AI Weld Quality Prediction From Melt Pool and Keyhole Imaging
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Solution Overview
Problem
Existing methods for assessing weld quality, such as laser welding in battery modules, are inadequate and can lead to poor welds that break under vibration, and non-destructive testing methods are not applicable to all products.
Innovation Solution
An apparatus and method using a learned artificial intelligence model and a camera to predict weld quality by acquiring images of the welding process, employing shape, depth, and strength prediction models to analyze melt pools and keyholes, leveraging convolutional and deep neural networks for high-accuracy predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cutting methods are used to check weld quality, then measurement precision can be achieved, but productivity decreases and the method cannot be applied to all products
Solution Approach 1:
The patent replaces the mechanical cutting method with an optical imaging system combined with AI analysis. The camera captures images of the welding area, and deep learning models automatically analyze the images to assess weld quality, eliminating the need for physical cutting and manual inspection while maintaining high measurement precision.
Solution Approach 2:
The patent creates a digital copy (image) of the weld area instead of physically cutting the workpiece. The camera captures visual information of the welding pool and keyhole, and AI models analyze this digital representation to determine weld quality, enabling non-contact inspection that preserves productivity.
2Strength
If laser welding is performed to ensure strong welds, then strength is improved, but weld quality may still be poor leading to breaks under vibration
Solution Approach 1:
The patent implements real-time feedback during the welding process by continuously monitoring the welding pool and keyhole characteristics through the camera and AI models. The system provides immediate quality assessment, allowing for real-time adjustments to welding parameters to ensure both strength and reliability, preventing defects that would cause vibration-related failures.
Solution Approach 2:
The patent performs preliminary quality assessment during the welding process itself by analyzing the welding pool and keyhole formation. This allows for early detection of potential quality issues before the weld is complete, enabling corrective actions to ensure the weld will have both the required strength and reliability under vibration conditions.
3Measurement precision
If multiple learning datasets are used to train AI models, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the quality assessment task into three separate AI models, each trained on specific learning datasets for different aspects: shape detection model for geometric features, depth prediction model for penetration depth, and strength prediction model for mechanical properties. This segmentation allows each model to specialize in one function with targeted training data, improving overall precision while managing complexity through modular architecture.
Data Source
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AI summary
An apparatus for predicting weld quality includes an image sensor acquiring an image of a welding portion of a workpiece, a storage storing a shape detection model, a depth prediction model and a strength prediction model, and a controller configured to acquire shapes of a melt pool and a keyhole by receiving the image and inputting the image to the shape detection model, acquire a penetration depth of the keyhole by inputting the shapes of the melt pool and the keyhole to the depth prediction model, and acquire a tensile strength by inputting one of the shape of the melt pool, the shape of the keyhole and the shapes of the melt pool and the keyhole and the penetration depth of the keyhole to the strength prediction model. The weld quality of all products may be predicted during a welding process.