in the 3D printed cement-based material and having a certain water retention capacity.

[0061] from Figure 3 , 4 As can be seen from Figures 9 and 10, Y1 gradually increases and Y2 decreases with the increase of C content. This is because C plays a dispersing role in mortar. When C increases, the anchoring groups and long side chains in its molecules form a hydration film on the surface of cement particles. Through steric hindrance and lubrication, it reduces particle aggregation and improves wettability, thereby increasing Y1 and decreasing Y2 in 3D printed cement-based materials.

[0062] from Figure 5 , 6 As can be seen from Figures 11 and 12, when B is at a high level, Y1 gradually increases with the increase of C; the slope of the response surface is relatively high and the gradient is relatively steep, and P is less than 0.05, indicating that the interaction between B and C has a significant impact on Y1.

[0063] After balancing the regression equations of Y1 and Y2, the resulting multi-response value overlay plot is shown below. Figure 13 As shown in the figure, the yellow area represents the range that simultaneously satisfies the above three response value constraint criteria, corresponding to multiple factor value schemes.

[0064] As can be seen from the above embodiments, the method provided by the present invention can determine the optimal mixing ratio of accelerator, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer according to the dynamic yield stress and fluidity of the required 3D printed cement-based material.

[0065] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the mix proportions of 3D printed cement-based materials based on response surface methodology, characterized in that, Includes the following steps: (1) Determine the levels of the test factors, such as quick-setting agent, hydroxypropyl methylcellulose ether solution and polycarboxylate superplasticizer, then determine the basic mix proportion of 3D printed cement-based materials, and then determine the test group; (2) Conduct experiments according to the experimental groups obtained in step (1) and obtain experimental results; (3) Perform variance analysis and polynomial fitting regression on the test results obtained in step (2), and establish regression equations for flowability and dynamic yield stress based on the test results; (4) Set the required flowability and dynamic yield stress of the 3D printed cement-based material, and then determine the optimal mixing ratio of the quick-setting agent, hydroxypropyl methyl cellulose ether solution and polycarboxylate superplasticizer according to the regression equation obtained in step (3); The basic mix proportions of the 3D printing cement-based material in step (1) are as follows: water-cement ratio of 0.3, mass ratio of cement:silica fume:mineral powder of 1:0.3:0.15, mortar ratio of 1.45:1.5, and mass of polypropylene fiber of 2% of the total mass of cement, silica fume and mineral powder.

2. The method according to claim 1, characterized in that, The quick-setting agent in step (1) is an alkali-free quick-setting agent.

3. The method according to claim 1, characterized in that, In step (1), the mass concentration of the hydroxypropyl methylcellulose ether solution is 1-3%, and the viscosity of the hydroxypropyl methylcellulose ether solution is 50-150 Pa·s.

4. The method according to claim 1, characterized in that, The water reduction rate of the polycarboxylate superplasticizer in step (1) is 35-45%.

5. The method according to claim 1, characterized in that, In step (1), the experimental groups are determined using DesignExpert 13, Origin, or Matlab software.

6. The method according to claim 1, characterized in that, In step (3), analysis of variance and multinomial fitting regression are performed using Design Expert 13, Origin or Matlab software.

7. The method according to claim 1, characterized in that, The regression equations in step (3) are shown in Equations I and II: Y1=156.20-3.12A-0.9375B+13.31C+0.125AB+1.13AC+1.00BC+2.02A 2 -5.60B 2 -4.60C 2 Formula I; In Formula I, Y1 represents fluidity in mm; A represents accelerator in wt%; B represents hydroxypropyl methylcellulose ether solution in wt%; and C represents polycarboxylate superplasticizer in wt%. Y2=626.40+45.58A-32.47B-358.30C-2.65AB+11.75AC+21.35BC+73.97A 2 +68.82B 2 -16.77C 2 Formula II; In Formula II, Y2 is the dynamic yield stress in Pa; A is the accelerator in wt%; B is a hydroxypropyl methylcellulose ether solution, in wt%; C represents polycarboxylate superplasticizer, measured in wt%.

8. The method according to claim 1, characterized in that, The required flowability of the 3D printed cement-based material in step (4) is 150~180mm.

9. The method according to claim 1, characterized in that, The dynamic yield stress of the 3D printed cement-based material required in step (4) is 200~800 Pa.

Images