3D building printing leveling device and method thereof

Abstract

The invention discloses a 3D building printing leveling device and method, and relates to the technical field of 3D building printing, environmental data and printing parameters are collected in real time through various sensors, and the data are fitted and processed to generate a printing data set Q; through analysis of a printing data set Q, the speed V of a printing head and the material flow M are dynamically adjusted, and optimized printing parameters Vn and Mn are obtained. According to the improvement, the defect that a traditional fixed parameter setting method is insufficient in adaptability under the complex printing condition is overcome, the printing strategy can be adjusted in a self-adaptive mode according to real-time data, and therefore the uniformity of material deposition and the transition smoothness between layers are improved. According to the quality evaluation result in the printing process, the printing parameters can be automatically adjusted, and the surface quality can be dynamically optimized. The function effectively solves the problem that a traditional printing technology is insufficient in adaptability to complex environment changes, the flatness of the printing surface and the overall building quality are improved, and material waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D building printing, and in particular to a 3D building printing leveling device and method thereof. Background Art

[0002] 3D architectural printing, a quintessential application of additive manufacturing technology in the construction sector, has garnered widespread attention in recent years. With its advantages such as high material utilization, flexible design, and short construction cycles, it has gradually become a key research direction in construction engineering technology. Within this field, printing accuracy and surface quality are key factors influencing the effectiveness of 3D architectural printing. In particular, the uniformity of material deposition during the printing process directly determines the surface smoothness and functional performance of the final structure.

[0003] One of the main challenges facing 3D architectural printing is surface flatness. This is primarily manifested by uneven deposition and uneven transitions between layers, resulting in uneven surfaces on the final building structure. The root cause of these issues lies in the inability to perceive and dynamically adjust to the complex changes in environmental factors and material properties during the printing process.

[0004] First, real-time data during the printing process is not fully utilized, resulting in a slow response to environmental changes and printing status. Second, existing methods lack the support of intelligent algorithms, making it difficult to achieve dynamic optimization in a complex environment with multiple interacting variables. These issues directly lead to quality defects on the printed building surface, manifesting as misalignment of printed layers, material waste, and reduced structural strength. These defects not only affect the aesthetics and functionality of the printed building, but also pose safety risks and increase the cost of subsequent repairs. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a 3D building printing and leveling device and method thereof, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A 3D building printing and leveling method, comprising the following steps:

[0007] S1. During the printing process, real-time data related to printing is collected through various sensors, processed, and fitted into a printing data set Q.

[0008] S2. Dynamically adjust the print head speed V and the material flow M of the print head according to the obtained print data set Q, and obtain an adjusted print head speed Vn and an adjusted material flow Mn;

[0009] S3. Observe the building printing situation through the adjusted print head speed Vn and the adjusted material flow Mn, and calculate and obtain the real-time surface error E;

[0010] S4. According to the surface error E, the print head speed V and the material flow M of the print head are adjusted by a feedback control algorithm, and the adjusted print head speed Vn and the adjusted material flow Mn are corrected to obtain a corrected speed Vx and a corrected flow Mx;

[0011] S5. Evaluate the flatness of each layer based on the corrected speed Vx and the corrected flow rate Mx, and perform a quality evaluation on the entire printing process to obtain an evaluation result;

[0012] S6. Based on the evaluation results, continuously correct the flatness and adjust the surface quality.

[0013] Preferably, said S1 includes S11 and S12;

[0014] S11. Real-time data is collected through a variety of sensors. The real-time data includes ambient temperature T, ambient humidity H, print head speed V, material flow M, and surface deposition thickness D, forming a real-time data set QW;

[0015] Among them, the ambient temperature T is collected by a temperature sensor, the ambient humidity H is collected by a humidity sensor, the print head speed V is collected by an encoder installed in the print head, the material flow M is collected by a flow meter, and the surface deposition thickness D is collected by a lidar;

[0016] S11, cleaning and standardizing the real-time data set QW to obtain the printed data set Q;

[0017] Cleaning includes missing value processing and outlier processing. Missing values ​​are filled by interpolation method in the real-time dataset QW. Outlier processing is to remove abnormal data in the real-time dataset QW through outlier detection algorithm.

[0018] The formula for the standardization process is:

[0019] ;

[0020] Where Qp represents the p-th data item in the print dataset Q, QWp represents the p-th data item in the real-time dataset QW, μQWp represents the mean of the p-th data item in the real-time dataset QW, and σQWp represents the standard deviation of the p-th data item in the real-time dataset QW.

[0021] Preferably, said S2 includes S21 and S22;

[0022] S21. Based on the printed data set Q, feature extraction is performed to extract the surface deposition error. ; and according to the surface deposition error Dynamically generate speed adjustment coefficient Kv and material flow adjustment coefficient Km;

[0023] The surface deposition error Obtained by the following formula:

[0024] ;

[0025] Where D(t) represents the surface deposition thickness at time t, Didea(t) represents the target surface deposition thickness at time t;

[0026] The speed adjustment coefficient Kv is obtained by the following formula:

[0027] ;

[0028] Where, represents the adjustment constant;

[0029] The material flow adjustment coefficient Km is obtained by the following formula:

[0030]

[0031] Where, represents the adjustment constant;

[0032] S22. Adjust the print head speed V and the material flow rate M according to the obtained speed adjustment coefficient Kv and the material flow rate adjustment coefficient Km, and obtain an adjusted print head speed Vn and an adjusted material flow rate Mn;

[0033] The adjusted print head speed Vn is obtained by the following formula:

[0034] ;

[0035] Where Kv represents the speed adjustment coefficient, T(t) represents the ambient temperature at time t, and H(t) represents the ambient humidity at time t;

[0036] The adjusted material flow Mn is obtained by the following formula:

[0037] ;

[0038] Where Km represents the material flow adjustment coefficient.

[0039] Preferably, said S3 includes S31 and S32;

[0040] S31, after the adjusted print head speed Vn and the adjusted material flow Mn are brought into the printing device, the printing condition of the building is observed by the laser radar, and the deposition thickness difference Dre is recorded;

[0041] The deposition thickness difference Dre is obtained by the following formula:

[0042] ;

[0043] Where D(t) represents the surface deposition thickness at time t, and D(t-1) represents the surface deposition thickness at time t-1;

[0044] S32, comparing the deposition thickness difference Dre with the preset printing thickness difference YTD, and calculating and obtaining a real-time surface error E;

[0045] The real-time surface error E is obtained by the difference between the deposition thickness difference Dre and the preset printing thickness YTD.

[0046] Preferably, said S4 includes S41 and S42;

[0047] S41, using the surface error E as a feedback signal, and analyzing the influence of the surface error E on the print head speed V and the material flow M;

[0048] Based on the surface error E, the velocity correction factor Kvn and the flow correction factor Kmn are generated through the PID controller;

[0049] The speed correction factor Kvn is obtained by the following formula:

[0050] ;

[0051] Where Kp represents the proportional gain coefficient, Ki represents the integral gain coefficient, Kd represents the differential gain coefficient, d represents the differential, dt represents the time period, E(t) represents the surface error at time t, and t represents time;

[0052] The flow correction factor Kmn is obtained by the following formula:

[0053] .

[0054] Preferably, S42 uses the acquired speed correction factor Kvn and flow correction factor Kmn to correct the adjusted print head speed Vn and the adjusted material flow Mn to obtain a corrected speed Vx and a corrected flow Mx;

[0055] The correction speed Vx is obtained by the following formula:

[0056] ;

[0057] Where Vn represents the adjusted print head speed;

[0058] The corrected flow rate Mx is obtained by the following formula:

[0059] ;

[0060] Where Vn represents the adjusted print head speed.

[0061] Preferably, said S5 includes S51 and S52;

[0062] S51, bringing the corrected speed Vx and the corrected flow rate Mx into the printing device, collecting the corrected actual deposition thickness DZ, evaluating the surface flatness index Rmse, and comparing it with the preset index threshold TRm to determine whether the flatness meets the standard;

[0063] The surface flatness index Rmse is obtained by the following formula:

[0064] ;

[0065] Where N represents the number of sampling points, DZi represents the actual deposition thickness at the i-th sampling point, and Dideai represents the target surface deposition thickness at the i-th sampling point;

[0066] The flatness state is obtained by matching in the following manner:

[0067] When the surface flatness index Rmse ≤ the index threshold TRm, it means that the flatness meets the standard and the correction speed Vx and correction flow Mx ​​are not adjusted;

[0068] When the surface flatness index Rmse>the index threshold TRm, it means that the flatness does not meet the standard, and the correction speed Vx and the correction flow Mx ​​are adjusted.

[0069] Preferably, S52, when the surface flatness index Rmse>the index threshold TRm, adjust the correction speed Vx and the correction flow Mx, and obtain a new print head speed Vy and a new material flow My according to the surface flatness index Rmse;

[0070] The new print head speed Vy is obtained by the following formula:

[0071] ;

[0072] Where, represents the adjustment parameter of the correction speed Vx, μRmse represents the mean value of the surface flatness index;

[0073] The new material flow My is obtained by the following formula:

[0074] ;

[0075] Where, Indicates the adjustment parameter for the corrected flow rate Mx.

[0076] Preferably, the S6 brings the acquired new print head speed Vy and new material flow My back into the printing device, continuously corrects the 3D building printing process, automatically adjusts the print head speed V and material flow M, and adjusts the surface quality.

[0077] A 3D building printing and leveling device, comprising the following modules:

[0078] Data acquisition and processing module: During the printing process, various sensors are used to collect real-time data related to printing, and the real-time data is processed and fitted into the printing data set Q;

[0079] Dynamic adjustment module: dynamically adjusts the print head speed V and the material flow M of the print head according to the obtained print data set Q, and obtains the adjusted print head speed Vn and the adjusted material flow Mn;

[0080] Surface flatness monitoring module: observes the building printing situation through the adjusted print head speed Vn and the adjusted material flow Mn, and calculates the real-time surface error E;

[0081] Flatness error correction module: According to the surface error E, the print head speed V and the material flow M of the print head are adjusted through the feedback control algorithm, and the adjusted print head speed Vn and the adjusted material flow Mn are corrected to obtain the corrected speed Vx and the corrected flow Mx;

[0082] Flatness evaluation module: Evaluates the flatness of each layer based on the corrected speed Vx and corrected flow Mx, and performs quality evaluation on the entire printing process to obtain evaluation results;

[0083] Comprehensive feedback module: Based on the evaluation results, the flatness is continuously corrected and the surface quality is adjusted.

[0084] The present invention provides a 3D building printing and leveling device and method thereof, which have the following beneficial effects:

[0085] (1) Environmental data and printing parameters are collected in real time through multiple sensors, and the data is fitted and processed to generate a printing data set Q. This method makes up for the problem of insufficient monitoring capabilities of complex changes in the printing environment in existing technologies, enabling the printing system to fully perceive and respond to real-time changes, laying the foundation for intelligent adjustment and dynamic optimization.

[0086] By analyzing the print data set Q, the print head speed V and material flow rate M are dynamically adjusted to obtain optimized printing parameters Vn and Mn. This improvement overcomes the lack of adaptability of traditional fixed parameter setting methods under complex printing conditions. It can adaptively adjust the printing strategy based on real-time data, thereby improving the uniformity of material deposition and the smoothness of transitions between layers. A feedback control algorithm is introduced to further correct the adjusted printing parameters Vn and Mn based on the real-time surface error E, resulting in more accurate corrected speed Vx and corrected flow rate Mx. This mechanism solves the problem of the existing technology's lack of dynamic correction capability for printing errors, enabling the system to continuously optimize during the printing process and gradually reduce error accumulation.

[0087] (2) The deposition thickness difference Dre is recorded in real time by a laser radar, and the real-time surface error E is calculated based on the comparison between the deposition thickness difference and the preset printing thickness difference YTD. This method can accurately reflect the deposition changes between printed layers, providing a scientific basis for subsequent parameter correction. Compared with the traditional method that lacks effective monitoring of deposition thickness changes, this embodiment significantly improves the dynamic perception capability of the printing process.

[0088] Using a PID control algorithm, the surface error E is converted into a velocity correction factor Kvn and a flow correction factor Kmn. This method achieves precise adjustment of surface error through proportional, integral, and differential control. Compared to static parameter adjustment methods, this embodiment responds more efficiently to error changes and dynamically optimizes print head velocity Vn and material flow Mn, improving the system's adaptability to complex printing conditions.

[0089] (3) The surface flatness index Rmse is calculated using the corrected actual deposition thickness DZ and compared with the preset flatness threshold TRm to determine whether the printed surface meets the standard. This embodiment overcomes the lack of quantitative surface quality assessment in traditional methods, enabling the printing system to scientifically evaluate the flatness status based on quantitative indicators, helping to promptly identify and address quality issues and ensure printing quality.

[0090] When the surface flatness indicator Rmse exceeds the threshold TRm, the system automatically adjusts the corrected speed Vx and flow rate Mx to generate a new print head speed Vy and material flow rate My. This mechanism optimizes flatness by adjusting parameters, significantly improving the smoothness of transitions between printed layers and addressing the poor adaptability of traditional printing processes to complex conditions.

[0091] (4) Through the data acquisition and processing module, the device uses a variety of sensors to comprehensively collect real-time data related to the environment and equipment during the printing process, and processes and fits the data to generate a high-quality printing data set Q. This function significantly enhances the system's ability to perceive complex printing environments, making up for the shortcomings of traditional methods such as limited data monitoring range and insufficient processing capabilities, and providing accurate data support for subsequent optimization.

[0092] The dynamic adjustment module intelligently adjusts print head speed V and material flow rate M based on the print data set Q, generating optimized parameters. Compared to fixed parameter settings, this module automatically adjusts the printing strategy based on real-time data, effectively responding to environmental changes and fluctuations in printing status, improving material deposition uniformity and reducing uneven transitions between layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 This is a schematic diagram of the steps of a 3D building printing and leveling method of the present invention;

[0094] Figure 2 This is a schematic diagram of a 3D building printing and leveling device module of the present invention. DETAILED DESCRIPTION

[0095] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0096] Example 1

[0097] The present invention provides a 3D architectural printing and leveling method, please refer to Figure 1 , including the following steps:

[0098] S1. During the printing process, real-time data related to printing is collected through various sensors, processed, and fitted into a printing data set Q.

[0099] S2. Dynamically adjust the print head speed V and the material flow M of the print head according to the obtained print data set Q, and obtain an adjusted print head speed Vn and an adjusted material flow Mn;

[0100] S3. Observe the building printing situation through the adjusted print head speed Vn and the adjusted material flow Mn, and calculate and obtain the real-time surface error E;

[0101] S4. According to the surface error E, the print head speed V and the material flow M of the print head are adjusted by a feedback control algorithm, and the adjusted print head speed Vn and the adjusted material flow Mn are corrected to obtain a corrected speed Vx and a corrected flow Mx;

[0102] S5. Evaluate the flatness of each layer based on the corrected speed Vx and the corrected flow rate Mx, and perform a quality evaluation on the entire printing process to obtain an evaluation result;

[0103] S6. Based on the evaluation results, continuously correct the flatness and adjust the surface quality.

[0104] In this embodiment, multiple sensors are used to collect environmental data and printing parameters in real time, and the data is fitted and processed to generate a print dataset Q. This approach overcomes the existing technology's inability to monitor complex changes in the printing environment, enabling the printing system to fully perceive and respond to real-time changes, laying the foundation for intelligent adjustment and dynamic optimization.

[0105] By analyzing the print data set Q, the print head speed V and material flow rate M are dynamically adjusted to obtain optimized printing parameters Vn and Mn. This improvement overcomes the lack of adaptability of traditional fixed parameter setting methods under complex printing conditions. It can adaptively adjust the printing strategy based on real-time data, thereby improving the uniformity of material deposition and the smoothness of transitions between layers. A feedback control algorithm is introduced to further correct the adjusted printing parameters Vn and Mn based on the real-time surface error E, resulting in more accurate corrected speed Vx and corrected flow rate Mx. This mechanism solves the problem of the existing technology's lack of dynamic correction capability for printing errors, enabling the system to continuously optimize during the printing process and gradually reduce error accumulation.

[0106] Based on the corrected printing parameters, the printing smoothness of each layer is quantitatively evaluated, and the entire printing process is quality analyzed to obtain key evaluation results such as the mean square error. This function improves the lack of refined monitoring and evaluation of printing quality in existing methods, helping operators to promptly identify problems and make optimization adjustments. Combining sensor data, error feedback, and machine learning algorithms, the present invention can automatically adjust printing parameters and dynamically optimize surface quality based on the quality assessment results during the printing process. This function effectively solves the problem of traditional printing technology's lack of adaptability to complex environmental changes, improves the flatness of the printed surface and the overall building quality, and reduces material waste and subsequent repair costs.

[0107] Example 2

[0108] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12;

[0109] S11. Real-time data is collected through a variety of sensors. The real-time data includes ambient temperature T, ambient humidity H, print head speed V, material flow M, and surface deposition thickness D, forming a real-time data set QW;

[0110] Among them, the ambient temperature T is collected by a temperature sensor, the ambient humidity H is collected by a humidity sensor, the print head speed V is collected by an encoder installed in the print head, the material flow M is collected by a flow meter, and the surface deposition thickness D is collected by a lidar;

[0111] S11, cleaning and standardizing the real-time data set QW to obtain the printed data set Q;

[0112] Cleaning includes missing value processing and outlier processing. Missing values ​​are filled by interpolation method in the real-time dataset QW. Outlier processing is to remove abnormal data in the real-time dataset QW through outlier detection algorithm.

[0113] The formula for the standardization process is:

[0114] ;

[0115] Where Qp represents the p-th data item in the print dataset Q, QWp represents the p-th data item in the real-time dataset QW, μQWp represents the mean of the p-th data item in the real-time dataset QW, and σQWp represents the standard deviation of the p-th data item in the real-time dataset QW.

[0116] Said S2 includes S21 and S22;

[0117] S21. Based on the printed data set Q, feature extraction is performed to extract the surface deposition error. ; and according to the surface deposition error Dynamically generate speed adjustment coefficient Kv and material flow adjustment coefficient Km;

[0118] The surface deposition error Obtained by the following formula:

[0119] ;

[0120] Where D(t) represents the surface deposition thickness at time t, Didea(t) represents the target surface deposition thickness at time t;

[0121] The speed adjustment coefficient Kv is obtained by the following formula:

[0122] ;

[0123] Where, represents the adjustment constant;

[0124] The material flow adjustment coefficient Km is obtained by the following formula:

[0125]

[0126] Where, represents the adjustment constant;

[0127] S22. Adjust the print head speed V and the material flow rate M according to the obtained speed adjustment coefficient Kv and the material flow rate adjustment coefficient Km, and obtain an adjusted print head speed Vn and an adjusted material flow rate Mn;

[0128] The adjusted print head speed Vn is obtained by the following formula:

[0129] ;

[0130] Where Kv represents the speed adjustment coefficient, T(t) represents the ambient temperature at time t, and H(t) represents the ambient humidity at time t;

[0131] The adjusted material flow Mn is obtained by the following formula:

[0132] ;

[0133] Where Km represents the material flow adjustment coefficient.

[0134] In this embodiment, the system comprehensively collects key data such as ambient temperature T, humidity H, print head speed VV, material flow M, and surface deposition thickness D through a variety of sensors, including temperature and humidity sensors, flow meters, and lidar, to form a real-time data set QW. Subsequently, the data is cleaned using a missing value interpolation method and an outlier detection algorithm, and standardized processing formulas are used to unify the data scale to generate the print data set Q. This approach effectively addresses the shortcomings of existing real-time data processing technologies, ensuring data integrity, consistency, and availability, and providing high-quality data support for subsequent dynamic adjustments.

[0135] By extracting features from the printed data set Q, the surface deposition error is calculated in real time. , and dynamically generates a speed adjustment factor, Kv, and a material flow adjustment factor, Km. This adjustment factor formula fully accounts for the influence of ambient temperature (T) and humidity (H), ensuring the system's high adaptability to complex printing environments. By comprehensively considering environmental conditions and real-time surface deposition errors, the system automatically optimizes printing parameters to ensure uniform material deposition and improve surface smoothness. This automated adjustment method overcomes the lag and inaccuracy of manual adjustments, enabling intelligent control of the printing process.

[0136] Example 3

[0137] This embodiment is explained in Example 2, please refer to Figure 1 , specifically: said S3 includes S31 and S32;

[0138] S31, after the adjusted print head speed Vn and the adjusted material flow Mn are brought into the printing device, the printing condition of the building is observed by the laser radar, and the deposition thickness difference Dre is recorded;

[0139] The deposition thickness difference Dre is obtained by the following formula:

[0140] ;

[0141] Where D(t) represents the surface deposition thickness at time t, and D(t-1) represents the surface deposition thickness at time t-1;

[0142] S32, comparing the deposition thickness difference Dre with the preset printing thickness difference YTD, and calculating and obtaining a real-time surface error E;

[0143] The real-time surface error E is obtained by the difference between the deposition thickness difference Dre and the preset printing thickness YTD.

[0144] Said S4 includes S41 and S42;

[0145] S41, using the surface error E as a feedback signal, and analyzing the influence of the surface error E on the print head speed V and the material flow M;

[0146] Based on the surface error E, the velocity correction factor Kvn and the flow correction factor Kmn are generated through the PID controller;

[0147] The speed correction factor Kvn is obtained by the following formula:

[0148] ;

[0149] Where Kp represents the proportional gain coefficient, Ki represents the integral gain coefficient, Kd represents the differential gain coefficient, d represents the differential, dt represents the time period, E(t) represents the surface error at time t, and t represents time;

[0150] The flow correction factor Kmn is obtained by the following formula:

[0151] .

[0152] S42 uses the obtained speed correction factor Kvn and flow correction factor Kmn to correct the adjusted print head speed Vn and the adjusted material flow Mn to obtain a corrected speed Vx and a corrected flow Mx;

[0153] The correction speed Vx is obtained by the following formula:

[0154] ;

[0155] Where Vn represents the adjusted print head speed;

[0156] The corrected flow rate Mx is obtained by the following formula:

[0157] ;

[0158] Where Vn represents the adjusted print head speed.

[0159] In this embodiment, a lidar system records the deposition thickness difference Dre in real time and calculates the real-time surface error E by comparing it with the preset printing thickness difference YTD. This method accurately reflects deposition variations between printed layers, providing a scientific basis for subsequent parameter correction. Compared to traditional methods that lack effective monitoring of deposition thickness variations, this embodiment significantly improves the dynamic perception of the printing process.

[0160] Using a PID control algorithm, the surface error E is converted into a velocity correction factor Kvn and a flow correction factor Kmn. This method achieves precise adjustment of surface error through proportional, integral, and differential control. Compared to static parameter adjustment methods, this embodiment responds more efficiently to error changes and dynamically optimizes print head velocity Vn and material flow Mn, improving the system's adaptability to complex printing conditions.

[0161] Example 4

[0162] This embodiment is explained in Example 3, please refer to Figure 1 Specifically: S5 includes S51 and S52;

[0163] S51, bringing the corrected speed Vx and the corrected flow rate Mx into the printing device, collecting the corrected actual deposition thickness DZ, evaluating the surface flatness index Rmse, and comparing it with the preset index threshold TRm to determine whether the flatness meets the standard;

[0164] The surface flatness index Rmse is obtained by the following formula:

[0165] ;

[0166] Where N represents the number of sampling points, DZi represents the actual deposition thickness at the i-th sampling point, and Dideai represents the target surface deposition thickness at the i-th sampling point;

[0167] The flatness state is obtained by matching in the following manner:

[0168] When the surface flatness index Rmse ≤ the index threshold TRm, it means that the flatness meets the standard and the correction speed Vx and correction flow Mx ​​are not adjusted;

[0169] When the surface flatness index Rmse>the index threshold TRm, it means that the flatness does not meet the standard, and the correction speed Vx and the correction flow Mx ​​are adjusted.

[0170] S52 , when the surface flatness index Rmse is greater than the index threshold TRm, adjusting the correction speed Vx and the correction flow rate Mx, and obtaining a new print head speed Vy and a new material flow rate My according to the surface flatness index Rmse;

[0171] The new print head speed Vy is obtained by the following formula:

[0172] ;

[0173] Where, represents the adjustment parameter of the correction speed Vx, μRmse represents the mean value of the surface flatness index;

[0174] The new material flow My is obtained by the following formula:

[0175] ;

[0176] Where, Indicates the adjustment parameter for the corrected flow rate Mx.

[0177] The S6 brings the acquired new print head speed Vy and new material flow My back into the printing device, continuously corrects the 3D building printing process, automatically adjusts the print head speed V and material flow M, and adjusts the surface quality.

[0178] In this embodiment, the surface flatness indicator Rmse is calculated using the corrected actual deposition thickness DZ and compared with a preset flatness threshold TRm to determine whether the printed surface meets the standard. This embodiment overcomes the lack of quantitative surface quality assessment in traditional methods, enabling the printing system to scientifically assess flatness based on quantitative indicators, helping to promptly identify and address quality issues and ensure printing quality.

[0179] When the surface flatness indicator Rmse exceeds the threshold TRm, the system automatically adjusts the corrected speed Vx and flow rate Mx to generate a new print head speed Vy and material flow rate My. This mechanism optimizes flatness by adjusting parameters, significantly improving the smoothness of transitions between printed layers and addressing the poor adaptability of traditional printing processes to complex conditions.

[0180] This embodiment introduces an automated closed-loop adjustment process. When surface flatness is detected to be substandard, the system continuously optimizes the printing parameters through dynamically updated printing parameters. This allows the printing device to adaptively adjust printing speed and material flow to ensure uniform material deposition and surface flatness. Compared to manual adjustments or static parameter settings, closed-loop control significantly improves the automation level of the printing process and the final building quality.

[0181] Example 5

[0182] A 3D architectural printing leveling device, please refer to Figure 2 , specifically: including the following modules:

[0183] Data acquisition and processing module: During the printing process, various sensors are used to collect real-time data related to printing, and the real-time data is processed and fitted into the printing data set Q;

[0184] Dynamic adjustment module: dynamically adjusts the print head speed V and the material flow M of the print head according to the obtained print data set Q, and obtains the adjusted print head speed Vn and the adjusted material flow Mn;

[0185] Surface flatness monitoring module: observes the building printing situation through the adjusted print head speed Vn and the adjusted material flow Mn, and calculates the real-time surface error E;

[0186] Flatness error correction module: According to the surface error E, the print head speed V and the material flow M of the print head are adjusted through the feedback control algorithm, and the adjusted print head speed Vn and the adjusted material flow Mn are corrected to obtain the corrected speed Vx and the corrected flow Mx;

[0187] Flatness evaluation module: Evaluates the flatness of each layer based on the corrected speed Vx and corrected flow Mx, and performs quality evaluation on the entire printing process to obtain evaluation results;

[0188] Comprehensive feedback module: Based on the evaluation results, the flatness is continuously corrected and the surface quality is adjusted.

[0189] In this embodiment, the data acquisition and processing module utilizes multiple sensors to comprehensively collect real-time data related to the environment and equipment during the printing process. This data is then processed and fitted to generate a high-quality print dataset, Q. This feature significantly enhances the system's ability to perceive complex printing environments, addressing the limitations of traditional methods, such as limited data monitoring and insufficient processing capabilities, and providing accurate data support for subsequent optimization.

[0190] The dynamic adjustment module intelligently adjusts print head speed V and material flow rate M based on the print data set Q, generating optimized parameters. Compared to fixed parameter settings, this module automatically adjusts the printing strategy based on real-time data, effectively responding to environmental changes and fluctuations in printing status, improving material deposition uniformity and reducing uneven transitions between layers.

[0191] The surface flatness monitoring module calculates surface error E in real time, providing data support for the flatness error correction module. The correction module, in turn, uses a feedback control algorithm to generate corrections for velocity Vx and flow rate Mx. This closed-loop control approach overcomes the slow response of traditional printing equipment to deposition errors, enabling the system to correct deviations in real time during the printing process, reducing error accumulation and significantly improving the flatness of the printed surface.

[0192] The Flatness Assessment module quantifies the surface quality of printed parts by evaluating the flatness of each layer and comprehensively analyzing the overall print quality. This function provides real-time insights into print results and a scientific basis for further optimization. Compared to traditional methods that rely solely on empirical quality assessment, this module provides quantitative assessment indicators, significantly improving the reliability of the printing process.

[0193] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A 3D architectural printing and leveling method, characterized by: The following steps are involved: S1. During the printing process, various sensors are used to collect and process real-time data related to printing. And fit it into the printed data set Q; S2. Dynamically adjust the print head speed V and the material flow M of the print head according to the obtained print data set Q, and obtain an adjusted print head speed Vn and an adjusted material flow Mn; S3. Observe the building printing situation through the adjusted print head speed Vn and the adjusted material flow Mn, and calculate and obtain the real-time surface error E; S4. According to the surface error E, the print head speed V and the material flow M of the print head are adjusted by a feedback control algorithm, and the adjusted print head speed Vn and the adjusted material flow Mn are corrected to obtain a corrected speed Vx and a corrected flow Mx; S5. Evaluate the flatness of each layer based on the corrected speed Vx and the corrected flow rate Mx, and perform a quality evaluation on the entire printing process to obtain an evaluation result; S6. Based on the evaluation results, continuously correct the flatness and adjust the surface quality.

2. A 3D architectural printing and leveling method according to claim 1, characterized in that: Said S1 includes S11 and S12; S11. Real-time data is collected through a variety of sensors. The real-time data includes ambient temperature T, ambient humidity H, print head speed V, material flow M, and surface deposition thickness D, forming a real-time data set QW; Among them, the ambient temperature T is collected by a temperature sensor, the ambient humidity H is collected by a humidity sensor, the print head speed V is collected by an encoder installed in the print head, the material flow M is collected by a flow meter, and the surface deposition thickness D is collected by a lidar; S11, cleaning and standardizing the real-time data set QW to obtain the printed data set Q; Cleaning includes missing value processing and outlier processing. Missing values ​​are filled by interpolation method in the real-time dataset QW. Outlier processing is to remove abnormal data in the real-time dataset QW through outlier detection algorithm. The formula for the standardization process is: ； Where Qp represents the p-th data item in the print dataset Q, QWp represents the p-th data item in the real-time dataset QW, μQWp represents the mean of the p-th data item in the real-time dataset QW, and σQWp represents the standard deviation of the p-th data item in the real-time dataset QW.

3. A 3D architectural printing and leveling method according to claim 1, characterized in that: Said S2 includes S21 and S22; S21. Based on the printed data set Q, feature extraction is performed to extract the surface deposition error. ; And according to the surface deposition error Dynamically generate speed adjustment coefficient Kv and material flow adjustment coefficient Km; The surface deposition error Obtained by the following formula: ； Where D(t) represents the surface deposition thickness at time t, Didea(t) represents the target surface deposition thickness at time t; The speed adjustment coefficient Kv is obtained by the following formula: ； Where, represents the adjustment constant; The material flow adjustment coefficient Km is obtained by the following formula: Where, represents the adjustment constant; S22. Adjust the print head speed V and the material flow rate M according to the obtained speed adjustment coefficient Kv and the material flow rate adjustment coefficient Km, and obtain an adjusted print head speed Vn and an adjusted material flow rate Mn; The adjusted print head speed Vn is obtained by the following formula: ； Where Kv represents the speed adjustment coefficient, T(t) represents the ambient temperature at time t, and H(t) represents the ambient humidity at time t; The adjusted material flow Mn is obtained by the following formula: ； Where Km represents the material flow adjustment coefficient.

4. A 3D architectural printing and leveling method according to claim 3, characterized in that: Said S3 includes S31 and S32; S31, after the adjusted print head speed Vn and the adjusted material flow Mn are brought into the printing device, the printing condition of the building is observed by the laser radar, and the deposition thickness difference Dre is recorded; The deposition thickness difference Dre is obtained by the following formula: ； Where D(t) represents the surface deposition thickness at time t, and D(t-1) represents the surface deposition thickness at time t-1; S32, comparing the deposition thickness difference Dre with the preset printing thickness difference YTD, and calculating and obtaining a real-time surface error E; The real-time surface error E is obtained by the difference between the deposition thickness difference Dre and the preset printing thickness YTD.

5. A 3D architectural printing and leveling method according to claim 4, characterized in that: Said S4 includes S41 and S42; S41, using the surface error E as a feedback signal, and analyzing the influence of the surface error E on the print head speed V and the material flow M; Based on the surface error E, the velocity correction factor Kvn and the flow correction factor Kmn are generated through the PID controller; The speed correction factor Kvn is obtained by the following formula: ； Where Kp represents the proportional gain coefficient, Ki represents the integral gain coefficient, Kd represents the differential gain coefficient, d represents the differential, dt represents the time period, E(t) represents the surface error at time t, and t represents time; The flow correction factor Kmn is obtained by the following formula: 。 6. A 3D architectural printing and leveling method according to claim 5, characterized in that: S42 uses the obtained speed correction factor Kvn and flow correction factor Kmn to correct the adjusted print head speed Vn and the adjusted material flow Mn to obtain a corrected speed Vx and a corrected flow Mx; The correction speed Vx is obtained by the following formula: ； Where Vn represents the adjusted print head speed; The corrected flow rate Mx is obtained by the following formula: ； Where Vn represents the adjusted print head speed.

7. A 3D architectural printing and leveling method according to claim 6, characterized in that: Said S5 includes S51 and S52; S51, bringing the corrected speed Vx and the corrected flow rate Mx into the printing device, collecting the corrected actual deposition thickness DZ, evaluating the surface flatness index Rmse, and comparing it with the preset index threshold TRm to determine whether the flatness meets the standard; The surface flatness index Rmse is obtained by the following formula: ； Where N represents the number of sampling points, DZi represents the actual deposition thickness at the i-th sampling point, and Dideai represents the target surface deposition thickness at the i-th sampling point; The flatness state is obtained by matching in the following manner: When the surface flatness index Rmse ≤ the index threshold TRm, it means that the flatness meets the standard and the correction speed Vx and correction flow Mx ​​are not adjusted; When the surface flatness index Rmse>the index threshold TRm, it means that the flatness does not meet the standard, and the correction speed Vx and the correction flow Mx ​​are adjusted.

8. A 3D architectural printing and leveling method according to claim 7, characterized in that: S52 , when the surface flatness index Rmse is greater than the index threshold TRm, adjusting the correction speed Vx and the correction flow rate Mx, and obtaining a new print head speed Vy and a new material flow rate My according to the surface flatness index Rmse; The new print head speed Vy is obtained by the following formula: ； Where, represents the adjustment parameter of the correction speed Vx, μRmse represents the mean value of the surface flatness index; The new material flow My is obtained by the following formula: ； Where, Indicates the adjustment parameter for the corrected flow rate Mx.

9. A 3D architectural printing and leveling method according to claim 8, characterized in that: The S6 brings the acquired new print head speed Vy and new material flow My back into the printing device, continuously corrects the 3D building printing process, automatically adjusts the print head speed V and material flow M, and adjusts the surface quality.

10. A 3D architectural printing and leveling device, characterized by: Includes the following modules: Data acquisition and processing module: During the printing process, it collects real-time data related to printing through various sensors and processes the real-time data; And fit it into the printed data set Q; Dynamic adjustment module: dynamically adjusts the print head speed V and the material flow M of the print head according to the obtained print data set Q, and obtains the adjusted print head speed Vn and the adjusted material flow Mn; Surface flatness monitoring module: observes the building printing situation through the adjusted print head speed Vn and the adjusted material flow Mn, and calculates the real-time surface error E; Flatness error correction module: According to the surface error E, the print head speed V and the material flow M of the print head are adjusted through the feedback control algorithm, and the adjusted print head speed Vn and the adjusted material flow Mn are corrected to obtain the corrected speed Vx and the corrected flow Mx; Flatness evaluation module: Evaluates the flatness of each layer based on the corrected speed Vx and corrected flow Mx, and performs quality evaluation on the entire printing process to obtain evaluation results; Comprehensive feedback module: Based on the evaluation results, the flatness is continuously corrected and the surface quality is adjusted.

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