Large interfacial strains in particles are crucial for promoting bonding in cold spraying(CS),initiated either by adiabatic shear instability(ASI)due to softening prevailing over strain hardening or by hydrostatic pla...Large interfacial strains in particles are crucial for promoting bonding in cold spraying(CS),initiated either by adiabatic shear instability(ASI)due to softening prevailing over strain hardening or by hydrostatic plasticity,which is claimed to promote bonding even without ASI.A thorough microstructural analysis is vital to fully understand the bonding mechanisms at play during microparticle impacts and throughout the CS process.In this study,the HEA CoCrFeMnNi,known for its relatively high strain hardening and resistance to softening,was selected to investigate the microstructure characteristics and bonding mech-anisms in CS.This study used characterization techniques covering a range of length scales,including electron channeling contrast imaging(ECCI),electron backscatter diffraction(EBSD),and high-resolution transmission microscopy(HR-TEM),to explore the microstructure characteristics of bonding and overall structure development of CoCrFeMnNi microparticles after impact in CS.HR-TEM lamellae were prepared using focused ion beam milling.Additionally,the effects of deformation field variables on microstructure development were determined through finite element modeling(FEM)of microparticle impacts.The ECCI,EBSD,and HR-TEM analyses revealed an interplay between dislocation-driven processes and twinning,leading to the development of four distinct deformation microstructures.Significant grain refinement occurs at the interface through continuous dynamic recrystallization(CDRX)due to high strain and temperature rise from adiabatic deformation,signs of softening,and ASI.Near the interface,a necklace-like structure of refined grains forms around grain boundaries,along with elongated grains,resulting from the coexistence of dynamic recovery and discontinuous dynamic recrystallization(DDRX)due to lower temperature rise and strain.Towards the particle or substrate interior,concurrent twinning and dislocation-mediated mechanisms refine the structure,forming straight,curved,and intersected twins.At the top of the particles,only deformed grains with a low dislocation density are observed.Our results showed that DRX induces microstructure softening in highly strained interface areas,facilitating atomic bonding in CoCrFeMnNi.HR-TEM investigation confirms the formation of atomic bonds between particles and substrate,with a gradual change in crystal lattice orientation from the particle to the substrate and the occurrence of some misfit dislocations and vacancies at the interface.Finally,the findings of this research suggest that softening and ASI,even in materials resistant to softening,are required to establish bonding in CS.展开更多
在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的...在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的反馈CSI会降低自适应系统的性能。针对该问题,提出了一种基于多智能体深度Q网络的资源分配(Multi-agent Deep Q Network Based Resource Allocation, MADQN-RA)方法。该方法将水声软频率复用网络视为多智能体系统,并使用过时的反馈CSI序列作为系统状态。通过建立有效的奖励表达式,智能体可以跟踪时变时延水声信道的变化特性并做出相应的资源分配决策。为了进一步提高智能体的决策准确度,同时避免状态空间维度增大时的部分学习成本,结合动态状态长度方法改进了MADQN-RA。仿真结果表明,所提方法实现的系统性能优于基于其他学习的方法和基于信道预测的方法,且更接近理论最优值。展开更多
文摘Large interfacial strains in particles are crucial for promoting bonding in cold spraying(CS),initiated either by adiabatic shear instability(ASI)due to softening prevailing over strain hardening or by hydrostatic plasticity,which is claimed to promote bonding even without ASI.A thorough microstructural analysis is vital to fully understand the bonding mechanisms at play during microparticle impacts and throughout the CS process.In this study,the HEA CoCrFeMnNi,known for its relatively high strain hardening and resistance to softening,was selected to investigate the microstructure characteristics and bonding mech-anisms in CS.This study used characterization techniques covering a range of length scales,including electron channeling contrast imaging(ECCI),electron backscatter diffraction(EBSD),and high-resolution transmission microscopy(HR-TEM),to explore the microstructure characteristics of bonding and overall structure development of CoCrFeMnNi microparticles after impact in CS.HR-TEM lamellae were prepared using focused ion beam milling.Additionally,the effects of deformation field variables on microstructure development were determined through finite element modeling(FEM)of microparticle impacts.The ECCI,EBSD,and HR-TEM analyses revealed an interplay between dislocation-driven processes and twinning,leading to the development of four distinct deformation microstructures.Significant grain refinement occurs at the interface through continuous dynamic recrystallization(CDRX)due to high strain and temperature rise from adiabatic deformation,signs of softening,and ASI.Near the interface,a necklace-like structure of refined grains forms around grain boundaries,along with elongated grains,resulting from the coexistence of dynamic recovery and discontinuous dynamic recrystallization(DDRX)due to lower temperature rise and strain.Towards the particle or substrate interior,concurrent twinning and dislocation-mediated mechanisms refine the structure,forming straight,curved,and intersected twins.At the top of the particles,only deformed grains with a low dislocation density are observed.Our results showed that DRX induces microstructure softening in highly strained interface areas,facilitating atomic bonding in CoCrFeMnNi.HR-TEM investigation confirms the formation of atomic bonds between particles and substrate,with a gradual change in crystal lattice orientation from the particle to the substrate and the occurrence of some misfit dislocations and vacancies at the interface.Finally,the findings of this research suggest that softening and ASI,even in materials resistant to softening,are required to establish bonding in CS.
文摘在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的反馈CSI会降低自适应系统的性能。针对该问题,提出了一种基于多智能体深度Q网络的资源分配(Multi-agent Deep Q Network Based Resource Allocation, MADQN-RA)方法。该方法将水声软频率复用网络视为多智能体系统,并使用过时的反馈CSI序列作为系统状态。通过建立有效的奖励表达式,智能体可以跟踪时变时延水声信道的变化特性并做出相应的资源分配决策。为了进一步提高智能体的决策准确度,同时避免状态空间维度增大时的部分学习成本,结合动态状态长度方法改进了MADQN-RA。仿真结果表明,所提方法实现的系统性能优于基于其他学习的方法和基于信道预测的方法,且更接近理论最优值。
