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.

用机器学习框架将高速彩色成像下的热像与熔池横截面轮廓进行相关联，以模拟超声成像所观察到的熔池亚表面形态，并通过与实验熔池观察结果进行比较评估性能。

基于视觉 Transformer 的熔池深度轮廓预测的深度学习方法

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

Defects in laser powder bed fusion (L-PBF) parts often result from the
meso-scale dynamics of the molten alloy near the laser, known as the melt pool.
For instance, the melt pool can directly contribute to the formation of
undesirable porosity, residual stress, and surface roughness in the final part.
Experimental in-situ monitoring of the three-dimensional melt pool physical
fields is challenging, due to the short length and time scales involved in the
process. Multi-physics simulation methods can describe the three-dimensional
dynamics of the melt pool, but are computationally expensive at the mesh
refinement required for accurate predictions of complex effects, such as the
formation of keyhole porosity. Therefore, in this work, we develop a generative
deep learning model based on the probabilistic diffusion framework to map
low-fidelity, coarse-grained simulation information to the high-fidelity
counterpart. By doing so, we bypass the computational expense of conducting
multiple high-fidelity simulations for analysis by instead upscaling
lightweight coarse mesh simulations. Specifically, we implement a 2-D diffusion
model to spatially upscale cross-sections of the coarsely simulated melt pool
to their high-fidelity equivalent. We demonstrate the preservation of key
metrics of the melting process between the ground truth simulation data and the
diffusion model output, such as the temperature field, the melt pool dimensions
and the variability of the keyhole vapor cavity. Specifically, we predict the
melt pool depth within 3 $\mu m$ based on low-fidelity input data 4$\times$
coarser than the high-fidelity simulations, reducing analysis time by two
orders of magnitude.

通过开发基于概率扩散框架的生成性深度学习模型，我们实现了低保真度、粗粒度模拟信息向高保真度对应物的映射，无需进行多次高保真度模拟分析，代之以放大轻量级粗网格模拟。我们通过实现一个二维扩散模型，将粗粒度模拟的熔池横截面空间放大到高保真度的等效尺寸，展示了熔化过程的关键度量，如温度场、熔池尺寸和关键孔造成的变异性在真实模拟数据和扩散模型输出之间的保持，并在低保真度输入数据的基础上预测了 3 微米的熔池深度，比高保真度模拟降低了两个数量级的分析时间。

低成本高保真融池模型在增材制造中的应用：基于生成式深度扩散的方法

Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing  Using Generative Deep Diffusion

A digital twin (DT) is a virtual representation of physical process, products
and/or systems that requires a high-fidelity computational model for continuous
update through the integration of sensor data and user input. In the context of
laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the
manufacturing process can offer predictions for the produced parts, diagnostics
for manufacturing defects, as well as control capabilities. This paper
introduces a parameterized physics-based digital twin (PPB-DT) for the
statistical predictions of LPBF metal additive manufacturing process. We
accomplish this by creating a high-fidelity computational model that accurately
represents the melt pool phenomena and subsequently calibrating and validating
it through controlled experiments. In PPB-DT, a mechanistic reduced-order
method-driven stochastic calibration process is introduced, which enables the
statistical predictions of the melt pool geometries and the identification of
defects such as lack-of-fusion porosity and surface roughness, specifically for
diagnostic applications. Leveraging data derived from this physics-based model
and experiments, we have trained a machine learning-based digital twin
(PPB-ML-DT) model for predicting, monitoring, and controlling melt pool
geometries. These proposed digital twin models can be employed for predictions,
control, optimization, and quality assurance within the LPBF process,
ultimately expediting product development and certification in LPBF-based metal
additive manufacturing.

本文介绍了基于参数化物理模型的数字孪生（PPB-DT），用于统计预测激光粉末床熔化金属增材制造过程中的熔池几何和缺陷识别在内的诊断应用。通过基于物理模型和实验数据训练的机器学习模型（PPB-ML-DT），这些数字孪生模型可用于预测、监测和控制熔池几何，加快基于激光粉末床熔化金属增材制造的产品开发和认证过程。