Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Abrasion Resistance of Concrete

Abrasion Resistance of Concrete

Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Viscosity Characterization of PDMS and Its Influence on the Performance of a Torsional Vibration Viscous Damper Under Forced Hydrodynamic Loading.

Materials (Basel, Switzerland)·2026

Same author

Optimization of Tribological Properties of Shot-Peened Surfaces via Multi-Criteria Decision-Making Using TOPSIS and GRA.

Materials (Basel, Switzerland)·2025

Same author

Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data.

Polymers·2025

Same author

Application of Machine Learning to the Prediction of Surface Roughness in the Milling Process on the Basis of Sensor Signals.

Materials (Basel, Switzerland)·2025

Same author

Analysis of Acoustic Absorption Coefficients and Characterization of Epoxy Adhesive Compositions Based on the Reaction Product of Bisphenol A with Epichlorohydrin Modified with Fillers.

Materials (Basel, Switzerland)·2024

Same author

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods.

Materials (Basel, Switzerland)·2024

Same journal

Correction: Yang et al. Microstructural Characteristics of High-Pressure Die Casting with High Strength-Ductility Synergy Properties: A Review. <i>Materials</i> 2023, <i>16</i>, 1954.

Materials (Basel, Switzerland)·2026

Same journal

Effect of La and Ce Microalloying on the Corrosion Resistance of 0.4Sb Low-Alloy Steel in a Harsh Marine Atmospheric Environment.

Materials (Basel, Switzerland)·2026

Same journal

High-Temperature Properties of Magnesium Ammonium Phosphate Cement Modified with Gold Tailings.

Materials (Basel, Switzerland)·2026

Same journal

A Study on the Evolution of Intermetallic Phase Microstructure and High-Temperature Creep Behavior in Mg-8.0Al-1.0Nd-1.5Gd-Mn Alloys.

Materials (Basel, Switzerland)·2026

Same journal

Material-Driven Clinical Complications in Mechanical Circulatory Support: From Blood-Material Interactions to Device-Related Adverse Events.

Materials (Basel, Switzerland)·2026

Same journal

Influence of Final Irrigation on Calcium Silicate-Based Sealer Dentinal Tubular Penetration: A Systematic Review.

Materials (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 18, 2025

Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing

Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing

Published on: July 3, 2020

Epoxy Adhesive Materials as Protective Coatings: Strength Property Analysis Using Machine Learning Algorithms.

Izabela Miturska-Barańska¹, Katarzyna Antosz²

¹Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland.

Materials (Basel, Switzerland)

|June 27, 2025

Summary

This summary is machine-generated.

This study optimized epoxy coatings using calcium carbonate fillers, achieving high mechanical strengths. Machine learning models accurately predicted properties, highlighting fillers and curing agents as key factors for industrial applications.

Keywords:

NN algorithm SEM SVM machine learning algorithms material characterization

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Author Spotlight: Characterizing Environmental Biofilm Mechanics Using Optical Coherence Elastography and its Applications in Wastewater Treatment

Author Spotlight: Characterizing Environmental Biofilm Mechanics Using Optical Coherence Elastography and its Applications in Wastewater Treatment

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Sep 18, 2025

Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing

Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing

Published on: July 3, 2020

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Author Spotlight: Characterizing Environmental Biofilm Mechanics Using Optical Coherence Elastography and its Applications in Wastewater Treatment

Author Spotlight: Characterizing Environmental Biofilm Mechanics Using Optical Coherence Elastography and its Applications in Wastewater Treatment

Published on: March 1, 2024

Area of Science:

Materials Science
Polymer Chemistry
Chemical Engineering

Background:

Epoxy adhesives are crucial functional coatings.
Understanding filler and curing agent effects on mechanical properties is vital.
Optimizing epoxy formulations requires detailed analysis.

Purpose of the Study:

To investigate the impact of physical modifications on epoxy adhesive mechanical properties.
To explore the use of mineral, active, and nanostructured fillers.
To develop predictive machine learning models for epoxy performance.

Main Methods:

Formulation of epoxy resins (Epidian 5, 53, 57) with various fillers (calcium carbonate, activated carbon, nanoclays).
Curing epoxy resins using TFF, Z-1, and PAC agents.
Experimental testing of mechanical properties (tensile, compressive, bending strength).
Application of machine learning algorithms and Shapley analysis for property prediction.

Main Results:

Calcium carbonate (10-20 wt%) in Epidian 5/53 resins cured with TFF/Z-1 yielded optimal strengths: up to 64 MPa (tensile), 145 MPa (compressive), and 123 MPa (bending).
Activated carbon and nanofillers provided moderate improvements, especially in flexible matrices.
Machine learning models achieved high accuracy (R² 0.93-0.95) for compressive and bending strength prediction.
Shapley analysis identified curing agents and fillers as significant predictive features.

Conclusions:

Calcium carbonate is a highly effective filler for enhancing epoxy adhesive mechanical properties.
Machine learning provides a powerful tool for optimizing epoxy formulations and predicting performance.
Further research is needed to improve tensile strength prediction models.