CN114115141B

CN114115141B - Solder paste production method and system

Info

Publication number: CN114115141B
Application number: CN202111289528.1A
Authority: CN
Inventors: 肖大为; 卢克胜; 肖健
Original assignee: Jiangsu Sanwal Electronic Technology Co ltd
Current assignee: Jiangsu Sanwal Electronic Technology Co ltd
Priority date: 2021-11-02
Filing date: 2021-11-02
Publication date: 2024-05-14
Anticipated expiration: 2041-11-02
Also published as: CN114115141A

Abstract

The embodiment of the specification provides a solder paste production method, which comprises the steps of obtaining first viscosity data of a solder paste semi-finished product based on a viscometer, and mixing and stirring the solder paste semi-finished product based on solder powder and soldering flux; determining whether a preset condition is met through a judging module based on the first viscosity data; responding to the failure, the early warning module sends out an alarm instruction; in response, the production module sends a finished product preparation instruction to enable the finished product preparation device to generate a finished solder paste product by vacuumizing and pouring the semi-finished solder paste product.

Description

Solder paste production method and system

Technical Field

The specification relates to the field of welding technology, in particular to a solder paste production method and a solder paste production system.

Background

Solder paste is a novel soldering material which is produced along with the surface mount technology (Surface Mounted Technology, SMT) and is a paste mixture formed by mixing solder powder, soldering flux, other surfactants, thixotropic agents and the like. With the development of electronic information devices, SMT has become one of the mainstream technologies for electronic assembly, and the use amount of solder paste has been increasing. The quality of the solder paste influences the service performance of the solder paste, so the quality control of the solder paste production is also very important.

Therefore, it is desirable to provide a solder paste production method, which realizes intelligent production and improves the quality of solder paste production by performing subsequent operations on data judgment of each stage of a solder paste semi-finished product.

Disclosure of Invention

One of the embodiments of the present disclosure provides a method for producing solder paste. The method comprises the following steps: acquiring first viscosity data of a semi-finished solder paste product based on a viscometer, wherein the semi-finished solder paste product is generated by mixing and stirring solder powder and soldering flux; determining whether a preset condition is met or not through a judging module based on the first viscosity data; responding to the failure, the early warning module sends out an alarm instruction; in response, the production module sends a finished product preparation instruction to cause the finished product preparation device to generate a finished solder paste product by vacuumizing and pouring the semi-finished solder paste product.

One of the embodiments of the present specification provides a solder paste production system, the system comprising: acquisition module, judgement module, early warning module and production module: the acquisition module is used for acquiring first viscosity data of a semi-finished solder paste product based on a viscometer, and the semi-finished solder paste product is generated based on mixing and stirring of solder powder and soldering flux; the judging module is used for determining whether a preset condition is met or not based on the first viscosity data; responding to the failure, the early warning module sends out an alarm instruction; in response, the production module sends a finished product preparation instruction to enable the finished product preparation device to generate a finished solder paste product by vacuumizing and pouring the semi-finished solder paste product.

One of the embodiments of the present disclosure provides a solder paste production apparatus, including at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the solder paste production method.

One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the solder paste production method.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

fig. 1 is a schematic view of an application scenario of a solder paste production system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow chart of a method of solder paste production according to some embodiments of the present description;

FIG. 3 is an exemplary flow chart for determining whether preset conditions are met according to some embodiments of the present description;

FIG. 4 is a schematic illustration of determining predicted viscosity data based on a viscosity identification model, according to some embodiments of the present disclosure;

Fig. 5 is another exemplary flow chart for determining whether a preset condition is satisfied according to some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

Fig. 1 is a schematic view of an application scenario of a solder paste production system 100 according to some embodiments of the present disclosure. A terminal 110, a network 120, a processor 130, a solder paste production device 140, a storage device 150 may be included in an application scenario.

The solder paste production system 100 may be used in facilities where solder paste production is desired.

The terminals 110, network 120, processor 130, solder paste production device 140, and storage device 150 may exchange data and/or information via network 120 to implement solder paste production functions. The memory device 140 may store all information during the performance of the solder paste production function. In some embodiments, the solder paste production apparatus 140 may send solder paste production results to the processor 130 and receive feedback information from the processor 130. The processor 130 may process solder paste production data including current inspection data and historical environmental data, wherein the historical solder paste production data may be retrieved from a storage device via the network 120. The processor 130 may process the solder paste production data, determine whether processing is required, generate an instruction based on the determination result of the processing, and send the instruction to the solder paste production apparatus 140 through a network, to instruct the solder paste production apparatus to process. The above interaction relationship between the devices is merely an example, and other interaction forms are possible according to the actual situation.

Terminal 110 may be configured to input and/or obtain data or information. For example, static data or dynamic data of the planting device may be acquired by terminal 110. In some embodiments, terminal 110 may be a personal terminal device or a public terminal device. For example, terminal 110 may be a mobile terminal, a wearable device, or a computing device, etc., of an object to be serviced. As another example, the terminal 110 may be a terminal device held by a service provider, and the service provider may issue instructions through a terminal acquisition server, or the like. In some embodiments, terminal 110 may include a mobile phone 110-1, a tablet computer 110-2, a notebook computer 110-3, a desktop computer 110-4, or the like, or any combination thereof.

The network 120 may connect components of the system and/or connect the system with external resource components. Network 120 enables communication between components and other parts of the system to facilitate the exchange of data and/or information. In some embodiments, network 120 may be any one or more of a wired network or a wireless network. For example, the network 120 may include a cable network, a fiber optic network, a telecommunications network, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), and the like, or any combination thereof. The network connection between the parts can be in one of the above-mentioned ways or in a plurality of ways. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired or wireless network access points, such as base stations and/or network switching points, through which one or more components of access point system 100 may connect to network 120 to exchange data and/or information.

Processor 130 may process data and/or information obtained from other devices or system components. The processor may execute program instructions to perform one or more of the functions described in this disclosure based on such data, information, and/or processing results. In some embodiments, processor 130 may contain one or more sub-processing devices (e.g., single-core processing devices or multi-core, multi-core processing devices). By way of example only, the processor 120 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a special purpose instruction processor (ASIP), a microprocessor, or the like, or any combination thereof.

The solder paste production apparatus 140 may be used to produce solder paste, including stirring apparatus, vacuum apparatus, and pouring apparatus. In some embodiments, the stirring device can stir and forcefully disperse, having stirring, dispersing, shearing and mixing functions. In some embodiments, the vacuum apparatus may be a nitrogen-filled apparatus. In some embodiments, the perfusion apparatus may be a fully automated dispensing apparatus.

In some embodiments, the solder paste production apparatus 140 may determine whether the first viscosity data satisfies the first sub-preset condition. In some embodiments, in response to satisfaction, the solder paste production apparatus 140 may determine predicted viscosity data for the solder paste semi-finished product based on the production recipe and the production process parameters. In some embodiments, the solder paste production apparatus 140 may determine whether to issue an alarm instruction based on whether the relationship between the predicted viscosity data and the first viscosity data satisfies a second sub-preset condition. In some embodiments, image data of the solder paste semi-finished product at different stirring speeds is acquired in response to satisfaction. In some embodiments, the solder paste production apparatus 140 may determine second viscosity data based on the image data at the different agitation speeds. In some embodiments, the solder paste production apparatus 140 may determine whether to issue the alarm instruction based on whether the relationship between the first viscosity data and the second viscosity data satisfies a third sub-preset condition.

Storage device 150 may be used to store data and/or instructions. Storage device 150 may include one or more storage components, each of which may be a separate device or may be part of another device. In some embodiments, storage device 150 may be implemented on a cloud platform.

It should be understood that the system shown in fig. 1 and its modules may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer-executable instructions and/or embodied in processor control code.

In some embodiments, the solder paste production system 100 includes an acquisition module, a judgment module, a prediction module, an early warning module, and a production module.

In some embodiments, the acquisition module is configured to acquire first viscosity data of the solder paste semi-finished product based on the viscometer. In some embodiments, the solder paste semi-finished product is generated based on solder powder and flux mixing and stirring. In some embodiments, the obtaining module is further configured to obtain image data of the solder paste semi-finished product at different agitation speeds in response to satisfaction.

In some embodiments, the determining module is configured to determine whether a preset condition is satisfied based on the first viscosity data. In some embodiments, the preset conditions include a first sub-preset condition and a second sub-preset condition, and the determining module is further configured to determine whether the first viscosity data meets the first sub-preset condition. In some embodiments, the determining module is further configured to determine whether to issue the alarm instruction based on whether a relationship between the predicted viscosity data and the first viscosity data satisfies a second sub-preset condition. In some embodiments, the preset conditions include a first sub-preset condition and a third sub-preset condition, and the determining module is further configured to determine whether to issue the alarm instruction based on whether a relationship between the first viscosity data and the second viscosity data satisfies the third sub-preset condition.

In some embodiments, the prediction module is further configured to determine, by the prediction module, predicted viscosity data for the solder paste semi-finished product based on the production recipe and the production process parameters in response to the satisfaction. In some embodiments, the prediction module is further to determine second viscosity data based on the image data at the different agitation speeds. In some embodiments, the determining module determines whether the preset condition is satisfied, and in response to the determining module determining whether the preset condition is satisfied, the warning module issues a warning command.

In some embodiments, the determining module determines whether a preset condition is met, and in response, the producing module sends a finished product preparation instruction to cause the finished product preparation apparatus to generate a finished solder paste product by evacuating and pouring the semi-finished solder paste product.

In some examples, the functions of the solder paste production method in one or more scenarios described in the embodiments of the present specification are implemented by executing different functions on different devices, respectively, or by executing multiple functions simultaneously on one device.

It should be noted that the above description of the system and its modules is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. For example, in some embodiments, for example, the acquisition module, the determination module, the prediction module, the early warning module, and the production module disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules. For example, the prediction module and the early warning module may be two modules, or may be one module having both the prediction function and the early warning function. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

Fig. 2 is an exemplary flow chart of a solder paste production method according to some embodiments of the present description. The flow 200 shown in fig. 2 includes steps 210, 220, 230, and 240.

At step 210, first viscosity data for the solder paste semi-finished product is obtained based on a viscometer. In some embodiments, step 210 is performed by the acquisition module.

The semi-finished solder paste product is an intermediate product of a finished solder paste product which is prepared by respectively pre-treating solder powder and soldering flux and stirring the solder powder and the soldering flux to a certain extent. In some embodiments, the solder paste semi-finished product is generated based on solder powder and flux mixing and stirring.

Solder powder refers to tin powder. In some embodiments, the solder powder includes an alloy composition. For example, the solder powder includes tin bismuth, tin silver copper alloy, and the like.

Soldering flux refers to a chemical substance that helps and facilitates the soldering process while protecting against oxidation reactions during the soldering process. In some embodiments, the flux may be solid, liquid, and gas. In some embodiments, the flux includes an activator, a thixotropic agent, a resin, a solvent, and the like.

The first viscosity data refers to the viscosity of the solder paste semi-finished product determined by a viscometer. For example, the first viscosity data may be viscosity data of the solder paste semifinished product determined by the viscometer at a preset detection time.

In some embodiments, the viscometer includes a capillary viscometer, a rotational viscometer, and a falling ball viscometer. In some embodiments, the first viscosity data may be read using a viscometer at the appropriate temperature and with the proper stirring speed.

Step 220, based on the first viscosity data, determining, by the determining module, whether the preset condition is satisfied.

The preset condition refers to the condition that the first viscosity data itself or the difference between the first viscosity data and the reference viscosity data acquired by other paths needs to be satisfied. In some embodiments, the preset condition may be that the value of the first viscosity data itself satisfies a preset threshold condition, or that the difference between the value and the reference viscosity data acquired by other paths is not greater than a preset threshold. In some embodiments, the reference viscosity data obtained by the other approach includes predicted viscosity data and second viscosity data, for relevant content with respect to the predicted viscosity data and the second viscosity data, see the relevant description of fig. 3-5. In some embodiments, the value of the preset threshold may be preset by the system or by a user.

In some embodiments, the preset conditions include three sub-preset conditions, namely a first sub-preset condition, a second sub-preset condition, and a third sub-preset condition. In some embodiments, the first sub-preset condition is mainly used for judging a condition to be met for acquiring the first viscosity data of the solder paste semi-finished product based on the viscometer. In some embodiments, the second sub-preset condition is used mainly to determine a condition to be satisfied for determining a relationship between the predicted viscosity data and the first viscosity data of the solder paste semi-finished product based on the production recipe and the production process parameters. In some embodiments, the third sub-preset condition is mainly used for judging a condition to be satisfied by a relation between the second viscosity data and the first viscosity data determined based on the image data at different stirring speeds. For more details on the first sub-preset condition, the second sub-preset condition, and the third sub-preset condition, see fig. 3 to 5 and the related description thereof.

In step 230, in response to no, the prediction module issues an alarm instruction.

The alarm instruction comprises a sound alarm instruction, a flash alarm instruction, a picture alarm instruction and the like. In some embodiments, the alarm instruction may be used as a warning of machine failure, may be based on field conditions or other conditions, and is not limited herein.

Based on the alarm instruction, the effectiveness of the working action of the machine can be ensured, redundant actions or invalid actions of the machine can be reduced or even avoided, and timely monitoring of the machine is also beneficial to timely identifying and correcting when the viscosity abnormal condition occurs. In some embodiments, the high or low viscosity of the solder paste semi-finished product can be avoided from affecting the quality of the finished product based on the alarm instruction. For example, the viscosity of the semi-finished solder paste is too high, and the solder paste is not easy to penetrate out of the leakage holes of the template. For another example, if the viscosity of the solder paste semi-finished product is too low, the solder paste is likely to fall off when used.

In some embodiments, the correction instructions may be further generated after the alert instructions are generated. The correction command can be generated in an auxiliary way according to the abnormality degree of the current situation, for example, the current first viscosity data deviate from the conventional value too much, or the correction command can be generated under other conditions which obviously need intervention before subsequent operation. Based on the correction instruction, correction processing may be instructed.

In some embodiments, the modification process may be to inform the user to adjust relevant parameters in the production process.

In some embodiments, the correction process may include informing a user to adjust the specific gravity of the feedstock composition, the production environment, or the operating parameters of the instrument device. For example, the specific gravity of the tin powder component or the production environment temperature is adjusted.

In response, the production module sends a finished product preparation instruction to cause the finished product preparation apparatus to generate a finished solder paste product by evacuating and pouring the semi-finished solder paste product, step 240.

The finished product preparation instruction refers to a code instruction for the machine to prepare the finished product, and the code instruction is sent by the production module. In some embodiments, the production module sends a paste end preparation instruction. The finished product preparation instruction indicates that the current semi-finished solder paste is fully stirred, and the next vacuumizing and finished product pouring link can be performed.

The finished product preparation device refers to a device for generating a finished solder paste product by vacuumizing and pouring a semi-finished solder paste product, and comprises stirring equipment, vacuumizing equipment and pouring equipment. For example, the vacuum apparatus may be a nitrogen-filled apparatus. In some embodiments, the evacuation device may be based on field conditions or other conditions.

Pouring means that the semi-finished product of the solder paste which is already mixed is poured into a mould as required. In some embodiments, the perfusion apparatus may be a fully automated dispensing apparatus or a manual dispensing, as may be appropriate for the situation or other conditions.

The first viscosity data of the semi-finished solder paste product is obtained based on the viscometer, so that the effectiveness of solder paste production can be guaranteed, redundant production or invalid production of equipment can be reduced or even avoided, and the solder paste production efficiency is improved. Meanwhile, whether preset conditions are met or not is determined through the judging module, and an alarm is given in time when abnormal conditions occur, so that intelligent production is realized, and the production quality of solder paste is improved.

It should be noted that the above description of the production of the solder paste is for illustration and description only, and does not limit the application scope of the present specification. Various modifications and alterations to the flow Cheng Xigao production may be made by those skilled in the art under the guidance of this specification. However, such modifications and variations are still within the scope of the present description.

Fig. 3 is an exemplary flow chart of a solder paste manufacturing method 300 for determining whether preset conditions are met according to some embodiments of the present disclosure. The flow 300 shown in fig. 3 includes steps 310, 320 and 330.

Step 310, determining whether the first viscosity data meets a first sub-preset condition. In some embodiments, step 310 is performed by a determination module.

For a description of the acquisition of the first viscosity data, refer specifically to the relevant description of fig. 2.

The first sub-preset condition is mainly used for judging whether the numerical value of the first viscosity data meets the condition. For example, the first sub-preset condition may be to determine whether the first viscosity data meets a preset threshold, and reach a reasonable threshold interval. The preset threshold value can be 180 Pa/s-200 Pa/s, 170 Pa/s-200 Pa/s or other possible ranges, and can be set according to actual production requirements.

In response to satisfaction, predicted viscosity data for the solder paste semi-finished product is determined by a prediction module based on the production recipe and the production process parameters, step 320. In some embodiments, step 320 is performed by a prediction module.

In some embodiments, the production recipe includes a flux content and a solder powder content, and the production process parameters include a stirring process parameter. In some embodiments, the process parameters of the agitation mainly include the rotational speed of the agitator, the duration of the agitation, and the like.

By way of example only, the production recipe may include 20-40 parts by weight rosin, 30-45 parts by weight synthetic resin, 2-5 parts by weight surfactant, 4-8 parts by weight organic solvent, 1-3 parts by weight co-solvent, 1-3 parts by weight corrosion inhibitor, 1-3 parts by weight film forming agent, 2-5 parts by weight succinic acid, 1-3 parts by weight corrosion inhibitor, 1-3 parts by weight antioxidant, 1-3 parts by weight organic amine, and the like.

In some embodiments, the production process parameters further include production equipment and production environment related parameters. For example, the type of equipment used for producing solder paste, the temperature and humidity during production, and other relevant parameters.

The predicted viscosity data refers to the predicted viscosity number of the solder paste.

In some embodiments, the predicted viscosity data may be determined based on the recipe or the time of detection.

In some embodiments, the detection time refers to the time corresponding to the first viscosity, possibly at some point in the preparation process.

In some embodiments, the predicted viscosity data may be empirical values obtained by look-up tables.

In some embodiments, the table may be a table based on historical data statistics. The table contains the types, the contents and the corresponding viscosities of the solder powder and the soldering flux with different formulations in various proportions, and can also comprise parameters of the environment and instruments during preparation, such as temperature, model of a stirrer and the like. In some embodiments, the table may be a historical data test set, and the relevant content may be seen in FIG. 5.

In some embodiments, the table may be retrieved and presented at the terminal device for query browsing based on manual input, cloud storage, and third party data sources (internet).

In some embodiments, the table is combined with the recipe and the test time, and the predicted viscosity data is calculated from the data obtained.

In some embodiments, the predicted viscosity data may be calculated based on parameters that set different values for the alloy powder content and particle size in the solder paste of the current formulation. For example, an increase in the content of solder paste alloy powder causes an increase in viscosity, and as the granularity of solder paste alloy powder increases, the viscosity decreases, and parameters of different values can be set based on this law and historical data.

In some embodiments, determining the predicted viscosity data may also take into account factors such as the production environment and instrumentation. For example, an increase in temperature in the production environment results in a decrease in viscosity, and a cessation of stirring force results in an increase in viscosity.

Step 330, determining whether to issue an alarm instruction based on whether the relation between the predicted viscosity data and the first viscosity data satisfies the second sub-preset condition. In some embodiments, step 330 is performed by a determination module.

In some embodiments, the second sub-preset condition is used primarily to determine a relationship between the predicted viscosity data and the first viscosity data. For example, the difference between the first viscosity data and the predicted viscosity data may not be greater than 10Pa/s (the specific values may be adjusted according to the production needs without limitation).

For a description of the alarm instruction, please refer to the corresponding part of fig. 2.

In some embodiments, comparing the first viscosity data with the predicted viscosity data can find out whether the viscometer has a fault in time, and ensure the accuracy of the first viscosity data so as not to interfere with the normal operation of solder paste production due to the fault of the viscometer.

Fig. 4 is a schematic diagram of a method of solder paste production for determining predicted viscosity data based on a viscosity recognition model, according to some embodiments of the present disclosure. The components of the schematic flow 400 shown in fig. 4 include a production recipe 411, production process parameters 412, a viscosity identification model 420, and predicted viscosity data 430.

Inputs to the viscosity identification model 420 include the production recipe 411 and the production process parameters 412, and outputs predicted viscosity data 430 including the solder paste semi-finished product. The viscosity identification model 420 is a neural network model. For example, the types of viscosity recognition models 420 include Convolutional Neural Networks (CNNs), deep Belief Networks (DBNs), recurrent Neural Networks (RNNs), long Short Term Memories (LSTMs), support Vector Machines (SVMs), and the like.

In some embodiments, the input to the viscosity identification model 420 also includes second viscosity data (not shown). The second viscosity data is determined based on image data at different stirring speeds, see the relevant description of fig. 5. In some embodiments of the present disclosure, inputting the second viscosity data for training may improve accuracy of the system.

In some embodiments, the input to the viscosity identification model 420 also includes a detection time (not shown).

In some embodiments of the present disclosure, by inputting the production recipe 411 and the production process parameters 412 and other related influencing parameters into the model for prediction, various influencing parameters can be integrated for prediction, thereby improving the comprehensiveness and accuracy of the prediction.

In some embodiments, the viscosity identification model 420 may be trained based on a number of identified training samples. For example, the training sample with the identification is input into the viscosity identification model 420, a loss function is constructed from the label and the predicted result of the viscosity identification model, and the parameters of the model are iteratively updated based on the loss function. And when the trained model meets the preset condition, finishing training. The preset conditions are that the loss function converges, the iteration times reach a threshold value, and the like.

In some embodiments, the training samples may be production recipes and production process parameters, test times, etc. of the solder paste recorded during the historical synthesis. The label may be real viscosity data or the like.

Fig. 5 is an exemplary flow chart of another solder paste production method for determining whether preset conditions are met according to some embodiments of the present description. The flow 500 shown in fig. 5 includes steps 510, 520, 530, and 540.

Step 510, determining whether the first viscosity data satisfies a first sub-preset condition. In some embodiments, step 510 is performed by a determination module.

For a description of determining whether the first viscosity data satisfies the first sub-preset condition, refer to fig. 3.

And step 520, responding to the satisfaction, and acquiring image data of the semi-finished solder paste at different stirring speeds. In some embodiments, step 520 is performed by the acquisition module.

In some embodiments, the image data at different agitation speeds may be image data acquired when only the agitation speed is different and the remaining conditions (e.g., production recipe, production process, production environment, and instrumentation) are the same.

In some embodiments, the different stirring speeds may be stirring speeds that are often selected for multiple solder paste productions. In some embodiments, the different stirring speeds may be several stirring speeds at manually preset numerical intervals within a stirring speed range suitable for solder paste production.

In some embodiments, the image data may be acquired by cameras, monitoring probes, etc. on the solder paste production apparatus 140.

In some embodiments, the time of acquisition of the image may refer to the time corresponding to the acquisition of the first viscosity, possibly at some point in time during the preparation process.

Step 530, determining second viscosity data based on the image data at the different agitation speeds. In some embodiments, step 530 is performed by a prediction module.

In some embodiments, the image may be presented to the user, the user input data obtained, and the second viscosity data determined. For example, the image may be sent to the user in the form of a video, which may be based on experience in conjunction with the image to estimate viscosity.

In some embodiments, the second viscosity data may be determined by a second viscosity identification model.

In some embodiments, the second viscosity identification model includes sub-identification models corresponding to a plurality of different agitation speeds, each sub-identification model for identifying an image corresponding to an agitation speed, and determining sub-second viscosity data corresponding to an agitation speed. A plurality of sub second viscosity data corresponding to a plurality of different stirring speeds are calculated to obtain second viscosity data. The operation mode comprises average, weighted average and the like.

In some embodiments, the type of sub-recognition model may be CNN.

In some embodiments, when weighting and summing sub-second viscosity data corresponding to a plurality of different agitation speeds, the weight of each sub-second viscosity data is determined based on the recognition accuracy of the corresponding sub-recognition model.

In some embodiments, the recognition accuracy of the sub-recognition model may be determined based on the test accuracy obtained at a test stage in the sub-recognition model training process. For example, when training meets the requirements, the test accuracy of the model is directly used as a weight.

In some embodiments, when weighting and summing sub-second viscosity data corresponding to a plurality of different agitation speeds, the weight of each sub-second viscosity data is determined based on the recognition confidence of the corresponding sub-recognition model output. For example, the recognition confidence is directly used as the weight.

In some embodiments, each recognition model may be trained separately.

In some embodiments, for a sub-recognition model, the sub-recognition model may be trained based on a number of identified training samples. For example, a training sample with an identification is input into the sub-recognition model, a loss function is constructed through the label and the prediction result of the sub-recognition model, and the parameters of the model are updated based on the loss function in an iterative manner. When the trained model meets the conditions, training is ended. The condition is that the loss function converges, the number of iterations reaches a threshold value, etc.

The training sample is training data of stirring speed corresponding to the sub-recognition model, and the training data is specifically an image of a sample solder paste semi-finished product or a historical solder paste semi-finished product at the stirring speed. The labels may be real viscosity data corresponding to the images, respectively. The label can be obtained by means of viscometer measurement and manual labeling.

Step 540, determining whether to issue the alarm instruction based on whether the relation between the first viscosity data and the second viscosity data satisfies a third sub-preset condition. In some embodiments, step 540 is performed by a determination module.

In some embodiments, the third sub-preset condition is primarily used to determine a relationship between the second viscosity data and the first viscosity data. For example, the difference between the first viscosity data and the second viscosity data may not be greater than 10Pa/s (the specific values may be adjusted according to the production needs without limitation).

In some embodiments of the present disclosure, by combining image data at different speeds with the second viscosity identification model and comparing the first viscosity data with the acquired second viscosity data, the first viscosity data measured by the viscometer can be effectively monitored, so as to ensure accuracy of the numerical value, and facilitate improvement of quality monitoring effect in the solder paste production process.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. A method of producing solder paste, the method comprising:

Acquiring first viscosity data of a semi-finished solder paste product based on a viscometer, wherein the semi-finished solder paste product is generated by mixing and stirring solder powder and soldering flux;

Determining whether preset conditions are met or not through a judging module based on the first viscosity data, wherein the preset conditions comprise a first sub-preset condition and a third sub-preset condition;

Responding to the failure, the early warning module sends out an alarm instruction and a correction instruction, and performs correction processing based on the correction instruction;

In response, the production module sends a finished product preparation instruction to enable the finished product preparation device to generate a finished solder paste product by vacuumizing and pouring the semi-finished solder paste product;

the determining, based on the first viscosity data, whether a preset condition is satisfied by a judging module includes:

Judging whether the first viscosity data meets the first sub-preset condition or not through the judging module;

responding to the satisfaction, acquiring image data of the solder paste semi-finished product at different stirring speeds;

Determining second viscosity data by a second viscosity recognition model based on the image data at the different agitation speeds; the second viscosity recognition model comprises a plurality of sub-recognition models, each sub-recognition model is used for recognizing the image data at the corresponding different stirring speeds, sub-second viscosity data of the corresponding different stirring speeds are determined, the second viscosity data are obtained through a plurality of sub-second viscosity data operation, the operation comprises weighted average, and the weight of the weighted average is determined based on the recognition accuracy of the corresponding sub-recognition model;

and determining whether to send the alarm instruction based on whether the relation between the first viscosity data and the second viscosity data meets the third sub-preset condition or not through the judging module.

2. The solder paste production method of claim 1, wherein the preset conditions further comprise a second sub-preset condition, and wherein the determining, based on the first viscosity data, by the judging module, whether the preset condition is satisfied comprises:

determining, by a prediction module, predicted viscosity data of the solder paste semi-finished product based on a production recipe and production process parameters, the production recipe including a content of the soldering flux and a content of the solder powder, the production process parameters including a process parameter of stirring;

And determining whether to send the alarm instruction based on whether the relation between the predicted viscosity data and the first viscosity data meets the second sub-preset condition or not through the judging module.

3. The solder paste production method of claim 2, wherein determining predicted viscosity data for the solder paste semi-finished product based on a production recipe and production process parameters comprises:

inputting the production formula and the production process parameters into a viscosity identification model in the prediction module, and outputting the predicted viscosity data of the solder paste semi-finished product;

the viscosity identification model is a neural network model.

4. The solder paste production system is characterized by comprising an acquisition module, a judgment module, an early warning module and a production module:

The acquisition module is used for acquiring first viscosity data of a semi-finished solder paste product based on a viscometer, and the semi-finished solder paste product is generated based on mixing and stirring of solder powder and soldering flux;

The judging module is used for determining whether preset conditions are met or not based on the first viscosity data, wherein the preset conditions comprise a first sub-preset condition and a third sub-preset condition;

In response, the production module sends a finished product preparation instruction to enable a finished product preparation device to generate a finished solder paste product by vacuumizing and pouring the semi-finished solder paste product;

the judging module is further configured to: judging whether the first viscosity data meets the first sub-preset condition or not;

the acquisition module is further to: responding to the satisfaction, acquiring image data of the solder paste semi-finished product at different stirring speeds;

The prediction module is further to: determining second viscosity data by a second viscosity recognition model based on the image data at the different agitation speeds; the second viscosity recognition model comprises a plurality of sub-recognition models, each sub-recognition model is used for recognizing the image data at the corresponding different stirring speeds, sub-second viscosity data of the corresponding different stirring speeds are determined, the second viscosity data are obtained through a plurality of sub-second viscosity data operation, the operation comprises weighted average, and the weight of the weighted average is determined based on the recognition accuracy of the corresponding sub-recognition model;

the judging module is further configured to: and determining whether to issue the alarm instruction based on whether the relation between the first viscosity data and the second viscosity data meets the third sub-preset condition.

5. The solder paste production system of claim 4, the preset conditions further comprising a second sub-preset condition, the determination module further configured to:

judging whether the first viscosity data meets the first sub-preset condition or not;

The solder paste production system further includes a prediction module, the prediction module further configured to:

Determining predicted viscosity data of the semi-finished solder paste product based on a production recipe and production process parameters in response to satisfaction, the production recipe including the content of the soldering flux and the content of the solder powder, the production process parameters including process parameters of stirring;

The judging module is further configured to:

and determining whether to send out the alarm instruction based on whether the relation between the predicted viscosity data and the first viscosity data meets the second sub-preset condition.

6. The solder paste production system of claim 5, the prediction module further to:

inputting the production formula and the production process parameters into a viscosity identification model, and outputting the predicted viscosity data of the solder paste semi-finished product;

the viscosity identification model is a neural network model.

7. A solder paste production device, the device comprising at least one processor and at least one memory;

the at least one memory is configured to store computer instructions;

the at least one processor is configured to execute at least some of the computer instructions to implement the solder paste production method of any one of claims 1-3.

8. A computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the solder paste production method of any one of claims 1 to 3.