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AI predicts particle traits in plasma spraying from video

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a method using high-speed video to predict particle characteristics in atmospheric plasma spraying (APS). This technique aims to non-invasively monitor particle temperature and velocity, which are crucial for coating quality. Various machine learning models, including TabPFN and CNNs, were evaluated, with pretrained CNNs achieving the highest accuracy in predicting both temperature and velocity directly from video frames. AI

IMPACT Enables real-time process monitoring and quality control in industrial coating applications.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and experimental results.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI predicts particle traits in plasma spraying from video

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The cluster contains an academic paper detailing a new research methodology and experimental results.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Abhijeet Praveen, Sareh Soleimani, Cormac Cureton, Aman Sidhu, Kintak Raymond Yu, Cristian Cojocaru, Narges Armanfard ·

    Video-Based Prediction of In-Flight Particle Characteristics in Atmospheric Plasma Spraying

    arXiv:2606.07416v1 Announce Type: new Abstract: 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 continuousl…

  2. arXiv cs.LG TIER_1 English(EN) · Narges Armanfard ·

    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 o…

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