Multimodal computer vision and machine learning for predictive monitoring of planar-flow casting of amorphous steel foils

Abstract Amorphous metallic foils produced by planar-flow casting (PFC) are central to emerging high-efficiency transformer and motor technologies. Despite its promise, the process remains highly sensitive to fluctuations in melt delivery, nozzle–wheel gap, and interfacial heat transfer, leading to free-surface ridging, uneven thickness, and compromised magnetic properties. Conventional monitoring, based on indirect variables or post-cast inspection, provides delayed feedback and cannot resolve the rapid dynamics of the melt–wheel interface.Here we present a data-driven framework for PFC monitoring that integrates synchronised video and infrared imaging with machine-learning analysis. A multimodal dataset of ≈ 200 runs (≈ 300,000 frames, ≈ 20 GB) was collected with front- and side-view cameras and infrared thermal imaging, paired with detailed metadata. Physics-inspired descriptors—including ridge density, reflection intensity, and thermal gradients—were extracted through automated computer vision pipelines and benchmarked against raw-frame deep learning. Analytical proxies, gradient-boosted decision trees, and multimodal fusion networks were systematically evaluated.Feature-based models achieved near-perfect gap prediction, while ridge and thermal-gradient analysis provided direct indicators of foil integrity. Deep-learning fusion models offered complementary robustness but at higher computational cost. Together, these results demonstrate a reproducible approach for linking process dynamics with foil quality, establishing a foundation for automated, closed-loop optimisation of amorphous steel-foil manufacturing.