This study investigated enhancing the wear resistance of Ti6Al4V alloys for medical applications by incorporating Ti C nanoreinforcements using advanced spark plasma sintering(SPS). The addition of up to 2.5wt% Ti C s...This study investigated enhancing the wear resistance of Ti6Al4V alloys for medical applications by incorporating Ti C nanoreinforcements using advanced spark plasma sintering(SPS). The addition of up to 2.5wt% Ti C significantly improved the mechanical properties, including a notable 18.2% increase in hardness(HV 332). Fretting wear tests against 316L stainless steel(SS316L) balls demonstrated a 20wt%–22wt% reduction in wear volume in the Ti6Al4V/Ti C composites compared with the monolithic alloy. Microstructural analysis revealed that Ti C reinforcement controlled the grain orientation and reduced the β-phase content, which contributed to enhanced mechanical properties. The monolithic alloy exhibited a Widmanstätten lamellar microstructure, while increasing the Ti C content modified the wear mechanisms from ploughing and adhesion(0–0.5wt%) to pitting and abrasion(1wt%–2.5wt%). At higher reinforcement levels, the formation of a robust oxide layer through tribo-oxide treatment effectively reduced the wear volume by minimizing the abrasive effects and plastic deformation. This study highlights the potential of SPS-mediated Ti C reinforcement as a transformative approach for improving the performance of Ti6Al4V alloys, paving the way for advanced medical applications.展开更多
Spark作为通用的计算引擎,以其简单、快速、可扩展的优势,被广泛地应用于大数据的处理和分析中.然而,Spark默认采用哈希分区或范围分区对数据进行划分,导致其在处理键倾斜分布的数据时,常常出现各分区数据量严重不均衡的问题.诸多优化...Spark作为通用的计算引擎,以其简单、快速、可扩展的优势,被广泛地应用于大数据的处理和分析中.然而,Spark默认采用哈希分区或范围分区对数据进行划分,导致其在处理键倾斜分布的数据时,常常出现各分区数据量严重不均衡的问题.诸多优化方法被提出,如迁移分区、贪心分区、反馈分区等,但往往存在数据传输量大、额外计算成本高、运行时间长等问题.为更好地缓解键倾斜分布问题带来的影响,本文提出了一种自适应的Spark数据均衡分区方法.该方法引入了奖惩思想对数据分区过程进行适当调控,同时对于数据量较大的键进行分割,使得各个分区的数据量相对均衡.该方法首先对数据采样并预估键权重.其次,按照键权重对样本数据降序排列,确保所有分区都有初始数据.再次,根据奖惩分配策略,自适应地更新各个分区的分配概率,并将待分配的键指向分配概率最高的分区.对于超过分区容量的键的数据,则分割为多个部分且指向不同分区.在所有样本数据分配完成后,获得自适应分区方案.在实际分区时,对于样本中出现的键对应的数据按照自适应分区方案进行分配;对于未出现的键对应的数据,则按照哈希方法进行分区.最后,通过实验验证,基于新方法设计的自适应均衡分区器(Adaptive Data Balanced Partitioner,ADBP)能够有效缓解键倾斜的负面影响.在真实数据集上,ADBP的WordCount程序总运行时间比自带分区器Hash、Range分别平均缩短了1.51%、29.90%,比现有基于学习自动机的自适应哈希分区器(Learning Automata Hash Partitioner,LAHP)、对倾斜的中间数据块进行拆分合并(Splitting and Combination algorithm for skew Intermediate Data block,SCID)算法、粗粒度放置和细粒度放置(Fined-Coarse Grained Intermediate Data Placement,FCGIDP)算法分别平均缩短了8.12%、21.64%、19.62%.展开更多
文摘This study investigated enhancing the wear resistance of Ti6Al4V alloys for medical applications by incorporating Ti C nanoreinforcements using advanced spark plasma sintering(SPS). The addition of up to 2.5wt% Ti C significantly improved the mechanical properties, including a notable 18.2% increase in hardness(HV 332). Fretting wear tests against 316L stainless steel(SS316L) balls demonstrated a 20wt%–22wt% reduction in wear volume in the Ti6Al4V/Ti C composites compared with the monolithic alloy. Microstructural analysis revealed that Ti C reinforcement controlled the grain orientation and reduced the β-phase content, which contributed to enhanced mechanical properties. The monolithic alloy exhibited a Widmanstätten lamellar microstructure, while increasing the Ti C content modified the wear mechanisms from ploughing and adhesion(0–0.5wt%) to pitting and abrasion(1wt%–2.5wt%). At higher reinforcement levels, the formation of a robust oxide layer through tribo-oxide treatment effectively reduced the wear volume by minimizing the abrasive effects and plastic deformation. This study highlights the potential of SPS-mediated Ti C reinforcement as a transformative approach for improving the performance of Ti6Al4V alloys, paving the way for advanced medical applications.
文摘Spark作为通用的计算引擎,以其简单、快速、可扩展的优势,被广泛地应用于大数据的处理和分析中.然而,Spark默认采用哈希分区或范围分区对数据进行划分,导致其在处理键倾斜分布的数据时,常常出现各分区数据量严重不均衡的问题.诸多优化方法被提出,如迁移分区、贪心分区、反馈分区等,但往往存在数据传输量大、额外计算成本高、运行时间长等问题.为更好地缓解键倾斜分布问题带来的影响,本文提出了一种自适应的Spark数据均衡分区方法.该方法引入了奖惩思想对数据分区过程进行适当调控,同时对于数据量较大的键进行分割,使得各个分区的数据量相对均衡.该方法首先对数据采样并预估键权重.其次,按照键权重对样本数据降序排列,确保所有分区都有初始数据.再次,根据奖惩分配策略,自适应地更新各个分区的分配概率,并将待分配的键指向分配概率最高的分区.对于超过分区容量的键的数据,则分割为多个部分且指向不同分区.在所有样本数据分配完成后,获得自适应分区方案.在实际分区时,对于样本中出现的键对应的数据按照自适应分区方案进行分配;对于未出现的键对应的数据,则按照哈希方法进行分区.最后,通过实验验证,基于新方法设计的自适应均衡分区器(Adaptive Data Balanced Partitioner,ADBP)能够有效缓解键倾斜的负面影响.在真实数据集上,ADBP的WordCount程序总运行时间比自带分区器Hash、Range分别平均缩短了1.51%、29.90%,比现有基于学习自动机的自适应哈希分区器(Learning Automata Hash Partitioner,LAHP)、对倾斜的中间数据块进行拆分合并(Splitting and Combination algorithm for skew Intermediate Data block,SCID)算法、粗粒度放置和细粒度放置(Fined-Coarse Grained Intermediate Data Placement,FCGIDP)算法分别平均缩短了8.12%、21.64%、19.62%.
