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Related Experiment Video

Updated: Feb 28, 2026

Application of Design Aspects in Uniaxial Loading Machine Development

Application of Design Aspects in Uniaxial Loading Machine Development

Published on: September 19, 2018

Data-Driven Design of Epoxy-Granite Machine Foundations: Bayesian Optimization for Enhanced Compressive Strength and

Mohammed Y Abdellah^1,2, Osama M Irfan^3,4, Hanafy M Omar³

¹Mechanical Engineering Department, Faculty of Engineering, Qena University, Qena 83521, Egypt.

|February 27, 2026

Summary

This study optimizes epoxy-granite composites for machine tools using data-driven simulations. The developed framework enhances compressive strength and vibration damping, offering a sustainable alternative to cast iron by utilizing granite waste.

Keywords:

Bayesian modeling Gaussian Process Regression epoxy granite composite mechanical properties optimization simulation

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Last Updated: Feb 28, 2026

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Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

Area of Science:

Materials Science and Engineering
Sustainable Manufacturing
Composite Materials

Background:

Epoxy-granite (EG) composites offer a sustainable alternative to cast iron for machine tool foundations, utilizing granite quarry waste and epoxy.
Enhancing mechanical properties like compressive strength and vibration damping is crucial for machining accuracy and dynamic stability.
Existing experimental data has limitations in fully exploring the design space for optimal EG composite formulations.

Purpose of the Study:

To develop and validate a data-driven simulation framework for optimizing epoxy-granite composite mechanical properties.
To simultaneously maximize compressive strength and vibration damping in EG composites.
To assess the reliability and reduce experimental effort in developing high-performance EG composites.

Main Methods:

Integration of published experimental data with Gaussian Process Regression (GPR) surrogate modeling.
Application of Bayesian optimization (BO) to explore the four-dimensional design space (epoxy content, aggregate fractions).
Utilizing Bayesian Weibull analysis for probabilistic reliability assessment.

Main Results:

Experimental validation showed compressive strengths up to 76.8 MPa and peak damping ratio of 0.0202.
The integrated GPR-BO framework identified optimal formulations (22-26 wt% epoxy, 55-70% fine aggregates) with predicted strengths of 78-85 MPa and damping ratios near 0.022.
Bayesian Weibull analysis indicated consistent performance with moderate variability (shape parameters α ≈ 2.4-2.9).

Conclusions:

The study presents the first integrated GPR-BO-Bayesian Weibull framework for epoxy-granite composites, enabling simultaneous optimization and reliability assessment.
The approach significantly reduces experimental effort (over 70%) and promotes the circular economy by valorizing granite waste.
Future experimental validation is recommended to address predictive uncertainties in under-sampled regions.