A Production Data Acquisition and Quality Management System for 12-Axis PCB Drilling Machines

Abstract

The present invention relates to the technical field of data management for intelligent manufacturing, and specifically discloses a production data acquisition and quality management system for a 12-axis PCB drilling machine. High-speed sensors are installed on each axis of the drilling machine to collect speed and pressure data in real time during the drilling process. A differential speed analysis method combined with Haar wavelet transform, zero-sequence pressure processing, and principal component analysis is used to extract speed change rate eigenvalues and pressure stability eigenvalues, respectively. These two types of eigenvalues are fused into a drilling quality feature vector, which is then input into a quality analysis model trained based on a gradient boosting tree to achieve intelligent evaluation and prediction of drilling quality. When quality anomalies are detected, the system automatically generates an early warning message and dynamically adjusts the drilling machine operating parameters in combination with a PID control algorithm, forming a closed-loop quality control mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of data management for intelligent manufacturing, and in particular to a production data acquisition and quality management system for a 12-axis PCB drilling machine. Background Art

[0002] With the rapid development of electronic information technology, printed circuit boards (PCBs), as core components of various electronic devices, are subject to increasingly stringent manufacturing precision and product quality requirements. In the PCB manufacturing process, the drilling process is a key link in determining the accuracy of circuit connections and the quality of subsequent assembly. 12-axis PCB drilling machines are widely used in the production of high-density, high-precision circuit boards due to their high efficiency and multi-hole simultaneous processing capabilities. To ensure the consistency of drilling quality and the stability of equipment operation, a quality management system is urgently needed that can collect key parameters in real time, intelligently analyze drilling status, and achieve closed-loop control. In recent years, with the development of industrial Internet of Things, big data analysis, and artificial intelligence technologies, intelligent manufacturing systems driven by sensor data have provided new solutions for improving the controllability and intelligence of the drilling process.

[0003] The existing technology has the following deficiencies:

[0004] Existing 12-axis PCB drilling machines remain at a relatively rudimentary stage in terms of data collection and quality monitoring. They rely primarily on simple statistical indicators and manual judgment, lacking the ability to extract and integrate features from speed and pressure data, making it difficult to accurately identify critical issues such as abnormal drill motion and pressure instability. Furthermore, the lack of machine learning-based quality assessment models and closed-loop feedback control mechanisms results in delayed responses to quality anomalies and inefficient adjustments, impacting production stability and product consistency. Therefore, an efficient quality management solution is urgently needed that can implement deep data mining, intelligent quality assessment, and automatic parameter optimization to address the current system's shortcomings in anomaly identification accuracy, intelligent assessment, and adaptive control capabilities. Summary of the Invention

[0005] The purpose of the present invention is to provide a 12-axis PCB drilling machine production data acquisition and quality management system to solve the problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A 12-axis PCB drilling machine production data acquisition and quality management system, including:

[0008] A data acquisition module, which collects speed and pressure data during the drilling process in real time through high-speed sensors installed on each axis of the drilling machine;

[0009] The high-speed sensor includes a speed sensor and a pressure sensor;

[0010] A speed data analysis module performs differential processing on the speed data, constructs a differential speed sequence, and calculates a speed change rate characteristic value for identifying abnormal fluctuations in drill bit motion;

[0011] A pressure data analysis module, which performs zero-sequence processing on the pressure data, constructs a zero-sequence pressure sequence, and calculates a pressure stability characteristic value for evaluating the stability of the drilling pressure;

[0012] a quality assessment module, wherein the module fuses the velocity change rate characteristic value and the pressure stability characteristic value into a drilling quality characteristic vector, and inputs the vector into a quality analysis model to perform drilling quality grade assessment;

[0013] The feedback control and optimization module generates quality warning information based on the evaluation results, and dynamically adjusts the speed and pressure parameters of the drilling machine to achieve closed-loop quality control of the drilling process.

[0014] As a further solution of the present invention, the identification of abnormal fluctuations in drill bit movement specifically includes:

[0015] By installing speed sensors on each axis of the drilling machine, speed data during the drilling process is collected in real time according to a time series. The speed data is differentially processed to construct a differential speed sequence. The speed change rate characteristic value is calculated to determine whether the speed change rate characteristic value is greater than or equal to a preset threshold. If so, the drill bit movement fluctuates abnormally; otherwise, the drill bit movement fluctuates normally.

[0016] As a further solution of the present invention: the velocity change rate characteristic value specifically includes:

[0017] The speed data collected in real time is obtained and integrated into a speed time series. The speed time series is processed by first-order difference, specifically, the speed value of the previous time point is subtracted from each speed value in the speed time series. That is, for the speed difference between any two adjacent time points, all speed differences are integrated to obtain a differential speed series.

[0018] The differential velocity series is analyzed by using Haar wavelet transform, and Haar wavelet is selected as the wavelet basis function. The differential velocity series is decomposed into multiple scales to generate a set of wavelet coefficients at different scales.

[0019] According to the wavelet coefficients obtained by Haar wavelet transform, the square sum of all wavelet coefficients at all scales is calculated to obtain the velocity change rate characteristic value.

[0020] As a further solution of the present invention: the evaluation of the stability of the drilling pressure specifically includes:

[0021] By installing pressure sensors on each axis of the drilling machine, pressure data during the drilling process is collected in real time according to the time series, the pressure data is processed in zero sequence, a zero-sequence pressure sequence is constructed, and the pressure stability characteristic value is calculated to determine whether the pressure stability characteristic value is greater than or equal to the preset threshold. If so, the drilling pressure is unstable; if not, the drilling pressure is stable.

[0022] As a further solution of the present invention: the process of obtaining the pressure stability characteristic value is:

[0023] Obtain real-time pressure data and integrate it into a pressure time series. Perform zero-sequence processing on the pressure data, i.e. remove the mean and construct a zero-sequence pressure series.

[0024] Based on the zero-sequence pressure sequence, calculate its covariance matrix; perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue set, and select the front The maximum eigenvalue is taken as the principal component, and the ratio of the eigenvalue of each principal component to the sum of the eigenvalue set is calculated to obtain the variance contribution rate of each principal component. The standard deviation of the variance contribution rates of all principal components is calculated to obtain the pressure stability eigenvalue.

[0025] As a further solution of the present invention, the fusion of the velocity change rate characteristic value and the pressure stability characteristic value into a drilling quality characteristic vector and inputting the vector into the quality analysis model specifically includes:

[0026] The speed change rate characteristic value and pressure stability characteristic value of the drilling work during the drilling machine production are obtained, and the speed change rate characteristic value and the pressure stability characteristic value are constructed into a drilling quality characteristic vector as the input of the quality analysis model to minimize the error between the predicted drilling quality score and the actual drilling quality score. This is the training target of the quality analysis model, and the quality analysis model is trained. According to the trained quality analysis model, the drilling quality score is output. The quality analysis model is a gradient boosting tree model.

[0027] As a further solution of the present invention: the training process of the quality analysis model is:

[0028] Multiple sets of velocity change rate characteristic values and pressure stability characteristic values in the historical drilling production process are obtained and constructed into a drilling quality characteristic vector; each drilling quality characteristic vector corresponds to an actual drilling quality score, and the multiple sets of historical drilling quality characteristic vectors and drilling quality scores are constructed into a training data set, which is input into the gradient boosting tree model for training. During the training process, an iterative method is used to construct a decision tree in each round. The newly generated tree in each round is used to fit the residual between the current model prediction value and the true label. The model parameters are optimized by continuously reducing the mean square error between the prediction value and the actual value. Finally, after the training is completed, a trained quality analysis model is obtained, which automatically predicts the drilling quality score of the current drilling operation based on the real-time collected velocity change rate characteristic value and pressure stability characteristic value.

[0029] As a further solution of the present invention: the drilling quality grade assessment specifically includes:

[0030] It is determined whether the drilling quality score of the drilling work during the production of the drilling machine is greater than or equal to a preset threshold. If so, the drilling quality during the production of the drilling machine is qualified; otherwise, the drilling quality during the production of the drilling machine is unqualified.

[0031] As a further solution of the present invention: if the drilling quality of the drilling machine during production is judged to be unqualified, the system automatically triggers the early warning mechanism, generates an early warning signal, and transmits it to the monitoring platform of the factory management via wireless communication.

[0032] As a further solution of the present invention, the dynamic adjustment of the speed and pressure parameters of the drilling machine to achieve closed-loop quality control of the drilling process specifically includes:

[0033] If the drilling quality is judged to be unqualified, the corresponding speed and pressure corrections are calculated based on the degree of deviation between the speed change rate characteristic value and the pressure stability characteristic value, and the new operating parameters are sent to the servo control systems of each axis of the drilling machine through the PLC controller. The corrections are generated according to a preset PID control algorithm. After completing the parameter adjustment, the system collects a new round of speed and pressure data and re-evaluates the drilling quality, forming a closed-loop feedback mechanism. The closed-loop feedback mechanism supports multiple rounds of iterative optimization until the drilling quality returns to within the acceptable range, thereby realizing online adaptive control of the operating status of the 12-axis PCB drilling machine.

[0034] Beneficial effects of the present invention:

[0035] (1) The present invention deploys high-speed sensors on each moving axis of the 12-axis PCB drilling machine to construct a high-frequency, multi-dimensional data acquisition system, thereby achieving millisecond-level synchronous acquisition of speed and pressure signals during the drilling process. The system adopts the method of differential velocity analysis combined with Haar wavelet transform to jointly model the speed data in the time and frequency domains, extract the velocity change rate eigenvalues with localized characteristics, and thus effectively capture the transient fluctuations and abnormal trends in the drill bit movement; at the same time, zero-sequence processing and principal component analysis technology are introduced for the pressure data. By decomposing the eigenvalues of the covariance matrix and calculating the standard deviation of the principal component variance contribution rate, the pressure stability eigenvalue that can reflect the stability of the multi-axis pressure distribution is obtained. The above two types of eigenvalues not only overcome the problems of insufficient sensitivity and poor robustness of traditional statistics (such as mean and variance) in non-stationary signal analysis, but also reveal the potential quality risks in the drilling process from the perspective of dynamic response, significantly improving the system's ability to identify small but significant process deviations, providing high-quality input for the subsequent quality assessment model based on machine learning, and thus achieving high-precision, real-time online assessment and early warning of drilling quality.

[0036] (2) The present invention innovatively introduces a drilling quality analysis model based on a gradient boosting tree, fully integrating multi-dimensional key parameters such as the velocity change rate characteristic value and the pressure stability characteristic value collected from high-speed sensors and extracted by the algorithm, and constructing a highly representative drilling quality feature vector as the model input to achieve high-precision prediction of the drilling quality score. The model optimized by training historical data can accurately identify the quality trend under different process parameter combinations and quickly evaluate the drilling quality grade based on the current feature vector during real-time operation. Once the predicted quality score is detected to be lower than the set threshold, the system determines that it is in a quality abnormality state. It not only automatically generates structured warning information and pushes it to the management terminal through the wireless communication module, but also triggers the feedback control mechanism. Combined with the proportional-integral-differential (PID) control algorithm, it calculates the optimal correction amount of the speed and pressure parameters according to the degree of deviation, dynamically adjusts the operating parameters of each axis of the drilling machine, and forms a closed-loop control process. This mechanism effectively improves the equipment's adaptive adjustment capabilities under complex working conditions, significantly enhances the stability and robustness of the production process, reduces dependence on human experience intervention, and realizes full-process intelligent management from data perception, intelligent decision-making to automatic control, greatly improving the product consistency and manufacturing efficiency of the 12-axis PCB drilling machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below with reference to the accompanying drawings.

[0038] Figure 1 This is a flow chart of a 12-axis PCB drilling machine production data acquisition and quality management system of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0040] See also Figure 1 As shown, the present invention is a 12-axis PCB drilling machine production data acquisition and quality management system, comprising:

[0041] A data acquisition module, which collects speed and pressure data during the drilling process in real time through high-speed sensors installed on each axis of the drilling machine;

[0042] The high-speed sensor includes a speed sensor and a pressure sensor;

[0043] A speed data analysis module performs differential processing on the speed data, constructs a differential speed sequence, and calculates a speed change rate characteristic value for identifying abnormal fluctuations in drill bit motion;

[0044] A pressure data analysis module, which performs zero-sequence processing on the pressure data, constructs a zero-sequence pressure sequence, and calculates a pressure stability characteristic value for evaluating the stability of the drilling pressure;

[0045] a quality assessment module, wherein the module fuses the velocity change rate characteristic value and the pressure stability characteristic value into a drilling quality characteristic vector, and inputs the vector into a quality analysis model to perform drilling quality grade assessment;

[0046] The feedback control and optimization module generates quality warning information based on the evaluation results, and dynamically adjusts the speed and pressure parameters of the drilling machine to achieve closed-loop quality control of the drilling process.

[0047] In the data acquisition module, the data acquisition module collects speed and pressure data of the drilling process in real time through high-speed sensors installed on each axis of the drilling machine, specifically including:

[0048] High-speed sensors installed on each axis of the drilling machine enable real-time monitoring and data collection of key physical quantities during the drilling process. Specifically, these sensors include speed sensors and pressure sensors. The speed sensors collect real-time motion speed data for each axis in the X, Y, and Z directions, while the pressure sensors collect drilling pressure data applied by the drill bit on the PCB. The sampling frequency is no less than 1kHz, ensuring the acquisition of high-precision time series raw data.

[0049] The high-speed sensor converts the collected analog signals into digital signals and transmits them to the local data acquisition unit via an industrial bus interface, generating a structured, time-stamped data sequence. The format includes, but is not limited to, {timestamp, axis number, speed value} and {timestamp, axis number, pressure value}. The system further caches and preprocesses the raw data to remove outliers and noise, providing a high-quality data foundation for the subsequent calculation of velocity change rate eigenvalues and pressure stability eigenvalues.

[0050] In the velocity data analysis module, the velocity data is differentially processed to construct a differential velocity sequence and calculate the velocity change rate characteristic value to identify abnormal fluctuations in drill bit motion. Specifically, the following are performed:

[0051] By installing speed sensors on each axis of the drilling machine, speed data during the drilling process is collected in real time according to a time series. The speed data is differentially processed to construct a differential speed sequence. The speed change rate characteristic value is calculated to determine whether the speed change rate characteristic value is greater than or equal to a preset threshold. If so, the drill bit movement fluctuates abnormally; otherwise, the drill bit movement fluctuates normally.

[0052] The speed change rate characteristic value specifically includes:

[0053] The speed data collected in real time is obtained and integrated into a speed time series. The speed time series is processed by first-order difference, specifically, the speed value of the previous time point is subtracted from each speed value in the speed time series. That is, for the speed difference between any two adjacent time points, all speed differences are integrated to obtain a differential speed series.

[0054] The differential velocity sequence is analyzed using Haar wavelet transform. Haar wavelet is selected as the wavelet basis function and multi-scale decomposition is performed on the differential velocity sequence to generate a set of wavelet coefficients at different scales. Haar wavelet transform is a simple form of discrete wavelet transform that achieves multi-level signal representation by recursively decomposing the signal into approximate and detail parts.

[0055] According to the wavelet coefficients obtained by the Haar wavelet transform, the sum of the squares of all wavelet coefficients at all scales is summed to obtain the velocity change rate characteristic value. For example, in the first level of decomposition, the energy sum of the detail coefficients is calculated, and the approximate coefficients are further decomposed and the energy is calculated until the predetermined number of decomposition levels is reached.

[0056] It should be noted that the present invention introduces a multi-scale analysis method based on Haar wavelet transform in the speed data analysis module, performs first-order difference processing on the speed time series collected during the drilling process, and obtains a speed change rate characteristic value with time-frequency localization characteristics through wavelet decomposition and energy aggregation calculation of the differential speed series. The speed change rate characteristic value not only reflects the instantaneous speed fluctuation intensity during the drill bit movement, but also can effectively identify abnormal fluctuation behaviors caused by factors such as mechanical vibration, servo system response delay or drill bit wear. By setting a reasonable threshold, the system can determine in real time whether the drill bit is in an abnormal motion state, thereby providing accurate data support for subsequent quality assessment and closed-loop control. This solution improves the sensitivity and recognition accuracy of small but critical speed mutation events, has stronger robustness and engineering practicality, and embodies significant technological progress and innovation.

[0057] In the pressure data analysis module, zero-sequence processing is performed on the pressure data to construct a zero-sequence pressure sequence and calculate the pressure stability characteristic value to evaluate the stability of the drilling pressure. Specifically, the following are performed:

[0058] By installing pressure sensors on each axis of the drilling machine, pressure data during the drilling process is collected in real time according to the time series, the pressure data is processed in zero sequence, a zero-sequence pressure sequence is constructed, and the pressure stability characteristic value is calculated to determine whether the pressure stability characteristic value is greater than or equal to the preset threshold. If so, the drilling pressure is unstable; if not, the drilling pressure is stable.

[0059] The process of obtaining the pressure stability characteristic value is as follows:

[0060] The real-time pressure data is acquired and integrated into a pressure time series. The original pressure data is processed by zero sequence, that is, the mean is removed to construct a zero sequence pressure series. The calculation expression is: ;in, represents the average pressure value in the pressure time series, Represents the pressure data in the zero-sequence pressure sequence after zero-sequence processing, represents the pressure data in the pressure time series, Indicates the collection time point;

[0061] Based on the zero-sequence pressure sequence, its covariance matrix is calculated, and the calculation expression is: ;

[0062] in, is the vector composed of zero-sequence pressure sequence, represents the transpose operation of a vector, Represents a vector The transposed vector of Indicates the total number of data collection points, Represents the covariance matrix; perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue set , Indicates the data points, express The characteristic values of the data points are selected The maximum eigenvalue is taken as the principal component, and the ratio of the eigenvalue of each principal component to the sum of the eigenvalue set is calculated to obtain the variance contribution rate of each principal component. The standard deviation of the variance contribution rates of all principal components is calculated to obtain the pressure stability eigenvalue.

[0063] It should be noted that the present invention introduces a feature extraction method based on the combination of zero-sequence processing and principal component analysis in the pressure data analysis module. By removing the mean of the pressure time series collected in real time during the drilling process, a zero-sequence pressure series is constructed, and its covariance matrix is further calculated. The principal components and their variance contributions are extracted by combining the eigenvalue decomposition technology, and finally the standard deviation of the principal component variance contribution is used as the pressure stability eigenvalue. The pressure stability eigenvalue can effectively reflect the concentration and volatility of the pressure distribution during the drilling process, and identify potential pressure instability caused by factors such as abnormal cooling system, unstable air pressure or drill bit blockage. By setting a reasonable judgment threshold, the system can realize online evaluation of drilling pressure stability, thereby improving the level of refinement of drilling quality control. Compared with the traditional method of relying solely on the average value or range for pressure judgment, this solution has stronger statistical robustness and sensitivity to multi-dimensional pressure change trends, reflecting significant technological innovation and engineering application value.

[0064] In the quality assessment module, the velocity change rate eigenvalue and the pressure stability eigenvalue are fused into a drilling quality eigenvector and input into the quality analysis model to evaluate the drilling quality level. Specifically, the following are performed:

[0065] The speed change rate characteristic value and pressure stability characteristic value of the drilling work during the drilling machine production are obtained, and the speed change rate characteristic value and the pressure stability characteristic value are constructed into a drilling quality characteristic vector as the input of the quality analysis model to minimize the error between the predicted drilling quality score and the actual drilling quality score. This is the training target of the quality analysis model, and the quality analysis model is trained. According to the trained quality analysis model, the drilling quality score is output. The quality analysis model is a gradient boosting tree model.

[0066] The training process of the quality analysis model is as follows:

[0067] Multiple sets of velocity change rate characteristic values and pressure stability characteristic values in the historical drilling production process are obtained and constructed into a drilling quality characteristic vector; each drilling quality characteristic vector corresponds to an actual drilling quality score, and the multiple sets of historical drilling quality characteristic vectors and drilling quality scores are constructed into a training data set, which is input into the gradient boosting tree model for training. During the training process, an iterative method is used to construct a decision tree in each round. The newly generated tree in each round is used to fit the residual between the current model prediction value and the true label. The model parameters are optimized by continuously reducing the mean square error between the prediction value and the actual value. Finally, after the training is completed, a trained quality analysis model is obtained, which automatically predicts the drilling quality score of the current drilling operation based on the real-time collected velocity change rate characteristic value and pressure stability characteristic value.

[0068] It is determined whether the drilling quality score of the drilling work during the production of the drilling machine is greater than or equal to a preset threshold. If so, the drilling quality during the production of the drilling machine is qualified; otherwise, the drilling quality during the production of the drilling machine is unqualified.

[0069] If the drilling quality of the drilling machine during production is judged to be unqualified, the system automatically triggers the early warning mechanism, generates an early warning signal, and transmits it to the monitoring platform of the factory management via wireless communication.

[0070] In the feedback control and optimization module, quality warning information is generated based on the evaluation results, and the speed and pressure parameters of the drilling machine are dynamically adjusted to achieve closed-loop quality control of the drilling process. Specifically, the following are included:

[0071] If the drilling quality is judged to be unqualified, the corresponding speed and pressure corrections are calculated based on the degree of deviation between the speed change rate characteristic value and the pressure stability characteristic value, and the new operating parameters are sent to the servo control systems of each axis of the drilling machine through the PLC controller; the corrections are generated according to the preset PID control algorithm to ensure that the adjustment process is smooth, responds quickly and does not cause new fluctuations. After completing the parameter adjustment, the system continues to collect a new round of speed and pressure data and re-evaluates the drilling quality to form a closed-loop feedback mechanism; the closed-loop feedback mechanism supports multiple rounds of iterative optimization until the drilling quality returns to the qualified range, thereby realizing online adaptive control of the operating status of the 12-axis PCB drilling machine and improving production stability and product consistency;

[0072] The specific calculation process of speed and pressure correction is as follows:

[0073] The system uses the difference between the current drilling quality score and the set quality acceptance threshold as the quality deviation signal; at the same time, the system combines the real-time calculated velocity change rate characteristic value and pressure stability characteristic value, and compares them with their respective set normal range thresholds to obtain the velocity deviation factor and pressure deviation factor; the proportional-integral-differential control algorithm is used to calculate the velocity adjustment amount and pressure adjustment amount, where the velocity adjustment amount is calculated as follows: the velocity proportional control coefficient multiplied by the velocity deviation factor, plus the velocity integral control coefficient multiplied by the time integral term of the velocity deviation factor, plus the velocity differential control coefficient multiplied by the time change rate of the velocity deviation factor; the pressure adjustment amount is calculated as follows: the pressure proportional control coefficient multiplied by the pressure deviation factor, plus the pressure integral control coefficient multiplied by the time integral term of the pressure deviation factor, plus the pressure differential control coefficient multiplied by the time change rate of the pressure deviation factor.

[0074] The system comprises a data acquisition module, a velocity data analysis module, a pressure data analysis module, a quality assessment module, and a feedback control and optimization module. High-speed sensors are deployed on each axis of the drilling machine to collect real-time velocity and pressure data during the drilling process. The velocity data is then subjected to first-order difference processing and Haar wavelet transform analysis to extract velocity change rate eigenvalues with time-frequency localization characteristics. Simultaneously, the pressure data is subjected to zero-sequence processing and principal component analysis to extract pressure stability eigenvalues based on the standard deviation of the variance contribution rate. These two eigenvalues are combined into a drilling quality feature vector, which is then input into a quality analysis model trained using a gradient boosting tree algorithm to achieve intelligent assessment of drilling quality. When unsatisfactory drilling quality is detected, the system automatically generates a quality warning and triggers a feedback control mechanism. Using a PID control algorithm, corrections to velocity and pressure parameters are calculated, dynamically adjusting the drilling machine's operating parameters to form a closed-loop quality control system. This system implements intelligent management of the entire process, from data acquisition, feature extraction, quality prediction, to automatic control. This significantly improves drilling process stability, product quality consistency, and the efficiency of responding to abnormal conditions, demonstrating promising engineering application prospects and widespread adoption.

[0075] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0076] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0077] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0078] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0079] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A 12-axis PCB drilling machine production data acquisition and quality management system, characterized in that: include: A data acquisition module, which collects speed and pressure data during the drilling process in real time through high-speed sensors installed on each axis of the drilling machine; The high-speed sensor includes a speed sensor and a pressure sensor; A speed data analysis module performs differential processing on the speed data, constructs a differential speed sequence, and calculates a speed change rate characteristic value for identifying abnormal fluctuations in drill bit motion; A pressure data analysis module, which performs zero-sequence processing on the pressure data, constructs a zero-sequence pressure sequence, and calculates a pressure stability characteristic value for evaluating the stability of the drilling pressure; a quality assessment module, wherein the module fuses the velocity change rate characteristic value and the pressure stability characteristic value into a drilling quality characteristic vector, and inputs the vector into a quality analysis model to perform drilling quality grade assessment; The feedback control and optimization module generates quality warning information based on the evaluation results, and dynamically adjusts the speed and pressure parameters of the drilling machine to achieve closed-loop quality control of the drilling process.

2. A 12-axis PCB drilling machine production data acquisition and quality management system according to claim 1, characterized in that: The identifying of abnormal fluctuations in drill bit movement specifically includes: By installing speed sensors on each axis of the drilling machine, speed data during the drilling process is collected in real time according to a time series. The speed data is differentially processed to construct a differential speed sequence. The speed change rate characteristic value is calculated to determine whether the speed change rate characteristic value is greater than or equal to a preset threshold. If so, the drill bit movement fluctuates abnormally; otherwise, the drill bit movement fluctuates normally.

3. A 12-axis PCB drilling machine production data acquisition and quality management system according to claim 2, characterized in that: The speed change rate characteristic value specifically includes: The speed data collected in real time is obtained and integrated into a speed time series. The speed time series is processed by first-order difference, specifically, the speed value of the previous time point is subtracted from each speed value in the speed time series. That is, for the speed difference between any two adjacent time points, all speed differences are integrated to obtain a differential speed series. The differential velocity series is analyzed by using Haar wavelet transform, and Haar wavelet is selected as the wavelet basis function. The differential velocity series is decomposed into multiple scales to generate a set of wavelet coefficients at different scales. According to the wavelet coefficients obtained by Haar wavelet transform, the square sum of all wavelet coefficients at all scales is calculated to obtain the velocity change rate characteristic value.

4. The 12-axis PCB drilling machine production data acquisition and quality management system according to claim 1, characterized in that: The evaluation of the stability of the drilling pressure specifically includes: By installing pressure sensors on each axis of the drilling machine, pressure data during the drilling process is collected in real time according to the time series, the pressure data is processed in zero sequence, a zero-sequence pressure sequence is constructed, and the pressure stability characteristic value is calculated to determine whether the pressure stability characteristic value is greater than or equal to the preset threshold. If so, the drilling pressure is unstable; if not, the drilling pressure is stable.

5. A 12-axis PCB drilling machine production data acquisition and quality management system according to claim 4, characterized in that: The process of obtaining the pressure stability characteristic value is as follows: Obtain real-time pressure data and integrate it into a pressure time series. Perform zero-sequence processing on the pressure data, i.e. remove the mean and construct a zero-sequence pressure series. Based on the zero-sequence pressure sequence, calculate its covariance matrix; Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue set and select The maximum eigenvalue is taken as the principal component, and the ratio of the eigenvalue of each principal component to the sum of the eigenvalue set is calculated to obtain the variance contribution rate of each principal component. The standard deviation of the variance contribution rates of all principal components is calculated to obtain the pressure stability eigenvalue.

6. The 12-axis PCB drilling machine production data acquisition and quality management system according to claim 1, characterized in that: The method of fusing the velocity change rate characteristic value and the pressure stability characteristic value into a drilling quality characteristic vector and inputting the vector into the quality analysis model specifically includes: The speed change rate characteristic value and pressure stability characteristic value of the drilling work during the drilling machine production are obtained, and the speed change rate characteristic value and the pressure stability characteristic value are constructed into a drilling quality characteristic vector as the input of the quality analysis model to minimize the error between the predicted drilling quality score and the actual drilling quality score. This is the training target of the quality analysis model, and the quality analysis model is trained. According to the trained quality analysis model, the drilling quality score is output. The quality analysis model is a gradient boosting tree model.

7. A 12-axis PCB drilling machine production data acquisition and quality management system according to claim 6, characterized in that: The training process of the quality analysis model is as follows: Multiple sets of velocity change rate characteristic values and pressure stability characteristic values in the historical drilling production process are obtained and constructed into a drilling quality characteristic vector; each drilling quality characteristic vector corresponds to an actual drilling quality score, and the multiple sets of historical drilling quality characteristic vectors and drilling quality scores are constructed into a training data set, which is input into the gradient boosting tree model for training. During the training process, an iterative method is used to construct a decision tree in each round. The newly generated tree in each round is used to fit the residual between the current model prediction value and the true label. The model parameters are optimized by continuously reducing the mean square error between the prediction value and the actual value. Finally, after the training is completed, a trained quality analysis model is obtained, which automatically predicts the drilling quality score of the current drilling operation based on the real-time collected velocity change rate characteristic value and pressure stability characteristic value.

8. The 12-axis PCB drilling machine production data acquisition and quality management system according to claim 1, characterized in that: The drilling quality grade assessment specifically includes: It is determined whether the drilling quality score of the drilling work during the production of the drilling machine is greater than or equal to a preset threshold. If so, the drilling quality during the production of the drilling machine is qualified; otherwise, the drilling quality during the production of the drilling machine is unqualified.

9. The 12-axis PCB drilling machine production data acquisition and quality management system according to claim 1, characterized in that: Generating quality warning information based on the evaluation results specifically includes: If the drilling quality of the drilling machine during production is judged to be unqualified, the system automatically triggers the early warning mechanism, generates an early warning signal, and transmits it to the monitoring platform of the factory management via wireless communication.

10. The 12-axis PCB drilling machine production data acquisition and quality management system according to claim 1, characterized in that: The dynamic adjustment of the speed and pressure parameters of the drilling machine to achieve closed-loop quality control of the drilling process specifically includes: If the drilling quality is judged to be unqualified, the corresponding speed and pressure corrections are calculated based on the degree of deviation between the speed change rate characteristic value and the pressure stability characteristic value, and the new operating parameters are sent to the servo control systems of each axis of the drilling machine through the PLC controller. The corrections are generated according to a preset PID control algorithm. After completing the parameter adjustment, the system collects a new round of speed and pressure data and re-evaluates the drilling quality, forming a closed-loop feedback mechanism. The closed-loop feedback mechanism supports multiple rounds of iterative optimization until the drilling quality returns to within the acceptable range, thereby realizing online adaptive control of the operating status of the 12-axis PCB drilling machine.

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