CN112632856A

CN112632856A - Conveyor belt speed and temperature control method of reflow furnace

Info

Publication number: CN112632856A
Application number: CN202011517309.XA
Authority: CN
Inventors: 梁广俊; 李梦; 范宇轩; 倪雪莉; 徐子洋; 刘清漠
Original assignee: JIANGSU POLICE INSTITUTE
Current assignee: JIANGSU POLICE INSTITUTE
Priority date: 2020-12-21
Filing date: 2020-12-21
Publication date: 2021-04-09
Anticipated expiration: 2040-12-21
Also published as: CN112632856B

Abstract

The invention discloses a method for controlling the speed and the temperature of a conveyor belt of a reflow oven, which comprises the steps of establishing and solving an oven temperature curve model, establishing the oven temperature curve model by actually measured actual temperature values of a group of welding points, the set temperature of each given temperature zone and the oven passing speed of the conveyor belt, reflecting the temperature change condition of the center of a welding area by the oven temperature curve model, acquiring the midpoint temperature of each small temperature zone or the end zone temperature of the small temperature zone by the oven temperature curve model, or determining the allowed maximum oven passing speed of the conveyor belt by using the oven temperature curve on the basis of ensuring that the oven temperature curve of a circuit board conforms to the process limit and assuming that the set values of the temperatures of the temperature zones are respectively given values. By utilizing the furnace temperature curve, under the condition of meeting the process limit, the invention can solve the optimal furnace temperature curve through a genetic algorithm, give the set temperature of each temperature zone and the furnace passing speed of the conveyor belt, and give the corresponding area or index value.

Description

Conveyor belt speed and temperature control method of reflow furnace

Technical Field

The invention relates to the field, in particular to a conveyor belt speed and temperature control method of a reflow oven.

Background

When electronic products such as integrated circuit boards are produced, electronic elements are required to be welded on the circuit boards in a reflow furnace heating mode, and the temperature of each part of the reflow furnace is important for the quality of the products. Today, much of this work is controlled and regulated by experimental tests.

A heating circuit is arranged in the reflow oven, air or nitrogen is heated to a high enough temperature and then blown to the circuit board with the attached component, and solder on two sides of the component is melted and then bonded with the mainboard. A plurality of small temperature zones are arranged in the reflow furnace, the length of each small temperature zone is 30.5cm, and a gap of 5cm is formed between every two adjacent small temperature zones. They can be functionally divided into 4 large temperature zones: a preheating zone, a constant temperature zone, a reflux zone and a cooling zone. The two sides of the circuit board are lapped on a conveyor belt and enter a furnace at a constant speed for heating and welding, and the thickness of a welding area is 0.15 mm. The temperature near the boundary of each temperature zone may also be affected by the temperature of the adjacent temperature zone. The temperature of the production plant was maintained at 25 ℃.

The reflow soldering process has the advantages that the temperature is easy to control, the oxidation can be avoided in the soldering process, and the manufacturing cost is easy to control. The soak and tent temperature profiles are the two most common types of temperature profiles used in reflow soldering processes.

For the control problem of the temperature curve of the reflow furnace, researchers have made researches:

an expert of D.C. Whalley and the like uses an open model [ Molhanec M, Mach P.the on testing based FMEA of lead free manufacturing process [ C ]//33rd International Spring on Electronics Technology, ISSE 2010.IEEE,2010], an idea of solving a multivariate problem by an ideal profile is adopted for a welding area on a circuit board, and a certain degree of error exists between an open model set point and an actual heater panel temperature. There may also be some variation in heater panel temperature within a zone and there may be interaction between zones, meaning that the transition from one zone to the next is not instantaneous, and the change in state of such a transition between zones is dependent on various factors, for ease of study the application weakens the problem of temperature transition between zones, assuming that the temperature change is instantaneous, so that the change between curved sections is abrupt but has little effect on the end result.

Meanwhile, the two articles do not mention the offset of the alignment between the stencil and the pad in the solder paste printing process [ national architecture energy saving professional committee of the building association? [ M ]// building energy conservation: how do? Chinese plan press 1997 and manhattan's uneven heating of components [ zhangdian, ten cloud maple ] how to correctly set the temperature profile of the reflow oven [ J ] communication and broadcast tv 2003(1):43-50 ], which causes certain errors in the reflow oven process and cannot be eliminated by model selection, so this application assumes that both effects on the results are not considered.

Disclosure of Invention

The technical problems to be solved by the invention are as follows:

firstly, assuming that the set temperature of each temperature zone and the furnace passing speed of the conveyor belt are respectively set values, how to establish a furnace temperature curve model according to the set temperature of each temperature zone and the furnace passing speed of the conveyor belt so as to reflect the temperature change condition of the center of a welding area, listing the temperatures of the middle points of the small temperature zones 3, 6 and 7 and the center of the welding area at the end of the small temperature zone 8 by using the furnace temperature curve model, drawing corresponding furnace temperature curves, and storing the temperatures of the centers of the welding areas at intervals of 0.5s into provided result.csv.

And secondly, on the basis of ensuring that the furnace temperature curve of the circuit board conforms to the process limit, assuming that the set values of the temperatures of the temperature zones are respectively set values, and determining the allowed maximum conveying belt furnace passing speed by using the furnace temperature curve.

And thirdly, in the welding process, the time that the temperature of the center of the welding area exceeds the threshold temperature of 217 ℃ is not too long, and the peak temperature is not too high. The ideal furnace temperature curve should minimize the area covered by the temperature from 217 ℃ to the peak value exceeding the threshold temperature, determine the optimal furnace temperature curve, set temperature of each temperature zone and the furnace passing speed of the conveyor belt, and give corresponding areas.

And fourthly, in the welding process, not only meeting the process limit, but also ensuring that furnace temperature curves with the peak temperature as a central line and two sides exceeding 217 ℃ are symmetrical as much as possible, further solving an optimal furnace temperature curve, the set temperature of each temperature zone and the furnace passing speed of a conveyor belt, and giving corresponding index values.

In order to solve the technical problems, the invention provides the following technical scheme:

the invention has the following beneficial effects:

(1) the mechanism analysis model and the mathematical tool software are mutually verified, so that the accuracy is higher;

(2) the optimal solution is screened by using the genetic algorithm, the operation efficiency is high, and the calculation time is saved;

(3) the application range is wide, and the temperature control device can be applied to the operation of common temperature control instruments.

(4) The stability analysis result shows that the reflow furnace temperature curve model can be suitable for most temperature control situations and has strong stability.

(5) The reflow furnace temperature curve drawn by the mechanism model and mathematical software is subjected to multiple fitting, so that the precision is high;

(6) the obtained reflow furnace temperature curve has reference value for furnace temperature control in actual production, and is simple and convenient to operate;

(7) by utilizing a multi-objective optimization algorithm, the sensitivity of the model is improved on the premise of ensuring the stability of the model;

(8) the improved and optimized genetic algorithm improves the operation convergence rate by about 40 percent.

Drawings

FIG. 1 is a technical roadmap for the present invention;

FIG. 2 is a schematic view of the radiation principle;

FIG. 3 is an attachment data piecewise fit curve;

FIG. 4 is a cubic polynomial fit curve of the attachment data;

FIG. 5 is a fourth order polynomial fit curve during the temperature ramp-up phase;

FIG. 6 is a first order polynomial fit curve during the temperature ramp down phase;

FIG. 7 is a graph fitted to equation (16);

FIG. 8 is a furnace temperature curve for technical problem one;

FIG. 9 is a comparison of a technical problem one and an accessory fitting curve;

FIG. 10 is a graph of the optimal solution furnace temperature for problem two;

FIG. 11 is a logic diagram of a genetic algorithm;

FIG. 12 is a graph of the optimal solution furnace temperature for technical problem three;

FIG. 13 is a logic diagram of a multi-objective optimization genetic algorithm;

fig. 14 is an optimal solution furnace temperature curve of technical problem four.

Detailed Description

The following examples are included to provide further detailed description of the present invention and to provide those skilled in the art with a more complete, concise, and exact understanding of the principles and spirit of the invention.

Example 1: the speed and temperature control method of the conveyor belt of the reflow oven is constructed according to the route shown in figure 1:

the total length of the reflow oven in this application constitutes: the furnace front area (25cm), the 11 small temperature areas (30.5cm multiplied by 11), the gaps (5cm multiplied by 10) and the furnace rear area (25cm), and the gaps are not specially controlled in temperature.

The accessory gives measured data of a furnace temperature curve (namely a welding area central temperature curve) in a certain experiment, the set temperature of each temperature area and the furnace passing speed of the conveyor belt are fixed constants, the set temperature of each small temperature area can be properly adjusted, and the temperature of partial small temperature areas is required to be kept consistent during adjustment. The furnace passing speed of the conveyor belt is adjusted within the range of 65-100 cm/min.

The measured data accessory of the furnace temperature curve in the reflow soldering furnace is lack of universality because the data in the accessory are obtained at a specific temperature zone temperature and a specific conveyor belt speed, and a rule implicit in the data is difficult to find.

In order to solve the problems and establish the model conveniently, the following assumptions are made in the application:

(1) the centering offset of the stencil and the welding plate in the solder paste printing process is not considered;

(2) neglecting the uneven heating of the element caused by the Manhattan phenomenon;

(3) the reflow furnace is a closed space, and the pressure intensity in the furnace is a fixed value after the circuit board enters;

(4) the temperature of each temperature zone is stable in the heating process of the reflow oven, and faults can not occur;

(5) errors in the measurement instrument due to temperature changes are ignored.

In the reflow heating process, the solder paste must go through a certain specific temperature curve to better play the role of solder alloy and soldering flux in the solder paste, which is called as a reflow temperature curve, while the furnace temperature curve referred to in the application is an actual temperature change curve at a welding spot obtained by actual measurement of a thermocouple on a printed circuit board, which is not the temperature on a PCB, nor the temperature of the surface or resistance of a heating element, but the temperature of hot air in the welding furnace [ Zhang Yun, Wuzi baby, and Erli, et al, test and comparison of heat transfer performance of clothes and fabrics thereof [ J ]. textile science, 2007(01):87-90 ]. Printing plates of different sizes, layers, component numbers and component densities can obtain the same furnace temperature curve through different temperature zone temperatures and chain speed settings. In the solder paste printing process, alignment offset exists between the stencil and the bonding pad, the circuit board is heated unevenly, and furnace temperature curve data errors can be caused, so that the alignment offset of the stencil and the bonding pad is ignored in the application.

The symbols and variables are as follows:

TABLE 1 legends

Second, establishment of furnace temperature curve model

2.1 model preparation

Heat transfer refers to the phenomenon of thermal energy transfer due to temperature differences, and there are three main basic forms: heat conduction (Q)_t) Thermal radiation (Q)_r) And thermal convection (Q)_c). As long as there is a temperature difference inside or between the objects, the thermal energy must be transferred from the high temperature to the low temperature in one or more of the above three ways. Assuming the total heat is Q, without calculating heat loss, the total heat equals the sum of three heats:

Q＝Q_t+Q_c+Q_r (1)

in this application, the circuit board carries to get into the reflow oven through the conveyer belt and heats, and the sensor receives the transmission of three kinds of forms heat energy respectively:

1) heat conduction

Thermal conduction is mainly manifested by thermal motion of microscopic particles, such as molecules, atoms, and free electrons, within the circuit board. The Fourier law shows that the heat transfer rate is proportional to the temperature gradient and the heat transfer area during heat conduction, and the volume of the circuit board is small, so that the heat energy increment caused by the heat conduction is negligible.

Equation u_tK Δ u is the heat conduction equation, where Δ is the laplacian of space variable, k is the thermal diffusivity, and is determined by the thermal conductivity, density and heat capacity of the material, and u is the body surface temperature.

The solution to the thermal equation has the property of smoothing the initial temperature, which represents the propagation of heat from high temperatures to low temperatures. Generally speaking, without other heat energy conversion, a plurality of different initial states tend to the same steady state, and the reflow furnace in the application is a closed space, and meets the characteristic.

The matlab can be used for fitting a curve in the accessory to obtain a linear function relation between the temperature of the object surface and the heat conduction energy, and then the product of the Laplace operator and the diffusivity is solved.

2) Thermal radiation

A phenomenon in which an object radiates electromagnetic waves due to having a temperature is called heat radiation. All objects with temperature higher than absolute zero can generate heat radiation, and the higher the temperature is, the greater the total energy radiated is [ Liu Yi, Wang Zi Juan. According to the Stefan-Boltzmann law, the radiation capability of a general object can be obtained, and let E represent the radiation flux radiated by the general object in unit time and unit area, namely

E＝εσT⁴ (2)

Wherein T is the absolute temperature (K) of the object and σ is the Stefan-Boltzmann constant (5.67X 10)^-8W/(m²·K⁴) Epsilon is the radiation coefficient of the surface of the object, and represents the ratio (value is 0-1) of the radiation capacity of the object to the black body radiation capacity.

The process of heat transfer between objects by mutual radiation and absorption of radiant energy is called radiative heat transfer. The radiation heat transfer of the heating plate in the furnace to the sensor is mainly considered, and the radiation flux of the heating plate is set as E_HThe radiation flux of the sensor is E_SThen, then

E_H＝ε_HσT⁴ _H (3)

E_S＝ε_SσT⁴ _S (4)

Wherein T is_HIs the temperature of the heating plate, T_SIs the temperature of the sensor,. epsilon_SIs the surface emissivity of the sensor, epsilon_HThe heating plate surface emissivity. Radiative heat transfer between objects requires consideration of reflection and absorption and phaseThe effect on position is shown in fig. 2.

Electromagnetic waves are radiated from the surface of the heating plate to the outside, and the initial radiation flux is E_HPenetrating air reaches the sensor surface, and the radiation flux E received by the sensor is proportional to the radiation power, since the absorption power of the sensor is proportional to the radiation power (Kirchoff's theorem)_H-SIs composed of

Similarly, the radiant flux E received by the heating plate_S-HIs composed of

Total heat quantity Q radiated per unit time_RThe product of radiant flux and radiant surface area, so the total amount of radiation obtained by subtracting the temperature of the sensor from the total amount of radiation from the heater, the increment of the radiant heat of the sensor is

Q_R＝E_H-S-E_S-H＝(T_H ⁴-T_S ⁴)ε_Hε_SσA_S (7)

Wherein A is_SIndicating the accessible surface area of the sensor.

In fact, the influence of relative position needs to be considered in real life, so that the angle coefficient is introduced

Finally obtaining the total radiant flux received by the sensor

3) Heat convection

The heat convection can occur in the fluid and is accompanied with the heat conduction effect generated by the molecular motion of the fluid, which is the heat transfer process caused by the relative displacement of the mass points, and the heat convection exchange formula is as follows

Q_C＝(T_H-T_S)KA (9)

Where K represents the convective heat transfer coefficient.

Any temperature sensor that obtains thermal energy will have a thermal mass (denoted C in this application) and will absorb heat Q per unit time. Given the construction of the sensor, there is no significant heat conduction inside; meanwhile, the sensor is sufficiently conductive, the internal temperature gradient can be ignored, and the analysis is combined to return the temperature change rate recorded by the sensor in the furnace

Is composed of

2.2 model building

Through observing the temperature setting of the small temperature zone of the reflow oven, we find that the temperature of the sensor can show the trend of rising before falling along with the time change after the circuit board enters the reflow oven through the conveyor belt, and the specific analysis is as follows:

a. rising phase

The circuit board enters a reflow furnace and firstly enters heating areas in the furnace, namely a preheating area, a constant temperature area and a reflow area, the temperature of the sensor in the stage is gradually increased, and the main influencing factors are a thermal convection effect and a thermal radiation effect. Combining the formula (1-10), the furnace temperature curve has the following change rate:

b. descending stage

When the circuit board reaches the cooling zone, the temperature of the sensor begins to drop. At this moment, the heat exchange efficiency of heat convection is obviously far higher than the efficiency of heat radiation, and the volume of the circuit board is negligible relative to the reflow furnace, so the heat transfer effect of the heat radiation of the circuit board is eliminated, and the following steps are included:

the piecewise function relationship between the heat effect and the furnace temperature curve slope obtained by integrating the lifting stage is as follows

The application considers an ideal model (neglecting the energy loss of electromagnetic waves in air propagation), but in the actual operation of the reflow oven heating welding, no matter the specification of the reflow oven or the model of the circuit board, the heat transfer effect in the model is slightly influenced. In order to make the result more accurate and closer to the real situation, the correction coefficient k can be introduced into the formula respectively₁、k₂Correcting the model; at the same time order

Represents T_H-T_SThe coefficient of (A) to (B),

To represent

Can see that a and b are unknown constants, and the model becomes

2.3 solving of the model

We have found, by observation (14), that the temperature difference (T) in the furnace_H-T_S)、

As key factors in the modelTherefore, the sensor temperature change curve model is a fourth order differential equation system, the unknown parameters are a and b, and the values of a and b are calculated by Matlab.

Due to the heater temperature T of each small temperature zone_HAs is known, we consider using the temperature difference (T) in the furnace_H-T_S) Instead of the sensor temperature T_SThen T can be obtained by fitting the rising phase data_SAn equation of order 4 with respect to time t, resulting in parameter b; then obtaining T by fitting the descending stage data_SA first order equation with respect to time t, resulting in parameter a, thereby establishing a complete closed-form solution for the sensor temperature profile.

Because more unknowns exist in the formula and effective numerical calculation cannot be carried out, an image formed by combining data points given by the accessory is drawn by means of Matlab, the image is observed to be in an irregular curve, and an approximate function expression of a data synthesis curve in the accessory is obtained through fitting in order to obtain the temperature change rate corresponding to the temperature difference of each point. As shown in fig. 3.

After many trials, when the cftool is used for six-degree polynomial fitting, the obtained time-temperature relationship curve is most fit with the original data (as shown in fig. 4).

At the moment, the derivative calculation is carried out on the function, so that the slope (namely the temperature change rate) of each time point is calculated, the difference value between the temperature and the temperature in the furnace at each time point is calculated, the rising stage and the falling stage of the function are respectively separated and sorted in an ascending order, and the slope and the temperature difference of each time point are in one-to-one correspondence to form a new image. A two-end fitting function is derived (as shown in fig. 5 and 6):

as can be seen from the fitting result parameters, the quartic coefficient of the fitting function image in the rising stage with respect to the temperature difference is-3.458 e^-9The coefficient of the first order term of the fit function image with respect to the temperature difference during the descent phase is 0.00598.

In the above description, a is the coefficient of the first order term in g (x), and b is the coefficient of the fourth order term in f (x). The above formula has cubic, quadratic and constant terms, but because the coefficient and the value are small, the positive and negative mutual cancellation influence is not large in the actual calculation, and the formula is regarded as a negligible term. The coefficients are respectively substituted into equation (14), and a function image is drawn by using Matlab for verification (as shown in fig. 7):

solving of technical problem one

Substituting known parameters such as the set temperature of the small temperature zone in the first technical problem into a formula (15) or a formula (16), calculating the temperature of a welding center area at corresponding time intervals of 0.5s, taking the time of the circuit board entering a reflow furnace or the displacement distance of the circuit board as an X axis, taking the actual temperature of the welding center area as a Y axis, drawing a furnace temperature curve, and drawing a furnace temperature curve as shown in figure 8:

the point temperature in the small temperature zone 3 can be obtained by solving the reflow furnace temperature curve model: 119.85 ℃, midpoint temperature of the small temperature zone 6: 169.06 ℃, middle point temperature of the small temperature zone 7: 187.56 ℃, temperature of the ending region of the small temperature region 8: 222.51 ℃ as shown in Table 2.

TABLE 2 actual temperature corresponding to different small temperature zones

In order to verify the accuracy of the result, the time of the abscissa is converted into the displacement distance of the circuit board, the measured temperature in the accessory and the data obtained by calculation in the first technical problem are refitted and compared, and the image is observed to know that the calculation result is closer to the measured data (as shown in fig. 9).

Example 2: solving a technical problem II:

after comprehensive consideration, the present application searches for an optimal solution by traversing the reflow oven temperature curve model obtained in embodiment 1, and under the condition that the set values of the temperatures of the 4 temperature zones are 182 ℃ (the small temperature zones 1-5), 203 ℃ (the small temperature zone 6), 237 ℃ (the small temperature zone 7), and 254 ℃ (the small temperature zones 8-9), it is ensured that the process is qualified (as shown in table 3): the threshold temperature for reflow soldering of the electronic board in this example is 217 ℃.

TABLE 3 Process limits

Matlab was used to perform 35 cycles of simulation, calculating floating point numbers with the help of python commands, and finally calculating the maximum speed of the conveyor belt in the reflow oven to be 73cm/min (as shown in FIG. 10).

Example 3: solution of technical problem three

The present embodiment adopts a genetic algorithm to solve, which comprises the following steps (logic is shown in fig. 11):

the first step is as follows: setting a genetic algebra of 400 times, an initial population number of 50, 5 individual genes (respectively representing the speed of an electronic board and the temperature of small temperature areas of 1-5, 6, 7 and 8-9), and a surrounding image area as an adaptive value, a cross probability of 0.8 and a mutation probability of 0.1;

the second step is that: initializing a population, randomly distributing gene values for 50 individuals, simultaneously carrying out cross and variation judgment, and combining an original population, two sub-populations generated by cross and a variation population into a whole;

the third step: calculating adaptive values of all individuals in the population, and simultaneously judging process limitation to screen out a part of individuals which do not accord with the process;

the fourth step: sorting the population in ascending order according to the adaptive value, simulating natural selection by using a roulette model under the condition of more population quantity, and randomly screening out a part of individuals until the population quantity is less than or equal to the initial population;

the fifth step: and randomly supplementing the population with too small number, wherein the generation rule is consistent with the initial population, and performing the next iteration.

According to the algorithm principle, the final population should have a normalization phenomenon, namely only one or a plurality of similar individuals in the population. In the experiment, part of the initial population and the final population are as follows:

table 4 technical problem three initial population and partial final population data comparisons

As is clear from FIG. 12 and Table 4, the adaptive value was the highest and the image area was the smallest 724.68 when the speed was 86cm/min and the four temperature values were 179 ℃, 196 ℃, 232 ℃ and 265 ℃.

Example 4: solution of technical problem four

The ideal goal is to have the image symmetrical about the center line of the highest point based on the technical problem three, i.e. to ensure that the difference between the areas of the left and right images is as small as possible. And (4) solving by adopting a multi-objective optimization genetic algorithm, wherein the basic steps are the same as the step (3). (logical process as shown in FIG. 13):

the third step: setting the area difference value of the left and right graphs as a second adaptive value, respectively calculating two adaptive values of each individual, and simultaneously screening the individuals which do not accord with the process limitation.

The fourth step: the population is respectively sorted according to the first adaptive value and the second adaptive value to form two new populations, the first half individuals of the two populations are taken to form the new populations, natural selection is simulated by using a sorting algorithm, and a part of individuals are randomly screened out until the population quantity is less than or equal to the initial population;

TABLE 5 initial population by population data comparison of technical problem four with partial final population data comparison

As is clear from Table 5 and FIG. 14, when the speed is 79cm/min and the four temperature values are 168 deg.C, 188 deg.C, 225 deg.C and 265 deg.C, respectively, the overall adaptive value is the highest, and the difference between the left and right pattern areas is 491.02.

Example 5: improvement of genetic algorithm for problem III

In order to accelerate the calculation convergence speed and reduce the running time, the following improvements are made on the basis of the problem three genetic algorithm:

1. because the optimal individual can be screened out in the roulette mode with a certain probability, the optimal solution can not be obtained finally, and the forced exclusion simulation natural selection is adopted, so that redundant and ranked individuals are directly excluded, and the optimal solution is protected to the maximum extent;

2. meanwhile, the concept of 'elite sense + selective right competition' is added, just like the selective mating rule of the human society, the mating rights of the society from the upper layer to the lower layer are reduced in sequence, and people at the bottommost layer cannot breed offspring due to economic problems, so that the size of the cross probability can be reflected in the algorithm. Therefore, the mating probability of individuals can be treated in a different way, the elite individuals directly enter the next iteration to ensure that the genotypes are not damaged due to crossing, and the underlying individuals are directly eliminated.

Through multiple tests, the improved genetic algorithm can generate data convergence only by 150 iterations, and the operation time is shortened to about 40% of the original operation time.

The method comprises the following specific steps:

the first step is as follows: setting a genetic algebra of 150 times, an initial population number of 50, 5 individual genes (respectively representing the speed of an electronic board and the temperature of small temperature areas of 1-5, 6, 7 and 8-9), and a surrounding image area as an adaptive value, a cross probability of 0.8 and a mutation probability of 0.1; (basic conditions are consistent with those before improvement except for the number of iterations)

The second step is that: the cross variation of the first generation population remained consistent with that before. If the population number is less than 30 from the second generation, random supplement is carried out, and the new individuals are ranked randomly but cannot become elite individuals. Then respectively considering the first ten and the last ten of the populations as elite individuals and bottom-layer individuals, wherein the elite individuals do not need to be crossed and directly enter the next iteration operation, and the bottom-layer individuals cannot be mated and are directly eliminated;

the third step: and (3) carrying out layering treatment on the ranking sequence of the middle individuals from high to low, wherein the cross probability of the first layer is unchanged, and the cross probabilities of the last two layers are 80% of the cross probability of the last layer in sequence.

The fourth step: the variation operation is kept unchanged, the original individuals and the new individuals generated by cross variation are combined into a new population after the variation is completed, the adaptive values of all the individuals in the population are calculated, meanwhile, the process limitation judgment is carried out, and a part of the individuals which do not accord with the process are screened out;

the fifth step: sorting the populations in ascending order according to the adaptive value, simulating natural selection by using a forced sorting method under the condition of more populations, randomly screening out a part of individuals until the population number is less than or equal to the initial population, and performing the next iteration;

according to the algorithm principle, the final population should have a convergence phenomenon, namely only one or a plurality of similar individuals in the population. In the experiment, part of the initial population and the final population are as follows:

TABLE 6 technical problem data comparison of three initial populations and partial final populations

The result can be obtained under the condition of less iteration times, and the effectiveness of the optimization operation is verified.

Example 6: model sensitivity analysis

And (3) carrying out sensitivity analysis according to the hypothesis of a mechanism model and data provided by the technical problem I, properly adjusting the speed of the circuit board and the set temperatures of the small temperature zones 1-5, 6, 7 and 8-9, and observing the change degree of the maximum value of the furnace temperature curve.

In the experiment, the five parameters are respectively processed by +/-1, and the variation degree of the maximum furnace temperature is calculated, so that the following results are obtained.

TABLE 7 sensitivity analysis parameter Table

Variation parameter	ΔT_max
		V_±1	0.614
T_1-5	0.237
		T₆	0.201
T₇	0.248
		T_8-9	1.281

Analysis shows that the set temperature of the small temperature zone 8-9 has the largest influence on the peak value of the curve, namely the sensitivity is the highest among all the parameters.

Example 7: model robustness analysis

Aiming at the steady analysis of the established model, the establishment of the model is based on the mutual verification of mechanism analysis and tool software, and the change rule of the furnace temperature curve is obtained by calculating the relationship between the temperature difference and the temperature change rate. Therefore, no matter the material of the reflow oven or the pcb is changed, the relevant parameters in the model can be used for practical calculation only by changing the relevant parameters, so that the model has strong robustness.

In summary, the temperature and the speed of the conveyor belt of each temperature zone provided by the data and the theme stem given in the accessory can be simply processed, the change situation of the center temperature of the welding area along with the time under the temperature of the corresponding temperature zone is expressed by the heat transfer efficiency, the data in the accessory shows that the heat transfer efficiency along with the temperature change is positively correlated, namely the heat transfer coefficient M of the heat transfer efficiency under different temperatures is a fixed value, the fixed value M is solved, the center temperature of the welding area is solved every 0.5s, and the corresponding furnace temperature curve is drawn according to the solved data. After a correct furnace temperature curve is obtained, the proportional coefficient of the thermal effect parameter can be calculated according to a related formula of the heat transfer effect, so that the corresponding relation between the temperature rise rate of the sensor, the environmental change temperature and the speed of the conveyor belt in the heating welding process is further deduced, a specific temperature-time slope equation is listed, and great convenience can be provided for solving the technical problems I, II and III.

Because the circuit board model provided by the application does not provide specific parameters and the volume of the circuit board is too small, the thermal resistance condition is not considered in the application, so the heat transfer mode only considers the thermal convection and the thermal radiation [ Sunky. design research [ D ].2015 ] of the PLC-based multi-temperature-zone reflow oven control system ]. Meanwhile, the temperature near the boundary of each temperature zone is also possibly influenced by the temperature of the adjacent temperature zone.

Aiming at the technical problem I, Matlab is used for modeling and fitting data to obtain a normalized heat transfer coefficient, and the normalized heat transfer coefficient is substituted into an equation to be solved. Or three heat transfer modes are precisely considered, classified modeling is carried out in two stages of the reflow oven process, and influence factors are found out and substituted for solving.

Aiming at the second technical problem, the known parameters in the first technical problem are combined, the optimal solution is searched by traversing a formula, the process limitation is ensured to be met, and the optimal solution can be obtained after the cyclic simulation operation is carried out.

Aiming at the third technical problem, a single-target genetic algorithm is adopted for solving, the adaptive populations are sorted in an ascending order according to adaptive values, and natural selection is simulated by utilizing a sorting algorithm, cross variation, catastrophe and the like.

And aiming at the fourth technical problem, a multi-target genetic algorithm is adopted for solving, and the steps are basically consistent with the third technical problem.

The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby, and any modification made on the basis of the technical scheme according to the technical idea proposed by the present invention falls within the protection scope of the present invention; the technology not related to the invention can be realized by the prior art.

Claims

1. The speed and temperature control method of the conveyor belt of the reflow oven is characterized by comprising the following steps of:

(1) establishing a furnace temperature curve model: in the whole process that the circuit board passes through the reflow oven through the conveyor belt, the actual temperature at a group of welding points is obtained through actual measurement of a heat sensor, the actual temperature is increased and then decreased along with the change of time, and the piecewise function relation between the heat effect and the oven temperature curve slope is obtained by integrating the lifting stage as follows:

(2) respectively introducing a correction coefficient k into the piecewise function₁、k₂Correcting the model; at the same time order

Represents T_H-T_SThe coefficient of (A) to (B),

To represent

A and b are unknown constants, and the scoring segment function is as follows:

(3) solving a piecewise function model: obtaining T by fitting the rise phase data according to the accessory data and the known temperature zone setting conditions by utilizing the proportional relation between the temperature change rate and the temperature difference_SAn equation of order 4 with respect to time t, resulting in parameter b; then obtaining T by fitting the descending stage data_SA first order equation related to the time t, so as to obtain a parameter a, and thus, establishing a complete closed-form solution of the temperature change curve of the thermal sensor;

(4) the set temperature of each small temperature zone is compared with the temperature difference (T) of the current sensor_H-T_S) Respectively substituting the temperature change rates into piecewise functions to calculate the temperature change rate of spot welding central areas at corresponding time intervals of 0.5s, adding the temperature change rates to the current temperature to obtain the changed temperature, drawing a furnace temperature curve by taking the time of the circuit board entering a reflow furnace as an X axis and the actual temperature of the welding point central areas as a Y axis, and solving the temperature of the middle point of each small temperature area or the temperature of the ending area of the small temperature area by adopting a furnace temperature curve model;

(5) meanwhile, the displacement distance of the circuit board is used as an X axis, the temperature is used as a Y axis, the time temperature given by the accessory data is drawn and compared with the image of the data calculated by using a formula, and therefore the accuracy of the piecewise function is verified;

(6) under the condition of ensuring to accord with the process limit, carrying out cycle simulation operation by using Matlab according to the variable speed value, and finally calculating the maximum speed of the conveyor belt in the reflow furnace according to the set value of the temperature of each temperature zone;

(7) solving a furnace temperature curve by using a genetic algorithm to obtain the speed of a conveyor belt with the highest adaptive value and the set temperature of a small temperature zone;

(8) the concept of 'elite sense + coupling right competition' is introduced to improve the genetic algorithm and accelerate the operation convergence speed of the result.

2. The conveyor speed and temperature control method for the reflow oven of claim 1, wherein: when the conditions 1 and 2 are met, solving a furnace temperature curve by using a genetic algorithm to obtain the speed of the conveyor belt with the highest adaptive value and the set temperature of the small temperature zone; the condition 1 is a process limit, and the condition 2 is that furnace temperature curves with the peak temperature as a central line and two sides exceeding threshold temperatures are symmetrical as much as possible.

3. The conveyor speed and temperature control method for the reflow oven as set forth in claim 1 or 2, wherein: the solving process of the genetic algorithm in the step (7) is as follows:

the first step is as follows: setting genetic algebra 400 times, initial population quantity 50, individual genes 5, image area enclosed by the genetic algebra as an adaptive value, cross probability 0.8 and mutation probability 0.1; the 5 individual genes respectively represent the speed of the electronic board and the set temperatures of the small temperature areas 1-5, 6, 7 and 8-9;

the fourth step: sorting the population in an ascending order according to the adaptive value, simulating natural selection by using a roulette model, and randomly screening a part of individuals until the population quantity is less than or equal to the initial population;

4. The conveyor speed and temperature control method for the reflow oven of claim 1, wherein: the method also comprises the step of verifying the accuracy of the result in the step (4), and comprises the following steps: and converting the time of the abscissa into the displacement distance of the circuit board, refitting and comparing the actual temperature of a group of welding points obtained by the actual measurement of the heat sensor with the temperature of the welding center area of the spot welding at the corresponding time interval of 0.5s obtained by the calculation of the piecewise function, and observing whether the image is fitted or not.

5. The conveyor speed and temperature control method for the reflow oven of claim 1, wherein: the specific method of the step (3) is as follows:

with the aid of the actual measurement data (measurement times and temperatures associated therewith) given in the accessory, the set temperature T for each individual small temperature zone_HKnown by the set temperature T_HMinus the sensor temperature T_STo obtain the temperature difference (T) in the furnace_H-T_S)；

Secondly, as can be seen from the above formula, the temperature change law is different between the rising and falling stages of the temperature, so that the time points and the temperature difference (T) between the rising and falling stages_H-T_S) Dividing the images into two groups, drawing linear images, and respectively calculating the slope k of each point of the images, namely the temperature change rate;

③ because the temperature change rate and the temperature difference are in positive correlation, and because of the arrangement of the temperature zone, the temperature difference of each point is not linearly increased, so two groups of temperature differences (T) are formed_H-T_S) And the temperature change rate k is sorted in an ascending order, and then two linear increasing images are respectively drawn so as to show the relationship between the temperature difference and the temperature change rate;

fourthly, performing polynomial fitting on the images of the two groups of data by using cftool of MATLAB: in the rising stage, a quartic relation between the temperature change rate and the temperature difference is obtained by utilizing quartic fitting; in the descending stage, a primary relational expression of the temperature change rate and the temperature difference is obtained;

at this time, k is obtained by fitting the rising stage data_H-T_S) To 4, thereby obtaining a parameter b; then obtaining k related to (T) by fitting the falling phase data_H-T_S) To obtain the a parameter, thereby establishing a complete closed-form solution of the temperature change curve of the thermal sensor.

6. The conveyor speed and temperature control method for the reflow oven of claim 5, wherein: the fitting method is characterized in that the cftool is used for carrying out sextic polynomial fitting, and the obtained time-temperature relation curve is most fit with the original data and is low in function complexity.

7. The method according to step (5) of claim 1, wherein the principle is as follows: under different set speeds, the furnace temperature curve length taking time as a vertical axis is not consistent; the speed is converted into the displacement, and the displacement of the sensor is taken as a longitudinal axis, so that the consistent furnace temperature curve lengths at different set speeds can be ensured, and the difference between the furnace temperature curve subjected to function fitting and a real data curve can be compared more intuitively.

8. The conveyor speed and temperature control method for the reflow oven of claim 3, wherein: the improvement of genetic algorithm in the step (8), which comprises the steps of:

The second step is that: the cross variation of the first generation population is consistent with that before; if the number of the population is less than 50 from the second generation, random supplementing is carried out, the new individuals are ranked randomly but cannot enter the first ten, then the first ten and the last ten in the population are respectively regarded as elite individuals and bottom-layer individuals, wherein the elite individuals do not need to be crossed and directly enter the next iteration operation, and the bottom-layer individuals cannot be mated and are directly eliminated;

the third step: carrying out layering treatment on the middle individual ranking sequence from high to low, wherein the cross probability of the first layer is unchanged, and the cross probabilities of the last two layers are 80% of that of the last layer in sequence;

the fifth step: and sorting the populations in an ascending order according to the adaptive values, simulating natural selection by using a forced sorting method under the condition of a large population number, randomly screening out a part of individuals until the population number is less than or equal to the initial population, and performing the next iteration.

9. The conveyor speed and temperature control method for the reflow oven of claim 8, wherein: the improvement of the genetic algorithm in the second step and the third step is that on the basis of the original algorithm, in the iterative process, the individuals ranked in the top ten of each population are regarded as elite individuals, and because the elite individuals are likely to be close to the optimal solution, the elite individuals do not participate in cross calculation to avoid damaging the genotype and are directly copied to the next generation; the latter ten individuals of each population are regarded as bottom-layer individuals, and are far from the optimal solution, so that the bottom-layer individuals do not participate in crossing, and the method is equivalent to depriving the chance-selecting right; for the middle individuals, the cross probability is reduced in sequence according to the ranking sequence, and the generated new generation of sub-individuals enter the next iteration.

10. The conveyor speed and temperature control method for the reflow oven according to claim 8 or 9, characterized in that: and in the fifth step, regarding the improvement of the genetic algorithm, the original roulette model is replaced by a forced exclusion method, and individuals with lower adaptive values are directly excluded so as to avoid losing better solutions.