| Title |
Performance Analysis of CNN-Based Magnesia-Chrome Refractory Crack Prediction According to Input Data Characteristics |
| Authors |
박재한(Park, Jae-Han) ; 김동환(Kim, Dong-Hwan) ; 김주석(Kim, Joo-Seok) ; 강상구(Kang, Sanggoo) |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.4.359 |
| Keywords |
Magnesia-Chrome Refractory Brick; Non-Destructive Testing; Impact-Based Signal; Deep Learning |
| Abstract |
This study presents a deep learning-based non-destructive testing method for detecting internal cracks in magnesia-chrome refractory bricks.
Five datasets were created using different signal representations, including time-domain, frequency-domain, time-frequency domain, and their
combinations. CNN models with identical structures were independently trained for each input type, with key hyperparameters optimized using
the Optuna framework. Time-domain models achieved high precision with relatively lower recall, while frequency-domain inputs improved
recall. The Time?Spectrogram Domain CNN (TSDC) achieved the best overall performance across all metrics. These results demonstrate that
fusing time and frequency information enhances feature learning and classification performance. This approach offers an efficient framework
for early crack detection in refractories. However, limitations due to dataset size and single-sensor input remain, indicating the need for future
work focused on data expansion and multi-sensor system integration to improve robustness and generalization. |