Carbon fiber plays an important role in fuel cells, providing water drainage and fuel distribution. Over time, its structure changes, which reduces the efficiency of fuel cells. Analysis of the microstructure of carbon fiber has become a necessary step in diagnosing the condition of fuel cells.
However, until now, it was not possible to perform high-resolution microstructure analysis without breaking the sample and using an electron microscope. To solve this problem, the team developed a technology that uses X-ray tomography and an AI-powered learning model to analyze the microstructure of carbon fiber. This allows accurate analysis without the use of an electron microscope and, as a result, a near real-time diagnosis of the condition of fuel cells.
More than 5,000 images of more than 200 carbon fiber samples were used to train the algorithm. The model is trained with over 98% accuracy, allowing it to accurately predict the distribution and location of key material components. This method significantly speeds up the diagnostic process by detecting damage and causes of performance degradation within a few seconds.
Source: Ferra
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