Real-Time Monitoring of the Inoculation Process in Cast Iron Production Using Deep Neural Networks

Grain refinement in cast iron is a critical operation in the casting process, achieved through the addition of inoculant powder to the molten metal. This process enhances mechanical properties such as strength, hardness, and wear resistance. The type, quantity, and method of adding the inoculant powder significantly influence grain size and distribution. Typically, the powder is added to the molten metal at the final stage before it enters the mold. However, challenges such as nozzle blockages and uneven powder distribution reduce efficiency and lower the quality of the casting part. Traditional monitoring methods, which rely on human operators, are often inefficient. Moreover, environmental risks around the casting equipment further complicate manual supervision. To address this issue, the present study employs machine vision techniques and deep neural networks for real-time monitoring of the inoculation process at the casting production line. This automated approach enables precise measurement of parameters such as inoculant quantity and spray angle, thereby enhancing production line efficiency. In this research, the U-Net architecture was utilized for image segmentation, while ResNet34, InceptionV3, and VGG16 networks were employed for image classification. The highest segmentation accuracy, 0.9841, was achieved using U-Net with an InceptionV3 encoder, and the best classification accuracy, 0.9463, was obtained using ResNet34. Compared to traditional monitoring methods, this approach demonstrates superior accuracy and has a positive impact on the casting process.