Microstructural characterization of bimodal composite metal foams under compression with machine learning

Cellular materials are gaining popularity in today’s major sectors. The aim is to develop high-performance materials to meet customer and application demands. This study revolves around the beginning of the failure; computed tomography and statistical image analysis assisted with machine learning we...

Full description

Saved in:
Bibliographic Details
Published in:Composites. Part A, Applied science and manufacturing Applied science and manufacturing, 2024-10, Vol.185, p.108292, Article 108292
Main Authors: Bubonyi, Tamás, Barkóczy, Péter, Kemény, Alexandra, Gácsi, Zoltán
Format: Article
Language:English
Subjects:
appearance (quality)
artificial intelligence
class
composite materials
Composite metal foam
Computed tomography
consumer demand
decision support systems
foaming
foams
Machine learning
Mathematical morphology
metals
sorting
statistical analysis
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cellular materials are gaining popularity in today’s major sectors. The aim is to develop high-performance materials to meet customer and application demands. This study revolves around the beginning of the failure; computed tomography and statistical image analysis assisted with machine learning were employed to quantitatively characterize the occurring processes within the structure at the beginning of the well-known plateau effect. Simple techniques (e.g., easily obtainable shape parameters, forest decision, 2D image slices) were employed to sort the structural elements into seven classes based on their visual appearances. The presented method can provide additional information about the fracture mode of the different foams, possibly applying/extending them for other types of closed-cell (composite or not) foams.
ISSN:1359-835X
DOI:10.1016/j.compositesa.2024.108292