Solder paste preparation method based on parameter control

Abstract

The present application relates to the technical field of solder paste preparation, and specifically to a method for preparing solder paste based on parameter control, the method comprising: adding a resin agent, an activator and an organic solvent into a mixing barrel, heating and stirring them uniformly to obtain a material to be mixed; cooling the material to be mixed, adding a thixotropic agent and stirring them uniformly to obtain a solder paste; wherein, the method for controlling the rate of decrease of the cooling temperature of the material to be mixed is specifically as follows: obtaining a material state sequence of the material to be mixed during the cooling process; analyzing the fluidity and dispersibility of the material to be mixed based on the material state sequence, determining the temperature matching factor of the material to be mixed, and controlling the rate of the cooling temperature of the material to be mixed in sections; adding solder powder into the solder paste, cooling it statically, stirring it, and then cooling it to room temperature to obtain a paste flux. The present application aims to achieve a fine response to temperature deviation and improve the preparation quality of solder paste.

Description

Technical Field

[0001] The present application relates to the technical field of solder paste preparation, and in particular to a method for preparing solder paste based on parameter control. Background Art

[0002] With the trend of electronic equipment becoming increasingly miniaturized and diversified in functions, the requirements for electronic assembly processes are becoming higher and higher. As a key material for connecting electronic components and circuit boards, the performance of solder paste directly affects the quality and reliability of electronic products. With the continuous advancement of science and technology, the demand for solder paste in the electronics industry continues to grow. The role of solder paste is mainly reflected in its role as a connecting medium between electronic components and circuit boards. Through the welding process, electronic components are firmly fixed on the circuit board to ensure the stable operation of the circuit. Its quality is directly related to the reliability and life of electronic products. High-quality solder paste can effectively reduce the failure rate of electronic products, improve production efficiency, and reduce maintenance costs.

[0003] During the preparation of solder paste, a certain amount of thixotropic agent is added to improve the stability and thixotropy of the solder paste, ensuring that the solder powder will not settle or collapse during the electronic assembly process, while maintaining good fluidity and rapid recovery during printing or dispensing. Before adding the thixotropic agent, the material being processed will be cooled to avoid thermal decomposition or denaturation of the thixotropic agent. During the cooling process, if the temperature is too low, the viscosity of the mixed material will be high, making it difficult for the thixotropic agent to be evenly dispersed. If the temperature is too high, the thixotropic agent will begin to decompose or denature, losing its proper thickening and stabilizing effects. In the prior art, a fixed cooling temperature is usually set. This method cannot adapt to the slight differences between different batches of raw materials, and cannot be adjusted according to the material status monitored in real time, resulting in problems with the quality of the material after cooling. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a solder paste preparation method based on parameter control to solve the existing problems.

[0005] The solder paste preparation method based on parameter control of the present application adopts the following technical solution:

[0006] An embodiment of the present application provides a method for preparing solder paste based on parameter control, the method comprising the following steps:

[0007] S1, weighing solder powder, activator, thixotropic agent, resin agent and organic solvent as raw materials for preparing solder paste;

[0008] S2, respectively adding a resin agent, an activator and an organic solvent into a mixing barrel, heating and stirring them evenly to obtain a material to be mixed;

[0009] S3, cooling the material to be mixed to 40°C to 80°C, adding a thixotropic agent and stirring uniformly to obtain a solder paste; wherein the method for controlling the decreasing rate of the cooling temperature of the material to be mixed is specifically as follows:

[0010] Obtaining a material state sequence of the material to be mixed during the cooling process; the material state sequence includes a temperature sequence, a viscosity sequence, and a particle size dispersion sequence;

[0011] Based on the number of outliers in the viscosity sequence, the position distribution of the outliers, and the fluctuation of the viscosity sequence, the dynamic stability characteristic value of the material flow of the material to be mixed is obtained;

[0012] Based on the distribution of the minimum value in the particle size dispersion sequence and the distribution difference of the data on both sides of the minimum value, the dispersion complexity index of the material to be mixed is obtained;

[0013] Taking the partial correlation coefficient between material state sequences as the weight, the dynamic stability characteristic value of material flow and the dispersion complexity index are forwardly integrated to obtain the temperature matching factor of the materials to be mixed;

[0014] Based on the negative correlation mapping of the temperature matching factor, the cooling rate of the mixed material is controlled in sections;

[0015] S4, adding solder powder into the soldering paste, cooling it to 30°C to 40°C, degassing it in vacuum and stirring it, and then cooling it to room temperature to obtain a paste flux.

[0016] Preferably, the weight ratio of the solder powder, the activator, the thixotropic agent, the resin agent and the organic solvent in S1 is 30:5:2:3:5.

[0017] Preferably, the activator in S1 is a mixture of one of succinic acid, sebacic acid, adipic acid and one of dibromosuccinic acid and dibromobutenediol.

[0018] Preferably, the resin agent in S1 is a mixture of KR-610, hydrogenated rosin and polymerized rosin.

[0019] Preferably, the organic solvent in S1 is a mixture of two or more of diethylene glycol dimethyl ether, diethylene glycol diethyl ether, ethylene glycol monobutyl ether and propylene glycol phenyl ether.

[0020] Preferably, in S2, when the resin agent is added into the mixing drum, it needs to be heated to 140°C to 150°C, and when the activator and the organic solvent are added into the mixing drum, the temperature is maintained at 130°C to 140°C.

[0021] Preferably, the process of acquiring the material flow dynamic stability characteristic value of the material to be mixed includes:

[0022] An outlier detection algorithm is used to obtain an outlier in the viscosity sequence; the number of outliers in the viscosity sequence is obtained; the average value of the difference between the maximum value and the minimum value in all preset size windows centered on the outliers is calculated; the product of the number of outliers and the average value is used as the first difference factor of the viscosity sequence;

[0023] Obtain the coefficient of variation of the viscosity sequence; obtain the fitted straight line of the viscosity sequence, and calculate the distance between the viscosity sequence and the fitted straight line; use the product of the coefficient of variation and the distance as the second difference factor of the viscosity sequence;

[0024] The first difference factor and the second difference factor are forwardly fused to determine the material flow dynamic stability characteristic value of the material to be mixed.

[0025] Preferably, the process of obtaining the dispersion complexity index of the material to be mixed includes:

[0026] Obtain the minimum value in the particle size dispersion sequence; obtain the difference sequence of the particle size dispersion sequence;

[0027] Obtain a preset size window centered on any minimum value; take any minimum value as the window splitting point, and calculate the mean difference values ​​of all data points on the left and right sides of the window of any minimum value; calculate the difference between the mean difference values ​​on the left and right sides of the window of any extreme value point, and record it as the particle sinking and floating difference factor of any extreme value point;

[0028] Calculate the average value of the collection time distance between any minimum value and all other minimum values, and record it as the sinking and floating closeness factor of any minimum value;

[0029] The dispersion complexity index of the material to be mixed is determined based on the particle sinking and floating difference factors and sinking and floating compact factors of all minimum values ​​in the particle size dispersion sequence; wherein the dispersion complexity index is positively correlated with the particle sinking and floating difference factors of all minimum values, and negatively correlated with the sinking and floating compact factors of all minimum values.

[0030] Preferably, the method of using the partial correlation coefficient between material state sequences as weights further includes: the weight of the dynamic stability characteristic value of material flow is the partial correlation coefficient between the temperature sequence and the viscosity sequence; the weight of the dispersion complexity index is the partial correlation coefficient between the temperature sequence and the particle size dispersion sequence.

[0031] Preferably, the negative correlation mapping based on the temperature matching factor is used to perform segmented control on the decreasing rate of the cooling temperature of the mixed material, including:

[0032] Performing segmented control on the decreasing rate of the cooling temperature at each preset control change interval after the start of cooling;

[0033] Obtain the temperature matching factor of the material to be mixed from the start of cooling to the current control change interval;

[0034] The temperature matching factor of the material to be mixed in the current control change interval is negatively correlated, and the result of the negative correlation mapping is normalized and multiplied by the preset gain coefficient and the proportional coefficient of the current control change interval PID algorithm to obtain the proportional coefficient of the next control change interval;

[0035] The proportional coefficient of the next control change interval is improved into the proportional coefficient in the PID algorithm, and the PID algorithm is used to control the decreasing rate of the cooling temperature of the next control change interval.

[0036] In the above scheme, the present application realizes precise control of the material cooling process by real-time collection of the temperature, viscosity and particle size dispersion data of the materials to be mixed; in the material mixing process, the stability of viscosity and particle size dispersion has a decisive influence on the quality of the final product, and real-time monitoring and analysis of the physical state of the material during cooling can effectively identify and adjust the non-uniformity that may occur during the mixing process. By discovering and responding to the abnormal conditions of uneven mixing or environmental conditions in the viscosity sequence during cooling, the uncertainty and potential quality risks in the cooling process are reduced; then, according to the characteristics of particle sedimentation and redispersion in the particle size dispersion sequence, data support is provided for the dispersion of the material during cooling, which helps to maintain the uniformity and stability of the material cooling; then, the mutual influence between temperature, viscosity and particle size dispersion is deeply understood, and the temperature control strategy is optimized to adapt to the changes in material properties; finally, a PID controller is set to perform segmented control of the cooling rate according to the physical state of the material, and the proportional coefficient of the PID controller is dynamically adjusted according to the temperature fit factor to achieve a fine response to the temperature deviation, ensure the uniformity and quality of the material during cooling, and improve the preparation quality of the solder paste. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 A flow chart of the steps of a method for preparing solder paste based on parameter control provided in one embodiment of the present application;

[0039] Figure 2 A flow chart of the control steps for the decreasing rate of the cooling temperature of the mixed material provided in one embodiment of the present application;

[0040] Figure 3 A schematic diagram of data collection provided for one embodiment of the present application;

[0041] Figure 4 A flowchart of the steps for obtaining an index of a temperature compatibility factor obtained by overall analysis of the viscosity and particle size of a material to be mixed provided in one embodiment of the present application;

[0042] Figure 5 A flowchart of the steps for constructing an indicator of a material flow dynamic stability characteristic value provided in one embodiment of the present application;

[0043] Figure 6 A flow chart of the steps for constructing an indicator of a decentralized complex index provided in one embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the method for preparing solder paste based on parameter control proposed in the present application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0046] The specific scheme of the solder paste preparation method based on parameter control provided by the present application is described in detail below with reference to the accompanying drawings.

[0047] An embodiment of the present application provides a method for preparing solder paste based on parameter control. For a preparation flow chart, please refer to Figure 1 , the specific preparation process is as follows:

[0048] S1, weigh solder powder, activator, thixotropic agent, resin agent and organic solvent as raw materials for preparing solder paste.

[0049] In the present application, the weight ratio is 30:5:2:3:5. The solder powder particle size is 15 μm to 25 μm; the activator is a mixture of one of succinic acid, sebacic acid, adipic acid and one of dibromosuccinic acid and dibromobutylene glycol; the thixotropic agent is a mixture of hydrogenated castor oil and amide thixotropic agents; the resin agent is a mixture of KR-610, hydrogenated rosin, and polymerized rosin; the organic solvent is a mixture of two or more of diethylene glycol dimethyl ether, diethylene glycol diethyl ether, ethylene glycol monobutyl ether, and propylene glycol phenyl ether.

[0050] In the first embodiment of the present application, a mixture of succinic acid and dibromosuccinic acid is used as the activator; a mixture of diethylene glycol dimethyl ether, diethylene glycol diethyl ether and ethylene glycol monobutyl ether is used as the organic solvent.

[0051] In the second embodiment of the present application, a mixture of sebacic acid and dibromobutylene glycol is used as an activator; a mixture of diethylene glycol diethyl ether, ethylene glycol monobutyl ether and propylene glycol phenyl ether is used as an organic solvent.

[0052] In the third embodiment of the present application, a mixture of adipic acid and dibromobutylene glycol is used as the activator; a mixture of diethylene glycol dimethyl ether, ethylene glycol monobutyl ether and propylene glycol phenyl ether is used as the organic solvent.

[0053] S2, respectively adding the resin agent, the activator and the organic solvent into the mixing barrel, heating and stirring evenly to obtain the material to be mixed.

[0054] In the present application, when the resin agent is added into the mixing barrel, it needs to be heated to 140°C to 150°C, and when the activator and the organic solvent are added into the mixing barrel, the temperature is maintained at 130°C to 140°C.

[0055] In the first embodiment of the present application, the resin agent is heated to 140° C. when added into the mixing barrel, and the temperature is maintained at 135° C. when the activator and the organic solvent are added into the mixing barrel.

[0056] In the second embodiment of the present application, the resin agent is heated to 150° C. when added into the mixing barrel, and the temperature is maintained at 140° C. when the activator and the organic solvent are added into the mixing barrel.

[0057] In the third embodiment of the present application, the temperature is heated to 145° C. when the resin agent is added into the mixing barrel, and the temperature is maintained at 130° C. when the activator and the organic solvent are added into the mixing barrel.

[0058] S3, cooling the material to be mixed to 40°C to 80°C, adding a thixotropic agent and stirring evenly for 30 minutes to obtain a soldering paste.

[0059] However, before adding the thixotropic agent, the material to be mixed is cooled to avoid thermal decomposition or denaturation of the thixotropic agent. During the cooling process, if the temperature is too low, the viscosity of the mixed material will be higher, making it difficult for the thixotropic agent to be evenly dispersed. If the temperature is too high, the thixotropic agent will begin to decompose or denature, losing its proper thickening and stabilizing effects. In the prior art, a fixed cooling temperature is usually set. This method cannot adapt to the slight differences between different batches of raw materials, and cannot be adjusted according to the material status monitored in real time, which leads to problems with the quality of the material after cooling. Accordingly, the present application controls the rate of decrease of the cooling temperature of the mixed material based on the above analysis. The specific control step flow chart is shown in the attached figure. Figure 2 As shown, specifically:

[0060] The first step is to obtain a material state sequence of the mixed material during the cooling process; the material state sequence includes a temperature sequence, a viscosity sequence and a particle size dispersion sequence.

[0061] The temperature data of the material to be mixed is collected in real time using a temperature sensor, the viscosity data of the material to be mixed is collected in real time using an online viscometer, and the particle size dispersion of the material to be mixed is collected using a probe-type particle size analyzer. The above three data are collected uniformly during the cooling process of the material to be mixed at a time interval t. In this embodiment, the value of t is 0.5s. Then, the three collected data are normalized respectively, and the normalized data are respectively composed of a temperature sequence, a viscosity sequence, and a particle size dispersion sequence. The schematic diagram of data collection is shown in FIG. Figure 3 As shown:

[0062] In the attached Figure 3 In the figure, numbers 1, 2, 3 are respectively the probes of the temperature sensor, the online viscometer and the particle size analyzer, and 4 is a mixing barrel.

[0063] At this point, the material state sequence of the material to be mixed can be obtained according to the above-mentioned acquisition device.

[0064] The second step is to analyze the fluidity and dispersibility of the materials to be mixed based on the material state sequence, further obtain the temperature requirements of fluidity and dispersibility, and determine the temperature matching factor of the materials to be mixed.

[0065] Viscosity and particle size dispersion are key parameters that affect material uniformity. Viscosity determines the fluidity of the material, while particle size dispersion affects the dispersibility of the material. Analysis of viscosity and particle size dispersion helps determine the optimal addition temperature of the thixotropic agent. Too high viscosity may lead to uneven dispersion of the thixotropic agent, while uneven particle size distribution may affect the combination of the thixotropic agent and solder powder. An overall analysis of viscosity and particle size helps reflect the real-time physical state of the material to be mixed.

[0066] Accordingly, in this application, the index obtaining step of the temperature matching factor obtained by overall analysis of the viscosity and particle size of the mixed material is as shown in the attached flowchart. Figure 4 As shown, this embodiment uses the same method to analyze the temperature sequence, viscosity sequence and particle size dispersion sequence at each acquisition moment, specifically:

[0067] In the first small step, the dynamic stability characteristic values ​​of the material flow of the material to be mixed are obtained based on the number of outliers in the viscosity sequence, the position distribution of the outliers, and the fluctuation of the viscosity sequence.

[0068] First, the viscosity sequence is used as input, and the abnormal value in the viscosity sequence is obtained using an abnormal detection algorithm. Since the viscosity increases as the temperature decreases, the abnormal value of the viscosity at a certain point means that the material is unevenly mixed when cooling at that point or an abnormal event is caused by environmental conditions such as temperature and air pressure, resulting in an abnormal change rate of the viscosity. Among them, this embodiment uses the LOF abnormal detection algorithm for abnormal detection. The LOF abnormal detection algorithm is a well-known technology and will not be repeated here. Other embodiments of the present application may also use Bayesian abnormal detection, KNN algorithm, etc.

[0069] The number of outliers in the viscosity sequence is recorded to reflect the number of abnormal situations occurring during the cooling process, and a window of size 1*N is created with each outlier in the viscosity sequence as the center. In this embodiment, N is 7, and the average value of the difference between the maximum value and the minimum value in all windows is calculated; the product of the number of outliers and the average value is taken as the first difference factor of the viscosity sequence.

[0070] Then, the coefficient of variation of the viscosity sequence is calculated to reflect the fluctuation degree of the viscosity value over time, and the viscosity sequence is used as input, a straight line fitting algorithm is used to output a fitting straight line, and the distance between the viscosity sequence and the fitting straight line is calculated; the product of the coefficient of variation and the distance is used as the second difference factor of the viscosity sequence. Among them, this embodiment uses the least squares method for straight line fitting, and the least squares method is a well-known technology and will not be described in detail.

[0071] In other embodiments of the present application, the data variance, information entropy, etc. of the viscosity sequence may also be used to calculate the fluctuation of the viscosity sequence, which is not limited in the present application.

[0072] In one embodiment of the present application, the absolute value of the difference between the actual viscosity value at each acquisition time of the viscosity sequence and the viscosity value on the corresponding fitting line is calculated, and the average of all the absolute values ​​of the difference at all acquisition times is used as the distance between the viscosity sequence and the fitting line. In other embodiments of the present application, the DTW distance between the viscosity sequence and the fitting line can also be used as the distance between the viscosity sequence and the fitting line. The DTW distance is a well-known technology and will not be described in detail.

[0073] This application forwardly merges the first difference factor and the second difference factor to determine the dynamic stability characteristic value of the material flow.

[0074] A positive fusion relationship is a situation where when the value of one or more features increases, the value of another one or more features also increases. Common positive fusion relationships include multiplication, addition, correlation coefficient and other methods, which are not limited in this application and are set by the implementer according to actual conditions.

[0075] In one embodiment of the present application, the product of the first difference factor and the second difference factor is used as the dynamic stability characteristic value of the material flow; in other embodiments of the present application, the sum of the first difference factor and the second difference factor is used as the dynamic stability characteristic value of the material flow.

[0076] It should be understood that the number of outliers characterizes the number of times the viscosity change rate is abnormal, and the average value of the difference between the maximum and minimum values ​​in all windows reflects the severity of the viscosity change near the abnormal point. The larger the first abnormal factor obtained by multiplying the two, the more severe the abnormal situation during the cooling process, the greater the stability and uniformity of the material are affected, and the larger the dynamic stability characteristic value of the material flow; at the same time, the coefficient of variation reflects the relative volatility of the viscosity over time, and the distance between the viscosity sequence and the fitting straight line reflects the average deviation between the actual value of the viscosity sequence and the theoretical expected value. The larger the second abnormal factor obtained by multiplying the two, the greater the impact of the fluctuation of the viscosity value on the overall preparation process, and the dynamic stability characteristic value of the material flow increases accordingly.

[0077] In one embodiment of the present application, the flow chart of the steps for constructing the index of the dynamic stability characteristic value of material flow is as shown in the attached figure. Figure 5 shown.

[0078] The second small step is to obtain the dispersion complexity index of the material to be mixed based on the distribution of the minimum value in the particle size dispersion sequence and the distribution difference of the data on both sides of the minimum value.

[0079] For the particles of the material to be mixed, the changes in their dispersion and quantity will be affected by many factors, including temperature, time, and the physical properties of the particles (such as melting point, particle size, etc.). When the material is at a high temperature, solid particles such as solder powder may partially or completely melt, resulting in a decrease in the number of particles and a decrease in dispersion. However, as the temperature decreases, the melted particles may re-solidify, so the dispersion of the particles is an uncontrollable factor, and the impact on the material is also complex and uncontrollable.

[0080] Therefore, the present application uses the collected particle size dispersion sequence as input, and uses the extreme value search algorithm to output all the minimum values ​​in the particle size dispersion sequence. The minimum value may indicate that the dispersion of particles in the material has reached a local minimum point. In this case, the particles may aggregate or settle due to factors such as temperature and viscosity, resulting in the dispersion reaching a relatively minimum. Subsequently, the dispersion increases again due to reasons such as temperature. Among them, the extreme value search algorithm is a well-known technology and will not be described in detail.

[0081] Obtain the differential sequence of the particle size dispersion sequence; establish a window of size 1*M with each minimum point as the center, in this embodiment, M is 7, take any minimum value as the window division point, calculate the mean differential values ​​of all data points on the left and right sides of the window of any minimum value, calculate the difference between the mean differential values ​​on the left and right sides of the window of any minimum value, and record it as the particle sinking and floating difference factor of any minimum value.

[0082] In this embodiment, the absolute value of the difference between the mean values ​​of the differential values ​​on the left and right sides of the window is taken as the difference between the mean values ​​of the differential values ​​on the left and right sides of the window.

[0083] Then, the average value of the collection time distance between each minimum value and all other minimum values ​​is calculated and recorded as the sinking and floating closeness factor corresponding to each minimum value. In this embodiment, the absolute value of the difference between the collection time of each minimum value and all other minimum values ​​is used as the collection time distance between each minimum value and all other minimum values.

[0084] The dispersion complexity index is determined based on the particle sinking and floating difference factors and sinking and floating compact factors of all minimum values ​​in the particle size dispersion sequence; wherein the dispersion complexity index is positively correlated with the particle sinking and floating difference factors of all minimum values, and negatively correlated with the sinking and floating compact factors of all minimum values.

[0085] It can be understood that a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship can be a multiplication relationship, an addition relationship, or the power of an exponential function, which is determined by actual application and is not specifically limited in this application. A negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by actual application.

[0086] In one embodiment of the present application, the mean of all minimum particle sinking and floating difference factors is calculated as the numerator; the mean of all minimum sinking and floating compactness factors is calculated as the denominator; and the ratio of the numerator to the denominator is used as the dispersion complexity index.

[0087] In other embodiments of the present application, the accumulation of all minimum particle sinking and floating difference factors is calculated as the exponent of the exponential function, and the calculation result of the exponential function is used as the numerator; the accumulation of all minimum sinking and floating tightness factors is calculated as the denominator; and the ratio of the numerator to the denominator is used as the dispersion complexity index.

[0088] It should be understood that the valley rebound characteristic of the particle size dispersion when the dispersion reaches the relatively lowest point is obtained by the particle sinking and floating difference factor of all minima. The larger the particle sinking and floating difference factor of all minima, the greater the difference in particle sedimentation and re-dispersion in the valley rebound characteristic, which shows a more complex dispersion change process, and the dispersion complexity index increases accordingly; the minimum distribution characteristics of the particle size dispersion are obtained by calculating the sinking and floating tightness factors of all minima. The smaller the sinking and floating tightness factors of all minima, the tighter the characteristics of the minimum distribution, that is, the density of minima appears is greater. At this time, the fluctuation of dispersion tends to be concentrated in one area, the smaller the complexity, and the dispersion complexity index decreases accordingly.

[0089] In one embodiment of the present application, the index construction step flow chart of the dispersed complexity index is as shown in the attached Figure 6 shown.

[0090] In the third small step, the partial correlation coefficient between the material state sequences is used as the weight to positively fuse the dynamic stability characteristic value of the material flow and the dispersion complexity index to obtain the temperature matching factor of the material to be mixed.

[0091] Temperature is one of the main factors affecting viscosity, and changes in temperature will also affect the dispersion of particles in the material. By analyzing the relationship between temperature and viscosity and particle size dispersion, we can better understand the impact of environmental factors on the material preparation process.

[0092] Based on this, the present application takes temperature sequence, viscosity sequence and particle size dispersion sequence as input, and uses partial correlation analysis algorithm to output the partial correlation coefficient between the temperature sequence and the particle size dispersion sequence, denoted as partial correlation coefficient X, and outputs the partial correlation coefficient between the temperature sequence and the viscosity sequence, denoted as partial correlation coefficient Y.

[0093] The partial correlation coefficient X and the partial correlation coefficient Y are used as the weights of the dynamic stability characteristic value of material flow and the dispersion complexity index respectively. The dynamic stability characteristic value of material flow and the dispersion complexity index are forward fused to obtain the temperature matching factor of the material to be mixed.

[0094] In one embodiment of the present application, the partial correlation coefficient X and the partial correlation coefficient Y are used as weights of the dynamic stability characteristic value of material flow and the dispersion complexity index, respectively, and the dynamic stability characteristic value of material flow and the dispersion complexity index are weightedly summed to obtain the temperature matching factor of the material to be mixed.

[0095] It should be understood that the partial correlation coefficient X is used as a weight, and the product of multiplying it with the dynamic stability characteristic value of material flow is recorded as the fluidity match confidence. The fluidity match confidence reflects the impact measure of material flow stability after considering the influence of temperature. The greater the fluidity match confidence, the higher the fit between the material fluidity change and temperature, and thus more precise temperature control is required to maintain the stability of material flow; the partial correlation coefficient Y is used as a weight, and the product of multiplying it with the dispersion complexity index is recorded as the dispersion match confidence. Similarly, the dispersion match confidence reflects the impact measure of material dispersion after considering the influence of temperature. The greater the dispersion match confidence, the more precise temperature control is required to maintain the stability of material dispersion.

[0096] The temperature matching factor comprehensively considers the influence of material flow stability and dispersion, and reflects the sensitivity of materials to temperature changes during the mixing process. The higher the temperature matching factor, the greater the influence of temperature on the material flowability and dispersion, and a more sophisticated temperature control strategy is needed to ensure the uniformity and stability of material mixing.

[0097] In one embodiment of the present application, the index construction step of the temperature matching factor of the mixed material is shown in the attached flowchart. Figure 6 shown.

[0098] In the third step, based on the negative correlation mapping of the temperature matching factor, the decreasing rate of the cooling temperature of the mixed material is controlled in sections.

[0099] A PID controller is set to regulate the cooling temperature of the mixed material. First, the target temperature range of the solder paste cooling process is determined, that is, the material temperature after cooling needs to be maintained between 40°C and 80°C. The PID parameters are set, namely, the proportional coefficient P, the integral coefficient I, and the differential coefficient D. In this embodiment, the values ​​are P=50, I=2, and D=1 respectively. It should be noted that the implementer can adjust the initial parameters of the PID algorithm according to actual conditions.

[0100] From the time when the mixed material is cooled, the control change interval of the cooling temperature is set to T. In this embodiment, T is set to 2s, that is, the temperature sequence in the cooling process is segmented every two seconds.

[0101] The temperature, viscosity and particle size dispersion sequence collected from the start of cooling to the current control change interval are used to calculate the temperature fit factor of the current control change interval, and the temperature fit factor of the current control change interval is used to improve the proportional coefficient of the PID algorithm of the next control change interval. The specific improvement method is:

[0102] The temperature matching factor is negatively correlated and mapped. Preferably, in this embodiment, a negative exponential function is selected for negative correlation mapping, and the gain coefficient is set to 2 in this embodiment. The calculation result of the negative exponential function is normalized and multiplied with the gain coefficient and the proportional coefficient of the current control change interval PID algorithm to obtain the proportional coefficient of the next control change interval.

[0103] Negative correlation mapping means that when the value of one feature increases, the value of another feature decreases. Common negative correlation mapping methods include taking the opposite number, taking the negative exponential function, taking the inverse, etc. This application does not restrict negative correlation mapping, and the implementer can set it according to the actual situation.

[0104] The proportional coefficient of the next control change interval is improved by the proportional coefficient in the PID algorithm, and the PID algorithm is used to control the decreasing rate of the cooling temperature of the next control change interval. The PID algorithm is a well-known technology and will not be described in detail.

[0105] It should be understood that the proportional coefficient adjusts the output according to the deviation between the current set value and the actual value. Here, the cooling target temperature is set to 40°C. The larger the proportional coefficient, the faster the response to the controlled temperature deviation, that is, the controller will cool the current temperature value to the target temperature value faster. After improving the proportional coefficient, when the temperature matching factor is larger, it means that the fluidity and dispersion of the material are more compatible with the temperature at this time, and more precise temperature control is required to maintain the stability of the material. At this time, the smaller the value after the negative exponential function mapping, the smaller the proportional coefficient of the next control change interval, and the slower the response to the temperature deviation.

[0106] After the improvement, the algorithm will update the proportional coefficient every 2 seconds and control the cooling temperature rate in stages according to the physical state of the material to be mixed to maintain a stable physical state of the material.

[0107] S4, adding solder powder into the solder paste, cooling it to 30°C to 40°C, degassing and stirring it in vacuum for 8 to 12 minutes, and then cooling it to room temperature to obtain a paste flux.

[0108] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0109] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0110] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0111] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. Solder paste preparation method based on parameter control, It is characterized in that The Method The following steps are involved: S1, weighing solder powder, activator, thixotropic agent, resin agent and organic solvent as raw materials for preparing solder paste; S2, respectively adding a resin agent, an activator and an organic solvent into a mixing barrel, heating and stirring them evenly to obtain a material to be mixed; S3, cooling the material to be mixed to 40°C to 80°C, adding a thixotropic agent and stirring uniformly to obtain a solder paste; wherein the method for controlling the decreasing rate of the cooling temperature of the material to be mixed is specifically as follows: Obtaining a material state sequence of the material to be mixed during the cooling process; the material state sequence includes a temperature sequence, a viscosity sequence, and a particle size dispersion sequence; Based on the number of outliers in the viscosity sequence, the position distribution of the outliers, and the fluctuation of the viscosity sequence, the dynamic stability characteristic value of the material flow of the material to be mixed is obtained; Based on the distribution of the minimum value in the particle size dispersion sequence and the distribution difference of the data on both sides of the minimum value, the dispersion complexity index of the material to be mixed is obtained; Taking the partial correlation coefficient between material state sequences as the weight, the dynamic stability characteristic value of material flow and the dispersion complexity index are forwardly integrated to obtain the temperature matching factor of the materials to be mixed; Based on the negative correlation mapping of the temperature matching factor, the cooling rate of the mixed material is controlled in sections; S4, adding solder powder into the soldering paste, cooling it to 30°C to 40°C, degassing it in vacuum and stirring it, and then cooling it to room temperature to obtain a paste flux.

2. The method for preparing solder paste based on parameter control as claimed in claim 1, It is characterized in that The weight ratio of the solder powder, the activator, the thixotropic agent, the resin agent and the organic solvent in S1 is 30:5:2:3:

5.

3. The method for preparing solder paste based on parameter control as claimed in claim 1, It is characterized in that The activator in S1 is a mixture of one of succinic acid, sebacic acid, adipic acid and one of dibromosuccinic acid and dibromobutenediol.

4. The method for preparing solder paste based on parameter control as claimed in claim 1, It is characterized in that The resin agent in S1 is a mixture of KR-610, hydrogenated rosin and polymerized rosin.

5. The method for preparing solder paste based on parameter control as claimed in claim 1, It is characterized in that The organic solvent in S1 is a mixture of two or more of diethylene glycol dimethyl ether, diethylene glycol diethyl ether, ethylene glycol monobutyl ether, and propylene glycol phenyl ether.

6. The method for preparing solder paste based on parameter control as claimed in claim 1, It is characterized in that In S2, when the resin agent is added into the mixing drum, it needs to be heated to 140°C to 150°C, and when the activator and the organic solvent are added into the mixing drum, the temperature is maintained at 130°C to 140°C.

7. The method for preparing solder paste based on parameter control according to claim 1, It is characterized in that The process of obtaining the material flow dynamic stability characteristic value of the material to be mixed includes: An outlier detection algorithm is used to obtain an outlier in the viscosity sequence; the number of outliers in the viscosity sequence is obtained; the average value of the difference between the maximum value and the minimum value in all preset size windows centered on the outliers is calculated; the product of the number of outliers and the average value is used as the first difference factor of the viscosity sequence; Obtain the coefficient of variation of the viscosity sequence; obtain the fitted straight line of the viscosity sequence, and calculate the distance between the viscosity sequence and the fitted straight line; use the product of the coefficient of variation and the distance as the second difference factor of the viscosity sequence; The first difference factor and the second difference factor are forwardly fused to determine the material flow dynamic stability characteristic value of the material to be mixed.

8. The method for preparing solder paste based on parameter control as claimed in claim 7, It is characterized in that The process of obtaining the dispersion complexity index of the material to be mixed includes: Obtain the minimum value in the particle size dispersion sequence; obtain the difference sequence of the particle size dispersion sequence; Obtain a preset size window centered on any minimum value; take any minimum value as the window splitting point, and calculate the mean difference values ​​of all data points on the left and right sides of the window of any minimum value; calculate the difference between the mean difference values ​​on the left and right sides of the window of any minimum value, and record it as the particle sinking and floating difference factor of any minimum value; Calculate the average value of the collection time distance between any minimum value and all other minimum values, and record it as the sinking and floating closeness factor of any minimum value; The dispersion complexity index of the material to be mixed is determined based on the particle sinking and floating difference factors and sinking and floating compact factors of all minimum values ​​in the particle size dispersion sequence; wherein the dispersion complexity index is positively correlated with the particle sinking and floating difference factors of all minimum values, and negatively correlated with the sinking and floating compact factors of all minimum values.

9. The method for preparing solder paste based on parameter control as claimed in claim 8, It is characterized in that The method of using the partial correlation coefficient between material state sequences as weights further includes: the weight of the dynamic stability characteristic value of material flow is the partial correlation coefficient between the temperature sequence and the viscosity sequence; the weight of the dispersion complexity index is the partial correlation coefficient between the temperature sequence and the particle size dispersion sequence.

10. The method for preparing solder paste based on parameter control according to claim 9, It is characterized in that The negative correlation mapping based on the temperature matching factor is used to control the decreasing rate of the cooling temperature of the mixed material in sections, including: Performing segmented control on the decreasing rate of the cooling temperature at each preset control change interval after the start of cooling; Obtain the temperature matching factor of the material to be mixed from the start of cooling to the current control change interval; The temperature matching factor of the material to be mixed in the current control change interval is negatively correlated, and the result of the negative correlation mapping is normalized and multiplied by the preset gain coefficient and the proportional coefficient of the current control change interval PID algorithm to obtain the proportional coefficient of the next control change interval; The proportional coefficient of the next control change interval is improved into the proportional coefficient in the PID algorithm, and the PID algorithm is used to control the decreasing rate of the cooling temperature of the next control change interval.

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