CN-121357813-B - Automatic assembly control method for PCBA production

CN121357813BCN 121357813 BCN121357813 BCN 121357813BCN-121357813-B

Abstract

The application provides an automatic assembly control method for PCBA production, which relates to the technical field of optimization control, and comprises the steps of collecting solder paste images after solder paste printing is performed on a target bonding pad in the PCBA production process, and identifying and obtaining solder paste form information; performing fitting assembly parameter configuration of a target element to obtain a first fitting assembly parameter, and performing reflow soldering positioning prediction by combining solder paste form information to obtain a first positioning parameter; and performing iterative optimization of the fitting assembly parameters to obtain optimal fitting assembly parameters with optimal positioning parameters, and performing fitting assembly control of the target element. The technical problem that the component position deviation is difficult to control when the surface tension is generated by melting the soldering paste in the traditional PCBA assembly control method in the prior art is solved.

Inventors

LIU DERONG
Ou Zhaolun
FANG MIN
CHEN YAN
WANG YIFENG
ZHU XI

Assignees

广东英创立科技有限公司

Dates

Publication Date: 20260512
Application Date: 20251118

Claims (8)

1. An automated assembly control method for PCBA production, the method comprising: in the PCBA production process, after solder paste printing is carried out on a target bonding pad, a solder paste image is acquired, and the form information of the solder paste is identified and acquired; Performing fitting assembly parameter configuration of a target element to obtain a first fitting assembly parameter, and performing reflow positioning prediction by combining the solder paste form information to obtain a first positioning parameter; Performing iterative optimization of the fitting assembly parameters to obtain optimal fitting assembly parameters with optimal positioning parameters, and performing fitting assembly control of the target element; the method for obtaining the first fitting parameters comprises the steps of performing fitting parameter configuration of a target element, performing reflow soldering positioning prediction by combining the solder paste form information to obtain the first positioning parameters, and comprises the following steps: Acquiring initial fitting parameters of fitting and assembling the current target element on the target bonding pad as first fitting and assembling parameters, wherein each fitting and assembling parameter comprises fitting and assembling control coordinates; obtaining a reflow soldering positioning predictor; Inputting the first fitting assembly parameters and solder paste form information into the reflow positioning predictor, and outputting to obtain first positioning parameters, wherein the first positioning parameters comprise positioning error parameters; wherein, obtain reflow soldering location predictor, include: Collecting a sample soldering paste form information set and a sample fitting assembly parameter set according to process record data of reflow soldering of the same bonding pad and the same element as the target bonding pad and the same element; collecting solder paste form information of different samples and distances between solder pad mark points and element mark points after reflow soldering under sample fitting assembly parameters, marking the distances as sample positioning parameters, and obtaining a sample positioning parameter set; constructing an underlying network structure of the reflow soldering positioning predictor based on machine learning; and taking the sample solder paste form information set and the sample fitting assembly parameter set as input data, taking the sample positioning parameter set as supervision data, performing supervision training and testing on the reflow positioning predictor, and obtaining the reflow positioning predictor after the test is qualified.
2. The automated assembly control method for PCBA production according to claim 1, wherein during PCBA production, after solder paste printing on the target pads, collecting solder paste images, identifying and obtaining solder paste morphology information, comprises: In the PCBA production process, after solder paste printing is carried out on a target bonding pad, a solder paste image is collected; Acquiring a solder paste form identifier; And inputting the solder paste image into the solder paste form identifier, and identifying and outputting to obtain solder paste form information, wherein the solder paste form information comprises solder paste amounts of a plurality of target pad areas in the target pad.
3. The automated assembly control method for PCBA production of claim 2, wherein obtaining a solder paste morphology identifier comprises: Collecting a sample soldering paste image set according to similar welding data records of a target bonding pad and a target element; Dividing a target bonding pad into a plurality of target bonding pad areas, and marking the solder paste quantity in the plurality of target bonding pad areas in each sample solder paste image to obtain a sample solder paste form information set; constructing a solder paste form identifier based on deep learning; And performing supervised training and verification on the solder paste form identifier by adopting the sample solder paste image set and the sample solder paste form information set, and completing training after convergence.
4. The automated assembly control method for PCBA production of claim 1, wherein performing iterative optimization of the fit assembly parameters to obtain optimal fit assembly parameters with optimal positioning parameters, performing fit assembly control of the target component comprises: Acquiring a fitting parameter space for fitting and fitting a target element in a target bonding pad; Randomly adjusting the first fitting assembly parameters in the fitting assembly parameter space to obtain second fitting assembly parameters; the second fitting assembly parameters are adopted, and the reflow soldering positioning predictor is input by combining solder paste form information, and the second positioning parameters are output and obtained; and continuing to perform iterative optimization of the fitting assembly parameters, obtaining the optimal fitting assembly parameters with the minimum positioning parameters, and performing fitting assembly control of the target element.
5. The automated assembly control method for PCBA production of claim 4, wherein continuing the iterative optimization of the fit parameters to obtain optimal fit parameters with minimum positioning parameters, performing fit control of the target component comprises: obtaining standard solder paste form information and preset iteration optimization times; Calculating error coefficients of the solder paste form information and the standard solder paste form information, wherein error magnitudes of solder paste amounts in a plurality of target pad areas are calculated respectively, and an average value is calculated as the error coefficient; calculating and obtaining solder paste quantity dispersion according to the solder paste quantity of the plurality of target pad areas; Acquiring a reference solder paste quantity dispersion, and calculating the ratio of the solder paste quantity dispersion to the reference solder paste quantity dispersion as a dispersion coefficient; According to the error coefficient and the discrete coefficient, adjusting and calculating the preset iterative optimization times to obtain iterative optimization times; And continuing to perform iterative optimization of the fitting assembly parameters until the iterative optimization times are reached, acquiring the optimal fitting assembly parameters with the minimum positioning parameters in the optimization process, and performing fitting assembly control of the target element.
6. An automated assembly control system for PCBA production, for performing the method of any one of claims 1-5, comprising: The image acquisition module is used for acquiring a solder paste image after the solder paste printing is carried out on the target bonding pad in the PCBA production process, and identifying and acquiring the solder paste form information; the positioning prediction module is used for configuring the fitting assembly parameters of the target element to obtain first fitting assembly parameters, and combining the solder paste form information to perform reflow soldering positioning prediction to obtain first positioning parameters; And the optimizing output module is used for performing iterative optimization on the fitting assembly parameters, obtaining the optimal fitting assembly parameters with the optimal positioning parameters, and performing fitting assembly control on the target element.
7. An electronic device, comprising: A memory for storing a computer software program; A processor for reading and executing the computer software program to further implement an automated assembly control method for PCBA production as claimed in any one of claims 1 to 5.
8. A computer readable storage medium, wherein a computer program is stored in the storage medium, which when executed by a processor, implements an automated assembly control method for PCBA production according to any one of claims 1-5.

Description

Automatic assembly control method for PCBA production Technical Field The invention relates to the field of optimization control, in particular to an automatic assembly control method for PCBA production. Background In the PCBA production process, surface tension generated after solder paste melts in a reflow soldering process is easy to cause the position deviation of the element relative to the bonding pad, the deviation not only can cause the position deviation of the element and the bonding pad, but also can destroy the preset electric connection path of the circuit board, cause functional faults such as abnormal signal transmission, short circuit and the like, and reduce the production yield and the long-term reliability of products. However, the conventional PCBA assembly control method only sets initial fitting parameters based on fixed process standards, does not take into account differences in actual form after each solder paste print, and lacks pre-judgment and active deviation control capability of the positional deviation, so that the positional deviation of the component is difficult to control when the solder paste melts to generate surface tension. Therefore, there is a need for an automated assembly control method for PCBA production that reduces the risk of reflow soldering offset from the source, guaranteeing high precision assembly requirements. Disclosure of Invention The invention provides an automatic assembly control method for PCBA production, which aims at the technical problem that the component position deviation is difficult to control when the surface tension is generated by melting solder paste in the traditional PCBA assembly control method in the prior art. The technical scheme for solving the technical problems is as follows: In a first aspect, the present invention provides an automated assembly control method for PCBA production, comprising: in the PCBA production process, after solder paste printing is carried out on a target bonding pad, a solder paste image is acquired, and the form information of the solder paste is identified and acquired; Performing fitting assembly parameter configuration of a target element to obtain a first fitting assembly parameter, and performing reflow positioning prediction by combining the solder paste form information to obtain a first positioning parameter; and performing iterative optimization of the fitting assembly parameters to obtain optimal fitting assembly parameters with optimal positioning parameters, and performing fitting assembly control of the target element. In a second aspect, the present invention provides an automated assembly control system for PCBA production, comprising: The image acquisition module is used for acquiring a solder paste image after the solder paste printing is carried out on the target bonding pad in the PCBA production process, and identifying and acquiring the solder paste form information; the positioning prediction module is used for configuring the fitting assembly parameters of the target element to obtain first fitting assembly parameters, and combining the solder paste form information to perform reflow soldering positioning prediction to obtain first positioning parameters; And the optimizing output module is used for performing iterative optimization on the fitting assembly parameters, obtaining the optimal fitting assembly parameters with the optimal positioning parameters, and performing fitting assembly control on the target element. In a third aspect, the present invention provides an electronic device, comprising: A memory for storing a computer software program; a processor for reading and executing the computer software program to further implement an automated assembly control method for PCBA production as described in any one of the first aspects. In a fourth aspect, the present invention provides a computer readable storage medium having a computer program stored therein, which when executed by a processor implements an automated assembly control method for PCBA production as described in the first aspect. The beneficial effects of the invention are as follows: compared with the prior art, the method and the device have the advantages that firstly, in the PCBA production process, after solder paste printing is carried out on the target bonding pad, the solder paste image is collected, the solder paste form information is identified and obtained, and key data support is provided for follow-up reflow soldering positioning prediction and fitting assembly parameter optimization. And thirdly, configuring the fitting assembly parameters of the target element to obtain a first fitting assembly parameter, carrying out reflow positioning prediction by combining the solder paste form information to obtain the first positioning parameter, and predicting the positioning error after reflow in advance to provide a reliable error prediction model for iterative optimization of the subsequent fitting