Cormac Cureton

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e

Narges Armanfard

Sujets de recherche

Apprentissage multitâche

Apprentissage profond

Données tabulaires

Site web

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

TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data

Cormac Cureton

Narges Armanfard

Prior-Data Fitted networks (PFNs) have been very successful in tabular contexts, handling prediction tasks in context. However, they are des… (voir plus)igned for single-task inference, meaning that predicting several target values within a context requires repeated forward calls and precludes inter-task information sharing. We propose TabPFN-MT, which is trained on an expanded multi-target synthetic prior to capture inter-task dependencies in context. This model uses an expanded

2026-05-15

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