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Found 11 papers, 0 papers with code
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring
To do so, a conditional latent probabilistic diffusion model is trained to produce realistic high-resolution images of the build plate from low-resolution webcam images, recovering the distribution of small-scale features and surface roughness.
Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion
Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure.
Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers
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.
ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning
We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data.
Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs).
Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion
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.
Surrogate Modeling of Melt Pool Thermal Field using Deep Learning
In this work, we create three datasets of single-trail processes using Flow-3D and use them to train a convolutional neural network capable of predicting the behavior of the three-dimensional thermal field of the melt pool solely by taking three parameters as input: laser power, laser velocity, and time step.
Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing
Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control.
MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning
In this work, we introduced a comprehensive framework for benchmarking ML for melt pool characterization.
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning
Powder-based additive manufacturing techniques provide tools to construct intricate structures that are difficult to manufacture using conventional methods.
Graph Convolutional Neural Networks for Body Force Prediction
The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements.
