A pin and socket header assembly process for improving assembly accuracy

Abstract

The invention discloses a pin-and-female header assembly process for improving assembly accuracy, relates to the field of circuit connection, and comprises the following steps: step 1: collecting temperature, humidity and air pressure environment parameters at an assembly site, measuring the size, weight and material attribute parameters of a workpiece to be installed, and recording the model, use status and adjustment setting attribute parameters of an assembly tool; step 2: acquiring order information, extracting and analyzing the target requirements and assembly specifications of the order, analyzing whether the attribute parameters of the workpiece to be installed and the assembly tool are qualified, and terminating the assembly if unqualified, and sending an alarm to a management end; by collecting assembly environment parameters, order information and attribute information of the assembly tool and the workpiece to be installed, a simulated assembly model is used to perform intelligent analysis on the assembly process, and by pre-simulating the assembly, possible problems are discovered before the actual assembly, so as to ensure that the best assembly scheme is selected.

Description

Technical Field

[0001] The invention relates to the technical field of circuit connection, and in particular to a pin and socket header assembly process for improving assembly accuracy. Background Art

[0002] Pin headers and female headers are common connection components, usually used to connect electronic components or circuit boards. Pin headers are multi-hole connectors, usually made of metal materials, with a series of pins that can be inserted into the holes in the circuit board for connection with other electronic components. Female headers are connectors that match pin headers, usually a plug that corresponds to the pin header pins, and are used to connect pin headers to ensure the connection of the circuit board. The goal of assembling pin headers and female headers is to ensure that they are correctly connected together to provide reliable connection and conductive function. This assembly can include inserting the pin header into the hole of the circuit board, or inserting the female header into the pin of another connector. Correct assembly can ensure that the connection is firm and reliable, and can effectively transmit signals and power.

[0003] The existing assembly process of pin headers and female headers still has some shortcomings, such as:

[0004] 1. In the manufacturing industry, the assembly process is a crucial link. However, the environment and tools in the assembly process often differ in different time periods or processing scenarios. These factors may cause errors between the final processing results and the expected results, thereby causing a series of production losses. In the actual production process, the assembly environment may be affected by various factors such as temperature, humidity, dust, and noise. For example, in a high temperature or humid environment, some assembly materials may be deformed or corroded, resulting in reduced assembly accuracy. The differentiation of assembly tools is also an important cause of errors. Different tools may have different accuracy, performance, and service life. For example, some tools may be worn or damaged due to long-term use, resulting in reduced assembly accuracy.

[0005] 2. During the assembly process, production errors are not only difficult to detect, but also often difficult to correct in a timely and effective manner after they are discovered. This delayed way of dealing with problems is not only likely to slow down the overall production progress, but also due to the lack of reasonable and effective corrective opinions, it may further aggravate the severity of the problem. The existence of production errors is often the result of the combined effect of multiple factors. For example, insufficient equipment accuracy, low operator skill level, and imperfect production technology may all lead to production errors. Due to the lack of reasonable and effective corrective opinions, the handling of production errors often lacks pertinence and effectiveness. This inefficient handling method not only increases production costs, but may also have a negative impact on the company's reputation and market competitiveness. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a pin and socket header assembly process for improving assembly accuracy, which can effectively solve the problems of the prior art.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] The present invention discloses a pin and socket header assembly process for improving assembly accuracy, comprising the following steps:

[0011] Step 1: Collect the temperature, humidity and air pressure environmental parameters of the assembly site, measure the size, weight and material attribute parameters of the workpiece to be installed, and record the model, usage status and adjustment setting attribute parameters of the assembly tools;

[0012] Step 2: Obtain order information, extract and analyze the target requirements and assembly specifications of the order, and analyze whether the attribute parameters of the workpiece to be installed and the assembly tool are qualified. If not, terminate the assembly and send an alarm to the management end;

[0013] Step 3: Based on the order information and the attribute parameters of the workpiece to be installed and the assembly tool, a comprehensive output of several assembly plans is performed, and based on the preset selection index, the best installation plan under the index is obtained, and the environmental parameters of the corresponding assembly site and the setting attribute parameters of the assembly tool are determined;

[0014] Step 4: Build a simulated assembly model, obtain the installation plan determined in step 3, input the corresponding parameters into the model, analyze the matching of the parameters, and perform simulated assembly in a virtual environment;

[0015] Step 5: During the simulated assembly process of step 4 or after the assembly is completed, analyze whether there is abnormal error information, analyze the abnormal error information that occurs during the simulated assembly process, and obtain the cause of the abnormal error information and related influencing factors;

[0016] Step 6: Record the abnormal error information that occurs during the simulated assembly process, and determine the specific content and cause of each error message;

[0017] Step 7: According to the error message, the environmental parameters of the assembly site and the setting attribute parameters of the assembly tools are adjusted accordingly, and the preset range is adjusted step by step until the reasonable adjustment range output according to the historical installation plan data and expert standard guidance is hit, and the adjusted content is submitted to step 4 again;

[0018] Step 8: When there is no abnormal error information or the abnormal error information is lower than the preset threshold, it is considered to have passed the simulated assembly verification. At this time, the simulated assembly instructions are sent to the attribute parameters of the assembly tool, the workpiece to be installed and the scheduling end of the assembly site to perform actual assembly, and record in real time whether there is abnormal feedback.

[0019] Furthermore, the workpieces to be installed in step 1 include: pin headers, female headers and connecting consumables.

[0020] Furthermore, in the process of obtaining the best installation plan for a certain selection indicator in step 3, if one or more of the attribute parameters of the current assembly tool, the attribute parameters of the workpiece to be installed, and the environmental parameters of the assembly site cannot meet the implementation requirements of the best installation plan, then the sub-priority target in the output assembly plan is obtained for role replacement.

[0021] Furthermore, the simulated assembly model in step 4 is constructed using a convolutional neural network to establish a nonlinear model, model and predict the associated factors in the assembly process, and output predicted data.

[0022] Furthermore, when the abnormal error information in step 5 is not unique, a comprehensive analysis is performed to determine a priority adjustment sequence for the custom adjustment in step 7.

[0023] Furthermore, the method for determining the priority adjustment order includes:

[0024] a. Perform machine evaluation on each abnormal error message to determine the degree of influence of one or more parameters on assembly accuracy;

[0025] b. Refer to expert opinions and historical data to determine the extent to which adjustment of one or more parameters will affect assembly accuracy.

[0026] Furthermore, in the determination method a, the candidate error features of several abnormal error messages are analyzed, and the environmental parameters of the assembly site, the attribute parameters of the workpiece to be installed, and the attribute parameters of the assembly tool are labeled one by one to generate labels. After the candidate error features are determined, it is necessary to select features with discrimination and related to the label as input data, and calculate the correlation between the candidate features and the label by Pearson correlation analysis. The calculation formula is:

[0027]

[0028] In the formula, the closer the value of Pcc is to 1 or -1, the greater the positive or negative correlation between the candidate error feature and the label. i and i are respectively used as the i-th data in the sample data, and are the average values ​​of x and y respectively.

[0029] Furthermore, in step 8, if abnormal feedback occurs during the actual assembly process, the target with the problem is traced back to its source, the operating settings and attribute parameters of the associated components are recorded, and the current parameters are input into the simulated assembly model as reference values, and the simulation adjustment is performed again to output assembly suggestions.

[0030] (III) Beneficial effects

[0031] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0032] 1. By collecting assembly environment parameters, order information, and attribute information of assembly tools and workpieces to be installed, the simulated assembly model can be used to intelligently analyze and optimize the assembly process. By pre-simulating assembly, possible problems can be discovered before actual assembly, and the environmental parameters of the assembly site and the setting attributes of the assembly tools can be customized to ensure that the best assembly solution is selected.

[0033] 2. By identifying and analyzing the abnormal error information that occurs during the simulated assembly process, customized adjustments can be made to the error information to improve the accuracy of the assembly. By simulating the assembly model and recording abnormal feedback in real time, when problems occur during the assembly process, the system can track the problems in a timely manner, trace them to the source and solve them, thereby continuously optimizing the assembly plan.

[0034] 3. Through the analysis and optimization of the simulated assembly model, the duplication of work caused by assembly errors can be effectively avoided. In this way, the accuracy, stability and efficiency of the assembly are improved, which provides a guarantee for enterprises to save costs, improve production efficiency and improve product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The present invention will be further described below in conjunction with the embodiments.

[0039] Example 1

[0040] A pin header and a female header assembly process for improving assembly accuracy in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0041] Step 1: Collect the temperature, humidity and air pressure environmental parameters of the assembly site, measure the size, weight and material attribute parameters of the workpiece to be installed, and record the model, usage status and adjustment setting attribute parameters of the assembly tools. The workpieces to be installed include: pin headers, female headers and connection consumables;

[0042] Step 2: Obtain order information, extract and analyze the target requirements and assembly specifications of the order, and analyze whether the attribute parameters of the workpiece to be installed and the assembly tool are qualified. If not, terminate the assembly and send an alarm to the management end;

[0043] Step 3: According to the order information and the attribute parameters of the workpiece to be installed and the assembly tool, a comprehensive output of several assembly schemes is performed, and according to the preset selection index, the best installation scheme under the index is obtained, and the environmental parameters of the corresponding assembly site and the setting attribute parameters of the assembly tool are determined. In the process of obtaining the best installation scheme for a certain selection index in step 3, if one or more of the attribute parameters of the current assembly tool, the attribute parameters of the workpiece to be installed and the environmental parameters of the assembly site cannot meet the implementation requirements of the best installation scheme, then the secondary priority target in the output assembly scheme is obtained for role replacement;

[0044] Step 4: Build a simulated assembly model, obtain the installation plan determined in step 3, input the corresponding parameters into the model, analyze the matching of various parameters, and simulate assembly in a virtual environment. The simulated assembly model uses a convolutional neural network to build a nonlinear model, model and predict the related factors in the assembly process, and output the predicted data;

[0045] Step 5: During the simulated assembly process of step 4 or after the assembly is completed, analyze whether there is abnormal error information, analyze the abnormal error information that occurs during the simulated assembly process, and obtain the cause of the abnormal error information and related influencing factors;

[0046] The extraction methods of correlation influencing factors include:

[0047] Carefully analyze the specific content of the exception error information, including error code, description, timestamp, etc., to determine the specific circumstances in which the exception occurred;

[0048] Investigate and analyze various parameters involved in the abnormal error information, including environmental parameters of the assembly site, size, weight and material attribute parameters of the workpiece to be installed, and setting attribute parameters of the assembly tool;

[0049] Eliminate the possibilities related to abnormal error information one by one, including equipment failure, data error, human operation error and other factors;

[0050] Compare the abnormal error information with the historical assembly data to find similarities and determine possible influencing factors;

[0051] Step 6: Record the abnormal error information that occurs during the simulated assembly process, and determine the specific content and cause of each error message;

[0052] Step 7: Based on the error message, simulate and customize the environmental parameters of the assembly site and the setting attribute parameters of the assembly tools, and make step-by-step adjustments within the preset range until a reasonable adjustment range based on historical installation plan data and expert standard guidance is reached, and the adjusted content is submitted to Step 4 again.

[0053] In this embodiment, data collection, analysis and simulated assembly technology are used to realize automated assembly scheme selection and environmental parameter adjustment, thereby improving the intelligent level of the assembly process. By real-time monitoring and analysis of environmental parameters and assembly tool attribute parameters during the assembly process, unqualified assembly conditions are discovered in a timely manner, and an alarm is issued to effectively prevent possible failures during the assembly process. A simulated assembly model is used to comprehensively output multiple assembly schemes, and the best installation scheme is obtained according to preset selection indicators, making the assembly process more efficient and accurate.

[0054] By recording and analyzing abnormal error information during the simulated assembly process, it can help identify the causes and related factors of the abnormalities, help further optimize the assembly process and improve assembly quality. It can adjust environmental parameters and assembly tool attribute parameters in real time according to abnormal conditions during the simulated assembly process, and record abnormal feedback in real time to improve the flexibility and timeliness of the assembly process.

[0055] Example 2

[0056] At other levels, when the abnormal error information in this embodiment is not unique, a comprehensive analysis is performed to determine the priority adjustment order for the custom adjustment in step 7;

[0057] The methods for determining the priority adjustment order include:

[0058] a. Perform machine evaluation on each abnormal error message to determine the degree of influence of one or more parameters on assembly accuracy;

[0059] b. Refer to expert opinions and historical data to determine the extent to which adjustment of one or more parameters affects assembly accuracy;

[0060] In the determination method a, candidate error features of several abnormal error messages are analyzed;

[0061] Candidate error features include:

[0062] Equipment fault number, fault start time, fault duration, matching completeness rate, assembly process impact level, processing technology impact level, whether there are workpieces to be assembled on the faulty equipment, number of remaining processing steps, number of remaining assembly steps, total remaining execution time, average remaining equipment load rate, proportion of workpieces affected, proportion of directly affected processes, proportion of indirectly affected processes, and proportion of time affected;

[0063] The environmental parameters of the assembly site, the attribute parameters of the workpiece to be installed, and the attribute parameters of the assembly tools are labeled one by one to generate labels. After the candidate error reporting features are determined, it is necessary to select features that are discriminative and related to the labels as input data, and calculate the correlation between the candidate features and the labels using Pearson correlation analysis. The calculation formula is:

[0064]

[0065] In the formula, the closer the value of Pcc is to 1 or -1, the greater the positive or negative correlation between the candidate error feature and the label. i and i are respectively used as the i-th data in the sample data, and as the mean values ​​of x and y respectively;

[0066] The correlation coefficients between all candidate features and labels are ranked, and the first few features are used as input data for subsequent integrated learning.

[0067] Compared with the existing technology, the use of machine evaluation and correlation analysis technology can automatically evaluate the impact of each abnormal error message on assembly accuracy, so as to make more intelligent adjustment decisions. By comprehensively analyzing the correlation between candidate error features and assembly environment parameters, attribute parameters of the workpiece to be installed, and attribute parameters of the assembly tool, it can more comprehensively identify the influencing factors of abnormal error messages, thereby providing more accurate adjustment guidance.

[0068] By referring to expert opinions and historical data, a more reliable basis can be provided for the priority adjustment of abnormal error information, combining the advantages of artificial intelligence and expert knowledge;

[0069] By more accurately determining the adjustment sequence, abnormal situations in the assembly process can be resolved more quickly, improving assembly accuracy and efficiency. By using correlation analysis and machine evaluation, a large amount of data generated during the assembly process can be effectively utilized to provide data support for decision-making.

[0070] Example 3

[0071] In this embodiment, when there is no abnormal error information or the abnormal error information is lower than the preset threshold, it is considered to have passed the simulated assembly verification. At this time, the simulated assembly instruction is sent to the attribute parameters of the assembly tool, the workpiece to be installed and the scheduling end of the assembly site to perform actual assembly, and whether there is abnormal feedback is recorded in real time;

[0072] If abnormal feedback occurs during the actual assembly process, the target with the problem will be traced back to its source, the operating settings and attribute parameters of the related components will be recorded, and the current parameters will be input into the simulation assembly model as a reference value. The simulation will be re-adjusted and assembly suggestions will be output.

[0073] Compared with existing technologies, by simulating assembly models and recording abnormal feedback in real time, when problems occur during the assembly process, the system can track the problems in a timely manner, trace them to the source and solve them, thereby continuously optimizing the assembly plan.

[0074] In summary, the present invention simulates the assembly model and records abnormal feedback in real time. When problems occur during the assembly process, the system can track the problems in time, trace the source and solve them, so as to continuously optimize the assembly plan. Through rigorous data collection, analysis and application of simulated assembly technology, automatic assembly plan selection and environmental parameter adjustment can be realized, which significantly improves the intelligent level of the assembly process.

[0075] The present invention can monitor and analyze the environmental parameters and assembly tool attribute parameters in the assembly process in real time. Once unqualified assembly conditions are found, an alarm will be issued immediately to effectively prevent potential failures. At the same time, by simulating the assembly model, the system can comprehensively output multiple assembly plans and determine the best installation plan based on preset selection indicators, making the assembly process more efficient and accurate.

[0076] In addition, the present invention will also record and analyze the abnormal error information in the simulated assembly process to help identify the abnormal causes and their associated factors, thereby optimizing the assembly process and improving the assembly quality. The system can adjust the environmental parameters and assembly tool attribute parameters in real time according to the actual situation, and record abnormal feedback, which enhances the flexibility and timeliness of the assembly process;

[0077] Using machine evaluation and correlation analysis technology, the system can automatically evaluate the impact of each abnormal error message on assembly accuracy and provide more accurate adjustment guidance. Combined with expert opinions and historical data, it can determine the priority adjustment sequence of abnormal error messages, quickly resolve abnormal situations in the assembly process, and further improve assembly accuracy and efficiency.

[0078] By simulating assembly models and recording abnormal feedback in real time, the system can track problems in a timely manner during the assembly process, trace them to their source and solve them, and continuously optimize the assembly plan. At the same time, by using correlation analysis and machine evaluation, the system can effectively utilize a large amount of data generated during the assembly process to provide strong support for decision-making.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pin header and a female header assembly process for improving assembly accuracy, characterized in that: The following steps are involved: Step 1: Collect the temperature, humidity and air pressure environmental parameters of the assembly site, measure the size, weight and material attribute parameters of the workpiece to be installed, and record the model, usage status and attribute parameters of the assembly tools; Step 2: Obtain order information, extract and analyze the target requirements and assembly specifications of the order, and analyze whether the attribute parameters of the workpiece to be installed and the assembly tool are qualified. If not, terminate the assembly and send an alarm to the management end; Step 3: Based on the order information and the attribute parameters of the workpiece to be installed and the assembly tool, a comprehensive output of several assembly plans is performed, and based on the preset selection index, the best installation plan under the index is obtained, and the environmental parameters of the corresponding assembly site and the setting attribute parameters of the assembly tool are determined; Step 4: Build a simulated assembly model, obtain the installation plan determined in step 3, input the corresponding parameters into the model, analyze the matching of the parameters, and perform simulated assembly in a virtual environment; Step 5: During the simulated assembly process of step 4 or after the assembly is completed, analyze whether there is abnormal error information, analyze the abnormal error information that occurs during the simulated assembly process, and obtain the cause of the abnormal error information and related influencing factors; Step 6: Record the abnormal error information that occurs during the simulated assembly process, and determine the specific content and cause of each error message; Step 7: According to the error message, the environmental parameters of the assembly site and the setting attribute parameters of the assembly tools are adjusted accordingly, and the preset range is adjusted step by step until the reasonable adjustment range output according to the historical installation plan data and expert standard guidance is hit, and the adjusted content is submitted to step 4 again; Step 8: When there is no abnormal error information or the abnormal error information is lower than the preset threshold, it is considered to have passed the simulated assembly verification. At this time, the simulated assembly instructions are sent to the attribute parameters of the assembly tool, the workpiece to be installed and the scheduling end of the assembly site to perform actual assembly and record in real time whether there is abnormal feedback; When the abnormal error message in step 5 is not unique, a comprehensive analysis is performed to determine the priority adjustment sequence for the custom adjustment in step 7; The method for determining the priority adjustment order includes: a. Perform machine evaluation on each abnormal error message to determine the degree of influence of one or more parameters on assembly accuracy; b. Refer to expert opinions and historical data to determine the extent to which adjustment of one or more parameters affects assembly accuracy; In the determination method a, several candidate error features of abnormal error information are analyzed, and the environmental parameters of the assembly site, the attribute parameters of the workpiece to be installed, and the attribute parameters of the assembly tool are labeled one by one to generate labels. After the candidate error features are determined, features with discrimination and related to the labels need to be selected as input data, and the correlation between the candidate features and the labels is calculated by Pearson correlation analysis. The calculation formula is: Pcc( , )={ }╱ In the formula, the closer the value of Pcc is to 1 or -1, the greater the positive or negative correlation between the candidate error feature and the label. and As the first data, and As and The respective average values.

2. A pin header and socket header assembly process for improving assembly accuracy according to claim 1, characterized in that: The workpieces to be installed in step 1 include: pin headers, female headers and connecting consumables.

3. A pin header and socket header assembly process for improving assembly accuracy according to claim 1, characterized in that: In the process of obtaining the best installation plan for a certain selection indicator in step 3, if one or more of the attribute parameters of the current assembly tool, the attribute parameters of the workpiece to be installed, and the environmental parameters of the assembly site cannot meet the implementation requirements of the best installation plan, then the sub-priority target in the output assembly plan is obtained for role replacement.

4. A pin header and socket header assembly process for improving assembly accuracy according to claim 1, characterized in that: The simulated assembly model in step 4 is constructed using a convolutional neural network, a nonlinear model is established, the associated factors in the assembly process are modeled and predicted, and the prediction data is output.

5. The pin header and socket header assembly process for improving assembly accuracy according to claim 1, characterized in that: In step 8, if abnormal feedback occurs during the actual assembly process, the target with the problem is traced back to the source, the operating settings and attribute parameters of the related components are recorded, and the current parameters are input into the simulation assembly model as reference values, and the simulation adjustment is performed again to output assembly suggestions.

Images