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.

翻译：激光粉末床熔融（L-PBF）过程中产生的熔池之间重叠不足可能导致融合缺陷以及力学与疲劳性能下降。熔池亚表面形态的原位监测需要专用设备，这些设备可能难以获取或扩展。因此，我们提出一种机器学习框架，将高速彩色成像观测到的原位双色热图像与熔池横截面的二维轮廓相关联。具体而言，我们采用混合CNN-Transformer架构，建立单焊道离轴热图像序列与光学显微镜测量的熔池横截面轮廓之间的关联。在该架构中，ResNet模型将热图像中的空间信息嵌入潜在向量，而Transformer模型对嵌入向量序列进行关联以提取时间信息。我们的框架能够模拟亚表面熔池结构的曲率，在高能量密度区域相比解析熔池模型具有更优性能。通过将模型预测与对应实验熔池观测结果进行尺寸和几何对比，评估了该模型的性能。