做涂层工艺或工业过程监控的团队，终于有了不依赖昂贵传感器的实时诊断方案——用高速视频就能预测关键粒子参数，建议做APS工艺优化的点开看具体特征工程方法。
大气等离子喷涂（APS）中，飞行粒子的温度和速度对涂层质量至关重要，但难以实时监测。该研究利用高速视频观测等离子体羽流，通过TabPFN、CNN等模型预测粒子特性。TabPFN在温度预测上表现稳定（R²=0.86），CNN在速度预测上更优（R²=0.81），而预训练CNN直接处理原始视频帧达到最佳效果（温度R²=0.90，速度R²=0.82）。结果表明，视频驱动的非侵入式诊断方法为APS实时过程监控提供了可扩展的解决方案。
Video-Based Prediction of In-Flight Particle Characteristics in Atmospheric Plasma Spraying
Atmospheric plasma spraying (APS) is a widely used coating process in which in-flight particle temperature and velocity strongly influence coating quality. However, these particle characteristics are difficult to monitor continuously during operation, motivating the development of non-invasive data-driven diagnostic methods. In this work, we investigate the predictive potential of high-speed video observations of the plasma plume for estimating in-flight particle characteristics in APS. We introduce three different video-derived feature representations and evaluate them using Tabular Prior-Data Fitted Networks (TabPFN), convolutional neural networks (CNN), and classical regression baselines including Random Forest, Gradient Boosting, Support Vector Regression, and XGBoost. Experiments are conducted using grouped leave-one-out cross-validation on 126 labeled pre- and post-spray video recordings from 63 APS spray runs. Across the engineered feature experiments, TabPFN achieves the most consistent performance for temperature prediction, reaching R2 = 0.86 using the combined feature representation. CNN models particularly perform stronger for velocity prediction, achieving R2 of 0.81. In addition, we evaluate models operating directly on raw video frames using pretrained CNNs and find that the highest performance is achieved by a pretrained CNN with a regression head with R2 of 0.90 and 0.82 for temperature and velocity, respectively. The results demonstrate that video-derived plume information provides a promising and scalable foundation for non-invasive APS diagnostics and real-time process monitoring.