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

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 model uti...

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Veröffentlicht in:	Advanced modeling and simulation in engineering sciences Jg. 12; H. 1; S. 22 - 23
Hauptverfasser:	Chaudhry, Shubham, Abdedou, Azzedine, Soulaïmani, Azzeddine
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V SpringerOpen
Schlagworte:	Accuracy Additive manufacturing Artificial neural networks Classical and Continuum Physics Complexity Computational Science and Engineering Convolutional autoencoder Costs Datasets Decomposition Deep learning Engineering Laser beam melting Lasers Machine learning Manufacturing Mechanical analysis Model reduction Multilayer perceptrons Neural networks Optimization Proper Orthogonal Decomposition Reduced order models Reduced-order model Regression models Simulation Statistical analysis Temperature Theoretical and Applied Mechanics Thermomechanical analysis Deep learning Proper orthogonal decomposition Additive manufacturing Convolutional autoencoder Reduced-order model
ISSN:	2213-7467, 2213-7467
Online-Zugang:	Volltext
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