Net bed data detection method and related equipment

Abstract

The invention relates to the field of 3D printing, in particular to a net bed data detection method and related equipment. The mesh bed data detection method comprises the following steps: acquiring mesh bed data of a printing platform in a three-dimensional printer, wherein the mesh bed data comprises the measurement height of each measurement point in the printing platform; determining measurement points with abnormal measurement heights based on the measurement heights of the measurement points in the net bed data to obtain abnormal points; and updating the measurement height of the abnormal point in the net bed data to obtain updated net bed data. The accuracy of the net bed data can be improved, and the printing effect is guaranteed.

Description

Technical Field

[0001] This application relates to the field of 3D printing, specifically to a method for detecting mesh bed data and related equipment. Background Technology

[0002] 3D printing, also known as additive manufacturing, is a technology that manufactures solid parts by adding materials layer by layer based on the three-dimensional data of the model to be printed. During the 3D printing process, the printing platform (such as a heated bed) of the 3D printer needs to be leveled to achieve better uniformity of the first layer.

[0003] For example, measurement points can be configured in different areas of the printing platform, and the height of each measurement point can be detected by the nozzle of the 3D printer to obtain the mesh bed data. Then, the mesh bed data can be used for printing to compensate for the irregularity of the printing bed surface.

[0004] However, due to factors such as shaking of the 3D printer, structural instability, or foreign objects on the heated bed, the measured mesh bed data may be incorrect, affecting the printing effect. Summary of the Invention

[0005] In view of the above, embodiments of this application provide a method and related equipment for detecting net bed data. The aim is to improve the accuracy of net bed data and ensure printing quality.

[0006] In a first aspect, embodiments of this application provide a method for detecting network bed data, including:

[0007] Acquire the mesh bed data of the printing platform in the 3D printer, the mesh bed data including the measured height of each measuring point in the printing platform;

[0008] Based on the measured height of each measurement point, measurement points with abnormal measured heights are identified, and abnormal points are obtained.

[0009] The measured height of the abnormal point is updated in the net bed data to obtain the updated net bed data.

[0010] This application embodiment can automatically locate measurement points with abnormal measurement heights (i.e., abnormal points) based on the measurement height of each measurement point, and update the measurement height of the abnormal points, thereby updating the mesh bed data, improving the accuracy of the mesh bed data, and thus improving the printing effect.

[0011] In some embodiments, the abnormal measurement points are determined based on the measured height of each measurement point, including:

[0012] The measured heights of each measurement point are filtered to obtain the filtered heights of each measurement point.

[0013] The difference between the filtered height and the measured height at the same measurement point is obtained to obtain the height difference value of the measurement point.

[0014] Anomaly detection is performed based on the height difference between the measurement points to obtain the anomaly points.

[0015] The embodiments of this application use filtering to make the height value of the measurement point smoother. The filtered height can reflect the height value within the normal range to a certain extent. Then, the difference between the filtered height and the measured height is calculated to remove the interference of normal values ​​from the measured height, making the abnormal values ​​more significant and improving the accuracy of abnormal point detection.

[0016] In some embodiments, the measured height of each measurement point is filtered to obtain the filtered height of each measurement point, including:

[0017] The net bed data is extended outward from the edge of each measurement point to obtain the extended net bed data;

[0018] The expanded net bed data is filtered to obtain the filtered height of each measurement point.

[0019] In this embodiment, the net bed data is first expanded and then filtered so that the measurement points located at the edge of the net bed data can be filtered smoothly.

[0020] In some embodiments, the extended net bed data includes the predicted height of the extended points. The process of extending the net bed data outwards along the edges of each measurement point to obtain the extended net bed data includes:

[0021] Add extension points outwards along the edges of each of the aforementioned measurement points;

[0022] Obtain measurement points adjacent to the extended point;

[0023] The predicted height of the extension point is determined based on the measured height of the adjacent measurement points.

[0024] During the data expansion process of the network bed, the embodiments of this application combine the height of adjacent measurement points to determine the height of the expansion point, making the prediction of the height of the expansion point more accurate, thereby making the filtering height of the edge measurement points more precise.

[0025] In some embodiments, the extended netbed data includes the predicted height of the extended points, and the filtering of the extended netbed data to obtain the filtered height of each measurement point includes:

[0026] Determine the neighborhood points of the current measurement point from the expanded net bed data;

[0027] Wherein, the current measurement point is the measurement point currently undergoing height filtering, and the neighboring points include the measurement points and extended points adjacent to the current measurement point in the expanded netbed data;

[0028] Determine the median height from the measured height and the predicted height of the neighboring points;

[0029] The median height is used as the filtered height of the current measurement point.

[0030] In some embodiments, anomaly detection is performed based on the height difference between the measurement points to obtain the anomalies, including:

[0031] Calculate the average value and standard deviation of the height difference at each measurement point;

[0032] The standard range of height difference is determined based on the average value and standard deviation.

[0033] If the height difference of the measurement point is not within the standard range, the measurement point is confirmed as an abnormal point.

[0034] In some embodiments, the measured height of the abnormal points in the net bed data is updated to obtain updated net bed data, including:

[0035] The nozzle of the 3D printer is controlled to move back to the abnormal point, and the remeasured height of the abnormal point is obtained;

[0036] The measured height of the abnormal point in the net bed data is updated to the remeasured height to obtain the updated net bed data.

[0037] Secondly, embodiments of this application also provide a wire bed data detection device, comprising:

[0038] The mesh bed measurement module is used to acquire mesh bed data of the printing platform in the 3D printer, the mesh bed data including the measured height of each measurement point in the printing platform;

[0039] An anomaly detection module is used to determine the measurement points with abnormal measurement heights based on the measurement heights of each measurement point, and to obtain the anomaly points;

[0040] The net bed update module is used to update the measured height of the abnormal points in the net bed data to obtain the updated net bed data.

[0041] Thirdly, embodiments of this application also provide an electronic device, the electronic device including a processor and a memory, the memory being used to store instructions, and the processor being used to call the instructions in the memory, causing the electronic device to execute the net bed data detection method described in the first aspect.

[0042] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the webbed data detection method as described in the first aspect. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the implementation environment of a netbed data detection method according to an embodiment of this application.

[0044] Figure 2 This is a flowchart of the steps of a netbed data detection method according to an embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the net bed data provided according to an embodiment of this application.

[0046] Figure 4 This is a flowchart of the steps of a netbed data detection method according to another embodiment of this application.

[0047] Figure 5 This is a flowchart of a sub-step of step 402 provided according to another embodiment of this application.

[0048] Figure 6 This is a schematic diagram of extended netbed data provided according to an embodiment of this application.

[0049] Figure 7 This is a schematic diagram of the structure of a net bed data detection device according to an embodiment of this application.

[0050] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0051] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0052] The following description sets forth many specific details to provide a full understanding of this application. The described embodiments are only some, not all, of the embodiments of this application.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0054] It should be further noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0055] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0056] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0057] During the 3D printing process, the bed surface (such as a heated bed) of the 3D printer's printing platform needs to be leveled to achieve better uniformity of the first layer. For example, measurement points can be configured in different areas of the printing platform, and the height of each measurement point can be detected by the nozzle of the 3D printer to obtain mesh bed data. Then, the mesh bed data is used to compensate for the irregularities of the printing bed surface.

[0058] However, due to factors such as shaking of the 3D printer, structural instability, or foreign objects on the heated bed, the measured mesh bed data may be incorrect, affecting the printing effect.

[0059] In view of the above, embodiments of this application provide a method, apparatus, electronic device, and computer-readable storage medium for detecting webbed data.

[0060] refer to Figure 1 As shown, Figure 1 This is a schematic diagram illustrating the implementation environment of a netbed data detection method provided in an embodiment of this application.

[0061] This implementation scenario may include an electronic device 100 and a 3D printer 200. The electronic device 100 may be externally connected to the 3D printer 200 or may be an embedded computer of the 3D printer 200; this application embodiment does not limit this.

[0062] The electronic device 100 can be used to execute the net bed data detection method provided in the embodiments of this application to automatically detect abnormal height measurement points of the net bed data and improve the accuracy of the net bed data.

[0063] This electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, processors, microprogrammed control units (MCUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0064] The 3D printer 200 includes a nozzle 210 and a printing platform 220. The nozzle 210 can move above the printing platform 220 to print slices of the model to be printed layer by layer to form the finished product.

[0065] The printing platform 220 has measurement points configured in different areas. Before 3D printing begins, the 3D printer 200 can control the nozzle 210 to move to each measurement point and detect the height of each measurement point to obtain the mesh bed data.

[0066] Figure 2 This is a flowchart illustrating the steps of an embodiment of the network bed data detection method of this application. Depending on different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0067] See Figure 2 As shown, the net bed data detection method may include the following steps.

[0068] Step 201: Obtain the mesh bed data of the printing platform in the 3D printer. The mesh bed data includes the measured height of each measurement point in the printing platform.

[0069] Specifically, the electronic device is equipped with the horizontal position of each measuring point on the printing platform. The nozzle of the 3D printer can move to each measuring point in sequence according to the horizontal position. With the center of the heated bed of the printing platform as the zero point, the relative height of each measuring point and the zero point is detected. The measured relative height is used as the measurement height of the measuring point, thereby obtaining the mesh bed data. Then, the 3D printer can transmit the mesh bed data to the electronic device.

[0070] For example, refer to Figure 3 As shown, in Figure 3 In the three-dimensional coordinate system shown, the Z-axis is parallel to the nozzle direction, the XOY plane is perpendicular to the Z-axis and passes through the center of the heated bed. When the printing platform is a rectangular plane, the X-axis and Y-axis of the XOY plane can be parallel to two sides of the printing platform plane, respectively; when the printing platform is circular, the X-axis and Y-axis of the XOY plane can be parallel to two mutually perpendicular radii of the printing platform plane, respectively.

[0071] Assuming there are m rows and n columns of measurement points configured in the printing platform, the wire bed data can be represented as an m row and n column matrix A. m×n (m,n∈[2,∞)).

[0072] That is,

[0073] Where (m,n∈[2,∞), the measurement point in the i-th row and j-th column is denoted as a. ij a ij =(x ij ,y ij ,z ij ), i∈[0,m-1],j∈[0,n-1], (x ij ,y ij) For measurement point a ij In the coordinates of the printing platform plane (i.e., the horizontal coordinates), z ij For measurement point a ij The height relative to the Z origin, that is, the measured height.

[0074] Step 202: Based on the measured height of each measurement point, determine the measurement points where the measured height is abnormal, and obtain the abnormal points.

[0075] In other words, electronic devices can detect measurement points where the measured height is abnormal and identify those points as abnormal.

[0076] In some embodiments, electronic devices may use methods such as the three-standard-deviation method, box method, z-score, and Hampel method to determine outliers in the measured height of each measurement point.

[0077] The following example, using the three-standard-deviation method with electronic equipment, illustrates one implementation of step 202: The electronic equipment can calculate the standard deviation and mean of each measured height in the net bed data. Then, based on the standard deviation and mean, it obtains the upper limit and lower limit of the height difference. For example, the upper limit of the height difference = mean + standard deviation × 3; the lower limit of the height difference = mean - standard deviation × 3. If the measured height is within the range of the upper limit to the lower limit of the height difference, then the measured height is normal. If the measured height is greater than the upper limit of the height difference or less than the lower limit of the height difference, the measurement point corresponding to the measured height is confirmed as an abnormal point.

[0078] Step 203: Update the measured height of the abnormal points in the net bed data to obtain the updated net bed data.

[0079] In some embodiments, step 203 can be implemented as follows: obtain the measured height of each measurement point adjacent to the anomaly point; take the mean, median, or mode of the measured heights of several measurement points adjacent to the anomaly point as the target height of the anomaly point, and update the measured height of the anomaly point in the net bed data to the target height, thereby obtaining the updated net bed data.

[0080] In other embodiments, step 203 can be implemented as follows: control the nozzle of the 3D printer to move back to the abnormal point to obtain the remeasured height of the abnormal point; update the measured height of the abnormal point to the remeasured height in the mesh bed data to obtain the updated mesh bed data.

[0081] This application embodiment can automatically locate measurement points with abnormal measurement heights (i.e., abnormal points) based on the measurement height of each measurement point, and update the measurement height of the abnormal points, thereby updating the mesh bed data, improving the accuracy of the mesh bed data, and thus improving the printing effect.

[0082] refer to Figure 4 As shown in the embodiment of this application, a method for detecting net bed data is also provided. In this embodiment, the electronic device can obtain the filter height of each measurement point, and perform anomaly detection based on the filter height and measurement height of the same measurement point to obtain anomaly points.

[0083] Specifically, refer to Figure 4 As shown, the method for detecting net bed data may include:

[0084] Step 401: Obtain the mesh bed data of the printing platform in the 3D printer. The mesh bed data includes the measured height of each measurement point in the printing platform.

[0085] Step 401 and Figure 2 The steps shown in step 201 are largely the same, and this application embodiment does not limit them.

[0086] After acquiring the net bed data, the electronic device can determine the measurement points with abnormal measurement heights based on the measurement heights of each measurement point in the net bed data, thus obtaining the abnormal points.

[0087] Specifically, the electronic device can filter the net bed data to obtain the filtered height of each measurement point, and then perform anomaly detection based on the filtered height and the measured height of each measurement point, for example, referring to steps 402 to 404 below.

[0088] Step 402: Filter the measured height of each measurement point to obtain the filtered height of each measurement point.

[0089] The filtering process can employ median filtering, Gaussian filtering, Fourier transform, etc., but is not limited to these. In this embodiment, filtering can make the measured height of each measurement point smoother, eliminate isolated noise points, and make the measured height of abnormal points more significant when performing subtraction in the subsequent process.

[0090] The following uses median filtering as an example to illustrate the possible implementation of step 402: The electronic device can determine the neighboring points of the current measurement point in the net bed data. The current measurement point is the measurement point currently undergoing height filtering. The neighboring points include the measurement points adjacent to the current measurement point in the expanded net bed data. Then, the median height is determined from the measured heights of the neighboring points, and the median height is used as the filtered height of the measurement point.

[0091] The neighborhood points of a measurement point can be determined based on the filter kernel. The filter kernel can be regarded as an empty matrix. The filter kernel can be a 3×3 filter kernel, a 5×5 filter kernel, a 7×7 filter kernel, etc., but is not limited to this.

[0092] The filter kernel can slide in the netbed data. Each time it slides to a position, the measurement point located at the center of the filter kernel is taken as the current measurement point, and the other measurement points in the filter kernel are taken as the neighborhood points of the current measurement point. All the measurement heights covered by the filter kernel are sorted, and the median height is taken as the filter height of the current measurement point. In this way, the filter height corresponding to each measurement point is obtained.

[0093] In other embodiments, in step 402, the netbed data can be expanded first to obtain expanded netbed data, and then the netbed data can be filtered.

[0094] For example, the electronic device extends the net bed data outward along the edge of each measurement point to obtain extended net bed data; the extended net bed data is then filtered to obtain the filtered height of each measurement point.

[0095] This application embodiment extends the edges of the net bed data and then performs filtering, so that the measurement points located at the edges of the net bed data can be filtered smoothly, and the filtering height of each measurement in the net bed data can be smoothly transitioned.

[0096] The following example illustrates one possible implementation of step 402, using an electronic device to extend the network bed data and then filtering the extended data using median filtering. Specifically, refer to... Figure 5 As shown, step 402 may also include:

[0097] Step 501: Extend the net bed data outwards along the edge of each measurement point to obtain the extended net bed data.

[0098] The number of rows and columns to be extended outwards can be determined based on the size of the filter core. For example, if the filter core is 3×3, an additional ring of extension points can be added around each measurement point of the netbed data; if the filter core is 5×5, two additional rings of extension points can be added around each measurement point of the netbed data.

[0099] The height (predicted height) corresponding to the expansion point can be a preset value or determined based on the measurement points adjacent to the expansion point.

[0100] For example, step 501 can be implemented in the following way:

[0101] Step 5011: Add extension points outwards along the edges of each measurement point.

[0102] Each extension point is aligned with the rows and columns of each measurement point in the netbed data.

[0103] For example, refer to Figure 6 As shown, new row extension points are added on the extension lines at both ends of each row measurement point; new column extension points are added on the extension lines at both ends of each column measurement point, so that each extension point is aligned with the row and column of each measurement point in the netbed data.

[0104] In step 5011, the X-axis coordinates and Y-axis coordinates of each expansion point can be determined.

[0105] Suppose that in a certain row of the network bed data, the X-axis coordinates of the row expansion point are marked as x. (i+1)j The X-axis coordinates of the edge measurement point that is closest to the row expansion point and is in the same row are marked as x. ij The distance between the two can be denoted as |x ij -x (i+1)j The spacing can be configured according to actual application requirements. For example, the spacing can be set as the distance between two adjacent measurement points in the row. The X-axis coordinate of the extension point in the row can be determined by the spacing. The Y-axis coordinate of the extension point is the same as the Y-axis coordinate of the measurement point in the row, so the X-axis and Y-axis coordinates of the extension point in the row can be determined.

[0106] Suppose that in a column of the net bed data, the y-axis coordinates of the column extension points are marked as y i(j+1) The y-axis coordinates of the edge measurement point that is closest to the y-axis coordinates of the column extension point and is located in the same column are marked. i(j+1) The distance between the two can be denoted as |y ij -y i(j+1) The spacing can be configured according to actual application requirements. For example, the spacing can be set as the distance between two adjacent measurement points in the column. The Y-axis coordinate of the column extension point can be determined by the spacing. The X-axis coordinate of the column extension point is the same as the X-axis coordinate of the measurement point in the same column, so the X-axis and Y-axis coordinates of the column extension point can be determined.

[0107] Step 5012: Obtain the measurement points adjacent to the expansion point.

[0108] For a row expansion point, the measurement points that are in the same row as the row expansion point and are adjacent to it can be obtained as the measurement points adjacent to the row expansion point.

[0109] For column expansion points, measurement points that are in the same column as the column expansion point and are adjacent to it can be obtained as measurement points adjacent to the column expansion point.

[0110] Step 5013: Determine the predicted height of the expansion point based on the measured height of the measurement points adjacent to the expansion point.

[0111] Electronic devices can use matrix interpolation methods to determine the predicted height of the expansion point. For example, linear interpolation can be used to determine the predicted height of the expansion point, but it is not limited to this.

[0112] For a row expansion point, assuming the coordinates of two adjacent measurement points are marked as (x1, y1, z1) and (x2, y2, z2), then the predicted height z of the row expansion point is... ij As shown below:

[0113]

[0114] For a column expansion point, assuming the coordinates of the two adjacent measurement points are labeled (x1, y1, z1) and (x2, y2, z2), the predicted height of the column expansion point can be shown below:

[0115]

[0116] The above steps will allow you to obtain the netbed data A. m×n Expanding to Netbed Data B (m+2)×(n+2) That is, B (m+2)×(n+2) For the expanded network bed data, B (m+2)×(n+2) As shown below:

[0117]

[0118] Among them, b ij =(c ij ,y ij ,z ij (i∈[-1,m],j∈(-1,n)).

[0119] Step 502: The extended net bed data is filtered using the median filtering method to obtain the filtered height of each measurement point.

[0120] Specifically, step 502 can be implemented in the following way: determining the neighboring points of the current measurement point in the expanded net bed data; wherein, the current measurement point is the measurement point currently undergoing height filtering, and the neighboring points include the measurement points and expanded points adjacent to the measurement point in the expanded net bed data; determining the median height between the measured height and the predicted height of the neighboring points; and using the median height as the filtered height of the current measurement point.

[0121] Assuming the median filtering method uses a 3×3 filter kernel, the 3×3 filter kernel is shown below:

[0122]

[0123] When filtering the expanded network bed data, the center of the filter kernel can be moved to the position corresponding to B. (m+2)×(n+2) Measurement point b in 00 Alignment is performed, and the other elements covered by the filter kernel are the neighborhood points of the measurement point. These neighborhood points can be either measurement points or expansion points. The heights corresponding to these neighborhood points are sorted, and the values ​​are used as the measurement point b. 00 The filter height is then determined, and the filter kernel can be moved continuously until the filter height of each measurement point is obtained.

[0124] The filter height at each measurement point can be denoted as B. m×n ′, B m×n As shown below:

[0125]

[0126] Among them, b ij ′ represents the filtered height of the measurement point in the i-th row and j-th column.

[0127] Step 403: Subtract the filtered height and the measured height at the same measurement point to obtain the height difference value of the measurement point.

[0128] Specifically, matrix B m×n ′ and matrix A m×nPoint-to-point subtraction involves subtracting the filtered height and the measured height of the measurement point in the i-th row and j-th column to obtain the height difference between each measurement point. This height difference is denoted as C. m×n C m×n As shown below:

[0129]

[0130] Among them, c ij =(x ij ,y ij ,z ij )(i∈[0,m-1],j∈(0,n-1)),z ij Characterizes the height difference.

[0131] Step 404: Anomaly detection is performed based on the height difference of each measurement point to obtain anomalies.

[0132] For example, outliers can be identified from the height differences at each measurement point using methods such as box-line method, z-score, Hampshire method, and three-standard-deviation method.

[0133] The following steps a, b, and c illustrate one implementation of step 404 using the three-standard-deviation method for anomaly detection as an example.

[0134] Step a. Calculate the average value and standard deviation of the height difference at each measurement point.

[0135] For example, electronic devices are based on matrix C m×n height difference z ij Calculate the average value C of the height difference at each measurement point. mean and standard deviation C std .

[0136] Step b. Determine the standard range of height difference based on the median and standard deviation.

[0137] The standard range for height difference is the lower limit of height difference. down ~Height difference limit up ,

[0138] Among them, the upper limit of height difference. up The calculation method is as follows:

[0139] limit up0 =C mean +3*C std ;

[0140] If limit up0 Less than the first preset value, such as C mean +0.1, then limit upIt equals the first preset value (i.e., C). mean +0.1); if limit up0 Greater than or equal to C mean +0.1, then limit uo equal to limit up0 .

[0141] Height difference lower limit down The calculation method is as follows:

[0142] limit down0 =C mean -3*C std ;

[0143] If limit down0 Greater than the second preset value (e.g., C) mean -0.1), then limit down Equal to the second preset value (e.g., C) mean -0.1); if limit down0 Less than or equal to C mean -0.1, then limit down equal to limit downo .

[0144] That is, the standard range is the limit. down ~limit up .

[0145] Step c. If the height difference of the measurement point is not within the standard range, the measurement point is confirmed as an abnormal point.

[0146] Traversing C m×n z in the data ij If z ij Not in limit down ~limit up Within, the coordinates (x) of this data ij ,y ij () indicates the location of the outlier.

[0147] Step 405: Update the measured height of the abnormal points in the net bed data to obtain the updated net bed data.

[0148] Step 405 is largely the same as step 203, and will not be repeated here.

[0149] This application embodiment can automatically locate measurement points with abnormal measurement heights (i.e., abnormal points) based on the measurement height of each measurement point, and update the measurement height of the abnormal points, thereby updating the mesh bed data, improving the accuracy of the mesh bed data, and thus improving the printing effect.

[0150] Furthermore, the embodiments of this application can make the height value of the measurement point smoother by filtering. The filtered height can reflect the height value within the normal range to a certain extent. Then, the difference between the filtered height and the measured height is calculated to remove the interference of normal values ​​from the measured height, making the abnormal values ​​more significant and improving the accuracy of abnormal point detection.

[0151] Furthermore, in this embodiment, the net bed data is first expanded and then filtered, so that the measurement points located at the edge of the net bed data can be filtered smoothly. During the expansion process, this embodiment combines the height of adjacent measurement points to determine the height of the expansion point, making the prediction of the height of the expansion point more accurate, and thus making the filtering height of the edge measurement points more precise.

[0152] Based on the same idea as the net bed data detection method in the above embodiments, this application also provides a net bed data detection device, which can be used to perform the above net bed data detection method. For ease of explanation, the structural schematic diagram of the net bed data detection device embodiment only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the device, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0153] like Figure 3 As shown, the net bed data detection device includes a net bed measurement module 701, an anomaly detection module 702, and a net bed update module 703. In some embodiments, the above modules can be programmable software instructions stored in memory and executable by a processor. It is understood that in other embodiments, the above modules can also be program instructions or firmware embedded in the processor.

[0154] The mesh bed measurement module 701 is used to acquire mesh bed data of the printing platform in the 3D printer, the mesh bed data including the measured height of each measurement point in the printing platform.

[0155] The anomaly detection module 702 is used to determine the measurement points where the measurement height is abnormal based on the measurement height of each measurement point, and obtain the anomaly points.

[0156] The net bed update module 703 is used to update the measured height of the abnormal point in the net bed data to obtain the updated net bed data.

[0157] Figure 8 This is a schematic diagram of an embodiment of the electronic device of this application.

[0158] The electronic device 100 includes a memory 20, a processor 30, and a computer program 40 stored in the memory 20 and executable on the processor 30. When the processor 30 executes the computer program 40, it implements the steps described in the above-described webbed data detection method embodiment, for example... Figure 2 Steps 201 to 203 are shown.

[0159] For example, computer program 40 can also be divided into one or more modules / units, which are stored in memory 20 and executed by processor 30. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 40 in electronic device 100. For example, it can be divided into... Figure 7 The net bed measurement module 701, the anomaly detection module 702, and the net bed update module 703 are shown.

[0160] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. It may include more or fewer components than shown in the diagram, or combine certain components, or different components. For example, the electronic device 100 may also include input / output devices, network access devices, buses, etc.

[0161] Processor 30 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, a single-chip microcomputer, or any conventional processor.

[0162] The memory 20 can be used to store computer programs 40 and / or modules / units. The processor 30 implements various functions of the electronic device 100 by running or executing the computer programs and / or modules / units stored in the memory 20 and by calling data stored in the memory 20. The memory 20 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0163] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the electronic device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and other division methods may be used in actual implementation.

[0165] Furthermore, the functional units in the various embodiments of this application can be integrated into the same processing unit, or each unit can exist physically separately, or two or more units can be integrated into the same unit. The integrated units described above can be implemented in hardware or in the form of hardware plus software functional modules.

[0166] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and not restrictive in all respects. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or electronic devices recited in the electronic device claims may also be implemented by the same unit or electronic device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A mesh bed data detection method, characterized by, The method comprises the following steps: obtaining bed data of a printing platform in a three-dimensional printer, the bed data comprising measured heights of each measuring point in the printing platform; determining a measuring point with abnormal measured height based on the measured heights of the measuring points, to obtain an abnormal point; updating the measured height of the abnormal point in the bed data, to obtain updated bed data.

2. The web bed data detection method of claim 1, wherein, The step of determining a measuring point with abnormal measured height based on the measured heights of the measuring points, to obtain an abnormal point, comprises the following steps: performing filtering processing on the measured heights of the measuring points, to obtain filtered heights of the measuring points; determining a height difference value of a measuring point by subtracting the filtered height from the measured height of the same measuring point; performing abnormal point detection based on the height difference values of the measuring points, to obtain the abnormal point.

3. The web bed data detection method of claim 2, wherein, The step of performing filtering processing on the measured heights of the measuring points, to obtain filtered heights of the measuring points, comprises the following steps: extending the bed data outward along the edges of the measuring points, to obtain extended bed data; performing filtering on the extended bed data, to obtain the filtered heights of the measuring points.

4. The web bed data detection method of claim 3, wherein, The extended bed data comprises predicted heights of extended points, and the step of extending the bed data outward along the edges of the measuring points, to obtain extended bed data, comprises the following steps: adding extended points outward along the edges of the measuring points; obtaining measuring points adjacent to the extended points; determining the predicted heights of the extended points based on the measured heights of the adjacent measuring points.

5. The web bed data detection method of claim 3, wherein, The extended bed data comprises predicted heights of extended points, and the step of performing filtering on the extended bed data, to obtain filtered heights of the measuring points, comprises the following steps: determining neighborhood points of a current measuring point in the extended bed data; wherein the current measuring point is a measuring point currently performing height filtering, and the neighborhood points comprise measuring points and extended points adjacent to the current measuring point in the extended bed data; determining a height median value from the measured heights and the predicted heights of the neighborhood points; taking the height median value as the filtered height of the current measuring point.

6. The web bed data detection method of claim 2, wherein, The step of performing abnormal point detection based on the height difference values of the measuring points, to obtain the abnormal point, comprises the following steps: calculating an average value and a standard deviation of the height difference values of the measuring points; determining a standard range of the height difference values based on the average value and the standard deviation; if the height difference value of a measuring point is not within the standard range, confirming that the measuring point is an abnormal point.

7. The web bed data detection method of any one of claims 1 to 6, wherein, The step of updating the measured height of the abnormal point in the bed data, to obtain updated bed data, comprises the following steps: controlling a nozzle of the three-dimensional printer to move to the abnormal point again, to obtain a re-measured height of the abnormal point; updating the measured height of the abnormal point to the re-measured height in the bed data, to obtain the updated bed data.

8. A mesh bed data detection apparatus, characterized by, The method comprises the following steps: a bed measuring module is configured to obtain bed data of a printing platform in a three-dimensional printer, the bed data comprising measured heights of each measuring point in the printing platform; an abnormal detection module is configured to determine a measuring point with abnormal measured height based on the measured heights of the measuring points, to obtain an abnormal point; The mesh bed updating module is configured to update the measured height of the abnormal point in the mesh bed data to obtain updated mesh bed data. 9.An electronic device comprising a processor and a memory, wherein, The memory is configured to store instructions, and the processor is configured to invoke the instructions in the memory to cause the electronic device to perform the mesh bed data detection method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and when the computer instructions run on the electronic device, cause the electronic device to perform the mesh bed data detection method in any one of claims 1 to 7.

Images