Component welding point temperature calculation method and device

Abstract

The invention provides a component welding point temperature calculation method and device, and relates to the technical field of component welding. The method comprises the steps that the height, the bottom area, the number of contact pins, the diameter, the length, the welding resistance temperature and the material of a target component and the material of a PCB are obtained; based on the height, the bottom area and the number of contact pins, comprehensive heat flow is obtained; obtaining a first temperature field component based on the comprehensive heat flow, the number of contact pins, the diameter of the contact pins and the length of the contact pins; a second temperature field component is obtained based on the comprehensive heat flow, the height, the bottom area, the pin diameter and the pin number; obtaining a third temperature field component based on the welding resistance temperature, the material of the target component and the material of the PCB; and obtaining the temperature of the welding point of the target component based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component and the third weight. According to the invention, the temperature of the welding point can be accurately calculated, and the temperature during welding can be accurately controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of component welding, and in particular to a component welding point temperature calculation method and device. Background Art

[0002] In the electronics manufacturing sector, the miniaturization and high-performance development of electronic products places higher demands on thermal management technology for through-hole components. Traditional through-hole components suffer from long heat conduction paths and high thermal resistance, which affect soldering quality and device performance. Furthermore, the base area affects thermal conductivity differently for through-hole components of different lengths. In recent years, short-lead through-hole technology has gained popularity due to its advantages in thermal conductivity, but it faces the challenge of precisely controlling the temperature field in its applications. The coupling of factors such as soldering method, component material, and solder resistance temperature, coupled with external influences such as nitrogen atmosphere and conveyor speed, complicates temperature field control.

[0003] The complexity of temperature field control means that the welding temperature cannot be accurately controlled, which in turn will affect the quality of the welding of through-hole components, causing the through-hole components to be unstable and unable to work stably. Summary of the Invention

[0004] The embodiments of the present invention provide a method and device for calculating the temperature of a component soldering point, so as to solve the problem that the soldering temperature cannot be accurately controlled.

[0005] In a first aspect, an embodiment of the present invention provides a method for calculating the temperature of a component soldering point, comprising:

[0006] The height, bottom area, number of pins, pin diameter, pin length, soldering temperature resistance, and material of the target component, as well as the material of the PCB board, are obtained; wherein the target component is soldered on the PCB board.

[0007] Based on the height, base area and number of pins, the comprehensive heat flow is obtained.

[0008] Based on the comprehensive heat flux, number of pins, pin diameter and pin length, the influence of the pins and solder holes of the target components on the temperature field of the soldering point is obtained and recorded as the first temperature field component.

[0009] Based on the comprehensive heat flow, height, bottom area, pin diameter and number of pins, the influence of the height, bottom area and pins of the target component on the temperature field of the soldering point is obtained and recorded as the second temperature field component.

[0010] Based on the soldering temperature, the material of the target component and the material of the PCB board, the influence of the soldering temperature of the target component, the material of the target component and the material of the PCB board on the temperature field of the soldering point is obtained and recorded as the third temperature field component.

[0011] Based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component and the third weight, the temperature of the soldering point of the target component is obtained; wherein the first weight is the weight corresponding to the first temperature field component, the second weight is the weight corresponding to the second temperature field component, and the third weight is the weight corresponding to the third temperature field component.

[0012] In one possible implementation, based on the height, base area, and number of pins, a comprehensive heat flow is obtained, including:

[0013] Based on the height, the nonlinear effect of height on thermal resistance was determined, recorded as the first heat flux component. Based on the base area, the effect of base area on the temperature field was determined, recorded as the second heat flux component. Based on the number of pins, the effect of the number of pins on the temperature field was determined, recorded as the third heat flux component. The first, second, and third heat flux components were combined using the geometric mean method to obtain the comprehensive heat flux.

[0014] In one possible implementation, the influence of the pins and solder holes of the target component on the temperature field of the solder joint is obtained based on the comprehensive heat flux, the number of pins, the pin diameter, and the pin length. This is recorded as the first temperature field component and includes:

[0015] Obtain the thickness of the PCB board, the cross-sectional area of ​​the soldering hole at the soldering point, and the thermal conductivity coefficient. Based on the pin length, pin diameter, and thermal conductivity coefficient, obtain the thermal resistance of a single pin. Based on the thickness of the PCB board, the cross-sectional area of ​​the soldering hole at the soldering point, and thermal conductivity coefficient, obtain the thermal resistance of a single solder hole. Based on the thermal resistance of a single pin and the thermal resistance of a single solder hole, obtain the total thermal resistance of the solder hole and the pin. Based on the pin length, the lateral dimension of the soldering point, and the longitudinal dimension of the soldering point, obtain the comprehensive size of the soldering point. Based on the comprehensive heat flow, the top temperature of the soldering hole at the soldering point, the bottom temperature of the soldering hole at the soldering point, the total thermal resistance of the solder hole and the pin, and the comprehensive size of the soldering point, calculate and obtain the first temperature field component.

[0016] In one possible implementation, based on the comprehensive heat flux, height, bottom area, pin diameter, and number of pins, the influence of the height, bottom area, and pins of the target component on the temperature field of the soldering point is obtained and recorded as the second temperature field component, including:

[0017] The thermal resistance influence component is calculated based on the total thermal resistance of the solder hole and pins, as well as their height, bottom area, pin diameter, and number of pins. The temperature difference influence component is calculated based on the top and bottom temperatures of the solder hole at the solder spot, as well as their height, bottom area, pin diameter, and number of pins. The morphology influence component of the target component is calculated based on its height, bottom area, and number of pins. The second temperature field component is calculated based on the thermal resistance influence component, the temperature difference influence component, and the morphology influence component.

[0018] In one possible implementation, based on the soldering temperature, the material of the target component, and the material of the PCB, the influence of the soldering temperature of the target component, the material of the target component, and the material of the PCB on the temperature field of the soldering point is obtained and recorded as a third temperature field component, including:

[0019] Based on the target component's material, the impact of the target component's material on the solder point temperature field is calculated, recorded as the material factor. Based on the PCB material, the impact of the PCB material on the solder point temperature field is calculated, recorded as the substrate factor. Based on the soldering resistance temperature, the impact of the soldering resistance temperature on the solder point temperature field is calculated, recorded as the soldering resistance factor. Based on the material factor, substrate factor, and soldering resistance factor, the third temperature field component is calculated.

[0020] In a possible implementation, the first weight, the second weight, and the third weight are calculated as follows:

[0021] The first weight, the second weight, and the third weight are used as a particle in the particle swarm algorithm, and the optimal particle is found with the minimum first target value as the objective function; wherein the first target value is the absolute value of the difference between the calculated value and the ideal value of the soldering point temperature of the target component.

[0022] Based on the simulated annealing algorithm, the hyperparameters of the particle swarm algorithm are updated, and the speed and position of the particles in the particle swarm algorithm are updated to obtain the updated optimal particles.

[0023] Based on the updated optimal particles, a new target component solder point temperature is calculated.

[0024] A new first target value is calculated based on the absolute value of the difference between the new calculated value and the ideal value.

[0025] If the new first target value meets the preset conditions or the particle swarm algorithm reaches the maximum number of iterations, the first weight, second weight, and third weight represented by the particle corresponding to the first target value are used as the final first weight, second weight, and third weight; if the new first target value does not meet the preset conditions, return to the execution step of "updating the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and updating the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particle" and subsequent steps.

[0026] In one possible implementation, obtaining the temperature of the target component soldering point based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight includes:

[0027] A temperature field of a target component is obtained based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight.

[0028] Based on the temperature field of the target component, the temperature of the soldering point of the target component is obtained.

[0029] In one possible implementation, obtaining the temperature of the target component soldering point based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight includes:

[0030] A temperature field of a target component is obtained based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight.

[0031] Obtain the concentration of nitrogen gas, the speed of the conveyor belt where the target components are placed, the ambient temperature, and the melting temperature of the solder paste.

[0032] The temperature field of the target component, the concentration of nitrogen gas, the speed of the conveyor belt, the melting temperature of the solder paste, the solder resistance temperature and the ambient temperature are input into the fuzzy logic control algorithm, and the first temperature difference is output based on the preset fuzzy control logic.

[0033] Based on the first temperature difference, the first weight, the second weight, and the third weight, a second temperature difference is calculated.

[0034] The second temperature difference is used as a particle in the particle swarm algorithm, and the objective function is to find the optimal particle with the minimum second target value; wherein the second target value is the absolute value of the difference between the calculated value of the second temperature difference and the actual safe temperature.

[0035] Based on the simulated annealing algorithm, the hyperparameters of the particle swarm algorithm are updated, and the speed and position of the particles in the particle swarm algorithm are updated to obtain the updated optimal particles.

[0036] Based on the updated optimal particle, a new second temperature difference is calculated.

[0037] A new second target value is calculated based on the absolute value of the difference between the new second temperature difference and the actual safety temperature.

[0038] If the new second target value meets the preset conditions or the particle swarm algorithm reaches the maximum number of iterations, the new second temperature difference is used as the third target value; if the new first target value does not meet the preset conditions, the process returns to the step "update the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and update the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particles" and subsequent steps.

[0039] The sum of the third target value and the solder paste melting temperature is used as the minimum temperature of the target component soldering point; the difference between the soldering resistance temperature and the third target value is used as the maximum temperature of the target component soldering point.

[0040] In one possible implementation, obtaining a temperature field of a target component based on a first temperature field component, a first weight, a second temperature field component, a second weight, a third temperature field component, and a third weight includes: calculating a product of the first temperature field component and the first weight to obtain a first result; calculating a product of the second temperature field component and the second weight to obtain a second result; calculating a product of the third temperature field component and the third weight to obtain a third result; and calculating the sum of the first result, the second result, and the third result to obtain the temperature field of the target component.

[0041] In a second aspect, an embodiment of the present invention provides a device for calculating the temperature of a component solder point, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method in the first aspect or any possible implementation of the first aspect.

[0042] In the embodiment of the present invention, the influence of the pins and solder holes of the target component on the temperature field of the soldering point, the influence of the height, bottom area and pins of the target component on the temperature field of the soldering point, and the influence of the soldering temperature resistance and material of the target component on the temperature field of the soldering point are respectively obtained through various parameters of the target component. The relationship between various parameters and the temperature field of the soldering point is comprehensively analyzed. By superimposing the temperature fields, the calculated temperature of the soldering point can be made closer to the actual situation, and the calculation results can be made more accurate. It is convenient to adjust the parameters of the supporting equipment in time according to the calculated values, ensure the quality of welding, and enable the target component to work stably after welding is completed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the implementation of the method for calculating the component soldering point temperature provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In modern electronic devices such as smartphones, tablets, and wearable devices, space utilization and performance requirements have made miniaturization a necessity. Short-pin plug-in components have become an important development direction for electronic manufacturing because they can reduce space occupation and are in line with the trend of miniaturization and thinness of electronic products. In the field of automotive electronics, autonomous driving and vehicle networking technologies have high requirements for the reliability of electronic control systems. The good thermal performance of short-pin plug-in components can ensure their stable operation in complex temperature environments. In the field of industrial automation, equipment needs to operate in harsh environments, and short-pin plug-in technology can improve the integration and anti-interference ability of equipment. However, in actual applications, the welding quality and temperature field control of short-pin plug-in components are the key factors restricting their widespread application. Different application scenarios have different requirements for the soldering temperature resistance and material properties of components, and welding factors affect each other. Therefore, the embodiments of the present invention are designed.

[0045] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0046] See also Figure 1 , which shows a flow chart of the implementation of the method for calculating the temperature of the component soldering point provided by an embodiment of the present invention, and is described in detail as follows:

[0047] Step 101, obtaining the height, bottom area, number of pins, pin diameter, pin length, soldering temperature and material of the target component, as well as the material of the PCB board; wherein the target component is soldered on the PCB board.

[0048] Exemplarily, the material of the target component refers to the material of the entire target component, and the material of the PCB board refers to the material of the substrate of the PCB board. In the present invention, the target component can be a through-hole component.

[0049] The most common PCB substrate material is glass fiber reinforced epoxy resin, also known as FR-4. This material offers excellent electrical insulation, mechanical strength, and fire resistance. Depending on the application requirements, composite materials such as CEM-1 and CEM-3, as well as specialized materials such as polytetrafluoroethylene (PTFE) and polyimide, are also used as substrates.

[0050] Step 102: Obtain a comprehensive heat flow based on the height, bottom area, and number of pins.

[0051] In one possible implementation, step 102 may include:

[0052] Based on the height, the nonlinear effect of height on thermal resistance is obtained, which is recorded as the first heat flux component.

[0053] Based on the bottom area, the influence of the bottom area on the temperature field is obtained and recorded as the second heat flux component.

[0054] Based on the number of pins, the influence of the number of pins on the temperature field is obtained and recorded as the third heat flux component.

[0055] The first heat flux component, the second heat flux component and the third heat flux component are combined based on the geometric mean method to obtain the comprehensive heat flux.

[0056] Among them, regarding the nonlinear effect of height on thermal resistance:

[0057] The height d of the through-hole component affects the heat conduction path, thus affecting the heat dissipation efficiency. Short through-hole components are close to the circuit board, making heat conduction easier; long through-hole components are suspended in the air, making the heat conduction path longer and reducing the heat dissipation efficiency. In order to analyze the effect of height on heat flow, the height is normalized and the dimensionless height is defined as Here, d0 is the reference height selected in the experiment, which is usually selected based on the median value of the common heights of through-hole devices to cover the height range in actual applications.

[0058] Through experiments (maintaining the thermal conductivity of the material k m The thermal resistance at different heights is measured and fitted with a power law to obtain (The derivation process is ).Right now p represents the effect of height on thermal resistance, usually taken as [0.8, 1], and C1 is the first normalized coefficient of heat conduction.

[0059] Among them, regarding the influence of the bottom area of ​​components on the temperature field:

[0060] For short through-hole devices, a discretized weight model needs to be introduced to describe the nonlinear effect of area on temperature field. The effect of bottom area can be described by discrete weights as: Q a =C2*ae bn , where Q a This represents the effect of the base area on the temperature field. C2 is the second normalized coefficient of heat conduction. a is the initial weight, describing the initial impact of the component base area on heat conduction efficiency. This factor is related to factors such as the material's thermal conductivity and the soldering process temperature. b is the area growth adjustment factor, indicating the degree to which an increase in base area affects the heat conduction rate. A represents the base area, and n represents the discrete level of base area. The values ​​involved are listed in the table below.

[0061] n illustrate <![CDATA[Range A in mm 2 > Applicable packaging <![CDATA[C2]]> a b 1 Ultra small <5 TO-92, SOT-23, etc. 0.05 0.1 0.1 2 Small 5～20 DIP-8, DIP-14, etc. 0.1 0.2 0.2 3 middle 20～50 TO-220, TO-247, etc. 0.3 0.3 0.3 4 big 50～100 Large IC, MOS tube, etc. 0.5 0.4 0.4 5 Oversized >100 Large integrated circuits, power switches, etc. 1.0 0.6 0.5

[0062] Among them, regarding the impact of the number of component pins on the temperature field:

[0063] Number of pins N pin Directly affects the number of heat conduction paths. Each pin provides a heat conduction channel, through which heat is transferred to the PCB or other heat dissipation structures. As the number of pins increases, the number of heat conduction paths also increases, thereby improving the heat dissipation capacity. This relationship is usually linearly positive, that is, the heat flow Q p Number of pins N pin The increase in the number of pins means that the heat can be dissipated through multiple paths, reducing the risk of local overheating. Therefore, a linear relationship is used to describe the relationship between heat flow and the number of pins, that is, Q p =C3*N pin , C3 is the third normalization coefficient.

[0064] In order to integrate the above three components, the geometric mean (square root model) is used to combine the effects of different factors on the comprehensive heat flux Q. This method meets the needs of modeling the relationship between different factors in practical applications. The effects of bottom area, height and number of pins are often coordinated with each other rather than simply linearly superimposed. The geometric mean can better handle these nonlinear and mutually restrictive relationships, avoid over-magnification or reduction of the effects of certain factors, and is especially suitable for combinations between different scales. Compared with a simple additive model, the geometric mean can balance the relationship between different factors, especially when facing changes in factors such as height and area, it can effectively avoid the problems of overfitting and unreasonable amplification, and the implementation of this method is simple, easy to adjust and verify.

[0065] Specifically, in actual production, the first formula can be used to calculate the comprehensive heat flow;

[0066] The first formula is:

[0067]

[0068] Among them, Q represents the comprehensive heat flow, C represents the normalization coefficient, a represents the initial impact of the component bottom area on the heat conduction efficiency, b represents the area growth adjustment coefficient, n represents the discrete level of the bottom area, N pin represents the number of pins, d represents the height of the target component, d0 represents the reference height, and p represents the effect of height on thermal resistance.

[0069] The range of the normalization coefficient, C, is typically determined by fitting experimental data and varies depending on the material, device type, and operating environment. Generally speaking, for low thermal conductivity materials, C values ​​may range from 0.1 to 0.3, while for high thermal conductivity materials, C values ​​are typically higher, perhaps between 0.5 and 1.0. Through experimental fitting, the C value can be adjusted based on actual conditions, ensuring that the model accurately reflects actual variations in heat flux. This is achieved by comprehensively considering the effects of height, base area, and number of pins on heat flux.

[0070] Step 103 : Based on the comprehensive heat flux, the number of pins, the pin diameter, and the pin length, the influence of the pins and solder holes of the target component on the temperature field of the soldering point is obtained, which is recorded as the first temperature field component.

[0071] In one possible implementation, step 103 may include:

[0072] Obtain the thickness of the PCB board, the cross-sectional area of ​​the soldering hole, and the thermal conductivity.

[0073] Based on the pin length, pin diameter and thermal conductivity, the thermal resistance of a single pin is obtained.

[0074] Based on the thickness of the PCB board, the cross-sectional area of ​​the soldering hole and the thermal conductivity coefficient, the thermal resistance of a single solder hole is obtained.

[0075] Based on the thermal conductivity resistance of a single pin and the thermal conductivity resistance of a single solder hole, the total thermal conductivity resistance of the solder hole and the pin is obtained.

[0076] Based on the pin length, the transverse dimension of the solder joint and the longitudinal dimension of the solder joint, the comprehensive dimension of the solder joint is obtained.

[0077] The first temperature field component is calculated based on the comprehensive heat flux, the top temperature of the solder hole of the solder spot, the bottom temperature of the solder hole of the solder spot, the total thermal conductivity resistance of the solder hole and the pin, and the comprehensive size of the solder point.

[0078] Specifically, for through-hole devices, N pin (Number of pins) = N pad (Number of solder holes), the number directly affects the number of heat conduction paths, the more the number, the lower the thermal resistance and the stronger the heat dissipation capacity. Thermal conductivity k pin and k pad can be regarded as k cu The same, that is, k pin =k pad =k cu =385W / m. According to the specifications of common electronic components, the pin diameter d pin The unit is mm, and the value is generally [0.75, 1.25]; standard FR4 PCB thickness L pad The unit is mm, and the value is generally 1.6; the pin length L pin The bit is mm, and the value is generally [7.5,12.5].

[0079] Since the pins are generally cylindrical, their cross-sectional area is Substitute into the calculation to get A pin The result is [4.42×10 -7 ,12.3×10 -7 ], unit is m 2 According to the thermal resistance formula, the thermal resistance of a single pin can be obtained Substitute into the calculation to get R pin The result is [1.58,7.35], the unit is K / W.

[0080] The actual solder hole diameter needs to be slightly larger than the pin diameter (usually add 0.1-0.2mm margin to meet assembly tolerances). The minimum pin diameter is 0.75mm, and the minimum solder hole margin is +0.1mm. The maximum pin diameter is 1.25mm, and the maximum solder hole margin is +0.2mm. The thermal resistance of a single solder hole is For a single in-line device, where multiple pins and solder holes conduct heat in parallel, the parallel resistance formula is used:

[0081]

[0082] but Number of pins N pin The more the total thermal resistance R total Decreases and heat dissipation capacity increases.

[0083] Specifically, the temperature difference between the bottom of the solder hole (under the board) and the top of the solder hole (on the board) is T bp -T tp (The temperature difference between the bottom and top of the pad is usually required to be at least greater than 20°C to ensure welding quality) which affects the stress distribution of the solder joint. The stress of the solder joint is proportional to the temperature difference:

[0084]

[0085] λ is the characteristic length of the welding area, which is approximately the equivalent diameter or equivalent height of the welding spot. The lateral size of the welding spot is mainly determined by the pin diameter d pin Determine, so λ h =d pin This representation method is applicable to the horizontal heat transfer path of the solder joint. The longitudinal dimension of the solder joint is determined by the PCB thickness L. pad and pin length L pin Determine together, so the characteristic length This representation method is applicable to the longitudinal heat transfer path of the solder joint.

[0086] In practical applications, the horizontal and vertical dimensions of the solder joint should be considered comprehensively. Simply adding the dimensions is unreasonable. Multiplying and then taking the square root can ensure that the dimension is length. Then the value range of λ is [6.825,17.625].

[0087] Specifically, during heat transfer, according to the definition of thermal resistance and Fourier's law, there is a relationship between temperature difference and thermal resistance:

[0088]

[0089] Get R total Q=T bp -T tp , substitute have to:

[0090] T bp -T tp Substitute σ weld Win

[0091] From an energy perspective, the thermal resistance formula reflects the heat accumulation and temperature change caused by the obstruction of heat flow. According to the law of conservation of energy, this will cause the internal energy of the solder joint to change, which is manifested as stress. For example, the heat flow causes the atomic spacing to change due to thermal expansion, which generates stress. The temperature difference is the driving force of heat transfer. According to Fourier's law, the uneven thermal deformation during heat transfer causes stress in the solder joint, so σ weld Closely related to heat transfer.

[0092] From a mechanical point of view, the geometric parameters of the pins and solder holes affect the heat transfer and mechanical properties. Under the action of external force, these parameters determine the stress distribution in the solder joint. The thermal expansion and contraction effect of heat transfer will also aggravate the change of crystal structure, thereby affecting the stress. The comprehensive consideration of I1 and σ weld Establish reasonable equivalence.

[0093] From the perspective of practical applications, solder joint reliability is related to the quality of electronic equipment, and failure is often related to stress. weld It can integrate thermal and mechanical factors to construct an index to evaluate solder joint reliability, helping engineers optimize designs, such as selecting parameters such as pins and PCBs, to reduce stress and improve equipment stability.

[0094] Specifically, in actual production, the second formula can be used to calculate the first temperature field component;

[0095] The second formula is:

[0096]

[0097] Where I1 represents the first temperature field component, Q represents the comprehensive heat flow, N pin Indicates the number of pins, d pin Indicates the pin diameter, L pad Indicates the thickness of the PCB board, L pin Indicates the pin length.

[0098] Step 104 , based on the comprehensive heat flux, height, bottom area, pin diameter and number of pins, the influence of the height, bottom area and pins of the target component on the temperature field of the soldering point is obtained, which is recorded as the second temperature field component.

[0099] In one possible implementation, step 104 may include:

[0100] The thermal resistance influencing component is obtained based on the total thermal conductivity resistance, height, bottom area, pin diameter and number of pins of the solder hole and pins.

[0101] The temperature difference influencing component is obtained based on the top temperature of the welding spot weld hole, the bottom temperature of the welding spot weld hole, the height, the bottom area, the pin diameter and the number of pins.

[0102] Based on the height, bottom area and number of pins, the morphological influence components of the target components are obtained.

[0103] The second temperature field component is calculated based on the thermal resistance influence component, the temperature difference influence component and the morphology influence component.

[0104] Among them, through-hole devices have pins. The thermal conductivity of these pins and the way they contact the PCB board are important factors affecting heat dissipation performance. Generally, the heat conduction path of through-hole devices is divided into two parts:

[0105] Thermal conductivity of the device itself: including the height and bottom area of ​​the device (including the thermal conductivity of the pins).

[0106] Thermal conductivity of pins: The number of pins and the contact area between the pins and the PCB board will significantly affect the heat transfer efficiency.

[0107] Specifically, for through-hole devices, in addition to considering the height d and bottom area A, the number of pins N also needs to be considered. pin The following analysis is based on the special structure of through-hole devices and combines the influence of height and bottom area:

[0108] Thermal resistance impact:

[0109] Thermal resistance R of through-hole device θ Determined by its height d and bottom area A, and the number of pins N pin and the cross-sectional area A of the pin pin It will also affect the thermal resistance. The calculation of thermal resistance can be expressed as:

[0110]

[0111] Where k is the thermal conductivity of the material, unit: W / (m*K).

[0112] This formula takes into account the heat transfer path between the pins and the PCB in addition to the thermal resistance of the device itself.

[0113] Impact of temperature difference:

[0114] Temperature difference T bp -T tp The calculation method is related to thermal resistance and is related to height d, bottom area A, number of pins N pin and the cross-sectional area A of the pin pin The formula is:

[0115]

[0116] The impact of the through-hole device’s morphology:

[0117] Since the number of pins of a through-hole device and the contact area between it and the PCB have a strong influence, the form factor can be expressed as:

[0118]

[0119] This form factor reflects the impact of the through-hole device's geometry on thermal conductivity, especially the number of its pins and the impact of the pad contact area.

[0120] Specifically, in actual production, the third formula can be used to calculate the second temperature field component;

[0121] The third formula is:

[0122]

[0123] Where I2 represents the second temperature field component, β∈[0.01, 0.02], d represents the height of the target component, A represents the bottom area, N pin Indicates the number of pins, A pin represents the cross-sectional area of ​​the pin, and Q represents the integrated heat flow.

[0124] Under normal conditions: β = 0.01 to 0.02. Under conditions with high thermal resistance (high-density layout, significant differences in device height): β = 0.02 to 0.05. Under conditions with good thermal conductivity (low-density layout, good heat dissipation conditions): β = 0.005 to 0.01.

[0125] This shows the effect of the ratio of height to base area on thermal resistance and temperature difference. The greater the height, the smaller the base area, the greater the thermal resistance and the greater the temperature difference.

[0126] The impact of the number of pins and the cross-sectional area of ​​the pins on thermal resistance and temperature difference is considered. The more pins, the larger the contact area, the better the heat conduction effect, and the smaller the temperature difference.

[0127] The morphological influence combines the impact of the device's height, base area, and number of pins on the temperature field.

[0128] The effect of heating power on temperature difference. The greater the heating power, the greater the temperature difference and the higher the device temperature.

[0129] The effects of heat generation power and pin cross-sectional area on temperature difference are considered. The larger the pin contact area, the better the heat transfer effect and the smaller the temperature difference.

[0130] The reasons for cubicizing the third formula include:

[0131] Scaling the numerical range: In actual calculations, the result range of I2 will tend to be stable after taking the cube root.

[0132] Nonlinear suppression: By taking the cube root, the contribution of certain extreme parameters to the final I2 can be weakened.

[0133] Physical consistency: Although the cube root scales the value, the relative relationship of the parameters remains unchanged, so the physical meaning still holds.

[0134] Step 105 : Based on the soldering temperature, the material of the target component, and the material of the PCB board, the influence of the soldering temperature of the target component, the material of the target component, and the material of the PCB board on the temperature field of the soldering point is obtained, which is recorded as the third temperature field component.

[0135] In one possible implementation, step 105 may include:

[0136] Based on the material of the target component, the influence of the material of the target component on the temperature field of the soldering point is obtained and recorded as the material factor.

[0137] Based on the material of the PCB board, the influence of the material of the PCB board on the temperature field of the soldering point is obtained and recorded as the substrate factor.

[0138] Based on the soldering resistance temperature, the influence of the soldering resistance temperature on the temperature field of the soldering point is obtained and recorded as the soldering resistance factor.

[0139] Based on the material factor, substrate factor and solder resistance factor, the third temperature field component is calculated.

[0140] Among them, the material factors are analyzed:

[0141] Metal materials have high thermal conductivity, uniform heat distribution, and low temperature control complexity. Common metal materials and their thermal conductivity are k Cu ≈385W / (m×K), k Al ≈205W / (m×K), k Mo ≈138W / (m×K), k 不锈钢 ≈205W / (m×K), equipment material factor (taking copper as a reference),

[0142] The thermal conductivity of semiconductor materials is low, the heat conduction efficiency is poor, local overheating is easy, and the temperature control is complex. Common semiconductor materials and their conductivity are k Si ≈149W / (m×K), k GaAs ≈46W / (m×K), device material factor (taking copper as a reference)

[0143] The thermal conductivity of ceramic materials is between that of metals and semiconductors, and they have good thermal stability, but the temperature control complexity is high. Common ceramic materials and their conductivity are k AIN≈170W / (m×K), device material factor (taking copper as a reference)

[0144] The thermal conductivity of plastic materials is extremely low, and the temperature difference and heat accumulation phenomenon are obvious. They are easily deformed by high temperature and have the highest temperature control complexity. Common plastic materials and their thermal conductivity include polyimide k PI ≈0.2W / (m×K), epoxy resin k EP ≈0.2W / (m×K), device material factor (taking copper as a reference) FR-4 (phenolic resin) k FR-4 ≈0.3W / (m×K),

[0145] Material Factor k 材质 is the thermal conductivity of the selected reference material. where k 基板 is the thermal conductivity of the current substrate.

[0146] Among them, the influence of welding resistance temperature on temperature field is analyzed:

[0147] formula It reflects the sensitivity of the material to temperature changes during welding. The smaller the value, the more complex the temperature control. T The normalized coefficient of heat conduction should be based on the thermal conductivity k of the material. 材质 and k 基板 , and a reference material (usually copper) for normalization. Different materials will have different C T ,T max The soldering temperature during the patch process. The smaller the value, the more difficult it is to control the temperature field and the more complex the temperature control is. See the table below:

[0148]

[0149] Specifically, in actual production, the fourth formula can be used to calculate the third temperature field component;

[0150] The fourth formula is:

[0151]

[0152] Where I3 represents the third temperature field component, T max Indicates soldering temperature, α 材质 Represents the material factor, α 基板 represents the substrate factor, and γ represents the adjustment coefficient.

[0153] This formula quantifies the difficulty of temperature control during the soldering process by normalizing a material's thermal conductivity and soldering temperature. Materials with high thermal conductivity (such as copper and aluminum) dissipate heat quickly, reducing the risk of localized overheating and making temperature control easier. Materials with low thermal conductivity or poor temperature resistance (such as FR-4 and stainless steel) are more susceptible to overheating or damage, making temperature control more difficult.

[0154] Among them, a larger temperature difference will increase the difficulty of temperature control of the system, resulting in more drastic changes in the temperature field. Therefore, in this formula, the soldering temperature T max Directly opposed to the thermal conductivity factor, the adjustment coefficient γ reflects the sensitivity of the temperature field complexity to temperature changes. Depending on the thermal conductivity characteristics of the material and the structural complexity, temperature changes can cause changes in the system's thermal conductivity efficiency. Therefore, the adjustment coefficient γ is introduced to describe this change. It is typically set in the range [1.2, 1.4], which meets the requirements of common temperature control systems.

[0155] Step 106, based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component and the third weight, obtain the temperature of the target component soldering point; wherein the first weight is the weight corresponding to the first temperature field component, the second weight is the weight corresponding to the second temperature field component, and the third weight is the weight corresponding to the third temperature field component.

[0156] In a possible implementation, the first weight, the second weight, and the third weight may be calculated as follows:

[0157] The first weight, the second weight, and the third weight are used as a particle in the particle swarm algorithm, and the optimal particle is found with the minimum first target value as the objective function; wherein the first target value is the absolute value of the difference between the calculated value and the ideal value of the soldering point temperature of the target component.

[0158] Based on the simulated annealing algorithm, the hyperparameters of the particle swarm algorithm are updated, and the speed and position of the particles in the particle swarm algorithm are updated to obtain the updated optimal particles.

[0159] Based on the updated optimal particles, a new target component solder point temperature is calculated.

[0160] A new first target value is calculated based on the absolute value of the difference between the new calculated value and the ideal value.

[0161] If the new first target value meets the preset conditions or the particle swarm algorithm reaches the maximum number of iterations, the first weight, second weight, and third weight represented by the particle corresponding to the first target value are used as the final first weight, second weight, and third weight; if the new first target value does not meet the preset conditions, return to the execution step of "updating the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and updating the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particle" and subsequent steps.

[0162] The particle swarm optimization (PSO) algorithm simulates the foraging process of a flock of birds, finding the optimal solution through cooperation and competition among particles. The weight coefficients are gradually adjusted based on the calculated objective function to achieve the best overall performance. The advantage of PSO is that it solves multi-objective optimization problems through cooperation and competition among particles, rather than relying on complex gradient information. However, PSO can become stuck in local optima. To avoid this, the simulated annealing (SA) algorithm can be introduced. Simulated annealing can escape local optima by randomly jumping through the solution space, exploring lower energy states and thus enhancing global search capabilities. In the early stages of the PSO algorithm, simulated annealing helps particles avoid local optima through random jumps with probability. As the PSO algorithm converges, simulated annealing gradually reduces the randomness and focuses on a detailed search of the nearby solution space to accurately determine the optimal solution.

[0163] The temperature decay mechanism of simulated annealing (SA) is highly consistent with the thermal cycle characteristics of the welding process. It conducts a large-scale exploration in the high-temperature stage, corresponding to the rapid temperature rise in the early stage of welding, allowing particles to jump significantly to explore potential optimal solutions (such as the impact of different pad layouts or pin configurations on thermal resistance); in the low-temperature stage, it conducts a detailed search, simulating the stable control of the welding cooling stage, and gradually converges to the optimal weight combination (such as accurately balancing heat conduction and temperature resistance). Annealing coefficient a SA The actual soldering cooling rate can be correlated (e.g., the typical cooling rate of lead-free soldering is 0.5-2°C / s) to ensure that the algorithm parameters are consistent with the physical process. Compared with other algorithms, PSO-SA can directly handle the conflicting objectives of I1, I2, and I3 (e.g., heat dissipation vs. temperature resistance) without pre-scaling. SA Random perturbations simulate soldering parameter fluctuations (such as reflow temperature deviations) to enhance the robustness of the solution. SA's random jumps effectively prevent PSO from falling into local optimality (such as over-optimizing heat dissipation while ignoring temperature resistance). Parameter design (such as annealing rate and perturbation amplitude) is mapped to the soldering thermal process, and the results can directly guide process adjustments. This hybrid algorithm is not only theoretically rigorous but also adapts to different soldering equipment (such as wave soldering vs. reflow soldering) by adjusting the annealing coefficient and particle swarm size, showing broad potential for industrial applications.

[0164] First, each particle in the swarm is initialized to a solution, namely a pair of first weight ω1, second weight ω2, and third weight ω3. The initial values ​​of these weights can be randomly generated, and the initial values ​​of the weights can be randomly generated in the interval [0,1] to ensure that the initial solution space of the particles is evenly distributed and not biased in any direction. Based on the position of the particles, the objective function is calculated to minimize the gap between the ideal index and the target. The optimization goal is:

[0165]

[0166] In each iteration, the particle speed is adjusted with a certain probability according to the simulated annealing temperature strategy. When the temperature is high, the particles are allowed to make large random jumps and escape the local optimum. When the temperature is low, the particles gradually tend to search for the global optimal solution. The particles adjust their positions based on their own experience and the experience of the group. The particle speed and position update rules are as follows:

[0167]

[0168] in, and is the velocity and position of the particle at the kth iteration, The initial velocity of the particle can be set as [-v max ,v max ],

[0169] Application scenarios:

[0170] 1. Weight ω range (ω max and ω min ) settings

[0171] During the soldering process of through-hole devices, the pin structure involves high thermal conductivity materials (such as copper pins) and low thermal conductivity materials (such as plastic packaging), resulting in large temperature gradients in different areas and complex heat flow paths. To avoid neglecting or overemphasizing certain key factors, dynamic weight adjustment is required to improve the adaptability and accuracy of optimization. The specific settings are as follows:

[0172] Initial stage: Use larger ω and smaller ω to enhance exploration capability and avoid falling into local optimum, especially in the area of ​​high thermal conductivity materials, to ensure that the search space is wide enough.

[0173] Mid-term: Gradually reduce ω to allow the particle swarm to converge stably towards the optimal solution.

[0174] Later stage: Increase ω to [0.2, 0.3] to improve the local search accuracy and ensure the reliability of the final solution, especially in areas with low thermal conductivity materials (such as packaging layers) to avoid being ignored.

[0175] Application scenarios:

[0176] In composite structures such as copper pins and plastic packages, the temperature field varies greatly. The large initial weight helps explore the entire temperature field and avoid optimization failure due to ignoring a certain part of the material (such as the packaging layer).

[0177] In the middle stage of the optimization process, the weights are gradually adjusted to effectively narrow the search range to ensure the stability of the temperature field.

[0178] 2. Setting of acceleration constants (c1 and c2)

[0179] In thin-walled pin structures or large-footprint packages, the thermal coupling effect between pin height and foot area is significant, leading to complex local thermal field variations. To prevent particles from becoming trapped in local optima due to over-reliance on individual historical optimal solutions during optimization, we enhance group collaboration by adjusting the acceleration constant, thereby improving overall search efficiency.

[0180] c1=1.5: Reduces the memory influence of individual particles, allowing particles to accept new solutions and enhance exploration capabilities.

[0181] c2=2.0: Strengthen the collaboration between groups, guide particles to move towards the global optimal solution, and improve the global optimization ability.

[0182] Application scenarios:

[0183] For composite PCB boards with pin and package structures, enhancing the group collaboration capability of particles can effectively avoid the search being concentrated in certain specific areas due to local thermal coupling effects.

[0184] A high acceleration constant helps to balance the exploration and exploitation capabilities between individual particles and the group, thus avoiding deviations from local solutions.

[0185] 3. Setting of random numbers (r1 and r2)

[0186] In the process of optimizing the welding temperature field, the temperature field is affected by many factors (such as thermal conductivity, packaging material, pad area, etc.). In order to increase the randomness of the particle swarm and avoid falling into local optimality, we introduce random numbers to enhance the search diversity and global exploration capabilities.

[0187] r1 (individual learning factor): affects the degree of convergence of particles to their own historical optimal position. By randomly selecting values ​​between [0,1], it prevents individuals from over-relying on historical experience and helps them escape from local optimality.

[0188] r2 (swarm learning factor): affects how quickly particles move toward the global optimal position. A larger r2 value helps particles quickly move toward the global optimal solution, but a certain degree of randomness must be maintained to prevent the search from becoming too concentrated.

[0189] Application scenarios:

[0190] The heat conduction path of the through-hole device is complex, and a large randomness is used to avoid ignoring the influence of certain low thermal conductivity materials during the optimization process.

[0191] Random adjustment ensures that particles explore different regions of the temperature field, effectively avoiding local optimal problems.

[0192] 4. Annealing perturbation (Δv SA ) settings

[0193] During PCB soldering, the temperatures in the pad area and package edge can exhibit multi-stable states (e.g., higher temperatures in the pad area and lower temperatures at the package edge). To prevent the algorithm from converging to an uneven temperature distribution, we introduce simulated annealing perturbations to disrupt this local thermal equilibrium and optimize the overall heat conduction path.

[0194] Annealing perturbation Δv SA =0.3v max : Apply disturbance near the melting point of solder paste to force it out of the local thermal equilibrium state, optimize the temperature distribution, and ensure the uniformity of the temperature field.

[0195] Application scenarios:

[0196] During the welding process, local temperature differences may affect the welding quality. Annealing perturbation helps to avoid such uneven temperature distribution and improve the overall optimization effect of the temperature field.

[0197] 5. Annealing coefficient (a SA ) settings

[0198] Different types of through-hole devices have different response speeds to temperature, and the specific annealing strategy needs to be adjusted according to the thermal response characteristics of the device.

[0199] For large devices, due to their large heat capacity and slow heat dissipation, the annealing coefficient is set to a SA =0.98, and a slow cooling strategy is adopted to ensure thermal stability and reduce welding stress.

[0200] For micro pin devices, due to their fast thermal response, the annealing coefficient is set to a SA =0.90, a faster cooling strategy is adopted to optimize welding strength and reliability.

[0201] Application scenarios:

[0202] During the reflow soldering process of large through-hole devices, a slower annealing process helps avoid the accumulation of soldering stress and ensure the quality of solder joints.

[0203] For small precision components, a rapid annealing process helps ensure soldering strength and reliability.

[0204] Summary: Comprehensive optimization parameters

[0205]

[0206] By adjusting and optimizing the above parameters, the temperature field optimization effect during PCB reflow soldering can be effectively improved, ensuring the soldering quality of through-hole devices and surface-mount devices, especially in scenarios with complex temperature fields and the interweaving of multiple materials, ensuring the accuracy and stability of the soldering process.

[0207] In one possible implementation, step 106 may include:

[0208] A temperature field of a target component is obtained based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight.

[0209] Based on the temperature field of the target component, the temperature of the soldering point of the target component is obtained.

[0210] For example, the temperature range of the target component soldering point can be directly read out based on the temperature field of the target component. The "temperature of the target component soldering point" obtained here specifically refers to a temperature range. If the temperature field calculation is precise enough, the "temperature of the target component soldering point" here can also refer to a temperature.

[0211] In one possible implementation, step 106 may include:

[0212] A temperature field of a target component is obtained based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight.

[0213] Obtain the concentration of nitrogen gas, the speed of the conveyor belt where the target components are placed, the ambient temperature, and the melting temperature of the solder paste.

[0214] The temperature field of the target component, the concentration of nitrogen gas, the speed of the conveyor belt, the melting temperature of the solder paste, the solder resistance temperature and the ambient temperature are input into the fuzzy logic control algorithm, and the first temperature difference is output based on the preset fuzzy control logic.

[0215] Based on the first temperature difference, the first weight, the second weight, and the third weight, a second temperature difference is calculated.

[0216] The second temperature difference is used as a particle in the particle swarm algorithm, and the objective function is to find the optimal particle with the minimum second target value; wherein the second target value is the absolute value of the difference between the calculated value of the second temperature difference and the actual safe temperature.

[0217] Based on the simulated annealing algorithm, the hyperparameters of the particle swarm algorithm are updated, and the speed and position of the particles in the particle swarm algorithm are updated to obtain the updated optimal particles.

[0218] Based on the updated optimal particle, a new second temperature difference is calculated.

[0219] A new second target value is calculated based on the absolute value of the difference between the new second temperature difference and the actual safety temperature.

[0220] If the new second target value meets the preset conditions or the particle swarm algorithm reaches the maximum number of iterations, the new second temperature difference is used as the third target value; if the new first target value does not meet the preset conditions, the process returns to the step "update the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and update the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particles" and subsequent steps.

[0221] The sum of the third target value and the solder paste melting temperature is used as the minimum temperature of the target component soldering point; the difference between the soldering resistance temperature and the third target value is used as the maximum temperature of the target component soldering point.

[0222] For example, solder paste melting temperatures are mainly divided into three categories, and the appropriate type is selected based on soldering requirements and component temperature resistance:

[0223] The melting point of low melting point solder (suitable for low temperature resistant components) is 143℃ (such as Sn 43 Bi 57 ), suitable for components with lower temperature resistance such as plastic packaged devices.

[0224] The melting point of medium melting point solder (general purpose solder) is 183℃ (such as Sn 63 Pb 37 ), suitable for common components, with a soldering temperature resistance of about 250°C.

[0225] The melting point of lead-free solder (environmentally friendly and high reliability requirements) is 217°C to 227°C (such as SAC305), which is suitable for devices with environmental and high reliability requirements.

[0226] Considering the complexity of the temperature control system, a fuzzy logic control system combined with a particle swarm optimization algorithm (PSO) can be used to adjust the welding temperature in real time. In this solution, PSO not only optimizes the temperature difference (the second temperature difference), but also dynamically adjusts the weight and search range to ensure the optimal welding temperature.

[0227] Among them, the input variables of fuzzy logic control include the temperature field of the target component, the concentration of nitrogen filled C N, conveyor belt speed V, solder paste melting temperature, solder resistance temperature and ambient temperature, the temperature field of the target component represents the comprehensive performance index. The higher each index is, the greater the complexity of the temperature field is, and the higher the soldering difficulty is. The output feature is the preliminary first temperature difference, and d, A, N pin and T max They have been used in the calculation of the temperature field of the target components, so there is no need to introduce these parameters again in the input characteristics of the fuzzy control system.

[0228] For each input variable, a corresponding membership function can be defined to convert the input value into a fuzzy set, the temperature field of the target component (from small to large), C N A (low to high) and V (slow to fast) can be defined as “low”, “medium”, “high”, indicating the strength of their impact.

[0229] In fuzzy rules, consider the following situation:

[0230] High soldering complexity (such as large devices or higher heights) combined with high nitrogen concentration and good heat conduction requires a lower safety temperature difference.

[0231] The bottom area has less impact and the conveyor belt speed is faster, which may cause uneven temperature, so a larger safety temperature difference is required.

[0232] Complex welding areas and large temperature differences require a larger safety temperature difference to ensure welding quality.

[0233] Through these rules, the system will generate a fuzzy output of the first temperature difference, such as "low temperature difference", "medium temperature difference" and "high temperature difference". For example, the temperature field of the target component corresponds to "low", C N corresponds to "low", V corresponds to "low", solder paste melting temperature corresponds to "low", solder resistance temperature corresponds to "low" and ambient temperature corresponds to "low", then the first temperature difference output corresponds to "low temperature difference".

[0234] Next, we use the membership function μ(ΔT1) in combination with the centroid method. The core idea is to obtain the specific temperature difference value (second temperature difference) ΔT2 by calculating the geometric center (centroid) of the fuzzy set. This method is calculated using the following formula:

[0235]

[0236] In fuzzy control, the position is the first temperature difference ΔT1, and the membership is μ(ΔT1) corresponding to each temperature difference (specifically, the first weight, the second weight, and the third weight). Since the value of is continuous rather than discrete, the integral is used to represent the weighted average, and the second temperature difference can be calculated as follows:

[0237]

[0238] The numerator of this formula represents the weighted sum of all possible temperature differences across the entire temperature range, multiplied by its membership, μ(ΔT1), and then integrated. The denominator, the integral of all memberships, is used to normalize the weights and ensure that the resulting temperature differences are reasonably proportional.

[0239] 2. Particle Swarm Optimization

[0240] The main task of the particle swarm optimization algorithm is to fine-tune the particle update rules and search range automatically according to the output of the fuzzy logic control system and the real-time welding conditions through an adaptive mechanism to ensure the optimal control of the temperature field.

[0241] Update rules:

[0242] In the current through-hole device welding scenario, the particle represents the value of ΔT2, so the particle update rule is to update the value. After combining the simulated annealing mechanism, the speed update formula may become:

[0243]

[0244] Where Δv SA is the random perturbation provided by simulated annealing.

[0245] Basis for formulation:

[0246] 1. Fuzzy Logic Control System Output: The fuzzy logic control system generates an output based on multiple factors during the welding process (such as welding current, welding time, and ambient temperature). This output reflects a desired or recommended temperature difference (the second temperature difference ΔT2) under the current welding conditions. The particle swarm algorithm uses this output as a reference to update particles in a direction that better meets welding requirements. For example, if the fuzzy logic control system output indicates that a larger temperature difference is currently required to ensure welding quality, the particle update rule will guide the particles toward a larger value (the second temperature difference ΔT2).

[0247] 2. Real-time welding conditions: These conditions include the characteristics of the welding material (such as thermal conductivity and melting point) and the state of the welding equipment (such as power stability). Different welding conditions have different requirements for the temperature field, and the particle update rules need to adapt to these conditions. For example, for materials with high thermal conductivity, a smaller temperature difference may be required to avoid overheating. The particle update rules will adjust the particle movement direction and amplitude accordingly.

[0248] 3. Simulated Annealing Mechanism: The simulated annealing mechanism is introduced to enhance global search capabilities. At high temperatures, particles have a high probability of random jumps, which makes the particle update rule more random and can escape from local optimal solutions. As the temperature decreases, the randomness decreases, and the particle update rule guides particles to conduct a detailed search near the current optimal solution, ultimately finding the global optimal solution.

[0249] 4. Fitness function:

[0250] f=|ΔT2-ΔT safe_value |

[0251] Weighs the predicted value (second temperature difference ΔT2) with the actual safety temperature ΔT safe_value The goal of the particle update rule is to make the particles move in the direction that reduces the fitness function value, that is, to continuously optimize the value (the second temperature difference ΔT2) to make it closer to the actual safe temperature difference, thereby achieving optimal control of the temperature field.

[0252] When the error of the fitness function is less than a preset threshold, or after a predetermined maximum number of iterations, the algorithm terminates and the optimal value (the third target value) is obtained.

[0253] 3. Output layer and execution module

[0254] The final safe temperature difference (third target value) is output by the particle swarm optimization module and then passed to the control execution module. In this module, the actual welding temperature range is calculated based on the (third target value) and the input solder resistance temperature and solder paste melting temperature. The minimum welding temperature is the sum of the third target value and the solder paste melting temperature, and the maximum welding temperature is the solder resistance temperature and the third target value.

[0255] In one possible implementation, obtaining the temperature field of the target component based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight may include:

[0256] A first temperature field component and a first weight product are calculated to obtain a first result.

[0257] The second temperature field component and the second weight product are calculated to obtain a second result.

[0258] The third temperature field component and the third weight product are calculated to obtain a third result.

[0259] The temperature field of the target component is obtained by calculating the sum of the first result, the second result, and the third result.

[0260] Specifically, higher heights can increase thermal resistance, slowing device heat conduction and affecting temperature distribution. The thermal conductivity of a material affects heat flow distribution proportionally to its height, so these two factors need to be analyzed together. The material's solder resistance temperature determines the device's ability to withstand high temperatures. Thermal fatigue and cracking issues at high temperatures require consideration of the material's stress response at high temperatures, particularly in relation to device height.

[0261] After the first temperature field component, the second temperature field component, and the third temperature field component are calculated, they need to be normalized according to their maximum and minimum values. Specifically:

[0262]

[0263] Among them, I1 norm It represents the first temperature field component after normalization, reflecting the heat conduction efficiency of the device through the pins and solder holes. The smaller the value, the better, in order to reduce the temperature rise, so I1 norm The smaller the value, the better the performance. norm It represents the normalized second temperature field component, reflecting the influence of height, bottom area, and pin / pad area on the temperature field. The higher the device is, the greater the thermal resistance is. The larger the bottom area of ​​the device is, the better the heat dissipation is. Therefore, I2 norm The smaller the better. I3 norm It represents the third temperature field component after normalization, reflecting the reliability of the device at high temperature, including the maximum temperature resistance of the device and the influence of the material. The higher the soldering temperature, the stronger the material's resistance to thermal fatigue. Therefore, I3 norm The smaller the value, the better. Using the normalized result can better ensure the accuracy of the calculation, and thus the calculated temperature field is more accurate.

[0264] The above-mentioned component soldering point temperature calculation method obtains the influence of the target component's pins and solder holes on the soldering point temperature field, the influence of the target component's height, bottom area and pins on the soldering point temperature field, and the influence of the target component's solder resistance temperature and material on the soldering point temperature field through various parameters of the target component. The relationship between various parameters and the soldering point temperature field is comprehensively analyzed. Through the superposition of temperature fields, the calculated soldering point temperature can be closer to the actual situation, and the calculation result can be made more accurate, which facilitates timely adjustment of the parameters of the supporting equipment according to the calculated value, ensures the quality of welding, and enables the target component to work stably after welding is completed.

[0265] As electronic manufacturing moves towards miniaturization and high performance, short-pin direct insertion technology, with its advantages of short heat conduction path and low thermal resistance, meets the needs of electronic products for compact space and high performance, and has become an important direction for future development. This invention focuses on direct insertion components and deeply analyzes the comprehensive impact of various key factors on the temperature field, providing theoretical and technical support for the application and optimization of short-pin direct insertion technology. Through dimensionless processing, a unified formula for the influence of device height on thermal resistance, temperature gradient and solder paste fluidity is constructed; a discretized weight model is used to distinguish the effect of the bottom area of ​​short and long direct insertion devices on the temperature field; the characteristics of microstructure to improve heat conduction are considered to optimize the heat conduction efficiency; the heat conduction effects of solder holes and pins are combined, and the solder hole spacing is optimized with the help of genetic algorithms; a formula for the thermal fatigue and cracking of the temperature field by solder resistance temperature and material is established. By comparing the difference in heat conduction between single-sided and double-sided welding, the principle of setting the melting temperature of solder paste and the applicable range of solder materials with different melting points are given. A PSO-SA hybrid optimization algorithm is used to determine the coupling weight coefficients of various factors. An innovative multi-level temperature field complexity analysis and real-time optimization control system are designed. Fuzzy logic and particle swarm optimization algorithms are integrated to adjust the soldering temperature in real time to ensure soldering quality. This provides an effective solution for precise temperature field control in short-lead direct insertion technology, promoting its widespread application in electronics manufacturing.

[0266] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0267] An embodiment of the present invention further provides a device for calculating the temperature of a component solder point, comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the above method embodiment. For example, the device for calculating the temperature of a component solder point can be a computer, a terminal server, or the like, without limitation herein.

[0268] In the above embodiments, the descriptions of each embodiment have their own focus. For parts not described or recorded in detail in one embodiment, please refer to the relevant descriptions of other embodiments. Unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features of different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0269] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for calculating the temperature of a component soldering point, characterized in that: include: Obtaining the height, bottom area, number of pins, pin diameter, pin length, soldering temperature resistance, and material of a target component, as well as the material of a PCB board on which the target component is soldered; Obtaining a comprehensive heat flow based on the height, the bottom area, and the number of pins; Based on the comprehensive heat flow, the number of pins, the pin diameter, and the pin length, the influence of the pins and solder holes of the target component on the temperature field of the soldering point is obtained, which is recorded as a first temperature field component; Based on the comprehensive heat flow, the height, the bottom area, the pin diameter, and the number of pins, the influence of the height and bottom area of ​​the target component and the pins on the temperature field of the soldering point is obtained, which is recorded as a second temperature field component; Based on the soldering temperature, the material of the target component, and the material of the PCB, the influence of the soldering temperature of the target component, the material of the target component, and the material of the PCB on the temperature field of the soldering point is obtained, which is recorded as a third temperature field component; Based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component and the third weight, the temperature of the soldering point of the target component is obtained; wherein the first weight is the weight corresponding to the first temperature field component, the second weight is the weight corresponding to the second temperature field component, and the third weight is the weight corresponding to the third temperature field component.

2. The method for calculating component soldering point temperature according to claim 1, wherein: The comprehensive heat flow is obtained based on the height, the bottom area and the number of pins, including: Based on the height, a nonlinear effect of the height on the thermal resistance is obtained and recorded as a first heat flow component; Based on the bottom area, the influence of the bottom area on the temperature field is obtained and recorded as the second heat flux component; Based on the number of pins, the effect of the number of pins on the temperature field is obtained and recorded as the third heat flux component; The first heat flow component, the second heat flow component and the third heat flow component are combined based on a geometric mean method to obtain the comprehensive heat flow.

3. The method for calculating component soldering point temperature according to claim 1, wherein: The influence of the pins and solder holes of the target component on the temperature field of the soldering point is obtained based on the comprehensive heat flow, the number of pins, the pin diameter, and the pin length, and is recorded as a first temperature field component, including: Obtain the thickness of the PCB board, the cross-sectional area of ​​the soldering holes, and the thermal conductivity; Obtaining a thermal resistance of a single pin based on the pin length, the pin diameter, and the thermal conductivity coefficient; Based on the thickness of the PCB board, the cross-sectional area of ​​the soldering hole of the soldering point and the thermal conductivity coefficient, the thermal resistance of a single soldering hole is obtained; Based on the thermal conductivity resistance of a single pin and the thermal conductivity resistance of a single solder hole, the total thermal conductivity resistance of the solder hole and the pin is obtained; Obtaining a comprehensive size of the solder joint based on the length of the pin, the transverse size of the solder joint, and the longitudinal size of the solder joint; The first temperature field component is calculated based on the comprehensive heat flux, the top temperature of the welding spot and the welding hole, the bottom temperature of the welding spot and the welding hole, the total thermal conductivity resistance of the welding hole and the pin, and the comprehensive size of the welding point.

4. The method for calculating component soldering point temperature according to claim 3, wherein: The influence of the height, bottom area, pin diameter, and number of pins of the target component on the temperature field of the soldering point is obtained based on the comprehensive heat flow, the height, the bottom area, the pin diameter, and the number of pins, and is recorded as a second temperature field component, including: Obtaining a thermal resistance influencing component based on the total thermal conductivity resistance of the solder hole and the pin, the height, the bottom area, the pin diameter, and the number of pins; Obtaining a temperature difference influence component based on the top temperature of the welding spot weld hole, the bottom temperature of the welding spot weld hole, the height, the bottom area, the pin diameter, and the number of pins; Obtaining a morphological influence component of a target component based on the height, the bottom area, and the number of pins; The second temperature field component is calculated based on the thermal resistance influence component, the temperature difference influence component, and the morphology influence component.

5. The method for calculating component soldering point temperature according to claim 1, wherein: The influence of the soldering temperature of the target component, the material of the target component, and the material of the PCB board on the temperature field of the soldering point is obtained based on the soldering temperature, the material of the target component, and the material of the PCB board, and recorded as the third temperature field component, including: Based on the material of the target component, the influence of the material of the target component on the temperature field of the soldering point is obtained and recorded as a material factor; Based on the material of the PCB board, the influence of the material of the PCB board on the temperature field of the soldering point is obtained and recorded as the substrate factor; Based on the soldering resistance temperature, the influence of the soldering resistance temperature on the temperature field of the soldering point is obtained and recorded as the soldering resistance factor; The third temperature field component is calculated based on the material factor, the substrate factor, and the solder resistance factor.

6. The method for calculating component soldering point temperature according to claim 1, wherein: The first weight, the second weight, and the third weight are calculated as follows: The first weight, the second weight, and the third weight are used as a particle in a particle swarm algorithm, and the optimal particle is found with the minimum first target value as the objective function; wherein the first target value is the absolute value of the difference between the calculated value and the ideal value of the soldering point temperature of the target component; Update the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and update the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particles; Based on the updated optimal particles, calculate the new target component solder point temperature; Calculating a new first target value based on the absolute value of the difference between the new calculated value and the ideal value; If the new first target value meets the preset conditions or the particle swarm algorithm reaches the maximum number of iterations, the first weight, second weight, and third weight represented by the particle corresponding to the first target value are used as the final first weight, second weight, and third weight; if the new first target value does not meet the preset conditions, the process returns to the step of "updating the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and updating the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particle" and subsequent steps.

7. The method for calculating component soldering point temperature according to claim 1, wherein: The step of obtaining the temperature of the target component soldering point based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight includes: Obtaining a temperature field of the target component based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight; Based on the temperature field of the target component, the temperature of the soldering point of the target component is obtained.

8. The method for calculating component soldering point temperature according to claim 1, wherein: The step of obtaining the temperature of the target component soldering point based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight includes: Obtaining a temperature field of the target component based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight; Obtaining the concentration of nitrogen gas, the speed of the conveyor belt on which the target component is placed, the ambient temperature, and the melting temperature of the solder paste; Inputting the temperature field of the target component, the concentration of the nitrogen gas, the speed of the conveyor belt, the solder paste melting temperature, the solder resistance temperature, and the ambient temperature into a fuzzy logic control algorithm, and outputting a first temperature difference based on a preset fuzzy control logic; calculating a second temperature difference based on the first temperature difference, the first weight, the second weight, and the third weight; The second temperature difference is used as a particle in the particle swarm algorithm, and the optimal particle is found with the minimum second target value as the objective function; wherein the second target value is the absolute value of the difference between the calculated value of the second temperature difference and the actual safe temperature; Update the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and update the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particles; Calculate a new second temperature difference based on the updated optimal particle; Calculating a new second target value based on the absolute value of the difference between the new second temperature difference and the actual safety temperature; If the new second target value meets the preset conditions or the particle swarm algorithm reaches the maximum number of iterations, the new second temperature difference is used as the third target value; if the new first target value does not meet the preset conditions, the process returns to the step of "updating the hyperparameters of the particle swarm algorithm based on the simulated annealing algorithm, and updating the speed and position of the particles in the particle swarm algorithm to obtain the updated optimal particles" and subsequent steps; The sum of the third target value and the solder paste melting temperature is used as the minimum temperature of the target component soldering point; the difference between the soldering resistance temperature and the third target value is used as the maximum temperature of the target component soldering point.

9. The method for calculating component soldering point temperature according to claim 7 or 8, characterized in that: The step of obtaining the temperature field of the target component based on the first temperature field component, the first weight, the second temperature field component, the second weight, the third temperature field component, and the third weight includes: Calculating a product of the first temperature field component and the first weight to obtain a first result; Calculating the product of the second temperature field component and the second weight to obtain a second result; Calculating the product of the third temperature field component and the third weight to obtain a third result; The sum of the first result, the second result and the third result is calculated to obtain the temperature field of the target component.

10. A device for calculating the temperature of a component soldering point, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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