材料科学
硼化物
微观结构
烧结
复合材料
原位
基质(化学分析)
物理
气象学
作者
Mustafa Hamamcı,A. Alper Cerit,Fehmi Nair
标识
DOI:10.1016/j.matchar.2022.112075
摘要
In this study, the influence of sintering parameters on the microstructure and micromechanical properties of ceramic reinforced iron matrix composites is investigated. Composites containing 5–10-20-30 vol% B 4 C were fabricated by in-situ powder metallurgy (IPM) and sintered at various temperatures and durations. Hot pressed powder mixtures were sintered at constant duration (60 min at 1000–1150-1300 °C) and constant temperature (1150 °C for 30–120 min) in a protective environment. The influence of these parameters is evaluated by investigating microstructural changes using SEM, XRD, EDX, and optical microscopy, density and porosity by Archimedes method, and finally, micromechanical properties by hardness determination. Results showed that while increasing reinforcement ratios of B 4 C particles caused reduced density in the green compacts, B 4 C particles under a certain size (<7 μm) were randomly distributed in the mixture and lead to the formation of iron borides. Additionally, increased hardness was witnessed for all temperatures with increasing reinforcement ratios until 20 vol% B 4 C, after which hardness was decreased. In fact, 20 vol% B 4 C showed excellent properties and was comparatively much sensitive to both, increasing sintering temperatures and duration. In-situ synthesized iron borides became the decisive factor in the increased hardness of the composites. • The properties of Fe-B 4 C composites were evaluated at various sintering parameters. • The iron boride phase was formed in different sizes and shapes as a result of the diffusion of boron from the B 4 C. • Composition of mixture powders and sintering time affected the dispersion of the boride phase. • The hardness of composites was significantly developed by in-situ synthesized boride phases. • Sintering time causes the phase formation in larger areas that enhanced the overall hardness.
科研通智能强力驱动
Strongly Powered by AbleSci AI