高压物理学报2025，Vol.39Issue(11)：85-102,18.DOI:10.11858/gywlxb.20251134
(Ti,Zr)(C,N)陶瓷调幅分解:数据驱动高效设计及硬度-韧性协同强化
Spinodal Decomposition in(Ti,Zr)(C,N)Ceramics:Data-Driven Efficient Design and Hardness-Toughness Synergy
摘要
Abstract
Traditional transition-metal carbide and nitride ceramics often exhibit a trade-off between hardness and toughness,leading to significantly reduced service life under severe conditions such as wear,corrosion,and high temperature.In this study,a spinodal decomposition-induced phasese paration strategy was employed to simultaneously enhance the hardness and toughness of(Ti,Zr)(C,N)carbonitride ceramics.Guided by thermodynamic calculations,a series of compositional variants of(Ti,Zr)(C,N)ceramics were synthesized,and the effects of aging temperature and duration on the microstructural evolution were systematically investigated.The experimental results demonstrate that spinodal decomposition induces the formation of a nanoscale phase-separated network,which strengthens the material while preserving fracture resistance.Furthermore,machine-learning models were developed to quantitatively correlate composition,microstructural features,and mechanical properties,enabling efficient screening and optimization of carbonitride ceramics.This work not only elucidates the intrinsic mechanisms by which spinodal decomposition enhances ceramic mechanical performance but also provides a data-driven framework for the rational design of high-performance ceramics for extreme environments.关键词
机器学习/(Ti,Zr)(C,N)/调幅分解/成分设计Key words
machine learning/(Ti,Zr)(C,N)/spinodal decomposition/composition design引用本文复制引用
张志轩,张宗耀,常国锐,王伟礼,李娜,张伟彬..(Ti,Zr)(C,N)陶瓷调幅分解:数据驱动高效设计及硬度-韧性协同强化[J].高压物理学报,2025,39(11):85-102,18.基金项目
国家重点研发计划(2023YFB3712600) （2023YFB3712600）
国家自然科学基金(52171009) （52171009）
