Search Results for author:
Found 3 papers, 0 papers with code
MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
Predicting mechanical properties in metal additive manufacturing (MAM) is essential for ensuring the performance and reliability of printed parts, as well as their suitability for specific applications.
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.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
