Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum Spartum Biocomposites

Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum Spartum Biocomposites

This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced...

Full description

Saved in:
Bibliographic Details
Main Authors: Khalil Benabderazag, Moussa Guebailia, Zouheyr Belouadah, Lotfi Toubal, Salah Eddine Tachi
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Fibers
Subjects:
3D printing
biocomposite
material behavior
acoustic emission
machine learning prediction
Online Access:https://www.mdpi.com/2079-6439/13/4/38
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items