Predictive modeling of PMMA-based polymer composites reinforced with hydroxyapatite - a machine learning and FEM approach

Aug 3, 2026

The study presents predictive modeling for PMMA-based polymer composites reinforced with hydroxyapatite (HAp) using a combined machine-learning and FEM framework. It targets elastic modulus and compressive strength as key indicators governing structural integrity and functional performance, while also addressing biomedical requirements such as improved biocompatibility and osteoconductivity. By incorporating HAp to mitigate PMMA limitations in osseointegration and thermal risks, the work aims to capture multiscale interactions and enable reliable property prediction for PMMA-HAp composites used in orthopedic and dental applications.

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Introduction

R. K. Singh et alii, Fracture and Structural Integriyt, 73 (2025) 74-87; DOI: 10.3221/IGF-ESIS.73.06 | Predictive modeling of PMMA-based polymer composites reinforced with hydroxyapatite: a machine learning and FEM approach
Rohit Kumar Singh, Khyati Verma, G. C. Mohan Kumar Department of Mechanical Engineering, National Institute of Technology Karnataka, India [rohitking1007@gmail.com](rohitking1007@gmail.com), [https://orcid.org/0009-0002-0387-0389](https://orcid.org/0009-0002-0387-0389)[ ](https://orcid.org/0009-0002-0387-0389)[kverma@nitk.edu.in](kverma@nitk.edu.in), [https://orcid.org/0000-0001-5912-3572](https://orcid.org/0000-0001-5912-3572)
[mkumargc@nitk.edu.in](mkumargc@nitk.edu.in), [https://orcid.org/0000-0002-7282-7512](https://orcid.org/0000-0002-7282-7512) | | | --- | --- | | |
Citation: Singh, R. K., Verma, K., Kumar, G C. M., Predictive modeling of PMMAbased polymer composites reinforced with hydroxyapatite: a machine learning and FEM approach, Fracture and Structural Integrity, 73 (2025) 74-87.
Received: 21.03.2025
Accepted: 30.04.2025
Published: 07.05.2025
Issue: 07.2025
Copyright: © 2025 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | | KEYWORDS. Multiscale modeling, RVE, PMMA, Hydroxyapatite (HAp), Polymer matrix composites, Machine learning | | | INTRODUCTION
P gaining popularity across a range of industries due to their many beneficial characteristics and immense usability.
olymer-based composite materials (PMCs) are highly durable high-performance engineering materials that are
Their low heat conductivity 0.2–0.5 W/m K, easy fabrication, corrosion resistance, and high strength-to-weight ratio make them suitable for use in biomedical, automotive, aerospace, and other technological industries [1-3] . Two major classes of PMCs can be indicated: thermoset and thermoplastic polymers. Thermoset materials provide sturdiness and lifetime performance but do not allow any reheating or remolding once formed due to irreversible chemical changes. Thermoplastic polymers such as PMMA can be reheated, remolded and cooled without affecting its chemical properties which can be used in multiple applications [4] . Polymethyl methacrylate (PMMA) is widely utilized in biomedical applications, including bone cement and dental prosthetics, due to its biocompatibility and ease of processing. However, PMMA exhibits poor osseointegration, and non-degradability, which can impede effective bone regeneration and integration with host tissues. | | R. K. Singh et alii, Fracture and Structural Integriyt, 73 (2025) 74-87; DOI: 10.3221/IGF-ESIS.73.06 Additionally, PMMA's high exothermic polymerization temperature poses risk of thermal necrosis to surrounding tissues, and the potential release of toxic monomers. To address these challenges, incorporating hydroxyapatite (HAp) into PMMA matrices has been explored, aiming to enhance boicompatibility and mechanical properties to improve osseointegration and overall performance in orthopedic and dental applications. Hydroxyapatite (HAp) is utilized within this context for improvement purposes due to its natural occurrence and calcium apatite composition which closely resembles human bone and teeth. Hydroxyapatite is available from naturally as well as artificially synthesized sources. The natural sources of hydroxyapatite from animal bones are subjected to treatments to extract pure HAp by deproteinizing and calcining under high temperatures. Some synthetic methods are used for producing HAp include wet chemical precipitation, sol-gel processes, and hydrothermal techniques. The process of synthesis basically involves controlled reactions between sources of calcium and phosphate to form HAp crystals, which can be synthesized in accordance to particle size, crystallinity, and morphology, towards the maximi

FAQs

Why is hydroxyapatite (HAp) added to PMMA-based composites?

HAp is added to improve biocompatibility and osteoconductivity while enhancing mechanical performance, thereby supporting better osseointegration in orthopedic and dental applications.

What are the main challenges in predicting the properties of PMMA-HAp composites?

Elastic modulus prediction is difficult due to complex interactions occurring across different material scales in the composite.

How does the combined machine learning and FEM approach support property prediction?

The approach leverages machine learning to learn predictive relationships and FEM to model structural behavior, enabling multiscale-informed estimation of composite performance indicators.

Aug 3, 2026

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