A method for predicting effective thermal conductivities(ETCs) of three-dimensional five-directional(3D5D) braided composites is presented. The effective thermal conductivity prediction method contains a digital image...A method for predicting effective thermal conductivities(ETCs) of three-dimensional five-directional(3D5D) braided composites is presented. The effective thermal conductivity prediction method contains a digital image processing technology. Multiple scanning electron microscopy(SEM)images of composites are analyzed to obtain actual microstructural features. These actual microstructural features of 3D5D braided composites are introduced into representative volume element(RVE) modeling. Apart from applying actual microstructural features,compression effects between yarns are considered in the modeling of RVE,making the RVE more realistic. Therefore,the ETC prediction method establishes a representative unit cell model that better reflects the true microstructural characteristics of the 3D5D braided composites. The ETCs are predicted with the finite element method. Then thermal conductivity measurements are carried out for a 3D5D braided composite sample.By comparing the predicted ETC with the measured thermal conductivity, the whole process of the ETC prediction method is proved to be effective and accurate,where a relative error of only 2.9 % is obtained.Furthermore,the effects of microstructural features are investigated,indicating that increasing interior braiding angles and fiber fill factor can lead to higher transverse ETCs. Longitudinal ETCs decrease with increasing interior braiding angles,but increase with increasing fiber fill factor. Finally,the influence of variations of microstructure parameters observed in digital image processing are investigated. To explore the influence of variations in microstructural features on variations in predicted ETCs,the actual probability distributions of microstructural features obtained from the 3D5D braided composite sample are introduced into the ETC investigation. The results show that,compared with the interior braiding angle,variations in the fiber fill factor exhibit more significant effects on variations in ETCs.展开更多
背景:3D打印技术可根据患者实际病情和治疗需求设计构建模型、手术导板和个性化植入体或固定物,在创伤性骨折修复中展示了巨大的应用前景。目的:综述3D打印技术在创伤性骨折中的应用。方法:检索Web of science、PubMed和中国知网数据库2...背景:3D打印技术可根据患者实际病情和治疗需求设计构建模型、手术导板和个性化植入体或固定物,在创伤性骨折修复中展示了巨大的应用前景。目的:综述3D打印技术在创伤性骨折中的应用。方法:检索Web of science、PubMed和中国知网数据库2020-2024年发表的创伤骨科领域3D打印技术应用的相关文献,英文检索词为“traumatic fracture,3D printing technology,digital model,surgical guide”,中文检索词为“创伤性骨折,3D打印技术,数字模型,手术导板”,经筛选和分析,最终纳入60篇文献进行分析。结果与结论:①创伤性骨折是各种致伤因素导致的骨骼连续性中断和完整性破坏的骨折现象,以可靠方案提高复位愈合效果,已成为骨外科相关研究领域亟需解决的热点问题;②3D打印技术是以数字模型数据为基础的,运用粉末状金属或聚合物等可黏合成型材料以立体光刻、沉积建模和光聚合物喷射等形式制造满足需求三维实体的技术,在数字骨科生物医学领域应用广泛;③3D打印技术在疾病诊断、术前规划、重建骨折三维模型、定制骨科植入体、定制固定支具及假肢、手术导板制作和骨缺损修复等方面发挥了显著的优势,可根据患者实际病情和治疗需求设计构建模型、手术导板和个性化植入体或固定物,为创伤性骨折的治疗提供了新的思路。展开更多
文摘A method for predicting effective thermal conductivities(ETCs) of three-dimensional five-directional(3D5D) braided composites is presented. The effective thermal conductivity prediction method contains a digital image processing technology. Multiple scanning electron microscopy(SEM)images of composites are analyzed to obtain actual microstructural features. These actual microstructural features of 3D5D braided composites are introduced into representative volume element(RVE) modeling. Apart from applying actual microstructural features,compression effects between yarns are considered in the modeling of RVE,making the RVE more realistic. Therefore,the ETC prediction method establishes a representative unit cell model that better reflects the true microstructural characteristics of the 3D5D braided composites. The ETCs are predicted with the finite element method. Then thermal conductivity measurements are carried out for a 3D5D braided composite sample.By comparing the predicted ETC with the measured thermal conductivity, the whole process of the ETC prediction method is proved to be effective and accurate,where a relative error of only 2.9 % is obtained.Furthermore,the effects of microstructural features are investigated,indicating that increasing interior braiding angles and fiber fill factor can lead to higher transverse ETCs. Longitudinal ETCs decrease with increasing interior braiding angles,but increase with increasing fiber fill factor. Finally,the influence of variations of microstructure parameters observed in digital image processing are investigated. To explore the influence of variations in microstructural features on variations in predicted ETCs,the actual probability distributions of microstructural features obtained from the 3D5D braided composite sample are introduced into the ETC investigation. The results show that,compared with the interior braiding angle,variations in the fiber fill factor exhibit more significant effects on variations in ETCs.
