ArticleMaterials (Basel, Switzerland)2025
Artificial Neural Networks for Impact Strength Prediction of Composite Barriers.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This study considers the impact and penetration of composite targets by steel projectiles. Firstly, experiments on the impact of homogeneous polymethyl methacrylate (PMMA) targets were simulated using the finite element method (FEM) and the incubation time fracture criterion (ITFC). Next, targets were assumed to be composed of cells with weakened mechanical properties, forming a composite barrier. The composite impact problems were then used to demonstrate an approach, which can be applied to overcome the typical difficulties for impact simulations-high demands on computing resources, long computation times, and potential numerical instabilities arising from high stresses in the contact zone and high strain rates. The approach is based on the use of artificial neural networks (ANNs) trained on arrays of numerical results obtained via finite element method.
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.