Advanced searches left 3/3

3D GLASS - Europe PMC

Summarized by Plex Scholar
Last Updated: 12 April 2022

* If you want to update the article please login/register

Low-Velocity Impact Response on Glass Fiber Reinforced 3D Integrated Woven Spacer Sandwich Composites.

This research presents an experimental investigation into the low-velocity impact of three-dimensional integrated woven spacer sandwich composites made of high-performance glass fiber reinforced fabric and epoxy resin. No significant effect of face sheet thickness on impact response is shown by data analysis. In addition, simple visualization of the specimen showed that the damage morphologies of 3D integrated woven spacer sandwich composites under various impact energies. Moreover, it was discovered that the damage caused by 3D integrated woven spacer sandwich composites samples in the affected region only affected the samples' integrity and does not influence the samples's integrity.

Source link: https://europepmc.org/article/MED/35329762


Influence of Short Glass Fibre Reinforcement on Mechanical Properties of 3D Printed ABS-Based Polymer Composites.

Constructed deposition modelling, one of the most commonly used additive manufacturing processes, is based on material extrusion and is most commonly used for producing thermoplastic parts for functional applications with the goals of low cost, minimal waste, and ease of conversion. Given that pure thermoplastic materials have a poor mechanical performance, it is important to improve the mechanical properties of thermoplastic parts made using FDM technology. The comparison was made between ABS + SGF composites and pure ABS of mechanical characteristics such as surface roughness, tensile strength, and low-velocity effect. The research also concentrated on the dispersion characters of SGF ABS matrix SGF and its effects on the properties. In comparison to pure ABS, a 57% rise in tensile strength was seen for the 30 wt% increase in tensile strength.

Source link: https://europepmc.org/article/MED/35335514


AI-driven quantification of ground glass opacities in lungs of COVID-19 patients using 3D computed tomography imaging.

Method We use PointNet++, an AI-based unsupervised machine learning approach used to detect and quantify GGOs in CT scans of COVID-19 patients and determine the severity of the disease. We've done our research into the "MosMedData" series, which includes CT lung scans of 1110 patients with or without COVID-19 infections. Using Minkowski tensors, we determine the morphologies of GGOs and calculate the abnormality score of individual regions of segmented lung and GGOs. On average, the shapes of GGOs in the COVID-19 datasets differ from sphericity by 15%, and anisotropies in GGOs are dominated by dipole and hexapole components. These anisotropies may help to distinguish GGOs of COVID-19 from other lung diseases in a quantitative manner. Conclusion The PointNet++ and the Minkowski tensor-based morphological study, as well as abnormality analysis, will provide radiologists and physicians with a useful set of tools for interpreting CT lung scans of COVID-19 patients.

Source link: https://europepmc.org/article/MED/35286309

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions