ArticleMaterials (Basel, Switzerland)2023
Machine Learning Estimation of Plateau Stress of Aluminum Foam Using X-ray Computed Tomography Images.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Three-Dimensional Geometry Correction of Scratch Grooves via X-Ray Computed Tomography for Fracture Characterization in Cementitious Materials.Materials (Basel, Switzerland) · 2026Article
- Modelling the Mechanical Properties of Architected Cellular Solids for Structural Applications: A Review.Materials (Basel, Switzerland) · 2026Review
- Prediction of Mechanical Properties and Stress-Strain Relation of Closed-Cell Aluminium Foam Under Compression Using Neural Network Models.Materials (Basel, Switzerland) · 2025Article
- Predicting the Compressive Properties of Carbon Foam Using Artificial Neural Networks.Materials (Basel, Switzerland) · 2025Article
- 2D Materials Nanoarchitectonics for 3D Structures/Functions.Materials (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Owing to its lightweight and excellent shock-absorbing properties, aluminum foam is used in automotive parts and construction materials. If a nondestructive quality assurance method can be established, the application of aluminum foam will be further expanded. In this study, we attempted to estimate the plateau stress of aluminum foam via machine learning (deep learning) using X-ray computed tomography (CT) images of aluminum foam. The plateau stresses estimated by machine learning and those actually obtained using the compression test were almost identical. Consequently, it was shown that plateau stress can be estimated by training using the two-dimensional cross-sectional images obtained nondestructively via X-ray CT imaging.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.