Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers

TL;DR

This paper presents a hybrid CNN-Transformer machine learning framework that predicts melt pool cross-section contours from surface thermal images, aiding in in-situ monitoring of laser powder bed fusion processes.

Contribution

It introduces a novel hybrid CNN-Transformer architecture that correlates thermal image sequences with melt pool morphology, improving prediction accuracy over existing models.

Findings

Accurately models melt pool curvature from thermal images.

Outperforms analytical models in high energy density regimes.

Demonstrates effective correlation between surface thermal data and subsurface melt pool structure.

Abstract

Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. Specifically, we employ a hybrid CNN-Transformer architecture to establish a correlation between single bead off-axis thermal image sequences and melt pool cross-section contours measured via optical microscopy. In this architecture, a ResNet model embeds the spatial information contained within the thermal images to a latent vector, while a Transformer model correlates the sequence of embedded vectors to extract temporal information. Our framework is able to model the curvature of the subsurface melt pool structure, with improved performance in high energy density regimes compared to analytical melt pool models. The performance of this model is evaluated through dimensional and geometric comparisons to the corresponding experimental melt pool observations.