SMT Process Prediction for PCB Printing and Soldering Quality
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Solution Overview
Problem
Current SMT production lines face challenges in achieving real-time quality prediction and automatic recommendation of process parameters due to numerous influencing factors, relying heavily on technician experience and traditional statistical process control, leading to limited quality improvement and increased costs.
Innovation Solution
An SMT process prediction tool incorporating a data fusion toolkit, printing parameter decision-making toolkit, soldering parameter decision-making toolkit, and human-computer interaction toolkit, utilizing multi-objective optimization and deep learning algorithms to automatically read and analyze design conditions, predict quality, and recommend process parameters, thereby enhancing automation and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional statistical process control and technician experience are used for quality control, then operational simplicity is maintained, but manufacturing precision and quality prediction accuracy are limited
Solution Approach 1:
The patent replaces traditional mechanical quality control methods (technician experience, manual inspection, statistical process control) with an intelligent system based on deep learning algorithms and multi-objective optimization. The system automatically collects production data from various equipment, processes it through neural networks, and generates quality predictions and parameter recommendations, substituting human expertise with automated intelligent analysis.
Solution Approach 2:
The patent introduces an intermediary intelligent decision-making system that acts as a bridge between raw production data and quality control decisions. This intermediary layer includes data collection modules, deep learning models, and optimization algorithms that process information between the production line and quality outcomes, enabling accurate quality prediction without direct human intervention.
2Manufacturing precision
If comprehensive monitoring of multiple influencing factors is implemented, then manufacturing precision improves, but loss of time and increased production costs occur
Solution Approach 1:
The patent implements preliminary action by using deep learning models to predict quality outcomes and optimize process parameters before actual production occurs. The system analyzes historical data and design conditions to pre-determine optimal printing and soldering parameters, allowing the production line to proceed efficiently without real-time interruptions for analysis or adjustment.
Solution Approach 2:
The patent replaces time-consuming manual monitoring and analysis of multiple influencing factors with automated intelligent algorithms. The system simultaneously processes numerous parameters (printing parameters, soldering parameters, material properties, equipment conditions) through parallel computational operations, achieving comprehensive monitoring without sequential processing delays.
3Manufacturing precision
If comprehensive monitoring of multiple influencing factors is implemented, then manufacturing precision improves, but production costs increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically collect, analyze, and optimize production parameters without requiring additional human resources or external consulting. The intelligent decision-making system uses its own accumulated data and learning capabilities to continuously improve quality control, eliminating the need for expensive external quality experts or manual analysis services.
Solution Approach 2:
The patent replaces costly manual quality control processes with automated intelligent algorithms. The system substitutes expensive human expertise, time-consuming manual inspections, and trial-and-error parameter adjustments with computationally efficient deep learning models that provide accurate predictions at minimal marginal cost once deployed.
4Productivity
If real-time quality prediction and automatic parameter recommendation are achieved, then productivity improves, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex quality control system into modular functional components: data collection modules for different equipment, deep learning model modules for different analysis tasks, optimization modules for different parameter types, and output modules for different decision-making levels. This modular architecture enables high productivity through automated parallel processing while managing complexity through clear separation of concerns.
Data Source
AI summary
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.