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

Abstract

The application provides a method for optimizing the mixing ratio of 3D printing cement-based materials based on a response surface method, and belongs to the technical field of 3D printing. The method for optimizing the mixing ratio of 3D printing cement-based materials based on the response surface method comprises the following steps: determining the level values of test factors, namely, a quick-setting agent, a hydroxypropyl methyl cellulose ether solution and a polycarboxylic acid water reducing agent, then determining the basic mixing ratio of the 3D printing cement-based materials, and then determining test groups; performing tests according to the test groups to obtain test results; performing variance analysis and polynomial fitting regression on the test results, and establishing a regression equation for the fluidity and dynamic yield stress according to the test results; setting the fluidity and dynamic yield stress of the required 3D printing cement-based materials, and then determining the optimal mixing ratio of the quick-setting agent, the hydroxypropyl methyl cellulose ether solution and the polycarboxylic acid water reducing agent according to the regression equation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of 3D printing, and particularly relates to a method for optimizing the mixing ratio of 3D printing cement-based materials based on a response surface method. BACKGROUND

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

[0003] Although the three admixtures of accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid superplasticizer only account for a small proportion in the material composition, they can significantly improve the performance of the material. This not only requires the material to exhibit excellent fluidity during transportation to ensure a smooth printing process, but also requires the material to quickly set after printing and exhibit high strength in the early stage. Yield stress and fluidity are key indicators for measuring the ability of 3D printing cement-based materials to resist plastic deformation and restore flow characteristics when subjected to shear strain. These two parameters can effectively describe the workability and buildability of 3D printing cement-based materials from a mechanism level. However, the influence of the three admixtures of accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid superplasticizer on the yield stress and fluidity of 3D printing cement-based materials is quite complex, and there is currently no research on the influence of these three admixtures on the dynamic yield stress and fluidity of 3D printing cement-based materials. Therefore, how to determine the optimal mixing ratio of accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid superplasticizer according to the dynamic yield stress and fluidity of the required 3D printing cement-based materials has become a technical problem to be solved in the field. SUMMARY

[0004] The purpose of the present application is to provide a method for optimizing the mixing ratio of 3D printing cement-based materials based on a response surface method. The method provided by the present application can determine the optimal mixing ratio of accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid superplasticizer according to the dynamic yield stress and fluidity of the required 3D printing cement-based materials.

[0005] In order to achieve the above-mentioned application purposes, the present application provides the following technical solutions:

[0006] The present application provides a method for optimizing the mixing ratio of 3D printing cement-based materials based on a response surface method, comprising the following steps:

[0007] (1) determining the level values of the test factors of the accelerator, the hydroxypropyl methyl cellulose ether solution and the polycarboxylic acid water reducing agent, then determining the basic mixture ratio of the 3D printing cement-based material, and then determining the test groups;

[0008] (2) performing the test according to the test groups obtained in the step (1) to obtain the test results;

[0009] (3) performing variance analysis and polynomial fitting regression on the test results obtained in the step (2), and establishing a regression equation for the fluidity and the dynamic yield stress according to the test results;

[0010] (4) setting the fluidity and the dynamic yield stress required for the 3D printing cement-based material, and then determining the optimal mixture ratio of the accelerator, the hydroxypropyl methyl cellulose ether solution and the polycarboxylic acid water reducing agent according to the regression equation obtained in the step (3);

[0011] The basic mixture ratio of the 3D printing cement-based material in the step (1) is that the water-binder ratio is 0.3, the mass ratio of cement: silica fume: mineral powder is 1:0.3:0.15, the mortar-sand ratio is 1.45:1.5, and the mass of the polypropylene fiber is 2% of the total mass of the cement, the silica fume and the mineral powder.

[0012] Preferably, the accelerator in the step (1) is an alkali-free accelerator.

[0013] Preferably, the mass concentration of the hydroxypropyl methyl cellulose ether solution in the step (1) is 1-3%, and the viscosity of the hydroxypropyl methyl cellulose ether solution is 50-150 Pa·s.

[0014] Preferably, the water-reducing rate of the polycarboxylic acid water reducing agent in the step (1) is 35-45%.

[0015] Preferably, the software of Design Expert 13, origin or Matlab is used to determine the test groups in the step (1).

[0016] Preferably, the software of Design Expert 13, origin or Matlab is used to perform the variance analysis and the polynomial fitting regression in the step (3).

[0017] Preferably, the regression equation in the step (3) is as shown in Formula I and Formula II:

[0018] 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;

[0019] In formula I, Y1 is fluidity, unit: mm; A is accelerator, unit: wt%; B is hydroxypropyl methyl cellulose ether solution, unit: wt%; C is polycarboxylic acid water reducer, unit: wt%;

[0020] 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;

[0021] In formula II, Y2 is dynamic yield stress, unit: Pa; A is accelerator, unit: wt%; B is hydroxypropyl methyl cellulose ether solution, unit: wt%; C is polycarboxylic acid water reducer, unit: wt%.

[0022] Preferably, the fluidity of the required 3D printing cement-based material in step (4) is 150-180 mm.

[0023] Preferably, the dynamic yield stress of the required 3D printing cement-based material in step (4) is 200-800 Pa.

[0024] 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

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 6 is a response surface plot of the effect of the interaction of B and C on Y1 when A is at the center level;

[0031] Figure 7 is a contour plot of the effect of the interaction of A and B on Y2 when C is at the center level;

[0032] Figure 8 is a response surface plot of the effect of the interaction of A and B on Y2 when C is at the center level;

[0033] Figure 9 is a contour plot of the effect of the interaction of A and C on Y2 when B is at the center level;

[0034] Figure 10 is a response surface plot of the effect of the interaction of A and C on Y2 when B is at the center level;

[0035] Figure 11 is a contour plot of the effect of the interaction of B and C on Y2 when A is at the center level;

[0036] Figure 12 is a response surface plot of the effect of the interaction of B and C on Y2 when A is at the center level;

[0037] Figure 13 is a superimposed plot of the multiple responses derived from the combined balance of the Y1 and Y2 regression equations in Example 1. DETAILED DESCRIPTION

[0038] The present application provides a method for optimizing the mix proportion of 3D printing cement-based materials based on the response surface method, comprising the following steps:

[0039] (1) Determine the level values of the test factors of accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid water reducer, then determine the basic mix proportion of the 3D printing cement-based materials, and then determine the test grouping;

[0040] (2) Perform tests according to the test grouping obtained in step (1) to obtain test results;

[0041] (3) Perform variance analysis and polynomial fitting regression on the test results obtained in step (2), and establish regression equations for the fluidity and dynamic yield stress according to the test results;

[0042] (4) Set the fluidity and dynamic yield stress of the required 3D printing cement-based materials, then determine the optimal mix proportion of the accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid water reducer according to the regression equations obtained in step (3);

[0043] 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.

[0044] 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.

[0045] 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.

[0046] In this invention, the quick-setting agent is preferably an alkali-free quick-setting agent.

[0047] 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.

[0048] As one implementation method, the alkali-free quick-setting agent can be Kezhijie alkali-free quick-setting agent.

[0049] 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.

[0050] In this invention, the water reduction rate of the polycarboxylate superplasticizer is preferably 35-45%, more preferably 40%.

[0051] 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).

[0052] 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.

[0053] 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.

[0054] In the present application, the cement is preferably P·0 42.5 ordinary portland cement; the content of silicon dioxide in the silica fume is preferably ≥ 88 wt%; the loss on ignition of the silica fume is preferably 0.41 wt%; the average particle size of the silica fume is preferably 1.33 µm; 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 0.5~1 mm; the length of the polypropylene fiber is preferably 6 mm; and the diameter of the polypropylene fiber is preferably 0.02 mm.

[0055] The present application is not particularly limited to the preparation method of the 3D printing cement-based material, and a preparation method known to those skilled in the art can be used.

[0056] In the present application, the determination of the test groups is preferably performed by using Design Expert 13, origin or Matlab software.

[0057] The present application is not particularly limited to the operation of determining the test groups by using Design Expert 13, origin or Matlab software, and an operation known to those skilled in the art can be used.

[0058] After obtaining the test groups, the present application performs tests according to the test groups to obtain test results.

[0059] The present application is not particularly limited to the operation of performing tests according to the test groups to obtain test results, and an operation known to those skilled in the art can be used.

[0060] After obtaining the test results, the present application performs variance analysis and polynomial fitting regression on the test results, and establishes a regression equation for fluidity and dynamic yield stress according to the test results.

[0061] In the present application, variance analysis and polynomial fitting regression are preferably performed by using Design Expert 13, origin or Matlab software.

[0062] The present application is not particularly limited to the operation of performing variance analysis and polynomial fitting regression by using Design Expert 13, origin or Matlab software, and an operation known to those skilled in the art can be used.

[0063] The present application is not particularly limited to the operation of establishing a regression equation for fluidity and dynamic yield stress according to the test results, and an operation known to those skilled in the art can be used.

[0064] In the present application, the regression equation is preferably as shown in formula I and formula II:

[0065] 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;

[0066] In Formula I, Y1 is fluidity, unit: mm; A is accelerator, unit: wt%; B is hydroxypropyl methyl cellulose ether solution, unit: wt%; C is polycarboxylic acid water reducing agent, unit: wt%;

[0067] 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;

[0068] In Formula II, Y2 is dynamic yield stress, unit: Pa; A is accelerator, unit: wt%; B is hydroxypropyl methyl cellulose ether solution, unit: wt%; C is polycarboxylic acid water reducing agent, unit: wt%.

[0069] In the application, the AB, AC and BC are preferably the interaction among the accelerator, the hydroxypropyl methyl cellulose ether solution and the polycarboxylic acid water reducing agent.

[0070] After obtaining the regression equation, the application sets the fluidity and dynamic yield stress of the required 3D printing cement-based material, and then determines the optimal mixing ratio of the accelerator, the hydroxypropyl methyl cellulose ether solution and the polycarboxylic acid water reducing agent according to the regression equation.

[0071] In the application, the fluidity of the required 3D printing cement-based material is preferably 150-180 mm; and the dynamic yield stress of the required 3D printing cement-based material is preferably 200-800 Pa.

[0072] In the application, the fluidity is preferably determined with reference to GB / T2419-2005 'Cement Mortar Fluidity Determination Method'; and the dynamic yield stress is preferably tested by using an ICAR Plus concrete rheometer.

[0073] The application does not have special limitations on the operation of determining the optimal mixing ratio of the accelerator, the hydroxypropyl methyl cellulose ether solution and the polycarboxylic acid water reducing agent according to the regression equation, and the operation well known to those skilled in the art can be adopted.

[0074] The application is based on a response surface method, first determines the level value of the test factor, then determines the basic mixing ratio, then determines the test grouping for testing, according to the test results, obtains the regression equation with the fluidity and dynamic yield stress of the 3D printing cement-based material as the response value, and the accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid water reducing agent as the response variable, and finally determines the optimal mixing ratio of the accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid water reducing agent according to the fluidity and dynamic yield stress of the required 3D printing cement-based material.

[0075] The method provided by the application is based on the Box-Behnken central combination principle, takes the fluidity and dynamic yield stress of the 3D printing cement-based material as the response value, selects the accelerator (accelerator, hydroxypropyl methyl cellulose ether solution and polycarboxylic acid water reducing agent) as the response variable, discusses the influence of the accelerator on the fluidity and dynamic yield stress of the 3D printing cement-based material, analyzes each response value using a quadratic regression equation, obtains the regression equation and the optimal mixing ratio, which is close to the measured value and meets the requirements of printability, and indicates that the response surface method can be used for the mixing ratio optimization of the 3D printing cement-based material.

[0076] The technical solutions in the application will be clearly and completely described below with reference to the embodiments in the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0077] Embodiment 1

[0078] A method for optimizing the mixing ratio of a 3D printing cement-based material based on a response surface method includes the following steps:

[0079] (1) determining the level value of the test factors, i.e. the accelerator, the hydroxypropyl methyl cellulose ether solution with a mass concentration of 2% and a viscosity of 100 Pa·s, and the polycarboxylic acid water reducing agent with a water reducing rate of 40%, then determining the basic mixing ratio of the 3D printing cement-based material, and then determining the test grouping by using Design Expert 13 software;

[0080] The accelerator is Kezhijie alkali-free accelerator.

[0081] The upper, middle and lower water levels of the accelerator, the hydroxypropyl methyl cellulose ether solution and the polycarboxylic acid water reducing agent are encoded and set as 1, 0 and -1; the -1 is a low level; the 1 is a high level; and the 0 is a central point.

[0082] The basic mixing ratio of the 3D printing cement-based material is: water-binder ratio is 0.3, mass ratio of cement: silica fume: mineral powder is 1:0.3:0.15, cement-sand ratio is 1.45:1.5, and the mass of polypropylene fiber is 2% of the total mass of cement, silica fume and mineral powder;

[0083] The cement is P·0 42.5 ordinary portland cement;

[0084] The content of silicon dioxide in the silica fume is 88.30wt%, the loss on ignition is 0.41wt%, and the average particle size is 1.33µm;

[0085] The mineral powder is S95 grade mineral powder, and the fineness is ≥800 mesh;

[0086] The sand is natural sand, and the particle size is 0.5~1mm;

[0087] The length of the polypropylene fiber is 6mm, and the diameter is 0.02mm;

[0088] The preparation of the 3D printing cement-based material is as follows:

[0089] 1) The cement, silica fume, mineral powder and sand are weighed and poured into a mixing pot, and dry mixing is performed for 180s to uniformly mix the raw materials to obtain a solid powder;

[0090] 2) The solid powder obtained in step 1) is added with a quick-setting agent, a hydroxypropyl methylcellulose ether solution, a polycarboxylic acid water reducer, and 3 / 4 of water, and stirred for 120s, then the remaining 1 / 4 of water is added and stirred for 60s, and then polypropylene fiber is added and stirred for 120s to obtain a 3D printing cement-based material;

[0091] (2) The test groups obtained according to step (1) are tested to obtain test results;

[0092] (3) The Design Expert 13 software is used to perform variance analysis and polynomial fitting regression on the test results obtained in step (2), and regression equations for flowability and dynamic yield stress are established according to the test results;

[0093] The regression equations are shown in formula I and formula II:

[0094] 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;

[0095] In formula I, Y1 is fluidity, unit: mm; A is accelerator, unit: wt%; B is hydroxypropyl methyl cellulose ether solution, unit: wt%; C is polycarboxylic acid water reducing agent, unit: wt%;

[0096] 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;

[0097] In formula II, Y2 is dynamic yield stress, unit: Pa; A is accelerator, unit: wt%; B is hydroxypropyl methyl cellulose ether solution, unit: wt%; C is polycarboxylic acid water reducing agent, unit: wt%;

[0098] (4) setting the fluidity of the required 3D printing cement-based material to 150-180 mm and the dynamic yield stress to 200-800 Pa, then obtaining a plurality of value schemes according to the regression equation obtained in the step (3), and evaluating by using a desirability index, wherein the optimal solution is shown in Table 1, and the desirability index reaches 1.

[0099] Table 1 Optimal scheme after response surface optimization and predicted value and actual value

[0100]

[0101] As shown in Table 1, the predicted value of the application is close to the actual value, meets the requirements of printability, and the method provided by the application can be used for the mix proportion optimization of the 3D printing cement-based material.

[0102] The fluidity is determined with reference to GB / T 2419-2005 'Cement mortar fluidity determination method':

[0103] (1) The mortar is filled into a truncated cone circular mold according to the provisions, tamped, and lifted vertically upward.

[0104] (2) Start the jump table, complete 25 times of jumping within 25 s, test the spread diameter, and take the average value.

[0105] The dynamic yield stress test adopts an ICAR Plus concrete rheometer, and the test procedure is as follows:

[0106] (1) Pre-shearing at a speed of 0.5 rps for 20 s;

[0107] (2) Uniformly reduce the initial speed of 0.5 rps until the final speed is 0.05 rps, and then complete shearing.

[0108] Example 1 uses Design Expert 13 software to design a response surface test according to the Box-Behnken Design (BBD) principle, the sample factors and level values are shown in Table 2, the test grouping and test results are shown in Table 3, and the variance analysis results are shown in Tables 4 and 5.

[0109] Table 2 Sample factors and level values

[0110]

[0111] Table 3 Test grouping and test results

[0112]

[0113] Table 4 Variance analysis results (Y1)

[0114]

[0115] Table 5 Variance analysis results (Y2)

[0116]

[0117] From Tables 4 and 5, the P value of the Y1 model is less than 0.0001, the P value of the dynamic yield stress model is less than 0.0001, both of which are less than 0.05, indicating that the regression equations Y1 and Y2 are significant, and the lack of fit is 0.0656 and 0.1770 respectively, both of which are greater than 0.05, indicating that the regression equation fitting is good; from the F value, the size of each factor on Y1 is C>A>B, and the dynamic yield stress influence significance is C>A>B; among the regression equation Y1, the P values of factors A, C and BC are all less than 0.05, indicating that the interaction of A, C and BxC has a more significant effect on Y1, while the interaction of AxB is smaller; in the regression equation Y2, the P value of factor C is less than 0.05, indicating that the interaction of C has a more significant effect on the dynamic yield stress, while the interaction between A, B and C is not significant.

[0118] On the basis of the variance analysis results, the Design Expert 13 software is used to draw the response surface and contour maps according to the regression equation, and the effects of A, B and C on the 3D printing cement-based materials Y1 and Y2 are analyzed. When one of A, B and C is fixed, the interaction between the other two factors on the response value can be represented by the contour map and response surface map, the results are shown in Figures 1-12 ; among them, the response surface and contour map can intuitively reflect the influence degree of the interaction on the response value, the steeper the surface, the more concentrated the contour, the more significant the influence, and the closer the contour to the ellipse, the stronger the interaction of the two factors.

[0119] 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.

[0120] 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.

[0121] from Figure 3 , 4As 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 polypropylene fiber of 2% of the total mass of cement, silica fume and mineral powder. 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%.

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

8. 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.

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