CN111385980B - Particle swarm-based PCB (printed Circuit Board) surface mounting method - Google Patents
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Abstract
The invention discloses a particle swarm-based PCB (printed circuit board) surface mounting method, which comprises the following steps of: classifying the particle swarm into a plurality of sub-populations according to the types of the electronic components, and initializing the position and the speed of each particle in the particle swarm; constructing a restriction function to restrict the searching mode of the particles according to the patch requirement of the PCB; replacing the initialized particles with electronic components according to a limiting function, and optimizing and updating the particles by adopting a particle swarm optimization algorithm to obtain an optimal distribution map of the electronic components; the PCB simulation module utilizes relevant software to manufacture a PCB diagram according to the optimal electronic component distribution diagram, and then manufactures a PCB without surface mounted components according to the PCB diagram; obtaining an optimal patch path by utilizing a CSO optimization algorithm according to a first-large-last-small component patch rule; and the micro control system controls a mechanical arm of the chip mounter to pick up the electronic components on the PCB without the chip components according to the optimal chip mounting path to carry out chip mounting, so that the PCB with the good chip mounting is obtained.
Description
Technical Field
The invention relates to the technical field of PCB (printed circuit board) surface mounting, in particular to a particle swarm based PCB surface mounting method.
Background
Compared with the base station quantity scale of million levels in the 4G era, the millimeter wave development can push the base station scale of the 5G era to break through the million levels. It is expected that with the gradual arrival of the 5G comprehensive commercial era, the mass construction and upgrading and updating of the communication base station will form a mass demand for high-frequency and high-speed boards such as PCB board factories, and the PCB circuit board will meet the demand of a new round of upgrading and replacing.
The number of the base stations and the value of a single base station are comprehensively considered for estimation, and the market space brought by the 5G base station to the multilayer PCB is more than 4-5 times of that of the 4G base station.
Meanwhile, the 5G network can bear larger bandwidth flow, the investment of the router, the switch, the IDC and other equipment is increased, and the demand of the high-speed PCB/copper-clad plate is greatly increased. Besides the increase of the required consumption, high-performance equipment adopts high-frequency (for antenna) and high-speed (for IDC/base station) plate materials with higher added values, and the added value and the consumption of the multilayer PCB/copper-clad plate industrial chain are increased.
According to data, the benefit of the large increase in demand, the global printed circuit board factory industry is accelerating, and the domestic industry is accelerating. According to the statistics of prism, the global PCB multi-layer circuit board production value in 2017 is 588.43 billion dollars, and the increase is 8.6%; wherein, the production value of Chinese PCB circuit board factory is 297.32 billion dollars, and the year-on-year increase is 9.9%.
The Prismark forecast benefits from structural upgrading and the explosion of increment requirements in the fields of communication, computers, consumer electronics and automotive electronics, the yield increase rate of a global smart phone PCB and an automotive PCB factory board in 2016 + 2021 year is 5% -8%, which is much higher than that of a PCB factory in other fields, the upward period of a PCB line kht lasts about 7 years, and the yield of the Chinese PCB factory industry breaks through $ 500 billion by 2021 year.
Therefore, it is therefore apparent that the demand for PCB boards must increase exponentially in the future. In the face of such a great demand, how to rapidly and accurately manufacture the PCB becomes an unavoidable problem while saving the cost, however, the conventional PCB mainly depends on manual experience in design, which greatly limits rapid design of excellent PCB layout, and the conventional mechanical arm pasting method is difficult to find the optimal pasting method, so that the production efficiency is low, which is undoubtedly the greatest loss of benefits in industry.
Disclosure of Invention
The invention aims to overcome the defects in the background technology, and provides a particle swarm-based PCB (printed circuit board) surface mounting method, which solves the problem of PCB manufacturing cost, saves manufacturing time and has better accuracy.
The technical scheme for realizing the purpose of the invention is as follows:
a particle swarm-based PCB (printed Circuit Board) surface mounting method comprises the following steps:
1) classifying the particle swarm into a plurality of sub-populations according to the types of the electronic components, and initializing the position and the speed of each particle in the particle swarm;
2) constructing a restriction function to restrict the searching mode of the particles according to the patch requirement of the PCB;
3) replacing the initialized particles with electronic components according to the restriction function constructed in the step 2), and optimizing and updating the particles by adopting a particle swarm optimization algorithm to obtain an optimal distribution map of the electronic components, wherein the optimization formula is as follows:
Vi(t+1)＝ωVi(t)+c1R1(t)(pbesti(t)-Xi(t))+c2R2(t)(gbest(t)-Xi(t)) (1)
Xi(t+1)＝Xi(t)+Vi(t+1) (2)
in the above formula Vi(t) is the velocity of the ith particle at the time of the t-th evolution, Xi(t) is the position of the ith particle at the time of the tth evolution, pbesti(t) is the optimal position of the ith particle in the previous t iterations, and gbest (t) is the position of the optimal particle among all particles in the population in the t evolution; omega is an updating weight and is used for controlling the speed of the particles; c. C1、c2、R1And R2Is a control parameter;
4) the PCB simulation module utilizes relevant software to manufacture a PCB diagram according to the optimal electronic component distribution diagram obtained in the step 3), and then manufactures a PCB without a surface mounted component according to the PCB diagram;
5) according to the first-big-last-small component patch rule, a CSO optimization algorithm is used for obtaining an optimal patch path, and the formula of a CSO optimizer is as follows:
Xl,k(t+1)＝Xl,k(t)+Vl,k(t+1) (4)
Xl,k(t) position of failed particle in t-th evolution in kth competition, Xw,k(t) represents the position of the winning particle in the t-th evolution in the kth competition,
represents the average position of all particles in the population; ri(i is 1, 2, 3) is a random number generated each time, and R isiThe value range is [0, 1 ]]Control parameters
The value range is [0.05, 0.2 ]]；
6) And the micro control system controls a mechanical arm of the chip mounter to pick up the electronic component on the PCB without the chip component according to the optimal chip mounting path obtained in the step 5) to carry out chip mounting, so that the PCB with the chip mounted is obtained.
In step 2), the searching mode comprises the following modes:
a. the core components are arranged firstly according to the arrangement principle of 'big first, small first and difficult first and easy later';
b. the total connecting line is as short as possible, and the key signal line is short;
c. high voltage, heavy current signal and low voltage, low current signal are totally separated;
d. the heating software is evenly distributed on the PCB, so that the over-high temperature of a certain area of the PCB is prevented;
e. right-angle and acute-angle wiring is avoided;
f. some space is left around the components to be tested for later testing.
In step 3), the particle swarm optimization algorithm comprises the following steps:
3-1) generating initialized particle swarm;
3-2) clustering the particles according to components, dividing the original particle swarm into a plurality of subgroups, and then independently optimizing each subgroup by adopting formulas (1) and (2);
3-3) judging whether an optimization result is achieved, if so, entering the next stage, otherwise, repeating the steps 3-1) and 3-2) to perform clustering analysis on the particle swarm again and optimizing until the requirements are met.
In step 5), the optimization process of the optimal patch path comprises the following steps:
5-1) initializing the position and speed of the particle swarm;
5-2) calculating an adaptive value of each particle in the particle swarm, and updating the position and the speed of each particle according to the obtained adaptive value;
5-3) repeating the step 5-2) until the set iteration times are obtained;
5-4) outputting the optimal path of the patch method according to the latest position and speed of each particle.
According to the particle swarm-based PCB surface mounting method provided by the invention, the particle swarm-based PCB surface mounting has the characteristics of full automation, high precision, high reliability, time saving and cost saving. Therefore, the technology can reduce the PCB pasting time, improve the production efficiency and reduce the production cost to a certain extent.
Drawings
FIG. 1 is a flow chart of a particle swarm-based PCB (printed Circuit Board) mounting method of the invention;
FIG. 2 is a flow chart of optimizing a distribution map of electronic components using a particle swarm optimization algorithm;
FIG. 3 is a flow chart of a multi-cluster coevolution algorithm part in a particle swarm optimization algorithm;
fig. 4 is an optimization flowchart for selecting an optimal patch path.
Detailed Description
The invention will be further elucidated with reference to the drawings and examples, without however being limited thereto.
Example (b):
a particle swarm-based PCB (printed Circuit Board) pasting method is shown in figure 1 and comprises the following steps:
1) the particle group is classified into a plurality of sub-groups according to the type of the electronic component, and the position and speed of each particle in the particle group are initialized.
2) According to the patch requirement of the PCB, a limit function is constructed to limit the searching mode of the particles,
the searching mode comprises the following modes:
a. the core components are arranged firstly according to the arrangement principle of 'big first, small first and difficult first and easy later';
b. the total connecting line is as short as possible, and the key signal line is short;
c. high voltage, heavy current signal and low voltage, low current signal are totally separated;
d. the heating software should be evenly distributed on the PCB as much as possible to prevent the temperature of a certain area of the PCB from being too high;
e. right-angle and acute-angle wiring is avoided;
f. some space is left around the components to be tested for later testing.
3) Replacing electronic components with the initialized particles according to the limit function constructed in the step 2), taking each type of electronic components as an optimization target, and optimizing and updating the particles by adopting a particle swarm optimization algorithm to obtain an optimal distribution diagram of the electronic components, wherein the optimization formula is as follows:
Vi(t+1)＝ωVi(t)+c1R1(t)(pbesti(t)-Xi(t))+c2R2(t)(gbest(t)-Xi(t)) (1)
Xi(t+1)＝Xi(t)+Vi(t+1) (2)
in the above formula Vi(t) is the velocity of the ith particle at the time of the t-th evolution, Xi(t) is the position of the ith particle at the time of the tth evolution, pbesti(t) is the optimal position of the ith particle in the previous t iterations, and gbest (t) is the position of the optimal particle among all particles in the population in the t evolution; omega is an update weight used for controlling the speed of the particles; c. C1、c2、R1And R2Are control parameters.
As shown in fig. 2, the particle swarm optimization algorithm includes the following steps:
3-1) generating initialized particle swarm;
3-2) clustering the particles according to components, dividing the original particle swarm into a plurality of subgroups, and then independently optimizing each subgroup by adopting formulas (1) and (2);
3-3) judging whether an optimization result is achieved, if so, entering the next stage, otherwise, repeating the steps 3-1) and 3-2) to perform clustering analysis on the particle swarm again and optimizing until the requirements are met.
4) The PCB simulation module utilizes relevant software to produce a PCB diagram according to the optimal electronic component distribution diagram obtained in the step 3), for example, EASY EDA, Protel, Altium Designer, Allegro and the like can be adopted to draw a corresponding PCB diagram on a computer, and then a PCB without a surface mounted component is produced according to the PCB diagram.
5) According to the first-big-last-small component mounting rule, a CSO optimization algorithm is used for obtaining an optimal mounting path, and an optimizer formula of the CSO optimization algorithm is as follows:
Xl,k(t+1)＝Xl,k(t)+Vl,k(t+1) (4)
Xl,k(t) position of failed particle in t-th evolution in kth competition, Xw,k(t) represents the position of the winning particle in the t-th evolution in the kth competition,
represents the average position of all particles in the population; ri(i ═ 1, 2, 3) is the random number generated each time, and R isiThe value range is [0, 1 ]]The algorithm adopts competition mechanism in human society, and the control parameter
Dynamically changing throughout the evolution process, controlling parameters
The value range is [0.05, 0.2 ]]；
As shown in fig. 3, the particle clusters are divided into a plurality of subgroups according to the types of components. Firstly, the particles in each subgroup are optimized independently, and secondly, the optimized particles and each subgroup are mixed together again for cluster analysis, so that information exchange among the subgroups is formed. Therefore, the algorithm utilizes multiple groups of coevolution to surprise and optimize the original population; the method comprises the following steps: after all the particles in the population are initialized, the positions and the speeds of the particles are updated by using a formula (3) and a formula (4), and in the optimization, the restriction function is only one optimal path, so that an optimal patch path can be obtained in a short time, and the patch time of the mechanical arm can be reduced to a great extent.
As shown in fig. 4, the optimization process of the optimal patch path includes the following steps:
5-1) initializing the position and speed of the particle swarm;
5-2) calculating an adaptive value of each particle in the particle swarm, and updating the position and the speed of each particle according to the obtained adaptive value;
5-3) repeating the step 5-2) until the set iteration times are obtained;
5-4) outputting the optimal path of the patch method according to the latest position and speed of each particle. 6) And the micro control system controls a mechanical arm of the chip mounter to pick up the electronic component on the PCB without the chip component according to the optimal chip mounting path obtained in the step 5) to carry out chip mounting, so that the PCB with the chip mounted is obtained.
6) And the micro control system controls a mechanical arm of the chip mounter to pick up the electronic component on the PCB without the chip component according to the optimal chip mounting path obtained in the step 5) to carry out chip mounting, so that the PCB with the chip mounted is obtained.
Claims (4)
1. A particle swarm-based PCB (printed Circuit Board) surface mounting method is characterized by comprising the following steps:
1) classifying the particle swarm into a plurality of sub-populations according to the types of the electronic components, and initializing the position and the speed of each particle in the particle swarm;
2) constructing a restriction function to restrict the searching mode of the particles according to the patch requirement of the PCB;
3) replacing the initialized particles with electronic components according to the restriction function constructed in the step 2), and optimizing and updating the particles by adopting a particle swarm optimization algorithm to obtain an optimal distribution map of the electronic components, wherein the optimization formula is as follows:
Vi(t+1)＝ωVi(t)+c1R1(t)(pbesti(t)-Xi(t))+c2R2(t)(gbest(t)-Xi(t)) (1)
Xi(t+1)＝Xi(t)+Vi(t+1) (2)
in the above formula Vi(t) is the velocity of the ith particle at the time of the t-th evolution, Xi(t) is the position of the ith particle at the time of the tth evolution, pbesti(t) is the optimal position of the ith particle in the previous t iterations, and gbest (t) is the position of the optimal particle among all particles in the population in the t evolution; omega is an update weight used for controlling the speed of the particles; c. C1、c2、R1And R2Is a control parameter;
4) the PCB simulation module utilizes relevant software to manufacture a PCB diagram according to the optimal electronic component distribution diagram obtained in the step 3), and then manufactures a PCB without a surface mounted component according to the PCB diagram;
5) according to the first-big-last-small component patch rule, a CSO optimization algorithm is used for obtaining an optimal patch path, and the formula of a CSO optimizer is as follows:
Xl,k(t+1)＝Xl,k(t)+Vl,k(t+1) (4)
Xl,k(t) position of failed particle in t-th evolution in kth competition, Xw,k(t) represents the position of the winning particle in the t-th evolution in the kth competition,
represents the average position of all particles in the population; ri(i ═ 1, 2, 3) is the random number generated each time, and R isiThe value range is [0, 1 ]]Control parameters
The value range is [0.05, 0.2 ]]；
6) And controlling a mechanical arm of the chip mounter to pick up the electronic components on the PCB without the chip components according to the optimal chip mounting path obtained in the step 5) by the micro control system, and carrying out chip mounting on the PCB without the chip components according to the optimal chip mounting path to obtain the PCB with good chip mounting.
2. The particle swarm based PCB board mounting method according to claim 1, wherein in the step 2), the searching method comprises the following steps:
a. the core components are arranged firstly according to the arrangement principle of 'big first, small first and difficult first and easy later';
b. the total connecting line is as short as possible, and the key signal line is short;
c. high voltage, heavy current signal and low voltage, low current signal are totally separated;
d. the heating software is evenly distributed on the PCB, so that the over-high temperature of a certain area of the PCB is prevented;
e. right-angle and acute-angle wiring is avoided;
f. some space is left around the components to be tested for later testing.
3. The particle swarm-based PCB (printed Circuit Board) mounting method according to claim 1, wherein in the step 3), the particle swarm optimization algorithm comprises the following steps:
3-1) generating initialized particle swarm;
3-2) clustering the particles according to components, dividing the original particle swarm into a plurality of subgroups, and then independently optimizing each subgroup by adopting formulas (1) and (2);
3-3) judging whether an optimization result is achieved, if so, entering the next stage, otherwise, repeating the steps 3-1) and 3-2) to perform clustering analysis on the particle swarm again and optimizing until the requirements are met.
4. The particle swarm based PCB board mounting method according to claim 1, wherein in the step 5), the optimal mounting path is optimized by the following steps:
5-1) initializing the position and speed of the particle swarm;
5-2) calculating an adaptive value of each particle in the particle swarm, and updating the position and the speed of each particle according to the obtained adaptive value;
5-3) repeating the step 5-2) until the set iteration times are obtained;
5-4) outputting the optimal path of the patch method according to the latest position and speed of each particle.
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