An intelligent optimization method and system for PCB back drilling process

Abstract

The application discloses an intelligent optimization method and system for a PCB back drilling process, and belongs to the technical field of circuit board processing.The point cloud data is obtained by scanning the cushion board, the surface height field function is constructed by using the thin plate spline interpolation, and the flatness of the cushion board is judged based on the grid node height value; if the flatness is unqualified, the cushion board is replaced; the continuous residual distribution function generated by the Kriging interpolation is superimposed on the thickness variation function fitted based on the historical production data to obtain a PCB predicted thickness distribution model, so that the predicted thickness of the back drilling through hole is obtained, and then the top surface height coordinate of the PCB is obtained; the difference between the initial vertical height of the drill needle and the top surface height coordinate of the PCB at the required back drilling through hole is used as the drill needle down distance when the back drilling is performed on the required back drilling through hole.The application realizes the automatic quantitative detection of the flatness of the cushion board, improves the detection accuracy, accurately analyzes the thickness of the back drilling position, effectively obtains the drill needle down distance, and improves the back drilling quality.

Description

Technical Field

[0001] This invention relates to the field of PCB processing technology, and in particular to an intelligent optimization method and system for the back drilling process of PCB boards. Background Technology

[0002] In high-frequency, high-speed PCB design, the portion of the metallized hole wall extending beyond the required signal conduction path in a through-hole will form parasitic capacitance and inductance, known as the "short stake effect," which severely degrades signal quality and increases insertion loss and return loss. To eliminate this harmful effect, the industry commonly uses back-drilling, a secondary drilling method that uses a drill bit with a diameter slightly larger than the through-hole diameter to drill away the unwanted copper-plated portion of the hole wall from the PCB surface. In back-drilling processes, a shim is typically placed under the PCB board to provide even support and prevent friction between the PCB board and the metal table. Therefore, the flatness of the shim is particularly important. A properly flat shim can prevent over-drilling or under-drilling. However, in the current process, the flatness of the shim is mainly checked manually, which is too subjective and leads to insufficient accuracy. At the same time, during the drilling process, the drill bit first moves down to the surface of the PCB board before rotating to drill. Accurately determining the downward distance of the drill bit is also very important. If the downward distance is too small, the drill bit may not reach the surface of the PCB board before drilling, resulting in insufficient drilling depth. If the downward distance is too large, it may damage the PCB board or the drill bit and result in excessive drilling depth. The prior art JP2017092259A discloses a back-drilling process method for multilayer printed circuit boards. The method uses a drill bit to detect the descent distance information and calculates the actual distance to the desired conductor layer based on the obtained descent distance information according to a certain formula. This method ignores the influence of the flatness of the pad on the drilling process. Furthermore, by continuously calculating the actual distance to the desired conductor layer instead of directly calculating the required descent distance first, it is easy to cause error accumulation, which may result in the actual controlled descent distance being too large or too small. Summary of the Invention

[0003] To address the technical problems existing in the prior art, this invention provides an intelligent optimization method for the back drilling process of PCB boards, comprising the following steps: S1. Using the scanned point cloud data of the padding board, construct the surface height field function of the padding board surface using the thin plate spline interpolation algorithm. Divide the padding board surface area into regular rectangular grids according to the preset spacing value. Obtain the padding board height value at each grid node through the surface height field function. Determine whether the flatness of the padding board is qualified based on the padding board height value at each grid node. If it is not qualified, notify the replacement of the padding board. If it is qualified, continue with the subsequent steps. S2. Based on historical production data of PCB boards of the same specifications, fit the board thickness variation function. The thickness of multiple PCB boards in the current batch is measured at the same thickness sampling point using a thickness measuring device, and the average value is calculated to obtain the actual average thickness of each thickness sampling point of the current batch of PCB boards. The estimated thickness of each thickness measurement sampling point is obtained by using the plate thickness variation function. For each thickness measurement sampling point, the residual between its actual average thickness and the estimated thickness is calculated to obtain the residual sampling point set. Based on the residual sampling point set, a continuous residual distribution function is generated for the PCB board using the Kriging interpolation method. The continuous residual distribution function satisfies that the value obtained by substituting the planar coordinates of each thickness sampling point of the PCB board in the PCB board design coordinate system into the calculation is the corresponding residual value. The PCB board predicted thickness distribution model is obtained by linearly superimposing the board thickness variation function and the continuous residual distribution function. The predicted thickness of the PCB board at the required back-drilled through hole is obtained through the PCB board predicted thickness distribution model. S3. Based on the predicted PCB thickness, obtain the PCB top surface height coordinates in the measurement coordinate system used when acquiring the scan point cloud data of the pad board for each required back-drilled through hole. S4. Obtain the initial vertical height of the drill bit relative to the surface of the back drilling platform. Use the difference between the initial vertical height and the height coordinate of the top surface of the PCB board at the required back drilling through hole as the downward distance of the drill bit when back drilling the required back drilling through hole.

[0004] Furthermore, the acquisition of the scanned point cloud data of the wooden board is specifically as follows: A measurement coordinate system is established using the prefabricated reference point on the back drilling platform as the origin. The measurement coordinate system has the X-axis parallel to the long side of the back drilling platform, the Y-axis parallel to the short side of the back drilling platform, and the Z-axis perpendicular to the surface of the back drilling platform. By scanning the wooden board placed on the back drill platform with a scanning device, the position coordinates of multiple scanning points on the surface of the wooden board in the measurement coordinate system are obtained.

[0005] Furthermore, the expression for the surface height field function is: ; in, Let n be the coordinates of the input point, and n be the number of scan points in the scanned point cloud data of the wooden board. , and The coefficient of the global linear trend term. Let be the weight coefficient for the i-th scan point. Here is the radial basis function kernel, and its expression is: , Let be the Euclidean distance from the input point to the i-th scan point; By substituting each of the scanned points in the scanned point cloud data into the surface height field function to form a system of equations, the coefficients of all global linear trend terms and the weight coefficients of each scanned point can be obtained.

[0006] Furthermore, the step of determining whether the flatness of the wooden board is acceptable based on the height value of the wooden board at each grid node specifically involves: S11. Combine all grid nodes in pairs to obtain each first node group. For each first node group, calculate the height difference between the two grid nodes. If any height difference is greater than or equal to the first preset threshold, the flatness of the pad board is determined to be unqualified. Otherwise, proceed to step S12. S12. Taking each grid node as the center, take the surrounding preset number of grid ranges as the sub-region of the corresponding grid node. For each sub-region, calculate its height value range. If the height value range is greater than or equal to the second preset threshold, the flatness of the corresponding sub-region is unqualified. If the flatness of any sub-region is unqualified, it is determined that the flatness of the pad board is unqualified. Otherwise, proceed to step S13. S13. Perform least squares plane fitting on the position data of the grid nodes to obtain the reference plane, calculate the height difference of each grid node relative to the reference plane, and calculate the root mean square deviation of the surface shape based on the height difference of each grid node relative to the reference plane. If the root mean square deviation of the surface shape is greater than or equal to the third preset threshold, the flatness of the pad board is determined to be unqualified.

[0007] Furthermore, based on historical production data of PCBs of the same specifications, a function for the variation of board thickness was fitted, specifically as follows: The expression for the plate thickness variation function is: ; (x1, y1) are the coordinates in the PCB design coordinate system, T0 is the nominal design thickness of the PCB, b1 is the coefficient describing the linear change trend of the thickness along the X-axis, b2 is the coefficient describing the linear change trend of the thickness along the Y-axis, b3 is the coefficient describing the degree of second-order nonlinear bending of the thickness along the X-axis, b4 is the coefficient describing the degree of second-order nonlinear bending of the thickness along the Y-axis, and b5 is the coefficient describing the thickness distortion trend under the coupling effect of the X-axis and Y-axis. The historical production data consists of a set of historical discrete sample points composed of the coordinates of historical thickness measurement sampling points of multiple PCB boards and the corresponding historical actual thickness. Based on the set of historical discrete sample points, the values ​​of b1, b2, b3, b4 and b5 are obtained by solving the least squares method.

[0008] Furthermore, the step of obtaining the PCB board top surface height coordinates in the measurement coordinate system used when acquiring the scanned point cloud data of the padding board for each required back-drilled through hole is specifically as follows: Extract the design coordinates of the required back-drill through holes from the PCB board drilling file; After placing the PCB board on the pad board, the design coordinates are transformed to planar coordinates in the measurement coordinate system used when the scanned point cloud data of the pad board is acquired. The Z-axis coordinates of each required back-drilled through hole on the surface of the pad board are obtained by using the planar coordinates through the surface height field function; The sum of the PCB thickness at the required back-drilled through hole location and the corresponding Z-axis coordinate value on the surface of the padding board is used as the top surface height coordinate of the PCB at the required back-drilled through hole location.

[0009] Furthermore, the design coordinates are coordinates in the PCB design coordinate system, which is constructed with one of the corner endpoints of the PCB as the origin, the long side of the PCB as the X-axis, and the short side of the PCB as the Y-axis.

[0010] This invention also provides an intelligent optimization system for the PCB back drilling process, which applies the intelligent optimization method for the PCB back drilling process as described above, including: The flatness judgment module for the wooden board uses the scanned point cloud data of the wooden board and a thin plate spline interpolation algorithm to construct a surface height field function for the surface of the wooden board. The surface area of ​​the wooden board is divided into regular rectangular grids according to a preset spacing value. The height value of the wooden board at each grid node is obtained through the surface height field function. The module judges whether the flatness of the wooden board is qualified based on the height value of the wooden board at each grid node. If it is not qualified, it notifies to replace the wooden board. The thickness prediction module is used to obtain the predicted thickness of the PCB at the required back-drilled through-hole location. This is achieved through the following steps: Based on historical production data of PCBs of the same specifications, a function for the variation of board thickness was fitted. The thickness of multiple PCB boards in the current batch is measured at the same thickness sampling point using a thickness measuring device, and the average value is calculated to obtain the actual average thickness of each thickness sampling point of the current batch of PCB boards. The estimated thickness of each thickness measurement sampling point is obtained by using the board thickness variation function. For each thickness measurement sampling point, the residual between its actual average thickness and the estimated thickness is calculated to obtain the residual sampling point set {(x1p,y1p,ΔTp)|p=1,2,...,M}, where (x1p,y1p) is the planar coordinate of the p-th thickness measurement sampling point in the PCB board design coordinate system, ΔTp is the residual corresponding to the p-th thickness measurement sampling point, and M is the number of thickness measurement sampling points. Based on the residual sampling point set, a continuous residual distribution function is generated for the PCB board using the Kriging interpolation method; The PCB board predicted thickness distribution model is obtained by linearly superimposing the board thickness variation function and the continuous residual distribution function. The PCB board predicted thickness at the required back-drilled through hole is obtained through the PCB board predicted thickness distribution model. The height coordinate analysis module obtains the height coordinates of the top surface of the PCB board in the measurement coordinate system used when acquiring the scan point cloud data of the pad board for each required back-drilled through hole, based on the predicted PCB thickness. The down-drilling distance analysis module obtains the initial vertical height of the drill bit relative to the surface of the back-drilling platform. The difference between the initial vertical height and the height coordinate of the top surface of the PCB board at the required back-drilling through hole is used as the down-drilling distance of the drill bit when back-drilling the required back-drilling through hole.

[0011] Furthermore, the expression for the surface height field function is: ; in, Let n be the coordinates of the input point, and n be the number of scan points in the scanned point cloud data of the wooden board. , and The coefficient of the global linear trend term. Let be the weight coefficient for the i-th scan point. Here is the radial basis function kernel, and its expression is: , Let be the Euclidean distance from the input point to the i-th scan point; By substituting each of the scanned points in the scanned point cloud data into the surface height field function to form a system of equations, the coefficients of all global linear trend terms and the weight coefficients of each scanned point can be obtained.

[0012] Furthermore, the step of determining whether the flatness of the wooden board is acceptable based on the height value of the wooden board at each grid node specifically involves: S11. Combine all grid nodes in pairs to obtain each first node group. For each first node group, calculate the height difference between the two grid nodes. If any height difference is greater than or equal to the first preset threshold, the flatness of the pad board is determined to be unqualified. Otherwise, proceed to step S12. S12. Taking each grid node as the center, take the surrounding preset number of grid ranges as the sub-region of the corresponding grid node. For each sub-region, calculate its height value range. If the height value range is greater than or equal to the second preset threshold, the flatness of the corresponding sub-region is unqualified. If the flatness of any sub-region is unqualified, it is determined that the flatness of the pad board is unqualified. Otherwise, proceed to step S13. S13. Perform least squares plane fitting on the position data of the grid nodes to obtain the reference plane, calculate the height difference of each grid node relative to the reference plane, and calculate the root mean square deviation of the surface shape based on the height difference of each grid node relative to the reference plane. If the root mean square deviation of the surface shape is greater than or equal to the third preset threshold, the flatness of the pad board is determined to be unqualified.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention acquires point cloud data by scanning a wooden board, constructs a surface height field function using thin-plate spline interpolation, and judges the flatness of the wooden board based on the grid node height values. If it fails to meet the standard, it is replaced. A predicted thickness distribution model for the PCB board is obtained by superimposing a board surface thickness variation function fitted based on historical production data and a continuous residual distribution function generated by Kriging interpolation. This model is used to obtain the predicted thickness at the back-drilled through-hole, thus obtaining the height coordinates of the PCB board top surface. The difference between the initial vertical height of the drill bit and the height coordinates of the PCB board top surface at the desired back-drilled through-hole is used as the drill bit's downward distance during back-drilling. This invention effectively achieves automatic quantitative detection of the flatness of the wooden board, improves detection accuracy, accurately analyzes the board thickness at the back-drilling location, effectively obtains the drill bit's downward distance, and improves back-drilling quality. By using the thin plate spline interpolation algorithm, a height field model of the pad board surface is constructed using the radial basis kernel function. This model accurately fits the discrete scan point cloud data, taking into account both the global linear trend and the local nonlinear deformation (weighted sum of radial basis functions), thus accurately reflecting the actual surface morphology of the pad board. The height values ​​of each grid node obtained in this way can be used for subsequent flatness determination, providing reliable pad board support surface data for the back drilling process. The flatness of the padding board is evaluated step by step through three levels: first, the maximum height difference between any two grid nodes is checked; then, the height fluctuation of each local sub-region is evaluated; and finally, the root mean square deviation of the overall surface shape relative to the fitted reference plane is calculated. This multi-level evaluation system, from point to local to overall, can comprehensively capture unevenness defects at different scales on the surface of the padding board and avoid omissions or misjudgments that may occur with a single indicator. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a flowchart of an intelligent optimization method for the back drilling process of a PCB board according to the present invention; Figure 2 This is a structural block diagram of an intelligent optimization system for the back drilling process of a PCB board according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0019] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" and "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0020] Example 1 See Figure 1 As shown, the present invention provides an intelligent optimization method for the back drilling process of PCB boards, which specifically includes the following steps: S1. Using the scanned point cloud data of the padding board, construct the surface height field function of the padding board surface using the thin plate spline interpolation algorithm. Divide the padding board surface area into regular rectangular grids according to the preset spacing value. Obtain the padding board height value at each grid node through the surface height field function. Determine whether the flatness of the padding board is qualified based on the padding board height value at each grid node. If it is not qualified, notify the replacement of the padding board. If it is qualified, continue with the subsequent steps. S2. Based on historical production data of PCB boards of the same specifications, fit the board thickness variation function. The thickness of multiple PCB boards in the current batch is measured at the same thickness sampling point using a thickness measuring device, and the average value is calculated to obtain the actual average thickness of each thickness sampling point of the current batch of PCB boards. The estimated thickness of each thickness measurement sampling point is obtained by using the board thickness variation function. For each thickness measurement sampling point, the residual between its actual average thickness and the estimated thickness is calculated to obtain the residual sampling point set {(x1p,y1p,ΔTp)|p=1,2,...,M}, where (x1p,y1p) is the planar coordinate of the p-th thickness measurement sampling point in the PCB board design coordinate system, ΔTp is the residual corresponding to the p-th thickness measurement sampling point, and M is the number of thickness measurement sampling points. Based on the residual sampling point set, a continuous residual distribution function is generated for the PCB board using the Kriging interpolation method; The PCB board predicted thickness distribution model is obtained by linearly superimposing the board thickness variation function and the continuous residual distribution function. The predicted thickness of the PCB board at the required back-drilled through hole is obtained through the PCB board predicted thickness distribution model. S3. Based on the predicted PCB thickness, obtain the PCB top surface height coordinates in the measurement coordinate system used when acquiring the scan point cloud data of the pad board for each required back-drilled through hole. S4. Obtain the initial vertical height of the drill bit relative to the surface of the back drilling platform. Use the difference between the initial vertical height and the height coordinate of the top surface of the PCB board at the required back drilling through hole as the downward distance of the drill bit when back drilling the required back drilling through hole.

[0021] In step S1, the acquisition of the scanned point cloud data of the wooden board is specifically as follows: A measurement coordinate system is established using the prefabricated reference point on the back drilling platform as the origin. The measurement coordinate system has the X-axis parallel to the long side of the back drilling platform, the Y-axis parallel to the short side of the back drilling platform, and the Z-axis perpendicular to the surface of the back drilling platform. By scanning the wooden board placed on the back drill platform with a scanning device, the position coordinates of multiple scanning points on the surface of the wooden board in the measurement coordinate system are obtained.

[0022] In step S1, the expression for the surface height field function is: ; in, Let n be the coordinates of the input point, and n be the number of scan points in the scanned point cloud data of the wooden board. , and The coefficient of the global linear trend term. Let be the weight coefficient for the i-th scan point. Here is the radial basis function kernel, and its expression is: , Let be the Euclidean distance from the input point to the i-th scan point; By substituting each of the scanned points in the scanned point cloud data into the surface height field function to form a system of equations, the coefficients of all global linear trend terms and the weight coefficients of each scanned point can be obtained.

[0023] In step S1, determining whether the flatness of the wooden board is acceptable based on the height value of the wooden board at each grid node specifically involves: S11. Combine all grid nodes in pairs to obtain each first node group. For each first node group, calculate the height difference between the two grid nodes. If any height difference is greater than or equal to the first preset threshold, the flatness of the pad board is determined to be unqualified. Otherwise, proceed to step S12. S12. Taking each grid node as the center, take the surrounding preset number of grid ranges as the sub-region of the corresponding grid node. For each sub-region, calculate its height value range. If the height value range is greater than or equal to the second preset threshold, the flatness of the corresponding sub-region is unqualified. If the flatness of any sub-region is unqualified, it is determined that the flatness of the pad board is unqualified. Otherwise, proceed to step S13. S13. Perform least squares plane fitting on the position data of the grid nodes to obtain the reference plane, calculate the height difference of each grid node relative to the reference plane, and calculate the root mean square deviation of the surface shape based on the height difference of each grid node relative to the reference plane. If the root mean square deviation of the surface shape is greater than or equal to the third preset threshold, the flatness of the pad board is determined to be unqualified.

[0024] In step S13, the position data of the grid nodes are fitted with least squares plane to obtain the reference plane, which is expressed as: z1(x,y)=A×x+B×y+C, where z1(x,y) is the reference height corresponding to the position of (x,y), and A, B and C are the coefficients that minimize the sum of squares of the fitting error.

[0025] In this solution, the flatness of the padding board is determined using a three-level judgment (i.e., steps S11 to S13), which covers multi-scale flatness characteristics from microscopic local to macroscopic overall, avoiding "missed judgment" that may be caused by a single indicator. Only padding boards that pass the three-level judgment will be used in the subsequent back drilling process, thereby ensuring that: the PCB board is subjected to uniform force when placed on the padding board, and the drill bit will not descend too far due to local depressions in the padding board, nor will it descend too far due to local protrusions.

[0026] In step S2, based on historical production data of PCBs of the same specifications, a function for fitting the board thickness variation is obtained, specifically: The expression for the plate thickness variation function is: ; (x1, y1) are the coordinates in the PCB design coordinate system, T0 is the nominal design thickness of the PCB, b1 is the coefficient describing the linear change trend of the thickness along the X-axis, b2 is the coefficient describing the linear change trend of the thickness along the Y-axis, b3 is the coefficient describing the degree of second-order nonlinear bending of the thickness along the X-axis, b4 is the coefficient describing the degree of second-order nonlinear bending of the thickness along the Y-axis, and b5 is the coefficient describing the thickness distortion trend under the coupling effect of the X-axis and Y-axis. The historical production data consists of a set of historical discrete sample points composed of the coordinates of historical thickness measurement sampling points of multiple PCB boards and the corresponding historical actual thickness. Based on the set of historical discrete sample points, the values ​​of b1, b2, b3, b4 and b5 are obtained by solving the least squares method.

[0027] In step S2, the continuous residual distribution function satisfies the condition that the value obtained by substituting the planar coordinates of each thickness measurement sampling point of the PCB board in the PCB board design coordinate system into the calculation is the corresponding residual value.

[0028] In step S2, the plate thickness residual is not independent random noise, but a spatially correlated structural variable caused by systematic factors such as lamination process, uneven material distribution, and local warping. To effectively model this correlation, this invention uses Kriging interpolation to generate a continuous residual distribution function. This method, based on variogram modeling, can provide the best linear unbiased estimate and quantify interpolation uncertainty. Compared with traditional interpolation methods such as inverse distance weighting and cubic spline methods, Kriging interpolation can more accurately restore the spatial distribution of the residual under limited and unevenly distributed sampling points, thereby improving the accuracy and robustness of the plate thickness prediction model and ultimately ensuring the drilling depth control quality of the back drilling process.

[0029] In step S3, the step of obtaining the PCB board top surface height coordinates in the measurement coordinate system used when acquiring the scanned point cloud data of the padding board for each required back-drilled through hole is specifically as follows: Extract the design coordinates of the required back-drill through holes from the PCB board drilling file; After placing the PCB board on the pad board, the design coordinates are transformed to planar coordinates in the measurement coordinate system used when the scanned point cloud data of the pad board is acquired. The Z-axis coordinates of each required back-drilled through hole on the surface of the pad board are obtained by using the planar coordinates through the surface height field function; The sum of the PCB thickness at the required back-drilled through hole location and the corresponding Z-axis coordinate value on the surface of the padding board is used as the top surface height coordinate of the PCB at the required back-drilled through hole location.

[0030] The design coordinates are coordinates in the PCB design coordinate system, which is constructed with one of the corner endpoints of the PCB as the origin, the long side of the PCB as the X-axis, and the short side of the PCB as the Y-axis.

[0031] Example 2 See Figure 2 As shown, the present invention also provides an intelligent optimization system for the back drilling process of PCB boards, specifically including: The flatness judgment module for the wooden board uses the scanned point cloud data of the wooden board and a thin plate spline interpolation algorithm to construct a surface height field function for the surface of the wooden board. The surface area of ​​the wooden board is divided into regular rectangular grids according to a preset spacing value. The height value of the wooden board at each grid node is obtained through the surface height field function. The module judges whether the flatness of the wooden board is qualified based on the height value of the wooden board at each grid node. If it is not qualified, it notifies to replace the wooden board. The thickness prediction module is used to obtain the predicted thickness of the PCB at the required back-drilled through-hole location. This is achieved through the following steps: Based on historical production data of PCBs of the same specifications, a function for the variation of board thickness was fitted. The thickness of multiple PCB boards in the current batch is measured at the same thickness sampling point using a thickness measuring device, and the average value is calculated to obtain the actual average thickness of each thickness sampling point of the current batch of PCB boards. The estimated thickness of each thickness measurement sampling point is obtained by using the board thickness variation function. For each thickness measurement sampling point, the residual between its actual average thickness and the estimated thickness is calculated to obtain the residual sampling point set {(x1p,y1p,ΔTp)|p=1,2,...,M}, where (x1p,y1p) is the planar coordinate of the p-th thickness measurement sampling point in the PCB board design coordinate system, ΔTp is the residual corresponding to the p-th thickness measurement sampling point, and M is the number of thickness measurement sampling points. Based on the residual sampling point set, a continuous residual distribution function is generated for the PCB board using the Kriging interpolation method; The PCB board predicted thickness distribution model is obtained by linearly superimposing the board thickness variation function and the continuous residual distribution function. The predicted thickness of the PCB board at the required back-drilled through hole is obtained through the PCB board predicted thickness distribution model. The height coordinate analysis module obtains the height coordinates of the top surface of the PCB board in the measurement coordinate system used when acquiring the scan point cloud data of the pad board for each required back-drilled through hole, based on the predicted PCB thickness. The down-drilling distance analysis module obtains the initial vertical height of the drill bit relative to the surface of the back-drilling platform. The difference between the initial vertical height and the height coordinate of the top surface of the PCB board at the required back-drilling through hole is used as the down-drilling distance of the drill bit when back-drilling the required back-drilling through hole.

[0032] The specific implementation of each module is the same as that in the above method embodiments, and will not be repeated here.

[0033] Example 3 The present invention also provides an electronic device, including: a processor, a transmitting device, an input device, an output device, and a memory. The processor may be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory may be implemented using a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), and is used to store computer program code, which includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any of the above possible implementation methods.

[0034] Example 4 The present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor of an electronic device, cause the processor to perform a method as described in any of the above possible implementations.

[0035] The beneficial effects of this invention are as follows: This invention acquires point cloud data by scanning a wooden board, constructs a surface height field function using thin-plate spline interpolation, and judges the flatness of the wooden board based on the grid node height values. If it fails to meet the standard, it is replaced. A predicted thickness distribution model for the PCB board is obtained by superimposing a board surface thickness variation function fitted based on historical production data and a continuous residual distribution function generated by Kriging interpolation. This model is used to obtain the predicted thickness at the back-drilled through-hole, thus obtaining the height coordinates of the PCB board top surface. The difference between the initial vertical height of the drill bit and the height coordinates of the PCB board top surface at the desired back-drilled through-hole is used as the drill bit's downward distance during back-drilling. This invention effectively achieves automatic quantitative detection of the flatness of the wooden board, improves detection accuracy, accurately analyzes the board thickness at the back-drilling location, effectively obtains the drill bit's downward distance, and improves back-drilling quality. By using the thin plate spline interpolation algorithm, a height field model of the pad board surface is constructed using the radial basis kernel function. This model accurately fits the discrete scan point cloud data, taking into account both the global linear trend and the local nonlinear deformation (weighted sum of radial basis functions), thus accurately reflecting the actual surface morphology of the pad board. The height values ​​of each grid node obtained in this way can be used for subsequent flatness determination, providing reliable pad board support surface data for the back drilling process. The flatness of the padding board is evaluated step by step through three levels: first, the maximum height difference between any two grid nodes is checked; then, the height fluctuation of each local sub-region is evaluated; and finally, the root mean square deviation of the overall surface shape relative to the fitted reference plane is calculated. This multi-level evaluation system, from point to local to overall, can comprehensively capture unevenness defects at different scales on the surface of the padding board and avoid omissions or misjudgments that may occur with a single indicator.

[0036] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0037] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0038] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A smart optimization method for the back drilling process of a PCB board, characterized in that, Includes the following steps: S1. Using the scanned point cloud data of the padding board, construct the surface height field function of the padding board surface using the thin plate spline interpolation algorithm. Divide the padding board surface area into regular rectangular grids according to the preset spacing value. Obtain the padding board height value at each grid node through the surface height field function. Determine whether the flatness of the padding board is qualified based on the padding board height value at each grid node. If it is not qualified, notify the replacement of the padding board. If it is qualified, continue with the subsequent steps. S2. Based on historical production data of PCB boards of the same specifications, fit the board thickness variation function. The thickness of multiple PCB boards in the current batch is measured at the same thickness sampling point using a thickness measuring device, and the average value is calculated to obtain the actual average thickness of each thickness sampling point of the current batch of PCB boards. The estimated thickness of each thickness measurement sampling point is obtained by using the plate thickness variation function. For each thickness measurement sampling point, the residual between its actual average thickness and the estimated thickness is calculated to obtain the residual sampling point set. Based on the residual sampling point set, a continuous residual distribution function is generated for the PCB board using the Kriging interpolation method. The continuous residual distribution function satisfies the condition that the value obtained by substituting the planar coordinates of each thickness sampling point of the PCB board in the PCB board design coordinate system into the calculation is the corresponding residual value. The PCB board predicted thickness distribution model is obtained by linearly superimposing the board thickness variation function and the continuous residual distribution function. The PCB board predicted thickness at the required back-drilled through hole is obtained through the PCB board predicted thickness distribution model. S3. Based on the predicted PCB thickness, obtain the PCB top surface height coordinates in the measurement coordinate system used when acquiring the scan point cloud data of the pad board for each required back-drilled through hole. S4. Obtain the initial vertical height of the drill bit relative to the surface of the back drilling platform. Use the difference between the initial vertical height and the height coordinate of the top surface of the PCB board at the required back drilling through hole as the downward distance of the drill bit when back drilling the required back drilling through hole.

2. The intelligent optimization method for the PCB back drilling process according to claim 1, characterized in that, The acquisition of the scanned point cloud data of the wooden board is as follows: A measurement coordinate system is established using the prefabricated reference point on the back drilling platform as the origin. The measurement coordinate system has the X-axis parallel to the long side of the back drilling platform, the Y-axis parallel to the short side of the back drilling platform, and the Z-axis perpendicular to the surface of the back drilling platform. By scanning the wooden board placed on the back drill platform with a scanning device, the position coordinates of multiple scanning points on the surface of the wooden board in the measurement coordinate system are obtained.

3. The intelligent optimization method for the PCB back drilling process according to claim 1, characterized in that, The expression for the surface height field function is: ； in, Let n be the coordinates of the input point, and n be the number of scan points in the scanned point cloud data of the wooden board. , and The coefficient of the global linear trend term. Let be the weight coefficient for the i-th scan point. Here is the radial basis function kernel, and its expression is: , Let be the Euclidean distance from the input point to the i-th scan point; By substituting each of the scanned points in the scanned point cloud data into the surface height field function to form a system of equations, the coefficients of all global linear trend terms and the weight coefficients of each scanned point can be obtained.

4. The intelligent optimization method for the PCB back drilling process according to claim 1, characterized in that, The method of determining whether the flatness of the wooden board is up to standard based on the height value of the wooden board at each grid node is as follows: S11. Combine all grid nodes in pairs to obtain each first node group. For each first node group, calculate the height difference between the two grid nodes. If any height difference is greater than or equal to the first preset threshold, the flatness of the pad board is determined to be unqualified. Otherwise, proceed to step S12. S12. Taking each grid node as the center, take the surrounding preset number of grid ranges as the sub-region of the corresponding grid node. For each sub-region, calculate its height value range. If the height value range is greater than or equal to the second preset threshold, the flatness of the corresponding sub-region is unqualified. If the flatness of any sub-region is unqualified, it is determined that the flatness of the pad board is unqualified. Otherwise, proceed to step S13. S13. Perform least squares plane fitting on the position data of the grid nodes to obtain the reference plane, calculate the height difference of each grid node relative to the reference plane, and calculate the root mean square deviation of the surface shape based on the height difference of each grid node relative to the reference plane. If the root mean square deviation of the surface shape is greater than or equal to the third preset threshold, the flatness of the pad board is determined to be unqualified.

5. The intelligent optimization method for the PCB back drilling process according to claim 1, characterized in that, Based on historical production data of PCBs of the same specifications, a function for the variation of board thickness was fitted, specifically: The expression for the plate thickness variation function is: ； (x1, y1) are the coordinates in the PCB design coordinate system, T0 is the nominal design thickness of the PCB, b1 is the coefficient describing the linear change trend of the thickness along the X-axis, b2 is the coefficient describing the linear change trend of the thickness along the Y-axis, b3 is the coefficient describing the degree of second-order nonlinear bending of the thickness along the X-axis, b4 is the coefficient describing the degree of second-order nonlinear bending of the thickness along the Y-axis, and b5 is the coefficient describing the thickness distortion trend under the coupling effect of the X-axis and Y-axis. The historical production data consists of a set of historical discrete sample points composed of the coordinates of historical thickness measurement sampling points of multiple PCB boards and the corresponding historical actual thickness. Based on the set of historical discrete sample points, the values ​​of b1, b2, b3, b4 and b5 are obtained by solving the least squares method.

6. The intelligent optimization method for the PCB back drilling process according to claim 1, characterized in that, The specific steps for obtaining the PCB board top surface height coordinates in the measurement coordinate system used when acquiring the scanned point cloud data of the padding board for each required back-drilled through hole are as follows: Extract the design coordinates of the required back-drill through holes from the PCB board drilling file; After placing the PCB board on the pad board, the design coordinates are transformed to planar coordinates in the measurement coordinate system used when the scanned point cloud data of the pad board is acquired. The Z-axis coordinates of each required back-drilled through hole on the surface of the pad board are obtained by using the planar coordinates through the surface height field function; The sum of the PCB thickness at the required back-drilled through hole location and the corresponding Z-axis coordinate value on the surface of the padding board is used as the top surface height coordinate of the PCB at the required back-drilled through hole location.

7. The intelligent optimization method for the PCB back drilling process according to claim 6, characterized in that, The design coordinates are coordinates in the PCB design coordinate system, which is constructed with one of the corner endpoints of the PCB as the origin, the long side of the PCB as the X-axis, and the short side of the PCB as the Y-axis.

8. An intelligent optimization system for the back drilling process of PCB boards, characterized in that, include: The flatness judgment module for the wooden board uses the scanned point cloud data of the wooden board and a thin plate spline interpolation algorithm to construct a surface height field function for the surface of the wooden board. The surface area of ​​the wooden board is divided into regular rectangular grids according to a preset spacing value. The height value of the wooden board at each grid node is obtained through the surface height field function. The module judges whether the flatness of the wooden board is qualified based on the height value of the wooden board at each grid node. If it is not qualified, it notifies to replace the wooden board. The thickness prediction module is used to obtain the predicted thickness of the PCB at the required back-drilled through-hole location. This is achieved through the following steps: Based on historical production data of PCBs of the same specifications, a function for the variation of board thickness was fitted. The thickness of multiple PCB boards in the current batch is measured at the same thickness sampling point using a thickness measuring device, and the average value is calculated to obtain the actual average thickness of each thickness sampling point of the current batch of PCB boards. The estimated thickness of each thickness measurement sampling point is obtained by using the board thickness variation function. For each thickness measurement sampling point, the residual between its actual average thickness and the estimated thickness is calculated to obtain the residual sampling point set {(x1p,y1p,ΔTp)|p=1,2,...,M}, where (x1p,y1p) is the planar coordinate of the p-th thickness measurement sampling point in the PCB board design coordinate system, ΔTp is the residual corresponding to the p-th thickness measurement sampling point, and M is the number of thickness measurement sampling points. Based on the residual sampling point set, a continuous residual distribution function is generated for the PCB board using the Kriging interpolation method; The PCB board predicted thickness distribution model is obtained by linearly superimposing the board thickness variation function and the continuous residual distribution function. The PCB board predicted thickness at the required back-drilled through hole is obtained through the PCB board predicted thickness distribution model. The height coordinate analysis module obtains the height coordinates of the top surface of the PCB board in the measurement coordinate system used when acquiring the scan point cloud data of the pad board for each required back-drilled through hole, based on the predicted PCB thickness. The down-drilling distance analysis module obtains the initial vertical height of the drill bit relative to the surface of the back-drilling platform. The difference between the initial vertical height and the height coordinate of the top surface of the PCB board at the required back-drilling through hole is used as the down-drilling distance of the drill bit when back-drilling the required back-drilling through hole.

9. The intelligent optimization system for PCB back drilling process according to claim 8, characterized in that, The expression for the surface height field function is: ； in, Let n be the coordinates of the input point, and n be the number of scan points in the scanned point cloud data of the wooden board. , and The coefficient of the global linear trend term. Let be the weight coefficient for the i-th scan point. Here is the radial basis function kernel, and its expression is: , Let be the Euclidean distance from the input point to the i-th scan point; By substituting each of the scanned points in the scanned point cloud data into the surface height field function to form a system of equations, the coefficients of all global linear trend terms and the weight coefficients of each scanned point can be obtained.

10. The intelligent optimization system for PCB back drilling process according to claim 8, characterized in that, The method of determining whether the flatness of the wooden board is up to standard based on the height value of the wooden board at each grid node is as follows: S11. Combine all grid nodes in pairs to obtain each first node group. For each first node group, calculate the height difference between the two grid nodes. If any height difference is greater than or equal to the first preset threshold, the flatness of the pad board is determined to be unqualified. Otherwise, proceed to step S12. S12. Taking each grid node as the center, take the surrounding preset number of grid ranges as the sub-region of the corresponding grid node. For each sub-region, calculate its height value range. If the height value range is greater than or equal to the second preset threshold, the flatness of the corresponding sub-region is unqualified. If the flatness of any sub-region is unqualified, it is determined that the flatness of the pad board is unqualified. Otherwise, proceed to step S13. S13. Perform least squares plane fitting on the position data of the grid nodes to obtain the reference plane, calculate the height difference of each grid node relative to the reference plane, and calculate the root mean square deviation of the surface shape based on the height difference of each grid node relative to the reference plane. If the root mean square deviation of the surface shape is greater than or equal to the third preset threshold, the flatness of the pad board is determined to be unqualified.

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