| Abstract |
| In this study, we analyzed the chemical composition of Al-Si cast alloys from microstructural images, using image recognition and machine learning. Binary Al-Si alloys of Si = 1~10 wt% were cast and prepared as reference images in the dataset used for machine learning. The machine learning procedure was constructed with Inception V3 model. Repeated training to relate the microstructural images to their chemical composition was carried out, for up to 10,000 steps, to increase the reliability of the analysis. The peaks of similarity existed in the dataset with chemical compositions corresponding to the known target composition. The heights of the peaks became higher and the distribution of similarity became sharper with further training steps. This means that the weighted average of the chemical composition approached the target composition with increasing training steps. The correctness of the analysis increased with training steps up to 10,000, then was saturated. It was found that the chemical composition outside the dataset range could not be analyzed correctly. Analysis of the compositions between the datasets showed incorrect but reasonable results. The reliability of the chemical composition analysis using machine learning and image recognition developed in this study will increase when a vast range of reference images are collected and verified.
(Received January 10, 2019; Accepted January 28, 2019) |
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| Key Words |
| artificial intelligence, image recognition, chemical composition, microstructure, aluminum alloy |
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