Pre-Reflow Solder Design Screening With ML Inspection Images
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Solution Overview
Problem
In the manufacturing of products through soldering, there is a need for high-precision determination of soldering-related designs to prevent reworking due to defects, and accurate inspection results post-reflow process.
Innovation Solution
An information processing apparatus is provided, which includes an image generation unit and a determination unit. The image generation unit creates input image data representing a pre-reflow state based on soldering-related design information and applies color or patterns corresponding to set color information for each component. The determination unit uses a machine learning model to assess the appropriateness of the design by inputting the generated image data, thereby determining the likelihood of defects in the post-reflow inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soldering-related designs are determined with high precision at the design stage, then manufacturing defects are prevented, but design complexity and determination time increase
Solution Approach 1:
The patent performs design appropriateness determination at the design stage before manufacturing, using machine learning models to predict potential defects. This preliminary action identifies design issues early, preventing manufacturing defects without requiring complex real-time monitoring during production.
Solution Approach 2:
The patent creates virtual images representing pre-reflow states based on design information and uses these copied representations for analysis. The machine learning model processes these virtual copies to predict post-reflow inspection results, avoiding the need for physical prototypes or complex manufacturing trials.
2Measurement precision
If machine learning models process detailed design information with color and pattern application, then inspection accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent applies color information and patterns selectively to different components in the generated images based on their specific characteristics. This local differentiation enhances the machine learning model's ability to distinguish and inspect specific components accurately without uniformly processing all elements with maximum detail.
Solution Approach 2:
The patent transforms design information into visual representations with varying parameters such as color, pattern, and image format. These parameter changes enable the machine learning model to process and analyze design appropriateness more efficiently while maintaining high inspection accuracy through multiple image generation modes.
Data Source
AI summary
In an embodiment, an information processing apparatus includes an image generation unit and a determination unit. The image generation unit generates input image data showing a pre-reflow state based on soldering-related design information, and applies a color or a pattern corresponding to color information set for each of a plurality of types of components, in the input image data, to each of the plurality of types of components. The determination unit determines appropriateness of a design shown in the design information by inputting, to a machine learning model outputting an inspection result of a post-reflow inspection in response to inputting of image data based on a pre-reflow image, the input image data.