Aman Sidhu

aman.sidhu@mila.quebec

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e

Narges Armanfard

Sujets de recherche

Apprentissage automatique appliqué

Apprentissage par renforcement

Apprentissage profond

IA appliquée

Site web

Google Scholar

GitHub

Publications

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

Abhijeet Praveen

Sareh Soleimani

Cormac Cureton

Aman Sidhu

Kintak Raymond Yu

Cristian Cojocaru

Narges Armanfard

Atmospheric plasma spraying (APS) is a widely used coating process in which in-flight particle temperature and velocity strongly influence c… (voir plus)oating 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.

2026-06-04

arXiv (prépublication)

doi.org

arxiv.org

Supervised Multimodal Model for Plasma Spray Diagnostics and Spray Health Monitoring

Cormac Cureton

Abhijeet Praveen

Sareh Soleimani

Aman Sidhu

Cristian Cojocaru

Kintak Raymond Yu

Narges Armanfard