Skew adjusting method and system for manufacturing short groove of printed circuit

Abstract

The invention relates to the technical field of printed circuits, and discloses a skew adjustment method and system for printed circuit short slot manufacturing, and the method comprises the steps: obtaining a short slot form data set of a target circuit board; determining a short slot feature record set based on the short slot form data set, and constructing a compensation level list; according to each compensation grade in the compensation grade list, determining an adjustment strategy set, and determining an adaptive metric value of each compensation strategy and the current process condition; determining a target strategy set based on the adaptation metric value distribution state and the compensation level list; a compensation level list and a target strategy set are input into a circuit board manufacturing execution system, accurate multi-parameter acquisition of a short slot form, graded quantitative evaluation of a skew degree, intelligent matching and screening of a compensation strategy and dynamic closed-loop optimization of process parameters are realized, and the position of an assembled circuit element is not affected.

Description

A method and system for adjusting the skew in the manufacturing of short slots for printed circuit boards. Technical Field

[0001] This invention relates to the field of printed circuit technology, and more specifically, to a method and system for adjusting the skewness in the manufacturing of short slots in printed circuits. Background Technology

[0002] Short slot processing of printed circuit boards is a key step in precision electronics manufacturing. By precisely slotting the substrate, electrical connections and structural positioning functions are formed, and it is widely used in the production process of high-density interconnect devices.

[0003] Existing methods rely on fixed process parameters for batch processing. When equipment thermal drift, material batch differences, or environmental vibrations cause complex skewnesses such as width deviation, length inaccuracy, and centerline offset in the short slots, equipment parameters are usually adjusted manually based on experience, or the entire board is scrapped. The shortcomings of existing technologies are: a lack of real-time quantitative characterization of short slot skew morphology, making it difficult to identify localized, progressive deviations; compensation adjustments are based on single-point measurement feedback, lacking a hierarchical, systematic strategy selection mechanism; the entire board-level short slot array lacks collaborative optimization capabilities, and compensation conflicts between adjacent units can easily lead to secondary defects; it reduces efficiency, easily causes scrapping, and suffers from inconsistent actual results. Summary of the Invention

[0004] This application provides a method and system for adjusting the skew of short slots in printed circuit manufacturing, which solves the problems of insufficient skew detection accuracy, lack of systematic compensation strategies, lag in process correction response, and lack of array-level coordination capability in the prior art. It realizes accurate acquisition of multiple parameters of short slot shape, graded and quantitative evaluation of skew degree, intelligent matching and screening of compensation strategies, and dynamic closed-loop optimization of process parameters.

[0005] To achieve the above objectives, the present invention provides a method for skew adjustment in the manufacturing of short slots in printed circuit boards, comprising: acquiring a short slot morphology dataset collected by each measurement unit deployed on a target circuit board, wherein the short slot morphology dataset includes edge contour records, centerline offset records, width deviation records, and length deviation records for each short slot unit; based on the short slot morphology dataset, determining a set of short slot feature records characterizing the skew state, classifying the short slot feature record set by skew degree, and constructing a compensation level list; determining a corresponding set of adjustment strategies according to each compensation level in the compensation level list, and determining the adaptation metric value of each compensation strategy with the current process conditions; based on the distribution of the adaptation metric value and the compensation level list, selecting strategies that meet the adaptation conditions from the set of adjustment strategies as target adjustment strategies, forming a target strategy set; inputting the compensation level list and the target strategy set into a circuit board manufacturing execution system, and displaying the original morphology data of the short slots corresponding to the feature records and the expected correction effect in the circuit board manufacturing execution system.

[0006] Furthermore, the compensation level list includes: arranging each feature record in the short groove feature record set in descending order according to the skew value; dividing the sorted feature records into several compensation levels according to a preset level span, with each compensation level corresponding to a skew value range; and assigning a level identifier to each compensation level.

[0007] Further, when determining the corresponding set of adjustment strategies based on each compensation level in the compensation level list, and determining the adaptation metric of each compensation strategy with the current process conditions, the process includes: extracting a list of baseline slotted units corresponding to the compensation level; determining the process baseline state corresponding to the compensation level, wherein the process baseline state is determined by the ranking of the compensation level in the compensation level list; extracting each slotted unit in the list of baseline slotted units, determining a set of basic deformation patterns for the slotted unit, wherein the set of basic deformation patterns includes at least one deformation type; determining the influence weight of each deformation type in the set of basic deformation patterns on the process baseline state, forming an influence weight distribution set; and determining the influence weight distribution... The degree of fit between each weight value in the distribution set and the process baseline state is used to form a fit evaluation set. Based on the fit evaluation set, deformation types with a fit degree greater than or equal to a first threshold are retained from the basic deformation pattern set to form a preferred deformation pattern set. The initial compensation amplitude corresponding to each deformation type in the preferred deformation pattern set is determined. Short slot units with an initial compensation amplitude less than a second threshold are removed from the baseline short slot unit list to form a valid short slot unit list. A candidate adjustment strategy table is constructed for the valid short slot unit list based on the preferred deformation pattern set. The adjustment strategy set is confirmed according to the candidate adjustment strategy table. The degree of adaptation between each candidate strategy in the adjustment strategy set and the standard process specification is determined as the adaptation metric.

[0008] Further, when forming a preferred deformation pattern set by retaining deformation types with a matching degree greater than or equal to a first threshold from the basic deformation pattern set based on the matching degree evaluation set, the process includes: deleting matching degree values ​​less than or equal to a third threshold from the matching degree evaluation set to form an effective matching degree set; determining the basic deformation pattern corresponding to each matching degree value in the effective matching degree set to form a set of undetermined patterns; for each deformation type in the set of undetermined patterns, counting the number of matching degree values ​​containing the deformation type to form a support frequency; extracting all matching degree values ​​containing the deformation type to form a type matching degree subset; determining the core contribution of the deformation type based on the support frequency and the type matching degree subset; and retaining deformation types whose core contribution satisfies a preset contribution condition to form the preferred deformation pattern set.

[0009] Further, when constructing a candidate adjustment strategy table for the list of effective short slot units based on the preferred deformation pattern set, the process includes: determining a set of historical process patterns for each short slot unit in the list of effective short slot units, wherein the set of historical process patterns includes at least one historical processing scheme; performing multiple rounds of heterogeneous combination of the preferred deformation pattern set corresponding to the list of effective short slot units with the set of historical process patterns to generate several combination strategy tables; determining the expected correction effect level corresponding to the combination strategy table; determining the median value of the initial compensation amplitude corresponding to each deformation type in the preferred deformation pattern set; comparing the expected correction effect level with the median value to form a level difference value; and eliminating combination strategy tables whose absolute value of the level difference value is greater than a fourth threshold to form a filtered strategy table.

[0010] Furthermore, after forming the filtered strategy table, the method further includes: when the filtered strategy table meets the first condition, determining the preferred deformation pattern set as a candidate adjustment strategy table; when the filtered strategy table meets the second condition, using the entire filtered strategy table as a candidate adjustment strategy table.

[0011] Furthermore, after inputting the compensation level list and the target strategy set into the circuit board manufacturing execution system, the process further includes: obtaining a short slot re-inspection pattern dataset after implementing the target strategy set; comparing the re-inspection pattern dataset with the short slot pattern dataset to determine the actual correction offset record; locking the current process constraint parameter group when the actual correction offset record achieves the expected correction effect; and triggering the dynamic correction process of the process constraint parameter group when the actual correction offset record does not achieve the expected correction effect.

[0012] Furthermore, when the dynamic correction process of the process constraint parameter group is triggered, it includes: retrieving case records with similarity higher than the fifth threshold from the historical compensation case library; extracting the optimized process constraint parameter group from the case records; making targeted adjustments to the current process constraint parameter group based on the optimized process constraint parameter group; and synchronously updating the adjusted process constraint parameter group to the generation rules of the compensation level list.

[0013] To achieve the above objectives, the present invention also provides a skew adjustment system for short slot manufacturing of printed circuit boards, comprising: a data acquisition module, used to acquire a short slot morphology dataset collected by each measurement unit deployed on the target circuit board, wherein the short slot morphology dataset includes edge contour records, centerline offset records, width deviation records, and length deviation records for each short slot unit; a compensation level module, used to determine a set of short slot feature records representing the skew state based on the short slot morphology dataset, classify the short slot feature record set by skew degree, and construct a compensation level list; a process adaptation module, used to determine a corresponding set of adjustment strategies according to each compensation level in the compensation level list, and determine the adaptation metric value between each compensation strategy and the current process conditions; a set generation module, used to select strategies that meet the adaptation conditions from the set of adjustment strategies as target adjustment strategies based on the distribution state of the adaptation metric value and the compensation level list, forming a target strategy set; and a skew adjustment module, used to input the compensation level list and the target strategy set into the circuit board manufacturing execution system, and display the original morphology data of the short slots corresponding to the feature records and the expected correction effect in the circuit board manufacturing execution system.

[0014] Furthermore, it also includes: an effect detection module, used for: obtaining a short slot re-inspection morphology dataset after implementing the target strategy set; comparing the re-inspection morphology dataset with the short slot morphology dataset to determine the actual corrected offset record; locking the current process constraint parameter group when the actual corrected offset record achieves the expected correction effect; triggering the dynamic correction process of the process constraint parameter group when the actual corrected offset record does not achieve the expected correction effect, retrieving case records with similarity higher than the fifth threshold from the historical compensation case library; extracting the optimized process constraint parameter group from the case records; making targeted adjustments to the current process constraint parameter group based on the optimized process constraint parameter group; and synchronously updating the adjusted process constraint parameter group to the generation rules of the compensation level list.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention discloses a skew adjustment method and system for short slot manufacturing of printed circuit boards, which acquires a short slot shape dataset of the target circuit board; based on the short slot shape dataset, determines the short slot feature record set and constructs a compensation level list; according to each compensation level in the compensation level list, determines an adjustment strategy set and determines the adaptation metric value of each compensation strategy with the current process conditions; based on the distribution state of the adaptation metric value and the compensation level list, determines the target strategy set; and inputs the compensation level list and the target strategy set into the circuit board manufacturing execution system, thereby realizing the accurate acquisition of multiple parameters of the short slot shape, the graded and quantitative evaluation of the skew degree, the intelligent matching and screening of compensation strategies, and the dynamic closed-loop optimization of process parameters, ensuring that the position of the assembled circuit components is not affected. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 shows a schematic flowchart of a method for adjusting the skew in the manufacture of printed circuit short slots according to an embodiment of the present invention; Figure 2 shows a schematic structural diagram of a system for adjusting the skew in the manufacture of printed circuit short slots according to an embodiment of the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0022] As shown in Figure 1, an embodiment of the present invention discloses a method for skew adjustment in the manufacturing of short slots in printed circuit boards, comprising: S110: acquiring a short slot morphology dataset collected by each measurement unit deployed on the target circuit board, wherein the short slot morphology dataset includes edge contour records, centerline offset records, width deviation records, and length deviation records for each short slot unit; in this embodiment, the measurement unit adopts a high-precision optical measurement device and is deployed at key process nodes in the circuit board production line. A short slot unit refers to a functional short slot structure on a circuit board, commonly found in high-frequency signal transmission interfaces and shielding structures. The edge contour record includes the burr height, sidewall roughness, and geometric shape dispersion of the short slot edge. The centerline offset record reflects the positional deviation of the actual processing centerline relative to the design centerline, including X-axis offset and Y-axis offset. The width deviation record refers to the difference between the actual slot width and the design slot width, divided into entrance width deviation, middle width deviation, and bottom width deviation. The length deviation record refers to the difference between the actual slot length and the design slot length, including overall length deviation and segmented length deviation.

[0023] S120: Based on the short groove morphology dataset, determine the short groove feature record set representing the skew state, classify the skew degree of the short groove feature record set, and construct a compensation level list; In this embodiment, the short groove feature record set is obtained by feature extraction from the morphology dataset, and weights are assigned to the edge contour records, centerline offset records, width deviation records, and length deviation records in the short groove morphology dataset in combination with actual working conditions. The weight is assigned to the edge contour records as 0.3, the weight to the centerline offset records as 0.35, the weight to the width deviation records as 0.2, and the weight to the length deviation records as 0.15. Before calculation, all data are normalized and then weighted and summed to obtain a comprehensive skew score. It should be noted that the short groove feature record set includes short groove morphology data corresponding to multiple measurement units and the comprehensive skew score.

[0024] In this embodiment, the degree of skewness is classified using a five-level classification system. The short groove feature record set is sorted according to the comprehensive skewness score, and the compensation level list is divided into five levels: severe skewness, significant skewness, moderate skewness, slight skewness, and normal.

[0025] In some embodiments of this application, the compensation level list includes: arranging each feature record in the short slot feature record set in descending order according to the skew value; dividing the sorted feature records into several compensation levels according to a preset level span, with each compensation level corresponding to a skew value range; and assigning a level identifier to each compensation level.

[0026] In this embodiment, the preset level span is set to 20%, that is, each level contains 20% of the total number of records after sorting.

[0027] S130: Based on each compensation level in the compensation level list, determine the corresponding set of adjustment strategies, and determine the adaptation metric value of each compensation strategy with the current process conditions; in some embodiments of this application, when determining the corresponding set of adjustment strategies based on each compensation level in the compensation level list, and determining the adaptation metric value of each compensation strategy with the current process conditions, the process includes: extracting a list of baseline slotted units corresponding to the compensation level; determining the process baseline state corresponding to the compensation level, wherein the process baseline state is determined by the ranking of the compensation level in the compensation level list; extracting each slotted unit in the list of baseline slotted units, determining the basic deformation mode set of the slotted unit, wherein the basic deformation mode set contains at least one deformation type; and determining each deformation type in the basic deformation mode set. The influence weights on the process baseline state are weighted to form an influence weight distribution set; the degree of fit between each weight value in the influence weight distribution set and the process baseline state is determined to form a fit evaluation set; based on the fit evaluation set, deformation types with a fit degree greater than or equal to a first threshold are retained from the basic deformation pattern set to form a preferred deformation pattern set; the initial compensation amplitude corresponding to each deformation type in the preferred deformation pattern set is determined; short slot units with an initial compensation amplitude less than a second threshold are removed from the baseline short slot unit list to form an effective short slot unit list; a candidate adjustment strategy table for the effective short slot unit list is constructed based on the preferred deformation pattern set; the adjustment strategy set is confirmed based on the candidate adjustment strategy table; the degree of adaptation between each candidate strategy in the adjustment strategy set and the standard process specification is determined as the adaptation metric.

[0028] In this embodiment, the reference slot element list is composed of representative slot elements selected from each compensation level. The selection rule is as follows: take the slot element corresponding to the median of the skew value within that level, and two slot elements near the boundary value of that level. The list includes complete identification information, precise location coordinates, morphological data snapshots, and the compensation level to which the slot elements belong.

[0029] In this embodiment, the process baseline state reflects the typical combination of process parameters for the current production line at the corresponding compensation level. For example, the first ranked state corresponds to a high intervention state, where the spindle speed is reduced to 70% of the rated value, the feed rate is reduced to 60% of the rated value, and the coolant flow rate is increased to 120% of the rated value. The third ranked state corresponds to a standard state, using the standard values ​​of the process specification. The fifth ranked state corresponds to an optimized state, allowing a 10% increase in processing efficiency based on the standard values. The process baseline state is recorded in the state configuration file, which includes the state code, applicable level range, parameter baseline value set, and equipment capability requirements. The system automatically loads the corresponding process baseline state according to the compensation level, serving as a reference benchmark for strategy adaptation calculation.

[0030] In this embodiment, the set of basic deformation modes is derived by analyzing the morphological deviation characteristics of the slot unit, including five deformation types: center drift, width expansion, length shortening, contour distortion, and composite anomaly. Center drift refers to a systematic offset of the slot centerline relative to the design position. Width expansion refers to the slot inlet or middle width exceeding the design tolerance range. Length shortening refers to the actual slot length being less than the design value. Contour distortion refers to burrs, serrations, or irregular sidewalls appearing on the slot edge. Composite anomaly refers to the simultaneous presence of two or more basic deformation characteristics. Each deformation type corresponds to a typical cause of process failure: center drift is often caused by positioning system errors; width expansion is often due to tool wear; length shortening is related to depth control failure; contour distortion reflects tool vibration problems; and composite anomaly suggests a multi-factor coupled failure.

[0031] In this embodiment, the influence weights are determined using process failure mode influence analysis. The influence weight of center drift on positioning accuracy is set to 0.35, and its influence weight on machining efficiency is 0.2. The influence weight of width expansion on tool condition is 0.4, and its influence weight on surface quality is 0.3. The influence weight of length shortening on depth control is 0.45, and its influence weight on product function is 0.25. The influence weight of contour distortion on tool vibration is 0.38, and its influence weight on edge integrity is 0.32. For composite anomalies, the influence weights are calculated using a weighted superposition method, with each individual factor's influence weight multiplied by a coupling coefficient of 0.7. The influence weight distribution set is stored in matrix form, with rows representing deformation types and columns representing process parameter dimensions.

[0032] In this embodiment, the degree of fit is calculated by matching the weight values ​​with the process baseline state parameters. The process baseline state parameters include three dimensions: upper limit of equipment capacity, allowable range of process specifications, and production cycle time requirements. When the influence weight of the deformation type falls within the adjustable range of equipment capacity, the fit score is increased by 30 points; when the influence weight exceeds the allowable range of process specifications but can be compensated for through adjustments, the fit score is increased by 20 points; when the influence weight causes a change in production cycle time of less than 10%, the fit score is increased by 25 points. The fit evaluation set is presented in percentage form, with scores above 75 indicating high fit, 60-75 indicating medium fit, and below 60 indicating low fit.

[0033] In this embodiment, the first threshold is set to 65 points.

[0034] In this embodiment, the initial compensation amplitude is determined based on the degree of quantification deviation of the deformation type. The compensation amplitude for center drift type is calculated as 1.5 times the offset, with an upper limit of 0.2 mm. The compensation amplitude for width expansion type is calculated as 1.2 times the deviation, with an upper limit of 0.15 mm. The compensation amplitude for length shortening type is calculated as 1.8 times the amount of shortening, with an upper limit of 0.3 mm. The compensation amplitude for contour distortion type is calculated as 2 times the deviation of burr height or sidewall roughness, with an upper limit of 0.1 mm. The compensation amplitude for composite anomaly type is calculated as 0.8 times the compensation amplitude of the dominant deformation type, or the weighted average of the individual compensation amplitudes. All compensation amplitude calculation results are recorded in the compensation amplitude configuration table, which includes four fields: deformation type code, deviation range, compensation coefficient, and compensation upper limit.

[0035] In this embodiment, the second threshold is 0.05 mm.

[0036] In this embodiment, the candidate adjustment strategy table is validated (infeasible strategies are eliminated) and redundancy is cleaned up (duplicate strategies are deleted), and the strategies that finally pass the validation are used as the adjustment strategy set.

[0037] The beneficial effects of the above technical solution are as follows: by constructing a dynamic matching mechanism between the compensation level list and the target strategy set, the precise classification and handling of the skew problem in the manufacturing process of printed circuit short slots is realized. The degree of adaptation between each candidate strategy in the adjustment strategy set and the standard process specification is determined as an adaptation metric. This not only improves the pertinence and effectiveness of the adjustment, but also allows the most appropriate adjustment strategy to be adopted according to different degrees of skew, thereby maximizing production efficiency while ensuring processing quality.

[0038] In some embodiments of this application, when forming a preferred deformation pattern set by retaining deformation types with a matching degree greater than or equal to a first threshold from the basic deformation pattern set based on the matching degree evaluation set, the process includes: deleting matching degree values ​​less than or equal to a third threshold from the matching degree evaluation set to form an effective matching degree set; determining the basic deformation pattern corresponding to each matching degree value in the effective matching degree set to form a set of undetermined patterns; for each deformation type in the set of undetermined patterns, counting the number of matching degree values ​​containing the deformation type to form a support frequency; extracting all matching degree values ​​containing the deformation type to form a type matching degree subset; determining the core contribution of the deformation type based on the support frequency and the type matching degree subset; and retaining deformation types whose core contribution satisfies a preset contribution condition to form the preferred deformation pattern set.

[0039] In this embodiment, the third threshold is 55 points.

[0040] In this embodiment, the set of undetermined patterns is constructed through mapping relationships, mapping each value in the effective fit set back to its corresponding deformation type code. Deformation types with the same code are merged into one record. Each record in the set includes a deformation type identifier, a fit value, an influence weight vector, and a source slot cell number.

[0041] In this embodiment, the support frequency reflects the prevalence of a certain deformation type in multiple short slot elements. The number of times each deformation type appears in the original fit evaluation set is counted. The frequency calculation includes a weighting factor; for example, when a deformation type appears in a severely skewed short slot element, the count weight is 1.5; when it appears in a slightly skewed element, the weight is 0.8. The final support frequency is a weighted count result.

[0042] In this embodiment, the type fit subset is constructed separately for each deformation type, containing the set of fit scores obtained for that type across all short slot units. The subset generation process employs a grouping aggregation method, grouping the fit evaluation set according to the deformation type code. All fit values ​​within each group constitute a subset for the corresponding type. The subset data is used to calculate the score distribution characteristics for that deformation type, including the maximum value, minimum value, average value, and standard deviation.

[0043] In this embodiment, the core contribution is determined by both the support frequency weight and the central tendency of the fit. The calculation formula adopts a weighted product model: Core Contribution = (Support Frequency / Total Frequency) × 0.4 + (Average of Fit Subset / 100) × 0.6. The core contribution calculation result is normalized to the 0-1 range and retained to three decimal places. When the core contribution of a certain deformation type is greater than 0.7, it is marked as a critical deformation pattern; between 0.5 and 0.7, it is marked as an important deformation pattern; and below 0.5, it is marked as a general deformation pattern. Different levels enjoy different policy formulation priorities.

[0044] In this embodiment, the preset contribution condition is set to a core contribution of not less than 0.55.

[0045] The beneficial effects of the above technical solution are as follows: By introducing a core contribution evaluation system, multi-dimensional quantitative decision-making is achieved in the process of selecting optimal deformation patterns. This effectively avoids decision-making biases caused by single indicators. Frequency weighting design ensures greater attention is paid to severe skewness issues, while the convergence trend analysis of fit ensures the stability of strategy formulation. The core contribution index calculated through a weighted product model accurately reflects the comprehensive impact of deformation type on overall processing quality.

[0046] In some embodiments of this application, when constructing a candidate adjustment strategy table for the list of effective short slot units based on the preferred deformation pattern set, the process includes: determining a set of historical process patterns for each short slot unit in the list of effective short slot units, wherein the set of historical process patterns includes at least one historical processing scheme; performing multiple rounds of heterogeneous combination of the preferred deformation pattern set corresponding to the list of effective short slot units and the set of historical process patterns to generate several combination strategy tables; determining the expected correction effect level corresponding to the combination strategy table; determining the median value of the initial compensation amplitude corresponding to each deformation type in the preferred deformation pattern set; comparing the expected correction effect level with the median value to form a level difference value; and eliminating combination strategy tables whose absolute value of the level difference value is greater than a fourth threshold to form a filtered strategy table.

[0047] In this embodiment, the historical process pattern set is extracted from the process archive database of the manufacturing execution system, containing all processing records of the short slot unit or similar short slots within the past six months. Each record includes processing time, deformation type, compensation strategy used, implementation parameters, processing result (success / failure), and quality inspection data. Historical processing schemes are sorted by effectiveness score; those with scores above 85 are marked as excellent, 70-85 as good, and below 70 as needing improvement. Only excellent and good schemes are retained in the set to ensure the reliability of historical experience.

[0048] In this embodiment, the heterogeneous combination adopts the Cartesian product method, pairing each preferred deformation mode with each historical processing scheme to form a strategy combination unit.

[0049] In this embodiment, the expected correction effect level is determined by evaluation through a simulation prediction model. The model input is the combined strategy parameters and the current morphological data of the short slot, and the output is the compensated morphological prediction value.

[0050] In this embodiment, the median amplitude value is taken as the median of the compensation amplitudes of all effective short slot elements under the same deformation type. The calculation process first sorts the compensation amplitude sequence; if the number is odd, the median value is taken; if it is even, the average of the two median values ​​is taken. The median amplitude value represents the typical compensation intensity of this deformation type and is used to evaluate the rationality of the combination strategy.

[0051] In this embodiment, the comparison process establishes a mapping table between effect levels and compensation ranges. An excellent level corresponds to a compensation range of 0.15-0.25 mm, a good level to 0.08-0.15 mm, and a satisfactory level to 0.05-0.08 mm. The level difference value is calculated by subtracting the median range value from the midpoint of the expected effect level's range. A positive level difference value indicates insufficient strategy compensation, while a negative value indicates overcompensation. An absolute level difference value less than 0.03 mm is considered a reasonable match, between 0.03 and 0.06 mm is considered an acceptable deviation, and greater than 0.06 mm is considered a significant mismatch.

[0052] In this embodiment, the fourth threshold is 0.06 mm.

[0053] The beneficial effects of the above technical solution are as follows: By integrating historical process data and deformation pattern characteristics, a data-driven candidate strategy generation framework is constructed. The historical process pattern screening mechanism ensures the reliability of strategy inheritance, and the heterogeneous combination method achieves a deep integration of deformation characteristics and processing experience. This improves the scientific nature and operability of strategy formulation and provides a complete quantitative decision support system for adjusting the skew of short slots in printed circuits.

[0054] In some embodiments of this application, after forming the filtered strategy table, the method further includes: when the filtered strategy table meets a first condition, determining the preferred deformation pattern set as a candidate adjustment strategy table; when the filtered strategy table meets a second condition, using the entire filtered strategy table as a candidate adjustment strategy table.

[0055] In this embodiment, it is determined whether the filtered strategy table is an empty set. If so, it is determined that the filtered strategy table meets the first condition. It is also determined whether the filtered strategy table is a non-empty set. If so, it is determined that the filtered strategy table meets the second condition.

[0056] S140: Based on the distribution status of the adaptation metric and the compensation level list, select the strategy that meets the adaptation conditions from the set of adjustment strategies as the target adjustment strategy to form a set of target strategies; in this embodiment, the adaptation conditions are set as follows: the comprehensive adaptation metric is not less than 75 points and the score of each individual dimension is not less than 60 points.

[0057] The beneficial effects of the above technical solution are: ensuring the overall effectiveness of the strategy while ensuring that each key dimension meets the basic requirements. It not only guarantees the success rate of strategy implementation but also achieves the optimal selection of adjustment strategies through quantitative indicators, effectively enhancing the engineering application value of skew adjustment in printed circuit board short slot manufacturing.

[0058] S150: Input the compensation level list and the target strategy set into the circuit board manufacturing execution system, and display the original morphological data of the short slot corresponding to the feature record and the expected correction effect in the circuit board manufacturing execution system.

[0059] In this embodiment, the circuit board manufacturing execution system presents a list of compensation levels in a visual dashboard format, with each level marked by a different color. Severe misalignment is indicated by red, while normal misalignment is indicated by green. The target strategy set is displayed as strategy cards, each containing a strategy summary, an adaptation metric radar chart, and an implementation button. The original morphological data of the short slots is displayed through a 3D visualization model, supporting rotation, scaling, and cross-sectional analysis. The expected correction effect is presented using a comparison view, with the current measured data on the left, the simulated corrected data on the right, and the improvement magnitude of key parameters marked in the middle. For example, assuming the measurement unit collects morphological data from 5 short slot units, where unit A005 has a centerline offset of 52μm, a width deviation of 35μm, and a contour burr of 28μm, resulting in a comprehensive misalignment score of 85; unit A001 has a centerline offset of 45μm, a width deviation of 30μm, and a contour burr of 25μm, resulting in a score of 78; unit A003 has a score of 62; and units A002 and A004 have scores of 21 and 15, respectively. A005 and A001 are classified as L-1 severely skewed, A003 as L-3 medium, and A002 and A004 as L-5 normal. In the Manufacturing Execution System (MES) dashboard, L-1 level units are highlighted in red at the top. For L-1 level units, the system automatically matches the target strategy set: A005, due to center drift and contour distortion, recommends a tool compensation scheme of "replacing the tool (current life 92%) + reducing the spindle speed by 30%"; A001, due to width expansion, recommends a strategy of "tool offset correction + reducing the feed rate by 20%". The system sends parameters to the CNC machining center: the tool offset of unit A005 is adjusted to +0.05mm, and the spindle speed is reduced to 42000rpm.

[0060] The beneficial effects of the above technical solution are: it realizes the accurate acquisition of multiple parameters of short slot shape, the graded and quantitative evaluation of skewness, the intelligent matching and screening of compensation strategies, and the dynamic closed-loop optimization of process parameters, ensuring that the position of assembled circuit components is not affected.

[0061] In some embodiments of this application, after inputting the compensation level list and the target strategy set into the circuit board manufacturing execution system, the method further includes: obtaining a short slot re-inspection pattern dataset after implementing the target strategy set; comparing the re-inspection pattern dataset with the short slot pattern dataset to determine the actual correction offset record; locking the current process constraint parameter group when the actual correction offset record achieves the expected correction effect; and triggering the dynamic correction process of the process constraint parameter group when the actual correction offset record does not achieve the expected correction effect.

[0062] In this embodiment, the re-examination morphology dataset is collected within 30 minutes after the implementation of the target strategy to ensure that the data reflects the real-time effect of the strategy. The acquisition equipment is the same as the initial measurement unit to ensure data comparability.

[0063] In this embodiment, the comparative analysis adopts a unit-by-unit comparison method to calculate the differences in various morphological parameters of each short slot unit before and after the strategy implementation. The correction offset record includes four core offset quantities: edge contour improvement (burr height reduction), centerline alignment (offset reduction), width convergence (deviation reduction), and length compensation (deviation correction). The record also includes a comprehensive correction effect score, which is calculated using a weighted method: centerline alignment (0.35 weight), width convergence (0.25 weight), edge contour improvement (0.25 weight), and length compensation (0.15 weight).

[0064] In this embodiment, the criterion for judging the expected correction effect is a comprehensive correction score of not less than 75 points. The locking operation marks the current process constraint parameter set as a valid parameter set for verification and stores it in the process parameter optimization library. The parameter sets in the library are accompanied by applicable condition labels (such as short slot size range, substrate material type). The locked parameter set will be the preferred recommended parameters in the next production run. When the actual correction offset record does not achieve the expected correction effect, the dynamic correction process of the process constraint parameter set is triggered.

[0065] In this embodiment, the process constraint parameter group includes six core parameters: spindle speed, feed rate, tool wear limit, positioning pressure, coolant flow rate, and substrate temperature.

[0066] The beneficial effects of the above technical solution are: the real-time data acquisition mechanism for re-inspection ensures the timeliness of effect evaluation, and the comparison-by-comparison method achieves precise location of deviations. The dynamic correction process can quickly respond to different morphological deviation characteristics, forming a complete closed loop of evaluation-feedback-optimization, which significantly improves the process stability and product yield of printed circuit short slot manufacturing.

[0067] In some embodiments of this application, when the dynamic correction process of the process constraint parameter group is triggered, the process includes: retrieving case records with similarity higher than the fifth threshold from the historical compensation case library; extracting the optimized process constraint parameter group from the case records; making targeted adjustments to the current process constraint parameter group based on the optimized process constraint parameter group; and synchronously updating the adjusted process constraint parameter group to the generation rules of the compensation level list.

[0068] In this embodiment, the historical compensation case library stores all successful corrective process cases that failed to meet the standards within the past year. Each case includes initial morphological data, implementation strategy, actual results, final adjustment parameters, and correction cycle. Similarity calculation employs a multi-dimensional feature matching algorithm, comparing the similarity between the current non-compliant case and historical cases in four dimensions: slot type, deformation mode, equipment status, and substrate material. The fifth threshold is set at 85%, meaning only historical cases with a comprehensive similarity higher than 85% are retrieved.

[0069] In this embodiment, targeted adjustment employs a parameter transfer learning method, mapping parameters that significantly improve performance in the optimized parameter group to the current parameter group. The adjustment rules are as follows: when a parameter value in the optimized parameter group deviates from the current value by more than 15% and this parameter is marked as a key parameter in the case study, the current value is replaced with the optimized value; when the deviation is in the range of 8%-15%, the average of the optimized value and the current value is taken as the new value; when the deviation is less than 8%, the current value remains unchanged.

[0070] In this embodiment, the synchronous update mechanism ensures that the new parameters are immediately applied to the compensation level determination of subsequent batches. The update operation modifies the baseline values ​​of the process constraint parameters in the compensation level generation rule file, so that the new parameter composition becomes the basis for the next skewness classification.

[0071] The beneficial effects of the above technical solution are as follows: a dynamic correction mechanism based on experience reuse is established through intelligent matching and parameter transfer learning from a historical case database. The multi-dimensional feature matching algorithm ensures the accurate retrieval of historical experience, the parameter transfer rules realize the targeted transfer of optimization parameters, and the synchronous update mechanism guarantees the real-time iteration of process standards.

[0072] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0073] Correspondingly, as shown in Figure 2, this application also provides a skew adjustment system for short slot manufacturing of printed circuit boards, comprising: a data acquisition module, used to acquire a short slot morphology dataset collected by each measurement unit deployed on the target circuit board, wherein the short slot morphology dataset includes edge contour records, centerline offset records, width deviation records, and length deviation records for each short slot unit; a compensation level module, used to determine a set of short slot feature records representing the skew state based on the short slot morphology dataset, classify the short slot feature record set by skew degree, and construct a compensation level list; a process adaptation module, used to determine a corresponding set of adjustment strategies according to each compensation level in the compensation level list, and determine the adaptation metric value between each compensation strategy and the current process conditions; a set generation module, used to select strategies that meet the adaptation conditions from the set of adjustment strategies as target adjustment strategies based on the distribution state of the adaptation metric value and the compensation level list, forming a target strategy set; and a skew adjustment module, used to input the compensation level list and the target strategy set into the circuit board manufacturing execution system, and display the original morphology data of the short slot corresponding to the feature records and the expected correction effect in the circuit board manufacturing execution system.

[0074] In some embodiments of this application, the method further includes: an effect detection module, configured to: obtain a short slot re-inspection morphology dataset after implementing the target strategy set; compare the re-inspection morphology dataset with the short slot morphology dataset to determine the actual corrected offset record; when the actual corrected offset record achieves the expected correction effect, lock the current process constraint parameter group; when the actual corrected offset record does not achieve the expected correction effect, trigger the dynamic correction process of the process constraint parameter group, retrieve case records with similarity higher than the fifth threshold from the historical compensation case library; extract the optimized process constraint parameter group from the case records; perform targeted adjustment of the current process constraint parameter group based on the optimized process constraint parameter group; and synchronously update the adjusted process constraint parameter group to the generation rules of the compensation level list.

[0075] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0076] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0077] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for adjusting the skewness in the manufacturing of short slots in printed circuit boards, characterized in that, include: Acquire the short slot shape dataset collected by each measurement unit deployed on the target circuit board, wherein the short slot shape dataset includes edge contour records, center line offset records, width deviation records and length deviation records for each short slot unit; based on the short slot shape dataset, determine the short slot feature record set representing the skew state, classify the skew degree of the short slot feature record set, and construct a compensation level list; Based on each compensation level in the compensation level list, a corresponding set of adjustment strategies is determined, and the fit metric of each compensation strategy with the current process conditions is determined. Based on the distribution of the fit metric and the compensation level list, strategies that meet the fit conditions are selected from the set of adjustment strategies as target adjustment strategies to form a target strategy set. The compensation level list and the target strategy set are input into the circuit board manufacturing execution system, and the original morphological data of the short slots corresponding to the feature records and the expected correction effect are displayed in the circuit board manufacturing execution system.

2. The method for adjusting the skewness in the manufacturing of short slots for printed circuits according to claim 1, characterized in that, The compensation level list includes: arranging each feature record in the short slot feature record set in descending order according to the skew value; dividing the sorted feature records into several compensation levels according to a preset level span, with each compensation level corresponding to a skew value range; and assigning a level identifier to each compensation level.

3. The method for adjusting the skewness in the manufacturing of short slots in printed circuits according to claim 1, characterized in that, When determining the corresponding set of adjustment strategies for each compensation level in the compensation level list, and determining the adaptation metric of each compensation strategy to the current process conditions, the process includes: extracting a list of baseline slotted units corresponding to the compensation level; determining the process baseline state corresponding to the compensation level, wherein the process baseline state is determined by the ranking of the compensation level in the compensation level list; extracting each slotted unit in the list of baseline slotted units, determining a set of basic deformation patterns for the slotted unit, wherein the set of basic deformation patterns contains at least one deformation type; determining the influence weight of each deformation type in the set of basic deformation patterns on the process baseline state, forming an influence weight distribution set; and determining the influence weight distribution set... The degree of fit between each weight value and the process baseline state forms a fit evaluation set; based on the fit evaluation set, deformation types with a fit degree greater than or equal to a first threshold are retained from the basic deformation pattern set to form a preferred deformation pattern set; the initial compensation amplitude corresponding to each deformation type in the preferred deformation pattern set is determined; short slot units with an initial compensation amplitude less than a second threshold are removed from the baseline short slot unit list to form an effective short slot unit list; a candidate adjustment strategy table for the effective short slot unit list is constructed based on the preferred deformation pattern set; the adjustment strategy set is confirmed based on the candidate adjustment strategy table; the degree of adaptation between each candidate strategy in the adjustment strategy set and the standard process specification is determined as the adaptation metric.

4. The method for adjusting the skewness in the manufacturing of short slots for printed circuits according to claim 3, characterized in that, When forming a preferred deformation pattern set by retaining deformation types with a matching degree greater than or equal to a first threshold from the basic deformation pattern set based on the matching degree evaluation set, the process includes: deleting matching degree values ​​less than or equal to a third threshold from the matching degree evaluation set to form an effective matching degree set; determining the basic deformation pattern corresponding to each matching degree value in the effective matching degree set to form a set of undetermined patterns; for each deformation type in the set of undetermined patterns, counting the number of matching degree values ​​containing the deformation type to form a support frequency; extracting all matching degree values ​​containing the deformation type to form a type matching degree subset; determining the core contribution of the deformation type based on the support frequency and the type matching degree subset; and retaining deformation types whose core contribution satisfies a preset contribution condition to form the preferred deformation pattern set.

5. The method for adjusting the skewness in the manufacturing of short slots for printed circuits according to claim 3, characterized in that, When constructing a candidate adjustment strategy table for the list of effective short slot units based on the preferred deformation pattern set, the process includes: determining a set of historical process patterns for each short slot unit in the list of effective short slot units, wherein the set of historical process patterns includes at least one historical processing scheme; performing multiple rounds of heterogeneous combination of the preferred deformation pattern set corresponding to the list of effective short slot units and the set of historical process patterns to generate several combination strategy tables; determining the expected correction effect level corresponding to the combination strategy table; determining the median value of the initial compensation amplitude corresponding to each deformation type in the preferred deformation pattern set; comparing the expected correction effect level with the median value to form a level difference value; and eliminating combination strategy tables whose absolute value of the level difference value is greater than a fourth threshold to form a filtered strategy table.

6. The method for adjusting the skewness in the manufacturing of short slots for printed circuits according to claim 5, characterized in that, After forming the filtered strategy table, the method further includes: when the filtered strategy table meets the first condition, determining the preferred deformation pattern set as a candidate adjustment strategy table; when the filtered strategy table meets the second condition, using the entire filtered strategy table as a candidate adjustment strategy table.

7. The method for adjusting the skewness in the manufacturing of short slots for printed circuits according to claim 1, characterized in that, After inputting the compensation level list and the target strategy set into the circuit board manufacturing execution system, the process further includes: obtaining the short slot re-inspection pattern dataset after implementing the target strategy set; comparing the re-inspection pattern dataset with the short slot pattern dataset to determine the actual correction offset record; locking the current process constraint parameter group when the actual correction offset record achieves the expected correction effect; and triggering the dynamic correction process of the process constraint parameter group when the actual correction offset record does not achieve the expected correction effect.

8. The method for adjusting the skewness in the manufacturing of short slots for printed circuits according to claim 7, characterized in that, When the dynamic correction process of the process constraint parameter group is triggered, it includes: retrieving case records with similarity higher than the fifth threshold from the historical compensation case library; extracting the optimized process constraint parameter group from the case records; making targeted adjustments to the current process constraint parameter group based on the optimized process constraint parameter group; and synchronously updating the adjusted process constraint parameter group to the generation rules of the compensation level list.

9. A skew adjustment system for manufacturing short slots in printed circuit boards, applied to the skew adjustment method for manufacturing short slots in printed circuit boards as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire the short slot morphology dataset collected by each measurement unit deployed on the target circuit board. The short slot morphology dataset includes edge contour records, centerline offset records, width deviation records, and length deviation records for each short slot unit. The compensation level module is used to determine a set of short slot feature records representing the skew state based on the short slot morphology dataset, classify the skewness degree of the short slot feature record set, and construct a compensation level list. The process adaptation module is used to determine a corresponding set of adjustment strategies for each compensation level in the compensation level list, and determine the adaptation metric value between each compensation strategy and the current process conditions. The set generation module is used to select strategies that meet the adaptation conditions from the set of adjustment strategies as target adjustment strategies based on the distribution of the adaptation metric value and the compensation level list, forming a target strategy set. The skew adjustment module is used to input the compensation level list and the target strategy set into the circuit board manufacturing execution system, and display the original short slot morphology data and expected correction effect corresponding to the feature records in the circuit board manufacturing execution system.

10. The skew adjustment system for manufacturing short slots in printed circuits according to claim 9, characterized in that, Also includes: The effect detection module is used to: obtain the short slot re-inspection pattern dataset after implementing the target strategy set; The re-inspection morphology dataset is compared with the short slot morphology dataset to determine the actual correction offset record; when the actual correction offset record achieves the expected correction effect, the current process constraint parameter group is locked. When the actual corrected offset record does not achieve the expected correction effect, the dynamic correction process of the process constraint parameter group is triggered, and case records with similarity higher than the fifth threshold in the historical compensation case library are retrieved; the optimized process constraint parameter group in the case records is extracted. Based on the optimized process constraint parameter set, the current process constraint parameter set is adjusted in a targeted manner; The adjusted process constraint parameter set will be synchronously updated to the generation rules of the compensation level list.

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