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Found 4 papers, 0 papers with code
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing
Comparison results demonstrate that 1) the source data selection method is general and supports integration with various TL methods and distance metrics, 2) compared with using all source data, the proposed method can find a small subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and 3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains.
Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks
Our work addresses this by introducing a physics-informed neural network framework specifically designed for temperature field prediction in metal AM.
Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls
With measured/predicted temperature profiles of several points on the same layer, the second stage proposes a reduced order model (ROM) (intra-layer prediction model) to decompose and construct the temperature profiles of all points on the same layer, which could be used to build the temperature field of the entire layer.
System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme
This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-property modeling and control approach to enhance the quality of modeling and control of part properties that cannot be measured in situ.
