A method and device for detecting the printing quality of 3D printed concrete

Abstract

The present application relates to the field of image data processing technology and provides a method and apparatus for detecting the print quality of 3D-printed concrete. In this scheme, the material properties of freshly mixed concrete are used as simulation input parameters, computational fluid dynamics software is used to simulate the printing process of a 3D concrete model, and the theoretical three-dimensional characteristics of the concrete 3D model are calculated based on the simulation results of the 3D printing process. Actual printing is carried out to generate a 3D printed concrete part, and an in-situ three-dimensional scan is performed on the 3D printed part. The macro-deviation, fine-deviation, surface defect rate, theoretical undulation, average expansion rate, and model expansion rate of the 3D-printed concrete are calculated using the theoretical three-dimensional characteristics and the actual three-dimensional characteristics of the 3D printed concrete part. These indicators can comprehensively and quantitatively detect and evaluate the print quality of 3D-printed concrete from various aspects, ensuring that the evaluation results are comprehensive and accurate, and improving the accuracy of 3D-printed concrete print quality detection.

Description

Technical Field

[0001] The present application relates to the field of image data processing technology, and in particular to a method and device for detecting the printing quality of 3D printed concrete. Background Art

[0002] Concrete 3D printing is a method of manufacturing concrete structures using 3D printing technology. This process involves gradually building up entire buildings or components by stacking concrete layer by layer. This technology enables the rapid construction of complex structures, reduces labor and material waste, and improves construction efficiency and quality.

[0003] The quality of building structures directly impacts their safety and stability. Therefore, quality testing of 3D-printed concrete is crucial. Testing the quality of 3D-printed concrete structures can promptly identify and address potential quality issues, ensuring that the building's structure meets design requirements and improving its reliability and durability. However, existing 3D-printed concrete quality testing technology lacks comprehensive, quantitative analysis, resulting in limitations in accuracy and unable to meet the demands for precise testing and evaluation of high-precision building structures.

[0004] Therefore, it is necessary to provide an improved technical solution to the above-mentioned deficiencies in the prior art. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for detecting the printing quality of 3D printed concrete, so as to solve or alleviate the problem of insufficient detection accuracy caused by the lack of comprehensive and quantitative analysis in the above-mentioned prior art.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for detecting the printing quality of 3D printed concrete, comprising:

[0008] The material properties of fresh concrete are used as input parameters for the simulation. Computational fluid dynamics software is used to simulate the printing process of the concrete 3D model. The theoretical three-dimensional characteristics of the concrete 3D model are calculated based on the simulation results.

[0009] Performing actual printing to generate a 3D printed concrete part, performing in-situ three-dimensional scanning on the 3D printed part to obtain point cloud data having three-dimensional coordinate information; and performing coordinate axis conversion and noise reduction processing on the point cloud data in sequence to obtain noise-reduced point cloud data;

[0010] Calculating actual three-dimensional features of the 3D printed part based on the noise-reduced point cloud data;

[0011] Based on the theoretical three-dimensional features and the actual three-dimensional features, a macro deviation, a fine deviation, a surface defect rate, a theoretical undulation, an average expansion rate, and a model expansion rate of the 3D printed part relative to the simulation result are calculated; and a comprehensive evaluation of the printing quality of the 3D printed concrete is performed based on the macro deviation, the fine deviation, the surface defect rate, the theoretical undulation, the average expansion rate, and the model expansion rate;

[0012] Among them, the macro deviation is used to describe the macro volume deviation between the actual 3D printing process and the ideal 3D printing process in the simulation results; the fine deviation is used to describe the fine volume deviation between the actual 3D printing process and the ideal 3D printing process in the simulation results at the hierarchical scale; the surface defect rate is used to describe the surface quality of the 3D printed part; the theoretical fluctuation is used to quantitatively describe the magnitude of the unfilled area of the 3D printed part; the average expansion rate and the model expansion rate are used to describe the utilization rate of concrete materials.

[0013] In the above solution, the theoretical three-dimensional characteristics include the total volume V0 of materials required for simulating and generating the concrete 3D model.

[0014] In the above solution, the actual three-dimensional feature includes the volume V1 of material actually used to print and generate the 3D printed part;

[0015] The calculation formula of the macro deviation is:

[0016] P1=(V1-V0) / V0;

[0017] Wherein, P1 represents the macro-deviation, V0 represents the total volume of materials required for simulating and generating the concrete 3D model, and V1 represents the volume of materials used for actually printing and generating the 3D printed part.

[0018] In the above solution, calculating the actual three-dimensional features of the 3D printed part based on the noise-reduced point cloud data includes:

[0019] Taking the stacking direction of the concrete material as the Z axis, performing interval sampling in the X axis or Y axis direction, and calculating the average value of all sampling points on the Z axis based on the coordinate values of the point cloud data to obtain a two-dimensional trajectory line of the 3D printed concrete;

[0020] Peak points and valley points in the two-dimensional trajectory are extracted, and the peak points and valley points are used as a basis for layering the 3D printed concrete in the graph of the two-dimensional trajectory to obtain a layered structure of the 3D printed part.

[0021] In the above solution, the theoretical three-dimensional features also include the theoretical volume of the material required to simulate and generate the i-th layer in the concrete 3D model;

[0022] The layered structure of the 3D printed part corresponds one-to-one to each layered structure of the concrete 3D model;

[0023] Accordingly, the calculation formula of the fine deviation is:

[0024] P2=(Σ(V2 i -C i )) / V0;

[0025] Wherein, P2 represents the fine deviation, V2 i represents the volume of the ith layer of the 3D printed part, V2 i is calculated based on the area occupied by the i-th layer in the two-dimensional graph of the two-dimensional trajectory line, C i represents the theoretical volume of material required to generate the i-th layer in the concrete 3D model; V0 is the total volume of material required to simulate the generation of the concrete 3D model.

[0026] In the above scheme, the theoretical fluctuation is calculated by the following steps:

[0027] In the two-dimensional graph of the two-dimensional trajectory line, a peak point of any layer is randomly selected as the current peak point, a horizontal line is drawn through the current peak point to the nearest layer, and the area enclosed by the horizontal line, the trajectory line of the layer where the current peak point is located, and the trajectory line of the nearest layer is regarded as an unfilled area;

[0028] Calculate the volume of the unfilled area corresponding to each interlayer area; and calculate the theoretical fluctuation according to the following formula:

[0029] P4=Σ(V3 j ) / V0;

[0030] Wherein, P4 represents the theoretical fluctuation, V3 j represents the volume of the unfilled area corresponding to the j-th interlayer region; V0 is the total volume of materials required to simulate and generate the concrete 3D model.

[0031] In the above solution, the average expansion rate and the model expansion rate are calculated by the following steps:

[0032] Calculating the mean of the two-dimensional trajectory line in the Z-axis direction, and drawing a horizontal line through the mean to obtain a mean line;

[0033] In the two-dimensional graph of the two-dimensional trajectory line, a region enclosed by the two-dimensional trajectory line and the mean line that is greater than the mean value is considered as a non-uniform region, and a volume of the non-uniform region is calculated;

[0034] Searching for the lowest valley point in the interlayer region of the two-dimensional trajectory, using the lowest valley point as a baseline, treating the region enclosed by the two-dimensional trajectory and the baseline that is larger than the lowest valley point as a protruding region, and calculating the volume of the protruding region;

[0035] Based on the volume of the non-uniform area and the volume of the protruding area, the average expansion rate and the model expansion rate are calculated according to the following formula:

[0036] P5=V4 / V0,P6=V5 / V0,

[0037] Among them, P5 represents the average expansion rate, P6 represents the model expansion rate, V4 represents the volume of the non-uniform area, V5 represents the volume of the protruding area, and V0 is the total volume of materials required to simulate and generate the concrete 3D model.

[0038] In the above scheme, the surface defect rate is obtained by the following steps:

[0039] Identifying and calibrating high-density areas in the point cloud data;

[0040] Processing the point cloud data using a plane projection algorithm to obtain point cloud data under plane projection;

[0041] The ratio of the area of the high-density region under the plane projection to the total area of the point cloud under the plane projection is calculated, and the obtained ratio is used as the surface defect rate.

[0042] In the above scheme, the printing quality of 3D printed concrete is comprehensively evaluated, including:

[0043] Obtaining weights corresponding to the macro deviation, the fine deviation, the surface defect rate, the theoretical fluctuation, the average expansion rate, and the model expansion rate;

[0044] Based on the weights, a weighted sum is performed on the macro deviation, the fine deviation, the surface defect rate, the theoretical fluctuation, the average expansion rate, and the model expansion rate to obtain a comprehensive evaluation result.

[0045] In a second aspect, this embodiment provides a device for detecting the printing quality of 3D printed concrete, comprising:

[0046] a simulation unit configured to use the material properties of the freshly mixed concrete as input parameters for simulation, simulate the printing process of the concrete 3D model using computational fluid dynamics software, and calculate theoretical three-dimensional characteristics of the concrete 3D model based on the simulation results of the 3D printing process;

[0047] a three-dimensional scanning unit configured to perform actual printing to generate a 3D printed concrete part, perform in-situ three-dimensional scanning on the 3D printed part to obtain point cloud data having three-dimensional coordinate information; and perform coordinate axis conversion and noise reduction on the point cloud data in sequence to obtain noise-reduced point cloud data;

[0048] a computing unit configured to compute actual three-dimensional features of the 3D printed part based on the noise-reduced point cloud data;

[0049] a comprehensive evaluation unit configured to calculate, based on the theoretical three-dimensional features and the actual three-dimensional features, a macro deviation, a fine deviation, a surface defect rate, a theoretical undulation, an average expansion rate, and a model expansion rate of the 3D printed concrete, and perform a comprehensive evaluation of the printing quality of the 3D printed concrete based on the macro deviation, the fine deviation, the surface defect rate, the theoretical undulation, the average expansion rate, and the model expansion rate;

[0050] Among them, the macro deviation is used to describe the macro volume deviation between the actual 3D printing process and the ideal 3D printing process; the fine deviation is used to describe the fine volume deviation between the actual 3D printing process and the ideal 3D printing process at the hierarchical scale; the surface defect rate is used to describe the surface quality of the 3D printed part; the theoretical fluctuation is used to quantitatively describe the magnitude of the unfilled area of the 3D printed part; the average expansion rate and the model expansion rate are used to describe the utilization rate of concrete materials.

[0051] The embodiments of the present application have the following beneficial effects:

[0052] Using the material properties of fresh concrete as input parameters, computational fluid dynamics software was used to simulate the printing process of the concrete 3D model. Based on the simulation results of the 3D printing process, the theoretical 3D characteristics of the concrete 3D model were calculated. This method can obtain the 3D characteristics of the concrete 3D model in an ideal state, providing an accurate evaluation baseline for subsequent printing quality evaluation. Actual printing was carried out to generate 3D printed concrete parts. The 3D printed parts were scanned in situ and subjected to a series of preprocessing to obtain high-quality point cloud data, which accurately reflected the morphological characteristics of the 3D printed parts and provided high-quality input parameters for the calculation of evaluation indicators. On this basis, the macro deviation, fine deviation, surface defect rate, theoretical undulation, average expansion rate, and model expansion rate of the 3D printed concrete were calculated using the theoretical 3D characteristics and the actual 3D characteristics of the 3D printed concrete parts. These indicators can comprehensively and quantitatively detect and evaluate the printing quality of 3D printed concrete from the aspects of macro volume deviation, fine volume deviation, surface quality status, filling status, and material utilization rate. Combining the comprehensive evaluation of multiple indicators ensures that the evaluation results are comprehensive and accurate, thereby improving the accuracy of 3D printed concrete printing quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings and descriptions that constitute part of this application are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. Among them:

[0054] Figure 1 A schematic flow chart of a method for detecting the printing quality of 3D printed concrete according to some embodiments of the present application.

[0055] Figure 2 A schematic diagram of the structure of an apparatus for detecting the printing quality of 3D printed concrete according to some embodiments of the present application. DETAILED DESCRIPTION

[0056] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as part of one embodiment can be used in another embodiment to produce yet another embodiment. Therefore, it is expected that the present application includes such modifications and variations within the scope of the appended claims and their equivalents.

[0057] In the following description, the terms "first / second / third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that "first / second / third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of the present disclosure. The terms used herein are only for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0059] Method Example

[0060] This embodiment provides a method for detecting the printing quality of 3D printed concrete. The execution subject of this method can be a personal computer, tablet, mobile phone, server and other electronic devices, such as Figure 1 As shown, the method includes:

[0061] Step S101: Using the material properties of fresh concrete as input parameters for simulation, computational fluid dynamics software is used to simulate the printing process of the concrete 3D model, and the theoretical three-dimensional characteristics of the concrete 3D model are calculated based on the simulation results.

[0062] In this embodiment, the material properties of the freshly mixed concrete are first measured and used as input parameters for the simulation. Since the physical properties of concrete (such as viscosity, density, shear stress, rheology, etc.) directly affect the flow and setting behavior during the printing process, using the actual property data of the freshly mixed concrete can ensure that the simulation is closer to the actual situation and provide more accurate results.

[0063] Computational Fluid Dynamics (CFD) software is an engineering software used to simulate and analyze fluid flow, heat transfer, and mass transfer. It uses numerical methods to solve fluid dynamics equations to simulate complex fluid behavior. Compared to traditional simulation methods, CFD software can simulate the complex flow behavior of fresh concrete in detail, while also simulating heat transfer. This helps accurately predict the flow of materials during 3D printing and provides an idealized 3D concrete model, resulting in more accurate calculated theoretical 3D features.

[0064] It should be noted that the CFD software may be any one of Fluent software, OpenFOAM software, and ANSYS CFX software. This embodiment does not limit the specific selection of the CFD software.

[0065] Step S102: Perform actual printing to generate a 3D printed concrete part, perform in-situ 3D scanning on the 3D printed part to obtain point cloud data with 3D coordinate information; and perform coordinate axis conversion and noise reduction on the point cloud data in sequence to obtain noise-reduced point cloud data.

[0066] In this embodiment, 3D concrete printing technology uses computer-generated layered modeling to control industrial robots or dedicated 3D printers, repeatedly laying concrete materials layer by layer to construct multi-story building structures. For example, based on the concrete 3D model, any 3D printer can be selected to perform actual printing, thereby generating a 3D printed concrete part. To improve the reliability of the test results, different printing parameters can be set during the printing process to obtain multiple 3D prints under different printing parameters. This not only allows the evaluation of concrete printing performance under different conditions, but also avoids accidental deviations, facilitates the overall assessment of concrete printing quality, and ensures the reliability and consistency of the results.

[0067] After obtaining the 3D printed part, an in-situ three-dimensional scan is performed on it using a 3D scanning device. Exemplarily, the 3D scanning device is a laser scanner or a structured light scanner.

[0068] Furthermore, in order to meet the reliability requirements, a 3D scanner with multiple cameras can be used to scan the 3D printed parts. The specific model of the scanner can be, for example, the Sense Plus 3D scanner provided by 3DPMAX (Shenzhen) Co., Ltd., which is equipped with a pair of binocular cameras, an infrared projector and a texture camera. By performing in-situ 3D scanning, using triangulation methods and binocular stereo vision 3D reconstruction, point cloud data with 3D coordinate information is obtained.

[0069] It should be noted that because imperfect concrete surfaces cannot reflect light, some of the original point cloud data obtained through in-situ 3D scanning contains holes, making it unsuitable for direct analysis. Therefore, this implementation uses post-processing steps to perform automatic filling, coordinate axis conversion, and noise reduction. The purpose of the coordinate axis conversion is to uniformly convert the point cloud data acquired in different reference systems to the real axis, thereby aligning them with the same coordinate system. Because the automatic filling algorithm causes some unrealistic noise points in the point cloud data, especially at the edges, noise reduction is required after automatic filling to improve data quality.

[0070] For example, considering that these noise points are usually far away from the scanning center, the following steps can be used to identify noise points: construct a KD tree, find the nearest points based on Euclidean distance, estimate the density of the point cloud based on the average distance from the nearest point to the center point to determine the threshold of the noise points, identify the points in the point cloud whose distance from the center point is greater than the threshold as noise points, and finally delete the noise points at the edge and inside of the point cloud data.

[0071] After the above post-processing steps, the noise-reduced point cloud data is finally obtained, that is, the scanned model of the complete concrete 3D printed part is obtained.

[0072] In addition, when the 3D scanning device uses a structured light scanner, the structured light scanner is usually cheaper than a laser scanner. Laser scanners use laser beams for scanning, which can cause damage to the human eye, while structured light scanners use visible light or infrared rays, which are relatively safe for the human body. Structured light scanners are usually faster than laser scanners in scanning speed, can complete scanning tasks faster, are simple to operate, and do not require complex calibration and adjustment.

[0073] Step S103: Calculating the actual three-dimensional features of the 3D printed part based on the denoised point cloud data.

[0074] Specifically, calculating the actual three-dimensional features of a 3D print based on point cloud data can be performed using the following steps: First, feature points (also known as key points) are extracted from the point cloud data. Feature points are used to describe the edges, corners, or other significant features of the 3D print. Then, cluster-based segmentation, edge-based segmentation, region-based segmentation, or other methods are used to divide the point cloud data into different hierarchical structures. Shape parameters of each hierarchical structure, such as volume and bounding box, are calculated. These shape parameters are used to quantitatively describe the actual three-dimensional features of the 3D print.

[0075] Step S104: Based on the theoretical three-dimensional features and the actual three-dimensional features, the macro deviation, fine deviation, surface defect rate, theoretical undulation, average expansion rate, and model expansion rate of the 3D printed part relative to the simulation result are calculated. Based on the macro deviation, fine deviation, surface defect rate, theoretical undulation, average expansion rate, and model expansion rate, a comprehensive evaluation of the printing quality of the 3D printed concrete is performed.

[0076] Among them, the macro deviation is used to describe the macro volume deviation between the actual 3D printing process and the ideal 3D printing process in the simulation results; the fine deviation is used to describe the fine volume deviation between the actual 3D printing process and the ideal 3D printing process in the simulation results at the layered scale; the surface defect rate is used to describe the surface quality of the 3D printed part; the theoretical fluctuation is used to quantitatively describe the magnitude of the unfilled area of the 3D printed part; the average expansion rate and model expansion rate are used to describe the utilization rate of concrete materials.

[0077] In this embodiment, 3D concrete print quality is evaluated using a combination of indicators such as macro-deviation, fine-deviation, surface defect rate, theoretical undulation, average expansion rate, and model expansion rate. These indicators cover multiple aspects of 3D concrete print quality, including dimensional accuracy, surface quality, and volume change. They provide a comprehensive and integrated reflection of print quality, thus improving the accuracy of 3D concrete print quality testing. Furthermore, these indicators are derived by comparing actual measurements with theoretical values, resulting in high accuracy and quantification. Using specific numerical values to quantitatively assess print quality makes the evaluation results more objective and comparable. This analysis and evaluation can identify key factors influencing print quality, facilitating quality control and process optimization.

[0078] Based on the aforementioned embodiment, in some optional embodiments, the theoretical three-dimensional feature includes the total volume V0 of materials required for simulating and generating the concrete 3D model.

[0079] In other words, after measuring the material properties of fresh concrete, Fluent software can be used to simulate and calculate the three-dimensional characteristics (theoretical three-dimensional) of the concrete material when produced under a preset 3D printing model, and the total material volume V0 required to generate the preset 3D printing model can be calculated. Using Fluent software to simulate and calculate the total material volume V0 required to theoretically simulate and generate the concrete 3D model ensures the accuracy and rationality of this numerical calculation.

[0080] Furthermore, the actual three-dimensional feature includes a material volume V1 used for actual printing to generate a 3D printed part; the material volume V1 is obtained by recording the material volume used for actual printing to generate a 3D printed part.

[0081] Accordingly, the calculation formula for macro deviation is:

[0082] P1=(V1-V0) / V0;

[0083] Among them, P1 represents the macro deviation, V0 represents the total volume of materials required to simulate the generation of concrete 3D models, and V1 represents the volume of materials used to actually print and generate 3D printed parts.

[0084] In the above formula, the deviation between the actual material volume V1 used to print the 3D print and the total material volume (theoretical volume) required to simulate the concrete 3D model is calculated, and then divided by the theoretical volume to obtain the macro-deviation, also known as relative deviation or percentage deviation. This calculation method allows measurement results of different dimensions and orders of magnitude to be directly compared, making it easier to evaluate the accuracy of different measurement results.

[0085] Taking into account the high complexity of directly calculating the actual three-dimensional features of the 3D printed part from the point cloud data in the method provided in the aforementioned embodiment, in some preferred embodiments, step S103: calculating the actual three-dimensional features of the 3D printed part based on the denoised point cloud data can be implemented by the following steps: taking the stacking direction of the concrete material as the Z axis, performing interval sampling in the X-axis or Y-axis direction, and calculating the average value of all sampling points on the Z axis based on the coordinate values of the point cloud data to obtain a two-dimensional trajectory line of the 3D printed concrete; extracting the peak points and valley points in the two-dimensional trajectory line, and using the peak points and valley points as the basis for the stratification of the 3D printed concrete in the graph of the two-dimensional trajectory line, and the area between two adjacent valley points is a layer, thereby obtaining a layered structure of the 3D printed part.

[0086] For example, the material stacking direction is set as the Z axis, and the other directions are set as the X axis and Y axis according to the characteristics of the model. Any axis of the X and Y axes is averaged to reduce the dimension of the three-dimensional point cloud coordinate information to generate a two-dimensional trajectory line, and the peak and valley points in the trajectory line are extracted as the basis for automatic identification of layers. The area between two adjacent valley values is a layer.

[0087] This embodiment converts three-dimensional point cloud data into two-dimensional data to obtain a two-dimensional trajectory line, and identifies the layered structure of the 3D printed part on the two-dimensional trajectory line, which is conducive to simplifying subsequent calculations and improving calculation efficiency. It is also conducive to carrying out complex calculations in the indicator calculation process and obtaining more accurate and refined calculation results.

[0088] In some embodiments, the theoretical three-dimensional features further include the theoretical volume C of the material required to simulate and generate the i-th layer in the concrete 3D model. i ; Where i is a positive integer. Theoretical volume C i The 3D printed part is the result of actual printing of the concrete 3D model, so the layered structure of the 3D printed part corresponds one-to-one with the layered structure of the concrete 3D model.

[0089] Accordingly, the calculation formula for fine deviation is:

[0090] P2=(Σ(V2 i -C i )) / V0;

[0091] Among them, P2 represents the fine deviation, V2 i represents the volume of the i-th layer of the 3D printed part.

[0092] Here, V2 iIt is calculated based on the area occupied by the i-th layer in the two-dimensional graph of the two-dimensional trajectory line. Specifically, after the layered structure of the 3D printed part is obtained by taking the peak point and the valley point as the layered basis in the two-dimensional trajectory line graph, the area A2 occupied by each layer in the two-dimensional graph can be obtained based on the two-dimensional trajectory line graph and the layered structure. i , and then calculate the volume V2 of each layer i .

[0093] In this embodiment, the fineness deviation is expressed by calculating the difference between the theoretical volume and the actual volume of each layer, adding up the total and dividing by the theoretical volume. This allows layered analysis and quantification of the difference between the actual printing results and the design model, making the deviation assessment more objective and accurate. At the same time, this method can evaluate the deviation layer by layer, making it possible to identify which specific layers or areas have large deviations, which helps to find problem areas in the printing process. Based on the layer-by-layer deviation analysis and assessment, more detailed adjustments and optimizations can be made, thereby improving the accuracy of the overall print quality detection. In addition, the volume of each layer is calculated based on the two-dimensional trajectory line, which improves the calculation speed.

[0094] In some embodiments, the theoretical undulation is calculated by the following steps: in the two-dimensional graph of the two-dimensional trajectory line, a peak point of any layer is randomly selected as the current peak point, a horizontal line is drawn through the current peak point to the nearest layer, and the area enclosed by the horizontal line, the trajectory line of the layer where the current peak point is located, and the trajectory line of the nearest layer is regarded as an unfilled area; the volume of the unfilled area corresponding to each inter-layer area is calculated; and the theoretical undulation is calculated according to the following formula:

[0095] P4=Σ(V3 j ) / V0;

[0096] Among them, P4 represents the theoretical fluctuation, V3 j represents the volume of the unfilled area corresponding to the j-th interlayer region; V0 is the total volume of materials required to simulate and generate the concrete 3D model.

[0097] In this embodiment, a horizontal straight line is drawn from the peak point of each layer to the nearest adjacent layer, and the area surrounded by this straight line and the trajectory lines of the two adjacent layers is regarded as the unfilled area. In this way, the unfilled area is identified in the two-dimensional image of the two-dimensional trajectory line, and the area A3 occupied by the unfilled area in the two-dimensional image is calculated. j Then we get its volume V3 j Compared with directly using three-dimensional point cloud data for calculation, the calculation efficiency is greatly improved. At the same time, due to the use of layered calculation method, the size of the unfilled area of each layer can be known, which improves the analyzability and calculation accuracy of the theoretical fluctuation.

[0098] In some embodiments, the average expansion rate and the model expansion rate are calculated by the following steps: calculating the mean value of the two-dimensional trajectory line in the Z-axis direction, drawing a horizontal line through the mean value to obtain the mean line; in the two-dimensional graph of the two-dimensional trajectory line, treating the area enclosed by the two-dimensional trajectory line with a value greater than the mean value and the mean line as a non-uniform area, and calculating the volume of the non-uniform area; searching for the lowest valley point in the interlayer area of the two-dimensional trajectory line, drawing a baseline through the lowest valley point, treating the area enclosed by the two-dimensional trajectory line with a value greater than the lowest valley point and the baseline as a protruding area, and calculating the volume of the protruding area; based on the volume of the non-uniform area and the volume of the protruding area, the average expansion rate and the model expansion rate are calculated according to the following formula:

[0099] P5=V4 / V0,P6=V5 / V0,

[0100] Among them, P5 represents the average expansion rate, P6 represents the model expansion rate, V4 represents the volume of the non-uniform area, V5 represents the volume of the protruding area, and V0 is the total volume of materials required to simulate and generate the concrete 3D model.

[0101] Specifically, the mean of the two-dimensional trajectory along the Z axis is calculated, that is, the two-dimensional trajectory is averaged into a straight line. The area of the region in the two-dimensional image that exceeds this straight line is calculated to obtain the non-uniform area A4, and then the volume V4 occupied by this area is obtained. The lowest valley point in the interlayer portion of the two-dimensional trajectory is searched, and a baseline is drawn at this valley point. The total protrusion area A5 beyond this baseline is calculated, and then the volume V5 occupied by this area is obtained.

[0102] This operation identifies non-uniform and protruding areas within the two-dimensional trajectory graph and determines the volumes occupied by these areas. This avoids the extensive computational effort required to directly calculate using three-dimensional point cloud data, improving computational efficiency. Furthermore, the average expansion rate reflects the actual material behavior of concrete during printing. By evaluating the average expansion rate, it is possible to assess whether the concrete exhibits significant shrinkage or expansion during the curing process, thereby understanding the material's stability and consistency. By evaluating the model's expansion rate, the printability of the design can be verified, ensuring that the design can be realized and meets the intended size and shape.

[0103] In some embodiments, the surface defect rate is obtained by the following steps: identifying and calibrating high-density areas in the point cloud data; processing the point cloud data using a plane projection algorithm to obtain point cloud data under the plane projection; calculating the ratio of the area A0 of the high-density area under the plane projection to the total area A1 of the point cloud under the plane projection, and using the obtained ratio as the surface defect rate.

[0104] It should be noted that the surface defects of 3D printed concrete can include deep cracks that are skipped during the scanning process and shallow holes on the surface of 3D printed concrete. In the point cloud data, these surface defects all have high-density characteristics.

[0105] In this embodiment, a K-means algorithm or a DBSCAN algorithm can be used to perform cluster analysis on the point cloud data, grouping the data into clusters with similar features. Then, an automatic processing program is written to identify and calibrate high-density areas in the point cloud data. For example, the identification and calibration may include the following steps: First, a KD tree is used to search for the nearest neighbor point sets at different scales for each point in the point cloud data of each cluster. For example, a k-nearest neighbor search can be performed using the knnsearch function in Matlab software to obtain 4, 6, 11, 16, 20, and 31 nearest neighbor points for each point, forming the nearest neighbor point sets at different scales. Then, for each point, the average distance of the nearest neighbor points at different scales is calculated to obtain an average distance matrix, in which each element corresponds to the average distance of each point at different nearest neighbor levels (scales). For example, at a scale of 4 points, a value in the average distance matrix represents the average distance of a point to its 4 nearest neighbors. Next, all values of the average distance matrix are added together to obtain the average nearest neighbor distance index of the entire cluster, which is used to measure the density characteristics of the point cloud in each cluster at various scales. Subsequently, the upper and lower limits of the point cloud density value are determined. For example, the percentile of the point cloud data can be calculated using the prctile function to define the upper and lower limit thresholds of the point cloud density value. By comparing whether each average nearest neighbor distance index is within the upper and lower limits of the cloud density value, high-density areas (surface defect areas) can be identified and calibrated.

[0106] In the above steps, the spatial distribution characteristics of point cloud data can be more comprehensively understood through the sets of nearest neighbor points at different scales. By calculating the average distance matrix and summing it, an overall average nearest neighbor distance indicator can be provided, which is used to quickly and effectively measure the distribution density and uniformity of point cloud data, which is conducive to better understanding the spatial relationship of point cloud data and improving recognition accuracy.

[0107] Before, after, or simultaneously with effectively identifying high-density areas, a planar projection algorithm can be used to process the point cloud data to obtain point cloud data under plane projection. This process can be achieved by importing the collected 3D point cloud data into software for point cloud data processing, and then using a planar projection algorithm to project the 3D point cloud data onto a plane, thereby simplifying the data and improving computational efficiency.

[0108] Based on the projection of the point cloud data onto the plane, the area of the high-density area is calculated to obtain the area size of the defect area A0. The total area of the point cloud after the plane projection is calculated as A1. Then, the high-density area and its proportion after the plane projection of the 3D point cloud are calculated as the surface defect rate. The specific calculation formula is as follows:

[0109] P3=A0 / A1,

[0110] Among them, P3 represents the surface defect rate, A0 represents the area of the high-density region under the plane projection, and A1 represents the total area of the point cloud under the plane projection.

[0111] This embodiment calculates the number of points in the high-density area after plane projection or uses an area calculation algorithm to calculate the area of the area, and then calculates the proportion of the high-density area by comparing the area of the high-density area with the area of the entire plane projection area, thereby avoiding a large amount of calculation of three-dimensional point cloud data and improving calculation efficiency.

[0112] In some optional embodiments, the printing quality of 3D printed concrete is comprehensively evaluated, including: obtaining the weights corresponding to the macro deviation, fine deviation, surface defect rate, theoretical fluctuation, average expansion rate, and model expansion rate; and performing weighted summation of the macro deviation, fine deviation, surface defect rate, theoretical fluctuation, average expansion rate, and model expansion rate based on the weights to obtain a comprehensive evaluation result.

[0113] Specifically, expert scoring, analytic hierarchy process (AHP) and other methods can be used to determine the weights corresponding to the macro deviation, fine deviation, surface defect rate, theoretical fluctuation, average expansion rate and model expansion rate.

[0114] Furthermore, in order to avoid the subjectivity of weights, the hierarchical analysis method can be combined with the entropy weight method to obtain the weights of each indicator to ensure that the determined weights are consistent with the actual situation and ensure the rationality and accuracy of weight distribution.

[0115] To unify the dimensions, we can first standardize each indicator, and then perform weighted summation of each indicator using the following formula to obtain a comprehensive evaluation result:

[0116] P=w1×P 11 +w2×P 21 +w3×P 31 +w4×P 41 +

[0117] w5×P 51 +w6×P 61 ,

[0118] Among them, P represents the comprehensive evaluation result, P 11 、P21 、P 31 、P 41 、P 51 、P 61 They represent the standardized macro deviation, fine deviation, surface defect rate, theoretical fluctuation, average expansion rate, and model expansion rate respectively. w1, w2, w3, w4, w5, and w6 represent the weights of the macro deviation, fine deviation, surface defect rate, theoretical fluctuation, average expansion rate, and model expansion rate respectively.

[0119] By performing a comprehensive evaluation in a weighted summation manner, an overall, quantitative comprehensive evaluation result is obtained, so that only one indicator is needed to obtain the evaluation result of the 3D printed concrete printing quality, which helps to intuitively and accurately judge the overall printing quality.

[0120] In summary, the method provided in this embodiment measures the volume deviation of 3D-printed concrete using both macro and fine deviation metrics, thereby determining the difference between the printing process and theoretical simulations. This allows assessment of the volumetric error caused by the material or printer during production, which can be used to guide the adjustment of concrete dosage for larger-scale production or quantification, or to provide feedback on the material's properties and printing process to reduce errors. The surface defect rate can be used to assess the surface condition of each layer of concrete after printing. A higher surface defect rate indicates a more incomplete concrete layer, with more cracks on the surface. These cracks can lead to stress concentration and rapid ingress of corrosive ions, seriously affecting the subsequent use of 3D-printed concrete. The theoretical undulation is used to assess the magnitude of unfilled areas, thereby estimating the channels and spaces for external corrosive ions to enter the concrete structure. The average expansion rate and model expansion rate are used to assess the utilization rate of concrete materials and determine how much material is wasted. Because unfilled areas are present, protruding parts on the model do not actually provide any benefit in terms of stress resistance or corrosion protection, but rather become a burden. This also indirectly reflects the smoothness of the print.

[0121] In summary, the method provided in this embodiment measures the properties of concrete materials, obtains the three-dimensional features of a preset 3D printing model through computational fluid dynamics software simulation calculations, actually prints and records the volume of the material used, performs in-situ three-dimensional scanning of the actual printed 3D printed concrete structure, identifies and deletes noise points in the point cloud data, calculates the volume occupied by the unfilled area, generates a two-dimensional trajectory line, extracts peak and valley points in the trajectory line as a basis for automatic layer identification, calculates the total difference in the actual volume of each layer between the actual 3D printed concrete and the simulated deformation, and calculates the evaluation index. This can verify the accuracy of the simulation results, ensure the consistency between the simulation calculation and the actual situation, ensure the quality and accuracy of the printed structure, and improve the detection accuracy.

[0122] By comparing and analyzing the three-dimensional features obtained by computational fluid dynamics software simulation calculations with the actual printed 3D printed concrete structure, the accuracy of the simulation results can be verified and the consistency between the simulation calculation and the actual situation can be ensured. By in-situ three-dimensional scanning, identifying and deleting noise in the point cloud data, and calculating the volume occupied by the unfilled area, the quality of the printing process can be monitored and controlled to ensure the quality and accuracy of the printed structure. By generating two-dimensional trajectory lines and extracting the peak and valley points as the basis for automatic identification of stratification, automatic stratification of the printed structure can be achieved, thereby improving work efficiency. By calculating the total difference in the actual volume of each layer between the actual 3D printed concrete and the simulated calculated deformation, the deformation of the printed structure can be deeply analyzed. Finally, by calculating the evaluation index, various factors can be comprehensively considered to comprehensively evaluate the printing results.

[0123] Device embodiment

[0124] This embodiment provides a device for detecting the printing quality of 3D printed concrete. Figure 2 As shown, the device includes: a simulation unit 201, a three-dimensional scanning unit 202, a calculation unit 203 and a comprehensive evaluation unit 204.

[0125] The simulation unit 201 is configured to use the material properties of fresh concrete as simulation input parameters, simulate the printing process of the concrete 3D model using computational fluid dynamics software, and calculate theoretical three-dimensional characteristics of the concrete 3D model based on the simulation results of the 3D printing process;

[0126] The 3D scanning unit 202 is configured to perform actual printing to generate a 3D printed concrete part, perform in-situ 3D scanning on the 3D printed part to obtain point cloud data having 3D coordinate information, and sequentially perform coordinate axis conversion and noise reduction on the point cloud data to obtain noise-reduced point cloud data;

[0127] a calculation unit 203 configured to calculate actual three-dimensional features of the 3D printed part based on the noise-reduced point cloud data;

[0128] The comprehensive evaluation unit 204 is configured to calculate the macro deviation, fine deviation, surface defect rate, theoretical undulation, average expansion rate, and model expansion rate of the 3D printed concrete based on the theoretical three-dimensional features and the actual three-dimensional features, and perform a comprehensive evaluation of the printing quality of the 3D printed concrete based on the macro deviation, fine deviation, surface defect rate, theoretical undulation, average expansion rate, and model expansion rate;

[0129] Among them, the macro deviation is used to describe the macro volume deviation between the actual 3D printing process and the ideal 3D printing process; the fine deviation is used to describe the fine volume deviation between the actual 3D printing process and the ideal 3D printing process at the hierarchical scale; the surface defect rate is used to describe the surface quality of the 3D printed part; the theoretical fluctuation is used to quantitatively describe the magnitude of the unfilled area of the 3D printed part; the average expansion rate and the model expansion rate are used to describe the utilization rate of concrete materials.

[0130] The device for detecting the print quality of 3D printed concrete provided in this embodiment can implement the steps and processes of the method for detecting the print quality of 3D printed concrete provided in any of the above embodiments and achieve the same technical effects, and will not be described in detail here.

[0131] The foregoing description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A device for detecting the printing quality of 3D printed concrete, characterized in that: include: a simulation unit configured to use the material properties of fresh concrete as input parameters for simulation, simulate the printing process of the concrete 3D model using computational fluid dynamics software, and calculate theoretical three-dimensional characteristics of the concrete 3D model based on the simulation results of the 3D printing process to provide an evaluation baseline for subsequent printing quality evaluation; a three-dimensional scanning unit configured to perform actual printing to generate a 3D printed concrete part, and perform in-situ three-dimensional scanning on the 3D printed part to obtain point cloud data having three-dimensional coordinate information; and performing coordinate axis conversion and noise reduction processing on the point cloud data in sequence to obtain noise-reduced point cloud data; a computing unit configured to compute actual three-dimensional features of the 3D printed part based on the noise-reduced point cloud data; a comprehensive evaluation unit configured to calculate, based on the theoretical three-dimensional features and the actual three-dimensional features, a macro deviation, a fine deviation, a surface defect rate, a theoretical undulation, an average expansion rate, and a model expansion rate of the 3D printed concrete, and perform a comprehensive evaluation of the printing quality of the 3D printed concrete based on the macro deviation, the fine deviation, the surface defect rate, the theoretical undulation, the average expansion rate, and the model expansion rate; Among them, the macro deviation is used to describe the macro volume deviation between the actual 3D printing process and the ideal 3D printing process; the fine deviation is used to describe the fine volume deviation between the actual 3D printing process and the ideal 3D printing process at the hierarchical scale; the surface defect rate is used to describe the surface quality of the 3D printed part; the theoretical fluctuation is used to quantitatively describe the magnitude of the unfilled area of the 3D printed part; the average expansion rate and the model expansion rate are used to describe the utilization rate of concrete materials.

2. A method for detecting the printing quality of 3D printed concrete, characterized in that: include: Using the material properties of fresh concrete as input parameters, computational fluid dynamics software was used to simulate the concrete 3D model printing process. Based on the simulation results, the theoretical three-dimensional characteristics of the concrete 3D model were calculated, providing an evaluation baseline for subsequent printing quality assessment. Performing actual printing to generate a 3D printed concrete part, and performing in-situ 3D scanning on the 3D printed part to obtain point cloud data having 3D coordinate information; and performing coordinate axis conversion and noise reduction processing on the point cloud data in sequence to obtain noise-reduced point cloud data; Calculating actual three-dimensional features of the 3D printed part based on the noise-reduced point cloud data; Based on the theoretical three-dimensional features and the actual three-dimensional features, a macro deviation, a fine deviation, a surface defect rate, a theoretical undulation, an average expansion rate, and a model expansion rate of the 3D printed part relative to the simulation result are calculated; and a comprehensive evaluation of the printing quality of the 3D printed concrete is performed based on the macro deviation, the fine deviation, the surface defect rate, the theoretical undulation, the average expansion rate, and the model expansion rate; Among them, the macro deviation is used to describe the macro volume deviation between the actual 3D printing process and the ideal 3D printing process in the simulation results; the fine deviation is used to describe the fine volume deviation between the actual 3D printing process and the ideal 3D printing process in the simulation results at the hierarchical scale; the surface defect rate is used to describe the surface quality of the 3D printed part; the theoretical fluctuation is used to quantitatively describe the magnitude of the unfilled area of the 3D printed part; the average expansion rate and the model expansion rate are used to describe the utilization rate of concrete materials.

3. The method for detecting the printing quality of 3D printed concrete according to claim 2, characterized in that: The theoretical three-dimensional characteristics include the total volume V0 of materials required for simulating and generating the concrete 3D model.

4. The method for detecting the printing quality of 3D printed concrete according to claim 3, characterized in that: The actual three-dimensional features include the volume V1 of material used to actually print and generate the 3D printed part; The calculation formula of the macro deviation is: P1=(V1-V0) / V0; Wherein, P1 represents the macro-deviation, V0 represents the total volume of materials required for simulating and generating the concrete 3D model, and V1 represents the volume of materials used for actually printing and generating the 3D printed part.

5. The method for detecting the printing quality of 3D printed concrete according to claim 3, characterized in that: Calculating actual three-dimensional features of the 3D printed part based on the noise-reduced point cloud data includes: Taking the stacking direction of the concrete material as the Z axis, performing interval sampling in the X axis or Y axis direction, and calculating the average value of all sampling points on the Z axis based on the coordinate values of the point cloud data to obtain a two-dimensional trajectory line of the 3D printed concrete; Peak points and valley points in the two-dimensional trajectory are extracted, and the peak points and valley points are used as a basis for layering the 3D printed concrete in the graph of the two-dimensional trajectory to obtain a layered structure of the 3D printed part.

6. The method for detecting the printing quality of 3D printed concrete according to claim 5, characterized in that: The theoretical three-dimensional features also include the theoretical volume of materials required to simulate and generate the i-th layer in the concrete 3D model; The layered structure of the 3D printed part corresponds one-to-one to each layered structure of the concrete 3D model; Accordingly, the calculation formula of the fine deviation is: P2=(Σ(V2 i -C i )) / V0; Wherein, P2 represents the fine deviation, V2 i represents the volume of the ith layer of the 3D printed part, V2 i is calculated based on the area occupied by the i-th layer in the two-dimensional graph of the two-dimensional trajectory line, C i represents the theoretical volume of material required to generate the i-th layer in the concrete 3D model; V0 is the total volume of material required to simulate the generation of the concrete 3D model.

7. The method for detecting the printing quality of 3D printed concrete according to claim 5, characterized in that: The theoretical fluctuation is calculated by the following steps: In the two-dimensional graph of the two-dimensional trajectory line, a peak point of any layer is randomly selected as the current peak point, a horizontal line is drawn through the current peak point to the nearest layer, and the area enclosed by the horizontal line, the trajectory line of the layer where the current peak point is located, and the trajectory line of the nearest layer is regarded as an unfilled area; Calculate the volume of the unfilled area corresponding to each interlayer area; and calculate the theoretical fluctuation according to the following formula: P4=Σ(V3 j ) / V0; Wherein, P4 represents the theoretical fluctuation, V3 j represents the volume of the unfilled area corresponding to the j-th interlayer region; V0 is the total volume of materials required to simulate and generate the concrete 3D model.

8. The method for detecting the printing quality of 3D printed concrete according to claim 5, characterized in that: The average expansion rate and the model expansion rate are calculated by the following steps: Calculating the mean of the two-dimensional trajectory line in the Z-axis direction, and drawing a horizontal line through the mean to obtain a mean line; In the two-dimensional graph of the two-dimensional trajectory line, a region enclosed by the two-dimensional trajectory line and the mean line that is greater than the mean value is considered as a non-uniform region, and a volume of the non-uniform region is calculated; Searching for the lowest valley point in the interlayer region of the two-dimensional trajectory, using the lowest valley point as a baseline, treating the region enclosed by the two-dimensional trajectory and the baseline that is larger than the lowest valley point as a protruding region, and calculating the volume of the protruding region; Based on the volume of the non-uniform area and the volume of the protruding area, the average expansion rate and the model expansion rate are calculated according to the following formula: P5=V4 / V0,P6=V5 / V0, Among them, P5 represents the average expansion rate, P6 represents the model expansion rate, V4 represents the volume of the non-uniform area, V5 represents the volume of the protruding area, and V0 is the total volume of materials required to simulate and generate the concrete 3D model.

9. The method for detecting the printing quality of 3D printed concrete according to claim 2, characterized in that: The surface defect rate is obtained by the following steps: Identifying and calibrating high-density areas in the point cloud data; Processing the point cloud data using a plane projection algorithm to obtain point cloud data under plane projection; The ratio of the area of the high-density region under the plane projection to the total area of the point cloud under the plane projection is calculated, and the obtained ratio is used as the surface defect rate.

10. The method for detecting the printing quality of 3D printed concrete according to claim 2, characterized in that: Comprehensive evaluation of the printing quality of 3D printed concrete, including: Obtaining weights corresponding to the macro deviation, the fine deviation, the surface defect rate, the theoretical fluctuation, the average expansion rate, and the model expansion rate; Based on the weights, a weighted sum is performed on the macro deviation, the fine deviation, the surface defect rate, the theoretical fluctuation, the average expansion rate, and the model expansion rate to obtain a comprehensive evaluation result.

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