Next-generation wear-resistant cast irons: A review of multicomponent systems

Multicomponent white cast iron (MWCI) is widely used in many critical engineering machine parts such as roll materials for steel hot rolling, mineral pulverizing mills, chute liners, slurry transportation pipelines, and blast furnace components, owing to superior wear resistance. This review integrates experimental evidence on MWCI wear behaviour across compositional design, microstructural evolution, and tribological performance. A PRISMA 2020-guided systematic search was conducted (1996–2025) across Scopus, Web of Science, and Google Scholar. Evidence was synthesized across four analytical classes: carbide morphology, carbide size, matrix properties, and testing conditions. The literature reveals that transitioning from M₇C₃-dominated to multi-carbide systems can reduce abrasive wear rates by 60–80%, depending on carbide type and test conditions, while carbide size governs shadow zone formation and matrix hardness controls carbide anchoring. For erosive wear, multi-carbide architectures reduced erosion by 57–80% relative to single-carbide counterparts under equivalent test conditions, while high-temperature oxidation protection through CoCr₂O₄ spinel formation suppressed erosion rates by up to 94.9% in specific alloy systems. Overall, MWCI wear resistance emerges from the synergistic interaction among carbide morphology, matrix composition, and thermal-oxidation control, rather than any single microstructural parameter. Abrasive wear is dominated by carbide size-shielding mechanisms, while erosive wear is controlled by interfacial bonding and oxidation resistance. The complexity of engineering factors in multicomponent systems causes dominant factors to vary across different testing conditions. Despite decades of research into the wear-resistance of MWCI, current literature lacks a comprehensive, focused analysis of these specific wear properties. This study fills the crucial gap by integrating analysis to bridge these specific areas, establishing its unique value and importance within the current literature, and guide future research direction in advanced manufacturing strategies and predictive modelling frameworks.