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From Conventional Dispersion Strengthening to Core–Shell Reinforcement Architectures in Iron-Based Metal Matrix Composites: A Review

Sep 2026 · Metals · 60 references
Aluminum Alloys Composites Properties Additive Manufacturing Materials and Processes

Abstract

Despite extensive research on iron-based metal matrix composites (Fe-based MMCs), an integrated assessment linking fabrication methods, reinforcement architectures, interface engineering, and strengthening mechanisms remains limited. This review summarizes recent advances in Fe-based MMCs, with emphasis on the relationships among processing routes, microstructural evolution, reinforcement characteristics, and mechanical performance. Powder metallurgy, casting, and additive manufacturing are critically compared in terms of their processing characteristics, advantages, limitations, and suitability for iron-based systems. The effects of reinforcement size, morphology, distribution, and volume fraction on composite performance are discussed. Particular attention is given to interface engineering strategies and architectured core–shell reinforcements produced through in situ reactions and solid-state diffusion, infiltration, laser cladding, sol–gel coating combined with additive manufacturing, electrochemical synthesis, and high-energy ball milling. Recent studies indicate that core–shell architectures can offer enhanced control of reinforcement–matrix interactions by combining hard ceramic or carbide phases with more ductile metallic components. Rod-like Me@MeC/Fe (Me = Ta, Nb, W) architectures and dispersed core–shell particles show promising combinations of strength, toughness, and wear resistance, although their performance depends strongly on shell architecture, interface characteristics, and processing conditions. Remaining challenges include reproducible and scalable fabrication, shell architecture control, interface stability, and long-term performance. Further progress may benefit from advanced reinforcement design, additive manufacturing, modelling, and AI-assisted optimization of high-performance Fe-based MMCs.

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