SMT process prediction tool for intelligent decision making on PCB quality

Abstract

A surface mounted technology (SMT) process prediction tool for intelligent decision making on PCB quality is disclosed. A data fusion tool automatically reads size parameters and component information on design conditions of a printed board to be assembled from a database and creates a design condition list for different components for a software execution layer; a printing parameter decision-making toolkit and a soldering parameter decision-making toolkit in the software execution layer perform comparisons on printing and soldering data according to design conditions of components on the printed board to be assembled, perform automatic decision making on printing and soldering parameters with a multi-objective optimization algorithm and a deep learning algorithm, predict printing quality and soldering quality, and send decided printing and soldering process parameters and corresponding predicted quality results to a human-computer interaction toolkit, to visually display the printing and soldering process parameters and the predicted quality values.

Claims

1. A surface mounted technology (SMT) process prediction tool for decision making on printed circuit board (PCB) quality, comprising: software layers of an SMT decision-making toolkit which comprises: a data fusion toolkit; a printing parameter decision-making toolkit; a soldering parameter decision-making toolkit; and a human-computer interaction toolkit all in communication with a database, wherein a data fusion tool automatically reads size parameters and component information on design conditions of a printed board to be assembled from the database and creates a design condition list for different components for a software execution layer; wherein the printing parameter decision-making toolkit and the soldering parameter decision-making toolkit in the software execution layer perform comparisons with prestored printing and soldering data of components under conditions of different printed boards according to design conditions of components on the printed board to be assembled, wherein data mining is performed to obtain one or more groups of data of alternative satisfactory printing parameter combinations and soldering parameter combinations for components respectively, wherein automatic decision making is performed on printing and soldering parameters with a multi-objective optimization algorithm and a deep learning algorithm, wherein printing quality and soldering quality is predicted according to size parameters and component information in a printed board design document, and wherein the printing and soldering process parameters obtained by automatic decision making and corresponding predicted quality results are sent to the human-computer interaction toolkit to visually display the printing and soldering process parameters and predicted quality values.

2. The SMT process prediction tool for decision making on PCB quality according to claim 1, wherein the condition list comprises data of printed board thickness, printed board material and component positions.

3. The SMT process prediction tool for decision making on PCB quality according to claim 1, wherein the database comprises design conditions, printing process parameters and corresponding solder paste inspection (SPI) results, and soldering process parameters and corresponding automated optical inspection (AOI) results of printed boards processed on SMT production line.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a constitution diagram of an SMT intelligent decision-making toolkit according to an embodiment of the present disclosure.

(2) The present disclosure will be further described below with reference to the accompanying drawings and embodiments, but is not thereby limited to the scope of the described embodiments.

DETAILED DESCRIPTION OF THE EMBODIMENTS

(3) Reference is made to FIG. 1. In a preferred embodiment described below, an SMT process prediction tool for intelligent decision making on PCB quality includes software layers of an SMT intelligent decision-making toolkit constituted of a data fusion toolkit, a printing parameter decision-making toolkit, a soldering parameter decision-making toolkit and a human-computer interaction toolkit all in communication with a database, where a data fusion tool automatically reads size parameters and component information on design conditions of a printed board to be assembled from the database and creates a design condition list for different components for a software execution layer; the printing parameter decision-making toolkit and the soldering parameter decision-making toolkit in the software execution layer perform comparisons with historical printing and soldering data of components under conditions of different printed boards according to design conditions of components on the printed board to be assembled, mine one or more groups of data of alternative satisfactory printing parameter combinations and soldering parameter combinations for components respectively, perform automatic decision making on printing and soldering parameters with a multi-objective optimization algorithm and a deep learning algorithm, predict printing quality and soldering quality according to size parameters and component information in a printed board design document, and send decided printing and soldering process parameters and corresponding predicted quality results to the human-computer interaction toolkit, to visually display the printing and soldering process parameters and predicted quality values to a technologist for reference and confirmation.

(4) The database includes design conditions, printing process parameters and corresponding solder paste inspection (SPI) results, and soldering process parameters and corresponding automated optical inspection (AOI) results of printed boards processed on SMT production line.

(5) In this embodiment, specific steps are as follows:

(6) S1, when a printed board needs to be assembled, the data fusion toolkit automatically reads the size parameters and component information in the printed board design document, creates the design condition list (including printed board thickness, printed board material, and component positions) for different components, and provides the design condition list to the printing parameter decision-making toolkit, the soldering parameter decision-making toolkit and the human-computer interaction tool for use.

(7) S2, when the printed board needs to be printed, the printing parameter decision-making toolkit performs automatic decision making on printing parameters according to the design conditions of the printed board to be assembled, predicts corresponding printing quality, and provides via the human-computer interaction tool the result to the technologist for interactive query and confirmation.

(8) S3, when the printed board needs to be soldered, the soldering parameter decision-making toolkit performs automatic decision making on soldering parameters according to the design conditions of the printed board to be assembled, predicts corresponding soldering quality, and provides via the human-computer interaction tool the result to the technologist for interactive query and confirmation.

(9) S4, after the printed board is assembled, the printing and soldering results of the printed board, together with the design conditions, the printing process parameters and the soldering process parameters of the printed board, are stored by the technologist in the database as historical data in future printed board assembling.

(10) The foregoing are descriptions of the preferred embodiment of the present disclosure. It should be noted that the above embodiment is illustrative of the present disclosure, but is not limited to the present disclosure. Moreover, alternative embodiments can be designed by a person skilled in the art without departing from the scope of the appended claims. Various variations and improvements can be made by a person of ordinary skill in the art without departing from the principle and essence of the present disclosure, and these variations and improvements should also be considered as falling within the protection scope of the present invention.