Dislocation Plasticity of TiC‑Reinforced Nickel Alloy Under Thermomechanical Conditions: Insights From Discrete Dislocation Dynamics
Please login to view abstract download link
Metal–matrix composites (MMCs) have experimentally been shown to offer substantial advantages over pure alloys in tribological loading conditions, combining hard particles that resist wear with a softer matrix that mitigates the brittleness of carbide‑based reinforcements. While MMCs are emerging as a promising material class in additive manufacturing, our understanding of the complex relationship between process parameters, microstructural evolution, and tribological performance remains limited. To advance material development and process optimization for additively manufactured MMCs, this contribution lays the groundwork for linking the additive process to the resulting plasticity through the modelling of dislocation dynamics. We employ discrete dislocation dynamics (DDD) to model the evolution of dislocation structures in an additively manufactured TiC‑reinforced nickel alloy, building on the solute‑strengthening framework of Varvenne et al. [1] and previous DDD studies on LPBF‑manufactured alloys by Sudmanns et al. [2]. This approach captures the detailed physics of dislocation motion and enables the extraction of insights relevant for continuum‑scale models. Particular attention is given to the numerical challenges associated with modelling temperature‑dependent dislocation–particle interactions and the transient thermomechanical conditions inherent to additive manufacturing. The influence of particle characteristics and process‑related variables on the dislocation structure evolution is investigated, showing the applicability of DDD as a tool for understanding the mechanical properties of additively manufactured MMCs. Overall, this work provides a first step toward modelling the complex interplay between thermomechanical parameters and particle-matrix interface characteristics. It establishes a foundation for future multiscale investigations, supports the generation of synthetic data for machine‑learning‑based materials design, and contributes to the development of constitutive descriptions for precipitate‑strengthened materials at larger simulation scales.
