Predictive Modeling and Sensitivity Analysis for Averting Nanoparticle Agglomeration in Metal Matrices

Abstract

Nanoparticle agglomeration is a significant challenge in improving the properties of metal matrix nanocomposites (MMNCs), as it leads to poor dispersion and weakens overall performance. Extensive experimental studies have explored ways to minimize agglomeration and identified key theoretical factors influencing this phenomenon. The present work formulates an effective yield strength prediction model that amalgamates several strengthening mechanisms - load transfer, increased dislocation density and Orowan strengthening. In addition, a degradation factor has also been included to address the effects of porosity. The model evaluates the impact of nanoparticle agglomeration on yield strength by introducing additional variables and identifies interfacial stress concentration and inadequate load distribution as primary contributors to strength reduction. It predicts a 69.29 % increase in yield strength with higher nanoparticle volume fractions, while larger nanoparticles (20-100 nm) and increased porosity (0-5 %) lead to reductions of 45.45 % and 5.50 %, respectively. Sensitivity analysis highlights key factors affecting yield strength, ensuring the model’s robustness and practical relevance. Validated against established theoretical frameworks and empirical data, the model demonstrates high accuracy, instilling confidence in its predictions. This study presents a unified approach to quantifying the interplay between strengthening mechanisms and agglomeration effects, providing valuable insights for optimizing MMNCs in advanced engineering applications.