PCBA manufacturing process optimization method, apparatus and device, and storage medium

By dividing the PCBA mounting point dataset into multiple mounting cycle subsets and using a deep learning model for feature encoding and decoding, the problem of poor mounting path optimization caused by relying on manual rules in existing technologies is solved. This enables automatic adjustment of optimization strategies based on PCB board characteristics, thereby shortening the mounting path.

CN121598754AInactive Publication Date: 2026-03-03SHENZHEN CMY OPTIMAL PRECISION ELECTRONICS CO LTD
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Patent Information

Application Number
CN202511680368.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-17
Publication Date
2026-03-03
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing PCBA mounting process optimization methods rely too heavily on manual rules, which cannot effectively capture the global feature relationships of mounting points, resulting in poor mounting path optimization performance and weak generalization ability.

Method used

By acquiring the placement point dataset of the PCB to be assembled, the placement point dataset is divided into multiple placement cycle subsets according to the number of placement heads of the placement machine and the distribution of component types. Then, a deep learning model is used for feature encoding and decoding to determine the matching relationship between placement points and placement heads, thereby optimizing the placement path.

Benefits of technology

It automatically learns the global feature relationships and constraints between placement points, and automatically adjusts the optimization strategy according to the characteristics of different PCB boards to shorten the placement path length and improve the optimization effect of the placement path.

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Abstract

The invention provides a PCBA manufacturing process optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining a mounting point data set of a to-be-assembled PCB, and dividing the mounting point data set into a plurality of mounting period subsets according to the number of mounting heads of a chip mounter and the type distribution of components; inputting the mounting period subset into a deep learning model, performing feature coding on the mounting point data through an encoder to obtain a feature matrix, and sequentially determining a matching relationship between each mounting point and a mounting head through a decoder according to the feature matrix to obtain a mounting point distribution scheme; and sorting the mounting points in each mounting period subset according to coordinate information and mounting angle information of each mounting point in the mounting point distribution scheme to obtain an execution sequence of the mounting head. According to the method, the global feature relationship and constraint conditions between the mounting points are automatically learned through the deep learning model, the optimization strategy is automatically adjusted according to the characteristics of different PCBs, and the mounting path length is shortened.
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