Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder

Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder

Abstract This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN...

Full description

Saved in:
Bibliographic Details
Main Authors: Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:Advanced Modeling and Simulation in Engineering Sciences
Subjects:
Reduced-order model
Convolutional autoencoder
Additive manufacturing
Deep learning
Proper orthogonal decomposition
Online Access:https://doi.org/10.1186/s40323-025-00305-6
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items