A PCB board production process management system and method based on intelligent traceability

Abstract

The invention discloses a PCB board production process management system and method based on intelligent tracing, and relates to the technical field of intelligent tracing. The invention extracts production features; obtains production links and parallel lines of each link in a process line by using the production features; constructs a process line framework of a PCB generation process; calculates the correlation between each production data in adjacent production links, judges the correlation between different production data in all adjacent production links, and uses the production data with the largest correlation as the transfer data of the corresponding production link; automatically learns the Bayesian network structure by maximum likelihood estimation to obtain the potential causal path of each transfer data; generates a chain quality assessment model; and after judging that the quality of a real-time PCB board is unqualified by using a qualified quality threshold, reverse tracing is performed by using the potential causal path of each production link in the chain quality assessment model.

Description

Technical Field

[0001] The present invention relates to the field of intelligent tracing technology, and specifically to a PCB board production process management system and method based on intelligent tracing. Background Technology

[0002] The widespread use of electronic products in modern society has led to increasing requirements for their quality and reliability. As the core component of electronic products, the quality of PCB boards directly affects product performance. In order to ensure the quality of electronic products, it is necessary to strictly monitor and trace the production process of PCB boards so that problems can be quickly located and solved when they occur; with the development of manufacturing industry towards intelligence and digitalization, and the deepening of concepts such as intelligent manufacturing, enterprises need to use advanced technologies to realize the visualization, controllability and traceability of the production process. Intelligent traceability technology has become a key means for PCB board manufacturers to improve production efficiency, quality control level and corporate competitiveness. Entering the 21st century, with the rapid development of information technology, especially the rise of technologies such as the Internet, the Internet of Things, and big data, the intelligent traceability technology of PCB board production process has developed rapidly. Enterprises have begun to introduce systems such as MES and ERP to realize the information management of the production process and the integrated sharing of data, and can conduct a more comprehensive traceability of the entire production process of PCB boards from raw materials to finished products. However, when tracing abnormal multi-layer PCBs, due to the numerous production links, there is no information exchange and communication between some production links during the PCB production process, which greatly delays the tracing time when tracing unqualified PCBs. SUMMARY OF THE INVENTION

[0003] The purpose of the present invention is to provide a PCB board production process management system and method based on intelligent traceability to solve the problems raised in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A PCB board production process management method based on intelligent traceability, the method comprising the following steps:

[0006] S100, collect all process lines in the PCB board production process, analyze the production data in all process lines, and extract production characteristics; use the production characteristics to obtain the production links and parallel lines of each link in the process line; build the process line framework of the PCB generation process;

[0007] Furthermore, the specific steps of constructing the process line framework of the PCB generation process are:

[0008] S101. Collect all the process lines in the PCB board production process, collect all the production data in each process line, and extract the data values of each production data at the start and end of the corresponding production line; calculate the change value of all the production data in the corresponding production line. The formula is:

[0009] ;

[0010] In the formula, Sc represents the change value of each production data in the calculated production line, S after represents the data value of each extracted production data at the start of the corresponding production line, and S before represents the data value of each extracted production data at the end of the corresponding production line;

[0011] After calculating the change values of all the production data in the production line, extract the production data corresponding to the maximum change value as the production feature of the production line; calculate the production features of all the production lines in the PCB production process in turn; use the production features to judge all the production lines. When the production features are the same, judge the corresponding production lines as parallel lines. When the production features are different, judge the corresponding production lines as different production links;

[0012] S102. When all the production links and the parallel lines of each production link in the PCB production process are judged, extract the time series of each production link in the PCB production process; use the bubble sort method to sort the extracted time series, and use the time sorting result to sort the production links to obtain the PCB production link chain; use the parallel lines and the production link chain in each production link to construct the process line framework of the PCB generation process.

[0013] By constructing the process line framework, the resources required for each link can be clearly displayed, and users can accurately allocate human, material and equipment resources accordingly; sorting out the process line can accurately locate repeated and redundant operations. The framework sets standards and specifications for each process, and operators operate according to the process, reducing the efficiency fluctuations caused by individual habit differences. New employees can also quickly get started with the help of the framework, reducing training costs and adaptation time, and ensuring the stability of the overall production rhythm. And the framework reflects the material flow speed, assisting in setting a reasonable inventory level. For parts with fast turnover, maintain a low inventory and high-frequency replenishment strategy; for those with slow turnover, accurately adjust the purchase quantity to avoid tying up funds and reducing warehousing costs.

[0014] S200. After generating the process line framework of the PCB production process, extract the production data in each production link, calculate the correlation degree between each production data in adjacent production links, judge the correlation degree of different production data in all adjacent production links, and take the production data with the largest correlation degree as the transfer data of the corresponding production link;

[0015] Further, the specific steps for using the production data with the highest correlation as the transfer data for the corresponding production link are as follows:

[0016] S201. After generating the process line framework of the PCB production process, collect the records of the historical PCB production process, extract the production data in each production link. Assume that the number of production data in each production link extracted is N. Draw a curve graph using the production data of adjacent production links, and use the linear regression algorithm to fit the two production data in the curve graph of adjacent production links. Calculate the linear regression equation of the two production data as , in the formula, Sa represents the production data of the latter production link in adjacent production links, Sb represents the production data of the former production link in adjacent production links; a represents the slope of the calculated linear regression equation, and b represents the intercept of the calculated linear regression equation;

[0017] In a pair of adjacent production links, calculate the linear regression equation of every two production data. Assume that the number of types of production data in each production link is m, then the number of linear regression equations calculated in a pair of adjacent production links is m 2 , and calculate the linear regression equations of the production data of every two adjacent production links in the process line framework;

[0018] S202. Extract the slope in each linear regression equation as the correlation degree of the two production data. Compare the m 2 correlation degrees in every two adjacent production links, and select the two production data corresponding to the maximum correlation degree as the transfer data of adjacent production links. Search in the process line framework to obtain the transfer data Sd of all adjacent production links.

[0019] S300. Collect the transfer data of each production link when an abnormality occurs in the historical PCB board production process. Use the transfer data of adjacent links as nodes to construct a Bayesian network, automatically learn the Bayesian network structure through maximum likelihood estimation, and obtain the potential causal path of each transfer data; and mark it in the process line framework;

[0020] Further, the specific steps for obtaining the potential causal path of each transfer data are as follows:

[0021] S301. Collect the transfer data of each production link during abnormal occurrences in the historical PCB board production process. Let the transfer data of the previous production link in adjacent production links be Sdb, and the transfer data of the subsequent production link be Sda; collect two sets of data values during the historical PCB production process, which are set as A and B respectively, where A represents the set of transfer data of the subsequent production link, and B represents the set of transfer data of the previous production link; initially set an undirected graph, use sets A and B as the nodes of the undirected graph, perform deletion, addition, and turning operations on each edge in the undirected graph, and calculate the network structure score of the undirected graph after each operation during the operation. The calculation model formula is constructed as follows:

[0022] ;

[0023] In the formula, BIC represents the network structure score of the undirected graph after each calculated operation, n represents the number of transfer data in the collected transfer data set; k represents the number of model parameters, represents the maximum likelihood estimate of the model; by calculating the network structure score, select the best network structure of the undirected graph when the network structure score is the largest; when there is an edge from B to A in the best network structure, judge that there is a causal flow from transfer data B to transfer data A;

[0024] S302. After judging that there is a causal flow between transfer data B and transfer data A, extract the linear regression equation of transfer data B and A in S200 as the causal relationship, combine the causal relationship with the edges of transfer data B and transfer data A in the best network structure to form a potential causal path, and mark the generated potential causal path in the process line framework; repeatedly calculate and mark the potential causal paths of all adjacent production links in the process line framework.

[0025] S400. In the adjacent production links in the process line framework, use the transfer data output by the previous production link as the input of the subsequent production link, and combine the marked potential causal paths to generate a chain - type quality assessment model; use the chain - type quality assessment model to calculate the quality of the PCB board produced in real - time;

[0026] Further, the specific steps for calculating the quality of the PCB board produced in real - time using the chain - type quality assessment model are as follows:

[0027] S401. Connect the undirected graphs of every two adjacent production links according to the process line framework, construct the transfer data output by the previous production link as the input of the subsequent production link, combine the marked potential causal paths, and use the Bayesian algorithm to build a chain - type quality assessment model; collect the production data during the historical PCB production process to construct a training set, and input the training set into the chain - type quality assessment model to train the model;

[0028] S402. When performing real-time PCB production, input the transfer data in the first production link of the process line framework into the chain quality assessment model, and use the chain quality assessment model for calculation until the transfer data of the last production link is calculated. Take the transfer data of the last production link as the quality Z of the produced PCB.

[0029] Since the model follows the chain structure of the production process, once a quality problem occurs in the product, the chain quality assessment model can accurately locate the specific production process, and can also dig deep into the root cause of the defect. During production, the chain model continuously receives real-time data of each process and gives an immediate quality assessment result. If it is monitored that the parameters of a certain process start to deviate from the normal range, and according to past rules, there is a high probability of quality problems in the subsequent processes, an early warning will be issued in time to reserve sufficient response time for the enterprise. By analyzing the contribution degree of each process in the chain model to the final quality and the mutual influence relationship, the bottleneck link in the production process can be accurately found.

[0030] S500. When producing a PCB board in real time, record the transfer data of each production link, draw internal codes on each layer of the final PCB board, and the internal codes record the transfer data and quality of all production links corresponding to the layer of the PCB board; then draw external codes outside the entire PCB board, and the external codes contain the position information of each internal code;

[0031] Furthermore, the specific steps for setting the internal code and the external code are as follows:

[0032] S501. When producing a multi-layer PCB board in real time, record the transfer data of each production link, and draw internal codes on each layer of the final PCB board. The internal code of each layer contains the transfer data of all production links for producing the corresponding layer of the PCB board;

[0033] S502. After drawing the internal codes on each layer of the multi-layer PCB board, extract the position of each internal code, and draw external codes outside the multi-layer PCB board. The external codes are formed by drilling holes on the Panel boards of the topmost and bottommost layers of the multi-layer PCB board; the external codes contain the position information of each internal code.

[0034] S600. Calculate the qualified quality threshold using the PCB board data during the normal production process of historical PCBs. The user scans the external code and the internal code to obtain the real-time quality of the PCB board. After judging that the real-time quality of the PCB board is unqualified using the qualified quality threshold, perform reverse tracing using the potential causal paths of each production link in the chain quality assessment model.

[0035] Furthermore, the specific steps for performing reverse tracing using the potential causal paths of each production link in the chain quality assessment model are as follows:

[0036] S601. Collect the transfer data of the last production link when the historical PCB board is produced qualified. Let the number of collected qualified transfer data of historical PCB production be G. Calculate the average value and standard deviation of the G collected transfer data, and calculate the qualified quality threshold when the PCB is produced qualified. The formula is:

[0037] ;

[0038] In the formula, In represents the calculated qualified quality threshold of PCB production, Fp represents the average value of the G calculated transfer data, and Fst represents the standard deviation of the G calculated transfer data;

[0039] S602. The user obtains the quality of the multi-layer PCB board by scanning the code, and uses the qualified quality threshold to judge the quality during real-time PCB production. When Z≥In, it is judged that the quality of the PCB board produced in real time is qualified. When Z<In, it is judged that the quality of the PCB board produced in real time is unqualified; when judging the PCB board produced in real time, first use the outer code of the multi-layer PCB to locate the position of the single-layer PCB board with unqualified quality, and then scan the inner code of the single-layer PCB board with unqualified quality. Use the potential causal path in the chain quality evaluation model to trace back reversely, and compare the transfer data of each production link with the transfer data of the qualified PCB board to find the production link with anomalies.

[0040] The inner and outer code systems give the PCB board a fine identification from the outer layer to the inner layer. When anomalies occur, the outer code can be used to quickly access the traceability system, and then the inner code can be used to accurately locate the specific layer or module, avoiding large-scale investigation and saving time and labor costs; then it is constructed in combination with the chain quality evaluation model according to the sequence of the production process and the causal relationship between processes. When anomalies are found, it can trace back reversely along the "chain" and accurately find the upstream process that causes the problem and locate the root cause of the failure.

[0041] A PCB board production process management system based on intelligent traceability. The PCB board production process management system includes a data collection module, a process line framework construction module, a transfer data search module, a potential causal path search module, a chain quality evaluation model construction module, a quality judgment module, and a traceability module;

[0042] The data collection module is used to collect production data during the historical PCB production process;

[0043] The process line framework construction module is used to analyze the production data in all process lines and construct a process line framework;

[0044] The production data lookup module is used to extract the production data in each production link, calculate the correlation degree between each production data in adjacent production links, and use the production data with the largest correlation degree as the transfer data for the corresponding production link;

[0045] The potential causal path lookup module is used to construct a Bayesian network with the transfer data of adjacent links as nodes, automatically learn the Bayesian network structure through maximum likelihood estimation, and obtain the potential causal path of each transfer data;

[0046] The chain - type quality assessment model construction module is used to use the transfer data output by the previous production link as the input of the next production link, and combine the marked potential causal path to generate a chain - type quality assessment model;

[0047] The quality judgment module is used to calculate the qualified quality threshold by using the transfer data of the last link of the historical production PCB board, and use the qualified quality threshold to judge the quality of the real - time produced PCB board;

[0048] The traceability module is used for the user to trace the abnormal production link by scanning the code and using the chain - type quality assessment model when it is determined that the quality of the real - time PCB board is unqualified.

[0049] The potential causal path lookup module includes an optimal network structure unit and a potential causal path unit;

[0050] The optimal network structure unit is used to calculate the network structure score when operating on the set undirected graph, and select the network structure corresponding to the maximum network structure score as the optimal network structure;

[0051] The potential causal path unit is used to use the linear regression equation of the transfer data of adjacent production links as the causal relationship, and combine the optimal network structure to generate the potential causal path.

[0052] The traceability module includes a code - scanning traceability unit and a model traceability unit;

[0053] The code - scanning traceability unit is used to judge the position of the single - layer PCB board with unqualified quality after the user scans the internal and external codes;

[0054] The model traceability unit is used to trace the abnormal production link by using the chain - type quality assessment model when the position of the single - layer PCB board with unqualified quality is determined.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] 1. By constructing a process line framework, the present invention can clearly display the resources required for each link, enabling users to accurately allocate human, material, and equipment resources accordingly. By sorting out the process lines, repetitive and redundant operations can be accurately located. The framework sets standard specifications for each process, and operators can operate according to the process, reducing efficiency fluctuations caused by individual habit differences.

[0057] 2. The present invention implements traceability in cooperation with internal and external codes and models. The internal and external code system assigns a fine-grained identification to the PCB board from the outer layer to the inner layer. When an abnormality occurs, the external code can be used to quickly access the traceability system, and then the internal code can be used to accurately locate the specific layer or module, avoiding large-scale troubleshooting and saving time and labor costs. Subsequently, a chain-type quality assessment model is constructed based on the sequence of the production process and the causal relationship between processes. When an abnormality is found, it can trace back reversely along the "chain" to accurately identify the upstream process that caused the problem and locate the root cause of the failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a module distribution diagram of a PCB board production process management system based on intelligent traceability according to the present invention;

[0059] Figure 2 is a step schematic diagram of a PCB board production process management method based on intelligent traceability according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution,

[0062] A PCB board production process management method based on intelligent traceability, the method comprising the following steps:

[0063] S100. Collect all process lines in the PCB board production process, analyze the production data in all process lines, extract production characteristics; obtain production links and parallel lines for each link in the process line using the production characteristics; construct a process line framework for the PCB generation process;

[0064] The specific steps for constructing the process line framework for the PCB generation process are as follows:

[0065] S101. Collect all the process lines in the PCB board production process, collect all the production data in each process line, and extract the data values of each production data at the start and end of the corresponding production line; calculate the change value of all the production data in the corresponding production line. The formula is:

[0066] ;

[0067] In the formula, Sc represents the change value of each production data in the calculated production line, S after represents the data value of each extracted production data at the start of the corresponding production line, and S before represents the data value of each extracted production data at the end of the corresponding production line;

[0068] After calculating the change values of all the production data in the production line, extract the production data corresponding to the maximum change value as the production feature of the production line; calculate the production features of all the production lines in the PCB production process in turn; use the production features to judge all the production lines. When the production features are the same, judge the corresponding production line as a parallel line. When the production features are different, judge the corresponding production line as different production links;

[0069] S102. When all the production links and the parallel lines of each production link in the PCB production process are judged, extract the time series of each production link in the PCB production process; use the bubble sort method to sort the extracted time series, and use the time sorting result to sort the production links to obtain the PCB production link chain; use the parallel lines and the production link chain in each production link to construct the process line framework of the PCB generation process.

[0070] By constructing the process line framework, the resources required for each link can be clearly shown, and users can accurately allocate human, material and equipment resources accordingly; by sorting out the process line, repeated and redundant operations can be accurately located. The framework sets standards and specifications for each process, and operators operate according to the process, reducing the efficiency fluctuations caused by personal habit differences. New employees can also quickly get started with the help of the framework, reducing training costs and adaptation time, and ensuring the stability of the overall production rhythm. And the framework reflects the material flow speed and helps to set a reasonable inventory level. For parts with fast turnover, maintain a low inventory and high-frequency replenishment strategy; for those with slow turnover, accurately adjust the purchase quantity to avoid overstocking funds and reduce warehousing costs.

[0071] S200. After generating the process line framework of the PCB production process, extract the production data in each production link, calculate the correlation degree between each production data in adjacent production links, judge the correlation degree of different production data in all adjacent production links, and take the production data with the largest correlation degree as the transfer data of the corresponding production link;

[0072] The specific steps for taking the production data with the highest degree of correlation as the transfer data for the corresponding production link are as follows:

[0073] S201. After generating the process line framework of the PCB production process, collect the records of the historical PCB production process, extract the production data in each production link. Let the number of production data in each production link extracted be N. Use the production data of adjacent production links to draw a curve graph, and use the linear regression algorithm to fit the two production data in the curve graph of adjacent production links. The linear regression equation of the two production data is calculated as , in the formula, Sa represents the production data of the latter production link in adjacent production links, and Sb represents the production data of the former production link in adjacent production links; a represents the slope of the calculated linear regression equation, and b represents the intercept of the calculated linear regression equation;

[0074] In a pair of adjacent production links, calculate the linear regression equation of every two production data. Let the number of types of production data in each production link be m, then the number of linear regression equations calculated in a pair of adjacent production links is m 2 , and calculate the linear regression equations of the production data of every two adjacent production links in the process line framework;

[0075] S202. Extract the slope in each linear regression equation as the correlation degree of the two production data. Compare the m 2 correlation degrees in every two adjacent production links, and select the two production data corresponding to the maximum correlation degree as the transfer data of adjacent production links. Search in the process line framework to obtain the transfer data Sd of all adjacent production links.

[0076] S300. Collect the transfer data of each production link when an abnormality occurs in the historical PCB board production process. Use the transfer data of adjacent links as nodes to construct a Bayesian network, and automatically learn the Bayesian network structure through maximum likelihood estimation to obtain the potential causal path of each transfer data; and mark it in the process line framework;

[0077] The specific steps for obtaining the potential causal path of each transfer data are as follows:

[0078] S301. Collect the transfer data of each production link during abnormal occurrences in the historical PCB board production process. Let the transfer data of the previous production link in adjacent production links be Sdb, and the transfer data of the subsequent production link be Sda; collect two sets of data values in the historical PCB production process, which are set as A and B respectively, where A represents the set of transfer data of the subsequent production link, and B represents the set of transfer data of the previous production link; initially set an undirected graph, use sets A and B as the nodes of the undirected graph, perform deletion, addition, and turning operations on each edge in the undirected graph, and calculate the network structure score of the undirected graph after each operation during the operation. The calculation model formula is constructed as follows:

[0079] ;

[0080] In the formula, BIC represents the network structure score of the undirected graph after each operation calculated, n represents the number of transfer data in the collected transfer data set; k represents the number of model parameters, represents the maximum likelihood estimate of the model; by calculating the network structure score, select the best network structure of the undirected graph when the network structure score is the largest; when there is an edge from B to A in the best network structure, determine that there is a causal flow from transfer data B to transfer data A;

[0081] S302. After determining that there is a causal flow between transfer data B and transfer data A, extract the linear regression equation of transfer data B and A in S200 as the causal relationship, combine the causal relationship with the edge of transfer data B and transfer data A in the best network structure to form a potential causal path, and mark the generated potential causal path in the process line framework; repeatedly calculate and mark the potential causal paths of all adjacent production links in the process line framework.

[0082] S400. In the adjacent production links in the process line framework, use the transfer data output by the previous production link as the input of the subsequent production link, and combine the marked potential causal paths to generate a chain - type quality assessment model; use the chain - type quality assessment model to calculate the quality of the PCB board produced in real - time;

[0083] The specific steps for calculating the quality of the PCB board produced in real - time using the chain - type quality assessment model are as follows:

[0084] S401. Connect the undirected graphs of every two adjacent production links according to the process line framework, construct the transfer data output by the previous production link as the input of the subsequent production link, combine the marked potential causal paths, and use the Bayesian algorithm to build a chain - type quality assessment model; collect the production data in the historical PCB production process to construct a training set, and input the training set into the chain - type quality assessment model to train the model;

[0085] S402. When performing real-time PCB production, input the transfer data in the first production link of the process line framework into the chain quality assessment model, and use the chain quality assessment model for calculation until the transfer data of the last production link is calculated. Take the transfer data of the last production link as the quality Z of the produced PCB.

[0086] Since the model follows the chain structure of the production process, once a quality problem occurs in the product, the chain quality assessment model can accurately locate the specific production process, and can also dig deep into the root cause of the defect. During production, the chain model continuously receives real-time data of each process and gives the quality assessment result immediately; if it is monitored that the parameters of a certain process begin to deviate from the normal range, and according to past rules, there is a high probability of quality problems in the subsequent processes, it will issue a warning in time, leaving enough response time for the enterprise. By analyzing the contribution degree of each process in the chain model to the final quality and the mutual influence relationship, the bottleneck link in the production process can be accurately found.

[0087] S500. When producing PCB boards in real time, record the transfer data of each production link, draw internal codes on each layer of the final PCB board, and the internal codes record the transfer data and quality of all production links corresponding to the layer of the PCB board; then draw external codes outside the entire PCB board, and the external codes contain the position information of each internal code;

[0088] The specific steps for setting the internal code and the external code are as follows:

[0089] S501. When producing multi-layer PCB boards in real time, record the transfer data of each production link, and draw internal codes on each layer of the final PCB board. The internal code of each layer contains the transfer data of all production links corresponding to the layer of the PCB board produced.

[0090] S502. After drawing the internal codes on each layer of the multi-layer PCB board, extract the position of each internal code, and draw external codes outside the multi-layer PCB board. The external codes are formed by drilling holes on the Panel boards of the topmost and bottommost layers of the multi-layer PCB board; the external codes contain the position information of each internal code.

[0091] S600. Calculate the qualified quality threshold using the PCB board data during the normal production process of historical PCBs. The user scans the external code and the internal code to obtain the real-time quality of the PCB board. After using the qualified quality threshold to determine that the real-time quality of the PCB board is unqualified, perform reverse tracing using the potential causal paths of each production link in the chain quality assessment model.

[0092] The specific steps for performing reverse tracing using the potential causal paths of each production link in the chain quality assessment model are as follows:

[0093] S601. Collect the transfer data of the last production link when the historical PCB board is produced qualified. Let the number of historical PCB production qualified transfer data collected be G, calculate the average value and standard deviation of the G transfer data collected, and calculate the qualified quality threshold when the PCB is produced qualified by using the average value and standard deviation. The formula is:

[0094] ;

[0095] In the formula, In represents the calculated qualified quality threshold of PCB production, Fp represents the average value of the G transfer data calculated, and Fst represents the standard deviation of the G transfer data calculated;

[0096] S602. The user obtains the quality of the multi-layer PCB board by scanning the code, and uses the qualified quality threshold to judge the quality during real-time PCB production. When Z≥In, it is judged that the quality of the PCB board produced in real time is qualified. When Z<In, it is judged that the quality of the PCB board produced in real time is unqualified; when judging the PCB board produced in real time, first use the outer code of the multi-layer PCB to locate the position of the single-layer PCB board with unqualified quality, and then scan the inner code of the single-layer PCB board with unqualified quality, and use the potential causal path in the chain quality evaluation model to trace back reversely, and compare the transfer data of each production link with the transfer data of the qualified PCB board to find the production link with abnormalities.

[0097] The inner and outer code systems give the PCB board a fine identification from the outer layer to the inner layer. When an abnormality occurs, the outer code can be used to quickly access the traceability system, and then the inner code can be used to accurately locate the specific layer or module, avoiding large-scale troubleshooting and saving time and labor costs; then combined with the chain quality evaluation model, it is constructed according to the sequence of the production process and the causal relationship between processes. When an abnormality is found, it can trace back reversely along the "chain" and accurately find the upstream process that causes the problem and locate the root cause of the failure.

[0098] A PCB board production process management system based on intelligent traceability. The PCB board production process management system includes a data collection module, a process line framework construction module, a transfer data search module, a potential causal path search module, a chain quality evaluation model construction module, a quality judgment module, and a traceability module;

[0099] The data collection module is used to collect production data during the historical PCB production process;

[0100] The process line framework construction module is used to analyze the production data in all process lines and construct a process line framework;

[0101] The transfer data search module is used to extract production data in each production link, calculate the correlation degree between each production data in adjacent production links, and use the production data with the largest correlation degree as the transfer data for the corresponding production link;

[0102] The potential causal path search module is used to construct a Bayesian network with the transfer data of adjacent links as nodes, automatically learn the Bayesian network structure through maximum likelihood estimation, and obtain the potential causal path of each transfer data;

[0103] The chain - type quality assessment model construction module is used to use the transfer data output by the previous production link as the input of the next production link, and combine the marked potential causal path to generate a chain - type quality assessment model;

[0104] The quality judgment module is used to calculate the qualified quality threshold using the transfer data of the last link of the historical production PCB board, and use the qualified quality threshold to judge the quality of the real - time produced PCB board;

[0105] The traceability module is used for the user to trace the abnormal production link using barcode scanning and the chain - type quality assessment model when it is determined that the quality of the real - time PCB board is unqualified.

[0106] The potential causal path search module includes an optimal network structure unit and a potential causal path unit;

[0107] The optimal network structure unit is used to calculate the network structure score when operating on the set undirected graph, and select the network structure corresponding to the maximum network structure score as the optimal network structure;

[0108] The potential causal path unit is used to use the linear regression equation of the transfer data of adjacent production links as the causal relationship, and combine the optimal network structure to generate a potential causal path.

[0109] The traceability module includes a barcode scanning traceability unit and a model traceability unit;

[0110] The barcode scanning traceability unit is used to judge the position of the single - layer PCB board with unqualified quality after the user scans the internal and external codes;

[0111] The model traceability unit is used to trace the abnormal production link using the chain - type quality assessment model when the position of the single - layer PCB board with unqualified quality is determined.

[0112] Example: Now, quality judgment is carried out on a factory producing multi - layer PCB boards. It is collected that the total number of process routes is 6, and through analysis, different production links are link 1 - link 2 - link 3; among them, there are two parallel lines in link 1 and three parallel lines in link 2, and finally a process line framework is constructed;

[0113] Extract the production data in each process. Through calculation, the transfer data of process 1 is material purity, the transfer data of process 2 is etching angle, and the transfer data of process 3 is line impedance;

[0114] Using the Bayesian network algorithm, the chain quality assessment models of two adjacent production processes are calculated as ZU = 1.5×SH + 4 and SH = 2.35×CH + 8.9;

[0115] And draw the inner code and outer code for the multi-layer PCB board. Suppose the quality judgment is carried out on a 6-layer PCB board, and it is found that the quality of the second layer is unqualified. The user determines the position of the second layer by scanning the outer code, and then uses the chain quality assessment model to trace back reversely to find that there is an abnormality in process 2.

[0116] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A PCB production process management method based on intelligent traceability, characterized by: The method comprises the following steps: S100, collect all process lines in the PCB board production process, analyze the production data in all process lines, and extract production characteristics; use the production characteristics to obtain the production links and parallel lines of each link in the process line; build a process line framework for the PCB generation process; The specific steps to build the process line framework of the PCB generation process are: S101. Collect all process lines in the PCB board production process, collect all production data in each process line, extract the data value of each production data at the beginning and end of the corresponding production line; calculate the change value of all production data in the corresponding production line, the formula is: ； In the formula, Sc represents the change value of each production data in the calculated production line, S after represents the data value of each extracted production data at the beginning of the corresponding production line, S before Indicates the data value of each extracted production data at the end of the corresponding production line; After calculating the change values ​​of all production data in the production line, extract the production data corresponding to the maximum change value as the production feature of the production line; calculate the production features of all production lines in the PCB production process in turn; use the production features to judge all production lines, when the production features are the same, judge the corresponding production lines as parallel lines, when the production features are different, judge the corresponding production lines as different production links; S102, after all the production links and the parallel lines of each production link in the PCB production process are determined, the time series of each production link in the PCB production process is extracted; the extracted time series is sorted by bubble sorting method, and the production links are sorted by the time sorting result to obtain the PCB production link chain; the parallel lines in each production link and the production link chain are used to construct the process line framework of the PCB generation process; S200, after generating the process line framework of the PCB production process, extract the production data in each production link, calculate the correlation between each production data in adjacent production links, determine the correlation between different production data in all adjacent production links, and use the production data with the largest correlation as the transfer data of the corresponding production link; S300, collect the transmission data of each production link when anomalies occur in the historical PCB board production process, use the transmission data of adjacent links as nodes to build a Bayesian network, automatically learn the Bayesian network structure through maximum likelihood estimation, and obtain the potential causal path of each transmission data; and mark it in the process line framework; S400, in adjacent production links in the process line framework, using the transfer data output by the previous production link as the input of the next production link, combined with the marked potential causal path, to generate a chain quality assessment model; using the chain quality assessment model to calculate the quality of the PCB board produced in real time; S500, when producing PCB boards in real time, record the transfer data of each production link, draw an internal code on each layer of the final PCB board, and the internal code records the transfer data and quality of all production links of the corresponding layer of PCB board; then draw an external code outside the entire PCB board, and the external code contains the location information of each internal code; S600, the qualified quality threshold is calculated using the PCB board data in the normal production process of the historical PCB. The user scans the external code and the internal code to obtain the real-time PCB board quality. After the qualified quality threshold is used to judge whether the real-time PCB board quality is unqualified, the potential causal path of each production link in the chain quality assessment model is used for reverse tracing.

2. According to a PCB board production process management method based on intelligent tracing according to claim 1, it is characterized in that: The specific steps of using the production data with the greatest correlation as the transmission data of the corresponding production link in S200 are: S201. After generating the process line framework of the PCB production process, collect the records of the historical PCB production process, extract the production data in each production link, set the number of each production data extracted in each production link as N, draw a curve graph using the production data of adjacent production links, use the linear regression algorithm to fit the two production data of adjacent production links in the curve graph, and calculate the linear regression equation of the two production data as follows: , where Sa represents the production data of the next production link in the adjacent production links, and Sb represents the production data of the previous production link in the adjacent production links; a represents the slope of the calculated linear regression equation, and b represents the intercept of the calculated linear regression equation; In a pair of adjacent production links, the linear regression equation of every two production data is calculated. Assuming that the number of production data types in each production link is m, the number of linear regression equations calculated in a pair of adjacent production links is m. 2 , the linear regression equation of production data of every two adjacent production links in the process line framework is calculated; S202, extract the slope of each linear regression equation as the correlation between the two production data, and compare m in every two adjacent production links. 2 The two production data corresponding to the maximum correlation are selected as the transfer data of adjacent production links, and the transfer data Sd of all adjacent production links are obtained in the process line framework.

3. A PCB board production process management method based on intelligent tracing according to claim 2, characterized in that: The specific steps of obtaining the potential causal path of each transfer data in S300 are: S301, collect the transmission data of each production link when an abnormality occurs in the historical PCB board production process, set the transmission data of the previous production link in the adjacent production links as Sdb, and the transmission data of the next production link as Sda; collect two data values ​​in the historical PCB production process to form sets A and B, where A represents the transmission data set of the next production link, and B represents the transmission data set of the previous production link; initially set an undirected graph, use sets A and B as nodes of the undirected graph, perform deletion, addition and steering operations on each edge in the undirected graph, calculate the network structure score of the undirected graph after each operation when performing the operation, and construct the calculation model formula as follows: ； In the formula, BIC represents the network structure score of the undirected graph after each operation, n represents the number of transfer data in the collected transfer data set, k represents the number of model parameters, Represents the maximum likelihood estimate of the model; by calculating the network structure score, select the optimal network structure of the undirected graph when the network structure score is the largest; when there is an edge from B to A in the optimal network structure, determine that there is a causal flow from B to A between the transmitted data B and the transmitted data A; S302. After determining that there is a causal flow between the transfer data B and the transfer data A, extract the linear regression equation of the transfer data B and A in S200 as the causal relationship, combine the causal relationship with the edges of the transfer data B and the transfer data A in the optimal network structure to form a potential causal path, and mark the generated potential causal path in the process line framework; repeatedly calculate and mark the potential causal paths of all adjacent production links in the process line framework.

4. The PCB production process management method based on intelligent tracing according to claim 3 is characterized in that: The specific steps of calculating the quality of the PCB produced in real time by using the chain quality assessment model in S400 are: S401, connect the undirected graphs of every two adjacent production links according to the process line framework, construct the transfer data output by the previous production link as the input of the next production link, combine the marked potential causal path, and use the Bayesian algorithm to build a chain quality assessment model; collect production data from the historical PCB production process to build a training set, and input the training set into the chain quality assessment model to train the model; S402. When real-time PCB production is carried out, the transfer data in the first production link in the process line framework is input into the chain quality assessment model, and the chain quality assessment model is used for calculation until the transfer data of the last production link is calculated, and the transfer data of the last production link is used as the quality Z of the produced PCB.

5. The PCB production process management method based on intelligent tracing according to claim 1 is characterized in that: The specific steps for setting the inner code and the outer code in S500 are: S501, when producing multi-layer PCB boards in real time, record the transfer data of each production link, draw an internal code on each layer of the final PCB board, and the internal code of each layer contains the transfer data of all production links of producing the corresponding layer of PCB boards; S502. After drawing the internal codes in each layer of the multi-layer PCB board, extract the positions of each internal code, and draw the external codes outside the multi-layer PCB board. The external codes are formed by drilling holes on the Panel boards of the topmost and bottommost layers of the multi-layer PCB board; the external codes contain the position information of each internal code.

6. The PCB production process management method based on intelligent tracing according to claim 5 is characterized in that: The specific steps for reverse tracing using the potential causal paths of each production link in the chain quality assessment model in S600 are as follows: S601. Collect the transfer data of the last production link when the historical PCB board is produced qualified. Let the number of collected historical PCB production qualified transfer data be G, calculate the average value and standard deviation of the G collected transfer data, and calculate the qualified quality threshold when the PCB is produced qualified using the average value and standard deviation. The formula is: ； In the formula, In represents the calculated qualified quality threshold of PCB production, Fp represents the average value of the G calculated transfer data, and Fst represents the standard deviation of the G calculated transfer data; S602. The user obtains the quality of the multi-layer PCB board by scanning the code, and uses the qualified quality threshold to judge the quality during real-time PCB production. When Z≥In, it is judged that the quality of the PCB board produced in real-time is qualified; when Z<In, it is judged that the quality of the PCB board produced in real-time is unqualified. When judging the PCB board produced in real-time, first use the external code of the multi-layer PCB to locate the position of the single-layer PCB board with unqualified quality, then scan the internal code of the single-layer PCB board with unqualified quality, and use the potential causal path in the chain quality assessment model for reverse tracing, compare the transfer data of each production link with the transfer data of the qualified PCB board, and find the production link with anomalies.

7. A PCB board production process management system based on intelligent tracing using a PCB board production process management method based on intelligent tracing as described in any one of claims 1 to 6, characterized in that: The PCB board production process management system includes a data collection module, a process line framework construction module, a transfer data search module, a potential causal path search module, a chain quality assessment model construction module, a quality judgment module, and a traceability module; The data collection module is used to collect the production data during the historical PCB production process; The process line framework construction module is used to analyze the production data in all process lines and construct a process line framework; The transfer data search module is used to extract the production data in each production link, calculate the correlation degree between each production data in adjacent production links, and use the production data with the largest correlation degree as the transfer data of the corresponding production link; The potential causal path search module is used to construct a Bayesian network with the transfer data of adjacent links as nodes, automatically learn the Bayesian network structure through maximum likelihood estimation, and obtain the potential causal path of each transfer data; The chain quality assessment model construction module is used to use the transfer data output by the previous production link as the input of the next production link, and combine the marked potential causal path to generate a chain quality assessment model; The quality judgment module is used to calculate the qualified quality threshold using the transfer data of the last link of the historical production PCB board, and use the qualified quality threshold to judge the quality of the real-time PCB board produced; The traceability module is used to trace the abnormal production link by the user using code scanning and the chain quality assessment model when it is judged that the quality of the real-time PCB board is unqualified.

8. A PCB board production process management system based on intelligent traceability according to claim 7, characterized in that: The potential causal path search module includes an optimal network structure unit and a potential causal path unit; The optimal network structure unit is used to calculate the network structure score when operating the set undirected graph, and select the network structure corresponding to the maximum network structure score as the optimal network structure; The potential causal path unit is used to utilize the linear regression equation of the transfer data of adjacent production links as the causal relationship and generate the potential causal path in combination with the optimal network structure.

9. A PCB board production process management system based on intelligent traceability according to claim 7, characterized in that: The traceability module includes a code scanning traceability unit and a model traceability unit; The code scanning and tracing unit is used to determine the location of the single-layer PCB board with unqualified quality after the user scans the internal and external codes; The model tracing unit is used to trace the abnormal production links by using the chain quality assessment model after the location of the single-layer PCB board with unqualified quality is determined.

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