Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics. [PDF]
Molaei S +10 more
europepmc +1 more source
Physics-Guided Machine Learning for Performance Prediction and Multi-Objective Optimization of High-Conductivity Aluminum Conductors. [PDF]
Miao Y +6 more
europepmc +1 more source
Recycled Thermoplastics for 3D Printing Filament Production: A Review of Circular Economy Drivers, Material Behavior, and Current Research Gaps. [PDF]
Mitaľová Z +3 more
europepmc +1 more source
Preparation of HMX/PMMA Composite Microspheres with Excellent Properties by Photoinitiated Emulsion Polymerization. [PDF]
Zhang S +7 more
europepmc +1 more source
Towards the design of artificial sensing materials via quantum-informed explainable AI. [PDF]
Chen L +4 more
europepmc +1 more source
Adhesion and Friction in Biological and Bioinspired Systems. [PDF]
Büscher TH, Gorb SN.
europepmc +1 more source
Pressure-Dependent Mechanical Behavior and Surface Degradation of Fluorocarbon Elastomer (FKM): Insights into Structure-Property Relationships Under Hydrogen Exposure. [PDF]
Subedi N +4 more
europepmc +1 more source
ANALYSING THE PHYSICAL AND MECHANICAL PROPERTIES OF CORE-SPUN YARNS
Core spun yarn is also named as complex, compound, composite or hybrid yarn can be defined as the combination of filament and staple fibres. A filament which is called core is covered by staple fibres called sheath. In this study, it was aimed to analyse physical and mechanical properties of ring core-spun and ring dual core-spun yarns.
openaire +3 more sources
Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models. [PDF]
Zhang M, Cole JM.
europepmc +1 more source
Mechanistic neural operator framework for multi-objective optimization of Ti-6Al-4 V metal matrix composites. [PDF]
Lakshmaiya N +7 more
europepmc +1 more source

