Printed Solder Joint Quality Identification and Repair Recommendation System and Method

Abstract

A system and method for identifying and providing repair suggestions for printed solder joint quality are disclosed. When abnormalities are detected in solder paste inspection data, component inspection data, or circuit inspection data, an analysis and computing device extracts operation data, equipment data, raw material data, process data, and environmental data corresponding to the manufacturing time of the pin position of the electronic component from the time data stream and generates a data feature profile. The analysis and computing device associates the positions of the solder paste inspection data, component inspection data, circuit inspection data, and repair data in the position data stream and sets corresponding reliability values. The analysis and computing device performs similarity calculations between the data feature profile and a reference data feature profile to calculate a similarity value. The reliability value of the reference data feature profile is multiplied by the similarity value and added to the reliability value of the data feature profile to calculate a relative reliability value. The analysis and computing device compares the relative reliability value with a threshold reliability value to generate repair suggestion information based on the comparison results. This achieves the technical effect of providing accurate identification and repair suggestions for printed solder joint quality.

Description

Technical Field

[0001] A quality identification and maintenance suggestion system and method, particularly a system and method for identifying and suggesting maintenance information for printed solder joints by associating and integrating data into time data streams and location data streams for analysis and calculation. Background Technology

[0002] In surface mount technology (SMT), solder paste printing is a crucial production step in circuit board manufacturing. Solder paste printing largely determines the quality of surface mount technology. Therefore, circuit board manufacturers generally use solder paste inspection (SPI) machines to inspect the printing quality.

[0003] Solder paste inspection machines judge solder paste quality based on thresholds of the physical shape of solder dots, or by using statistical process control (SPC) methods to judge the changing trends of solder joint area, volume, height, etc. However, since the quality of solder paste printing is not entirely equivalent to the quality of actual circuit connectivity, if printing quality problems occur, it may not necessarily result in actual circuit board electronic component connectivity problems. Therefore, the arbitrariness in judging solder paste printing quality in actual production becomes very large.

[0004] Existing methods for detecting and judging solder paste printing quality, which rely on simple threshold judgments, can lead to an excessive number of anomalies, making it impossible for maintenance personnel to handle them specifically. Furthermore, maintenance personnel cannot accurately describe the on-site data to provide reference. Even if some equipment data exists, it is of limited help in tracing the cause of the problem due to a large amount of invalid and distorted information mixed in with the equipment data. The measurement data of printing quality is not very representative of the actual problem, and there is a lack of guiding information for on-site troubleshooting and improvement.

[0005] In summary, it is clear that existing technologies have long suffered from difficulties in identifying the quality of printed solder joints and a lack of targeted repair suggestions. Therefore, it is necessary to propose improved technical methods to solve this problem. Summary of the Invention

[0006] In view of the problems of existing technologies, such as difficulty in identifying the quality of printed solder joints and lack of targeted repair suggestions, this invention discloses a system and method for identifying and providing repair suggestions for printed solder joint quality, wherein:

[0007] The printed solder joint quality identification and repair suggestion system disclosed in this invention is applicable to analysis and calculation devices and includes: a data receiving module, a data integration module, a location mapping module, a data acquisition module, a data feature profiling module, a reliability setting module, a calculation module, and a generation module.

[0008] The data receiving module receives operation data, equipment data, raw material data, process data, and environmental data from the solder paste printing workstation; solder paste inspection data from the solder paste inspection machine; component inspection data from the automatic optical inspection machine; and circuit inspection data from the online circuit inspection machine. It also receives maintenance data corresponding to the solder paste inspection data, component inspection data, and circuit inspection data. The data integration module integrates operation data, equipment data, raw material data, process data, and environmental data into a time-based data stream. The position mapping module integrates solder paste inspection data, component inspection data, circuit inspection data, and maintenance data into a position-based data stream based on the pin positions of electronic components. The data acquisition module extracts data from the time-based data stream when anomalies are detected in the solder paste inspection data, component inspection data, or circuit inspection data. The system includes: operation data, equipment data, raw material data, process data, and environmental data corresponding to the pin positions and manufacturing times of electronic components; a data feature profiling module to project the extracted operation data, equipment data, raw material data, process data, and environmental data to generate a data feature profile; a reliability setting module to set corresponding reliability values ​​for the location-related solder paste detection data, component detection data, circuit detection data, and maintenance data in the location data stream; a calculation module to calculate the similarity value by comparing the data feature profile with a reference data feature profile, multiplying the reliability value of the reference data feature profile by the similarity value, and adding the reliability value of the data feature profile to calculate the relative reliability value; and a generation module to compare the relative reliability value with the threshold reliability value to generate maintenance suggestion information based on the comparison results.

[0009] The method for identifying and recommending repairs for printed solder joint quality disclosed in this invention includes the following steps:

[0010] First, the analysis and computing device receives operation data, equipment data, material data, process data, and environmental data from the solder paste printing workstation. Next, it receives solder paste inspection data from the solder paste inspection machine. Then, it receives component inspection data from the automatic optical inspection machine. Next, it receives circuit inspection data from the in-circuit testing machine. Next, it receives maintenance data corresponding to the solder paste inspection data, component inspection data, and circuit inspection data. Next, the analysis and computing device integrates the operation data, equipment data, material data, process data, and environmental data into a time data stream based on time. Next, the analysis and computing device integrates the solder paste inspection data, component inspection data, circuit inspection data, and maintenance data into a position data stream based on the pin positions of electronic components. Finally, when an anomaly is detected in the solder paste inspection data, component inspection data, or circuit inspection data... The analysis and computing device extracts operational data, equipment data, raw material data, process data, and environmental data corresponding to the manufacturing time of the pin positions of electronic components from the time data stream. Next, it projects the extracted operational, equipment, raw material, process, and environmental data to generate a data feature profile. Then, it associates the positions of solder paste inspection data, component inspection data, circuit inspection data, and maintenance data from the position data stream with corresponding reliability values. Next, it calculates the similarity between the data feature profile and a reference data feature profile to obtain a similarity value. It then multiplies the reliability value of the reference data feature profile by the similarity value and adds it to the reliability value of the data feature profile to calculate a relative reliability value. Finally, it compares the relative reliability value with a threshold reliability value to generate maintenance suggestion information based on the comparison results.

[0011] The system and method disclosed in this invention are as described above. The difference between them and the prior art is that when an anomaly is detected in solder paste inspection data, component inspection data, or circuit inspection data, the analysis and computing device extracts operation data, equipment data, raw material data, process data, and environmental data corresponding to the pin position manufacturing time of the electronic component from the time data stream and generates a data feature profile. The analysis and computing device associates the positions of solder paste inspection data, component inspection data, circuit inspection data, and maintenance data in the position data stream and sets corresponding reliability values. The analysis and computing device performs similarity calculation between the data feature profile and the reference data feature profile to calculate a similarity value. The reliability value of the reference data feature profile is multiplied by the similarity value and added to the reliability value of the data feature profile to calculate a relative reliability value. The analysis and computing device compares the relative reliability value with the threshold reliability value to generate maintenance suggestion information based on the comparison result.

[0012] Through the above-mentioned technical means, the present invention can achieve the technical effect of providing accurate identification of printed solder joint quality and repair suggestions. Attached Figure Description

[0013] Figure 1 The system block diagram of the printed solder joint quality identification and repair suggestion system of the present invention is shown.

[0014] Figure 2 This diagram illustrates a data feature profile of the printed solder joint quality identification and repair recommendations of the present invention.

[0015] Figure 3A as well as Figure 3B The diagram illustrates the method flowchart for identifying and providing repair recommendations for printed solder joint quality according to the present invention.

[0016] In the attached figures, the following labels are used:

[0017] 10. Analytical computing device

[0018] 11 Data Receiving Module

[0019] 12 Data Integration Module

[0020] 13. Location mapping module

[0021] 14 Data Acquisition Module

[0022] 15 Data Feature Profiling Module

[0023] 16 Reliability Setting Module

[0024] 17 Calculation Module

[0025] 18 Generation Module

[0026] 21 Data Feature Profiling

[0027] Step 101: The analysis and computing device receives operation data, equipment data, raw material data, process data, and environmental data from the solder paste printing workstation.

[0028] Step 102: The analysis and calculation device receives solder paste inspection data from the solder paste inspection machine.

[0029] Step 103: The analysis and calculation device receives component detection data from the automatic optical inspection machine.

[0030] Step 104: Analyze the circuit detection data received by the online circuit tester from the computing device.

[0031] Step 105: The analysis and computing device receives the maintenance data corresponding to the solder paste detection data, component detection data, and circuit detection data.

[0032] Step 106: The analysis and calculation device integrates operational data, equipment data, raw material data, process data, and environmental data into a time data stream based on time.

[0033] Step 107: The analysis and computing device integrates solder paste inspection data, component inspection data, circuit inspection data, and maintenance data into a location data stream based on the pin positions of the electronic components.

[0034] Step 108: When abnormalities are detected in solder paste testing data, component testing data, or circuit testing data, the analysis and computing device extracts the operation data, equipment data, material data, process data, and environmental data corresponding to the pin position manufacturing time of the electronic component from the time data stream.

[0035] Step 109: The analysis and computing device projects the extracted operation data, equipment data, raw material data, process data, and environmental data to generate a data feature profile.

[0036] Step 110: The analysis and calculation device associates the locations of solder paste detection data, component detection data, circuit detection data, and maintenance data in the location data stream and sets corresponding reliability values.

[0037] Step 111: The analysis and computing device calculates the similarity between the data feature profile and the control data feature profile to obtain a similarity value. The reliability value of the control data feature profile is multiplied by the similarity value, and then added to the reliability value of the data feature profile to calculate the relative reliability value.

[0038] Step 112: The analysis and calculation device compares the relative reliability value with the threshold reliability value to generate maintenance suggestion information based on the comparison results. Detailed Implementation

[0039] The following will describe in detail the implementation of the present invention with reference to the accompanying drawings and embodiments, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0040] The following section will first describe the printed solder joint quality identification and repair suggestion system disclosed in this invention, and please refer to [the relevant documentation]. Figure 1 As shown, Figure 1 The diagram illustrates a system block diagram of the printed solder joint quality identification and repair suggestion system of the present invention.

[0041] The printed solder joint quality identification and repair suggestion system disclosed in this invention is applicable to the analysis and calculation device 10, which includes: a data receiving module 11, a data integration module 12, a position mapping module 13, a data acquisition module 14, a data feature profiling module 15, a reliability setting module 16, a calculation module 17, and a generation module 18.

[0042] The analysis and computing device 10 can be a computer, server, etc. The analysis and computing device 10 will establish a connection with the solder paste printing workstation, solder paste inspection machine, automatic optical inspection machine, online circuit inspection machine, and repair workstation through wired or wireless transmission methods. Wired transmission methods include cable networks, fiber optic networks, etc., and wireless transmission methods include Wi-Fi, mobile communication networks (e.g., 3G, 4G, 5G, etc.). These are just examples and are not intended to limit the scope of application of the present invention.

[0043] The data receiving module 11 of the analysis and computing device 10 receives operation data, equipment data, raw material data, process data and environmental data from the solder paste printing workstation.

[0044] The aforementioned operational data includes, for example, the operations performed by the operator at the solder paste printing workstation, basic operator information (e.g., employee name, employee number, etc.), and operator advancement information (e.g., employee skills, employee entry time, employee exit time, etc.). These are merely illustrative examples and are not intended to limit the scope of application of this invention.

[0045] The aforementioned equipment data includes, for example, bracket positioning information, stress values, scraper pressure, demolding speed, stencil inspection data, stencil scraper maintenance, etc. These are merely illustrative examples and are not intended to limit the scope of application of this invention.

[0046] The aforementioned raw material data include, for example, PCB board thickness, green paint thickness, measurement temperature, batch number, solder paste temperature, warm-up time, stirring time, etc. These are merely illustrative examples and are not intended to limit the scope of application of this invention.

[0047] The aforementioned process data includes, for example, the equipment parameters used in the solder paste printing workstation, the time of each step in the process, etc. These are merely illustrative examples and are not intended to limit the scope of application of this invention.

[0048] The aforementioned environmental data include, for example, the temperature, humidity, and electrostatic discharge (ESD) of the production environment. These are merely illustrative examples and are not intended to limit the scope of application of this invention.

[0049] The data receiving module 11 of the analysis and computing device 10 can also receive solder paste inspection data from the solder paste inspection machine, component inspection data from the automatic optical inspection machine, circuit inspection data from the online circuit inspection machine, and maintenance data corresponding to the solder paste inspection data, component inspection data, and circuit inspection data from the maintenance workstation.

[0050] Next, the data integration module 12 of the analysis and computing device 10 will associate and integrate the operation data, equipment data, raw material data, process data and environmental data into a time data stream based on time. That is, the data integration module 12 of the analysis and computing device 10 will find the corresponding data from the operation data, equipment data, raw material data, process data and environmental data at the selected time point or time range. The data integration module 12 of the analysis and computing device 10 can then associate and integrate the found data with the corresponding time point or time range into a time data stream.

[0051] Next, the position mapping module 13 of the analysis and computing device 10 is used to integrate solder paste detection data, component detection data, circuit detection data and maintenance data into a position data stream based on the pin positions of electronic components. That is, the position mapping module 13 of the analysis and computing device 10 maps the solder joint positions of the solder paste detection data to the electronic component positions of the component detection data, and then maps the electronic component positions of the component detection data to the pin positions of the electronic components in the circuit detection data and maintenance data to integrate them into a position data stream.

[0052] When an anomaly is detected in the solder paste test data, component test data, or circuit test data, the data acquisition module 14 of the analysis and computing device 10 extracts the operation data, equipment data, raw material data, process data, and environmental data corresponding to the pin position manufacturing time of the electronic component from the time data stream.

[0053] When the data acquisition module 14 of the analysis and computing device 10 extracts the operation data, equipment data, raw material data, process data, and environmental data corresponding to the pin positions and manufacturing times of electronic components from the time data stream, the data feature profiling module 15 of the analysis and computing device 10 is used to perform data projection (i.e., normalization) on the extracted operation data, equipment data, raw material data, process data, and environmental data to generate a data feature profile 21. The data feature profile 21 performs data projection (i.e., normalization) on each detailed item of the operation data, equipment data, raw material data, process data, and environmental data. The data of each sub-item can be converted to data between 0 and 1 using a pre-established conversion table (i.e., normalization). Alternatively, the data of each sub-item can be converted to data between 0 and 1 using a pre-established conversion formula. This is merely an example and does not limit the scope of application of the invention. Dividing the sub-items into a range of 0 to 1 and then sequentially connecting the converted data of each sub-item forms the data feature profile 21. Please refer to the diagram of the data feature profile 21. Figure 2 As shown, Figure 2 The illustration is a schematic diagram of the data feature profile for identifying and providing repair suggestions for the printed solder joint quality of this invention.

[0054] Next, the reliability setting module 16 of the analysis and calculation device 10 sets the corresponding reliability value by associating the location of solder paste detection data, component detection data, circuit detection data and maintenance data in the location data stream.

[0055] Specifically, when an anomaly is detected in the solder paste inspection data, the reliability setting module 16 of the analysis and calculation device 10 can set the reliability value to a first reliability value (e.g., 0.5); when an anomaly is detected in the solder paste inspection data and an anomaly is also detected in the position of the electronic component that is mapped to the component inspection data, the reliability setting module 16 of the analysis and calculation device 10 can set the reliability value to a second reliability value (e.g., 0.5). 0.7); When an abnormality is detected in the solder paste detection data and an abnormality is also detected in the pin position of the electronic component in the circuit detection data, the reliability setting module 16 of the analysis and calculation device 10 can set the reliability value to the third reliability value (e.g., 0.7); When an abnormality is detected in the solder paste detection data and an abnormality is detected in the electronic component position in the component detection data and an abnormality is also detected in the pin position of the electronic component in the circuit detection data, the reliability setting module 16 of the analysis and calculation device 10 can set the reliability value to the fourth reliability value (e.g., 0.9); When an abnormality is detected in the solder paste detection data and an abnormality is detected in the electronic component position in the component detection data and an abnormality is detected in the pin position of the electronic component in the circuit detection data and an abnormality is also detected in the pin position of the electronic component in the repair data, the reliability setting module 16 of the analysis and calculation device 10 can set the reliability value to the fifth reliability value (e.g., 1). This is only an example and is not intended to limit the scope of application of the present invention.

[0056] Next, the calculation module 17 of the analysis and calculation device 10 performs a similarity calculation on the data feature profile 21 and the reference data feature profile (the reference data feature profile is established in advance through the above process) to calculate a similarity value. The reliability value of the reference data feature profile is multiplied by the similarity value and then added to the reliability value of the data feature profile to calculate a relative reliability value. It is worth noting that the similarity value is between 0 and 1. This is only an example and is not intended to limit the scope of application of the present invention.

[0057] The calculation module 17 of the analysis and calculation device 10 calculates the similarity between the data feature profile 21 and the reference data feature profile to obtain a similarity value. This is done by calculating the difference between each data point in the data feature profile and multiplying it by the corresponding weight value to obtain multiple weighted differences. The average of these multiple weighted differences is then calculated to obtain the similarity value. The weight value corresponding to each data point (i.e., the details in the operation data, equipment data, raw material data, process data, and environmental data) is determined based on the root cause analysis and statistical results of each detail. The weight values ​​corresponding to each data point may be partially the same or completely different. This is only an example and is not intended to limit the application scope of the present invention.

[0058] The generation module 18 of the analysis and calculation device 10 compares the relative reliability value with the threshold reliability value to generate maintenance suggestion information based on the comparison result. Specifically, when the relative reliability value is greater than the threshold reliability value, the generation module 18 of the analysis and calculation device 10 can obtain the root cause (RC) and corrective action (CA) corresponding to the feature profile of the comparison data to generate maintenance suggestion information. Maintenance personnel can then perform preliminary maintenance based on the maintenance suggestion information, thereby avoiding the problem of time-consuming maintenance.

[0059] Next, the operation method of the present invention will be described below, and please refer to the following: Figure 1 as well as Figure 2 As shown, Figure 2 The diagram illustrates the method flowchart for identifying and providing repair recommendations for printed solder joint quality according to the present invention.

[0060] First, the analysis and computing device receives operation data, equipment data, raw material data, process data, and environmental data from the solder paste printing workstation (step 101); next, the analysis and computing device receives solder paste inspection data from the solder paste inspection machine (step 102); next, the analysis and computing device receives component inspection data from the automatic optical inspection machine (step 103); next, the analysis and computing device receives circuit inspection data from the in-line circuit inspection machine (step 104); next, the analysis and computing device receives maintenance data corresponding to the solder paste inspection data, component inspection data, and circuit inspection data (step 105); next, the analysis and computing device integrates the operation data, equipment data, raw material data, process data, and environmental data into a time data stream based on time (step 106); next, the analysis and computing device integrates the solder paste inspection data, component inspection data, circuit inspection data, and maintenance data into a position data stream based on the pin positions of electronic components (step 107); next, when the solder paste inspection data, component inspection data, or circuit inspection data... When an anomaly is detected, the analysis and computing device extracts operation data, equipment data, raw material data, process data, and environmental data corresponding to the manufacturing time of the pin position of the electronic component from the time data stream (step 108); then, the analysis and computing device projects the extracted operation data, equipment data, raw material data, process data, and environmental data to generate a data feature profile (step 109); next, the analysis and computing device sets corresponding reliability values ​​for the location association of solder paste detection data, component detection data, circuit detection data, and maintenance data in the location data stream (step 110); next, the analysis and computing device performs similarity calculation on the data feature profile and the reference data feature profile to calculate a similarity value, multiplies the reliability value of the reference data feature profile by the similarity value and adds it to the reliability value of the data feature profile to calculate a relative reliability value (step 111); finally, the analysis and computing device compares the relative reliability value with the threshold reliability value to generate maintenance suggestion information based on the comparison result (step 112).

[0061] In summary, the difference between this invention and the prior art lies in the following: when abnormalities are detected in solder paste testing data, component testing data, or circuit testing data, the analysis and computing device extracts operation data, equipment data, raw material data, process data, and environmental data corresponding to the pin position manufacturing time of the electronic component from the time data stream and generates a data feature profile. The analysis and computing device associates the positions of solder paste testing data, component testing data, circuit testing data, and maintenance data in the position data stream and sets corresponding reliability values. The analysis and computing device performs similarity calculations between the data feature profile and the reference data feature profile to calculate a similarity value. The reliability value of the reference data feature profile is multiplied by the similarity value and added to the reliability value of the data feature profile to calculate a relative reliability value. The analysis and computing device compares the relative reliability value with the threshold reliability value to generate maintenance suggestion information based on the comparison results.

[0062] This technology can solve the problems of existing technologies, such as difficulty in identifying the quality of printed solder joints and lack of targeted repair suggestions, thereby achieving the technical effect of providing accurate identification and repair suggestions for printed solder joint quality.

[0063] While the embodiments disclosed in this invention are as described above, the content is not intended to directly limit the scope of patent protection of this invention. Anyone skilled in the art to which this invention pertains may make minor modifications in form and detail without departing from the spirit and scope disclosed herein. The scope of patent protection of this invention shall still be determined by the appended claims.

Claims

1. A printed solder joint quality identification and repair recommendation system, adapted for use in an analysis computing device, characterized by, The system comprises: a data receiving module for receiving operation data, equipment data, raw material data, process data and environment data from a solder paste printing station, receiving solder paste inspection data from a solder paste inspection machine, receiving component inspection data from an automatic optical inspection machine, receiving circuit inspection data from an online circuit inspection machine, and receiving repair data corresponding to the solder paste inspection data, the component inspection data and the circuit inspection data from a repair station; a data integration module for integrating the operation data, the equipment data, the raw material data, the process data and the environment data into time data streams according to time; a position mapping module for integrating the solder paste inspection data, the component inspection data, the circuit inspection data and the repair data into position data streams according to pin positions of electronic components; a data obtaining module for obtaining the operation data, the equipment data, the raw material data, the process data and the environment data corresponding to manufacturing time of the pin positions of electronic components from the time data streams when the solder paste inspection data, the component inspection data or the circuit inspection data detects an abnormality; a data feature profiling module for profiling the obtained operation data, the equipment data, the raw material data, the process data and the environment data to generate data feature profiles; a reliability setting module for setting reliability values corresponding to the position association of the solder paste inspection data, the component inspection data, the circuit inspection data and the repair data in the position data streams; a calculation module for calculating similarity values by comparing the data feature profiles with reference data feature profiles, and calculating relative reliability values by multiplying the reliability values of the reference data feature profiles with the similarity values and adding the reliability values of the data feature profiles; and a generation module for comparing the relative reliability values with a threshold reliability value to generate repair suggestion information according to comparison results.

2. The printed solder joint quality identification and repair recommendation system of claim 1, wherein, The position mapping module maps solder joint positions of the solder paste inspection data to electronic component positions of the component inspection data, and maps the electronic component positions of the component inspection data to pin positions of electronic components in the circuit inspection data and the repair data to integrate the solder paste inspection data, the component inspection data, the circuit inspection data and the repair data into the position data streams.

3. The printed solder joint quality identification and repair recommendation system of claim 1, wherein, The reliability setting module sets the reliability value as a first reliability value when the solder paste detection data detects an abnormality, sets the reliability value as a second reliability value when the solder paste detection data detects an abnormality and the electronic component position mapped to the component detection data also detects an abnormality, sets the reliability value as a third reliability value when the solder paste detection data detects an abnormality and the pin position of the electronic component mapped to the circuit detection data also detects an abnormality, sets the reliability value as a fourth reliability value when the solder paste detection data detects an abnormality and the pin position of the electronic component mapped to the component detection data also detects an abnormality and the pin position of the electronic component mapped to the circuit detection data also detects an abnormality, and sets the reliability value as a fifth reliability value when the solder paste detection data detects an abnormality and the pin position of the electronic component mapped to the component detection data also detects an abnormality, the pin position of the electronic component mapped to the circuit detection data also detects an abnormality, and the pin position of the electronic component mapped to the repair data also detects an abnormality.

4. The printed solder joint quality identification and repair recommendation system of claim 1, wherein, The calculation module calculates the similarity value by calculating the distance between each data point in the data feature image, multiplying the distance by a corresponding weight value to obtain a plurality of weighted distances, and calculating the average of the plurality of weighted distances.

5. A method of print joint quality identification and repair recommendation, characterized in that, The method comprises the following steps: An analysis computing device receives operation data, equipment data, raw material data, process data, and environmental data from a solder paste printing workstation; The analysis computing device receives solder paste detection data from a solder paste inspection machine; The analysis computing device receives component detection data from an automatic optical inspection machine; The analysis computing device receives circuit detection data from an online circuit inspection machine; The analysis computing device receives repair data corresponding to the solder paste detection data, the component detection data, and the circuit detection data from a repair workstation; The analysis computing device integrates the operation data, the equipment data, the raw material data, the process data, and the environmental data into a time data stream according to time; The analysis computing device integrates the solder paste detection data, the component detection data, the circuit detection data, and the repair data into a position data stream according to the pin position of the electronic component; When the solder paste detection data, the component detection data, or the circuit detection data detects an abnormality, the analysis computing device extracts the operation data, the equipment data, the raw material data, the process data, and the environmental data corresponding to the manufacturing time of the pin position of the electronic component from the time data stream; The analysis computing device projects the extracted operation data, equipment data, raw material data, process data, and environmental data to generate a data feature image; The analysis computing device sets a corresponding reliability value for the position correlation of the solder paste detection data, the component detection data, the circuit detection data, and the maintenance data in the position data stream; The analysis computing device calculates a similarity value by similarity calculation of the data feature image and a reference data feature image, multiplies the similarity value by a reliability value of the reference data feature image, and adds the reliability value of the data feature image to calculate a relative reliability value; and The analysis computing device compares the relative reliability value with a threshold reliability value to generate maintenance suggestion information according to the comparison result.

6. The printed solder joint quality identification and repair recommendation method of claim 5, wherein, The step of the analysis computing device correlating the solder paste detection data, the component detection data, the circuit detection data, and the maintenance data according to the pin position of the electronic component to integrate into the position data stream is mapping the solder joint position of the solder paste detection data to the electronic component position of the component detection data, and then mapping the electronic component position of the component detection data to the pin position of the electronic component in the circuit detection data and the maintenance data to correlate and integrate into the position data stream.

7. The printed solder joint quality identification and repair recommendation method of claim 5, wherein, The step of the analysis computing device setting a corresponding reliability value for the position correlation of the solder paste detection data, the component detection data, the circuit detection data, and the maintenance data in the position data stream is setting the reliability value as a first reliability value when the solder paste detection data detects an abnormality, setting the reliability value as a second reliability value when the solder paste detection data detects an abnormality and the electronic component position of the component detection data also detects an abnormality, setting the reliability value as a third reliability value when the solder paste detection data detects an abnormality and the pin position of the electronic component in the circuit detection data also detects an abnormality, setting the reliability value as a fourth reliability value when the solder paste detection data detects an abnormality, the electronic component position of the component detection data also detects an abnormality, and the pin position of the electronic component in the circuit detection data also detects an abnormality, and setting the reliability value as a fifth reliability value when the solder paste detection data detects an abnormality, the electronic component position of the component detection data also detects an abnormality, the pin position of the electronic component in the circuit detection data also detects an abnormality, and the pin position of the electronic component in the maintenance data also detects an abnormality.

8. The printed solder joint quality identification and repair recommendation method of claim 5, wherein, The step of the analysis computing device calculating the similarity value by similarity calculation of the data feature image and the reference data feature image is calculating the distance between each data point in the data feature image, multiplying the corresponding weight value to obtain a plurality of weighted distances, and calculating the average of the plurality of weighted distances to obtain the similarity value.

Images