Copper matrix composites doped with ceramic particles are known to effectively enhance the mechanical properties,thermal expansion behavior and high-temperature stability of copper while maintaining high thermal and e...Copper matrix composites doped with ceramic particles are known to effectively enhance the mechanical properties,thermal expansion behavior and high-temperature stability of copper while maintaining high thermal and electrical conductivity.This greatly expands the applications of copper as a functional material in thermal and conductive components,including electronic packaging materials and heat sinks,brushes,integrated circuit lead frames.So far,endeavors have been focusing on how to choose suitable ceramic components and fully exert strengthening effect of ceramic particles in the copper matrix.This article reviews and analyzes the effects of preparation techniques and the characteristics of ceramic particles,including ceramic particle content,size,morphology and interfacial bonding,on the diathermancy,electrical conductivity and mechanical behavior of copper matrix composites.The corresponding models and influencing mechanisms are also elaborated in depth.This review contributes to a deep understanding of the strengthening mechanisms and microstructural regulation of ceramic particle reinforced copper matrix composites.By more precise design and manipulation of composite microstructure,the comprehensive properties could be further improved to meet the growing demands of copper matrix composites in a wide range of application fields.展开更多
To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization...To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization(PPO) has become an effective solution. Aiming at the multi-objective quality control problem of a company's cold-rolled products, based on industrial production data, we proposed a process parameter design and optimization method that combined multi-objective quality prediction and PPO. This method used the multi-output support vector regression(MSVR) method to simultaneously predict multiple quality indices. The MSVR prediction model was used as the effect verification model of the PPO results. It performed multi-process parameter collaborative design and realized the optimization of production process parameters for customized multi-objective quality requirements. The experimental results showed that, compared with the traditional single-objective quality prediction model based on support vector regression(SVR), the multi-objective prediction model could better take into account the coupling effect between process parameters and quality index, the MSVR model prediction accuracy was higher than that of the SVR, and the optimized process parameters were more capable and reflected the influence of metallurgical mechanism on the quality index,which were more in line with actual production process requirements.展开更多
基金supported by National Natural Science Foundation of China(No.51971101)Science and Technology Development Program of Jilin Province,China(20230201146G X)Exploration Foundation of State Key Laboratory of Automotive Simulation and Control(asclzytsxm-202015)。
文摘Copper matrix composites doped with ceramic particles are known to effectively enhance the mechanical properties,thermal expansion behavior and high-temperature stability of copper while maintaining high thermal and electrical conductivity.This greatly expands the applications of copper as a functional material in thermal and conductive components,including electronic packaging materials and heat sinks,brushes,integrated circuit lead frames.So far,endeavors have been focusing on how to choose suitable ceramic components and fully exert strengthening effect of ceramic particles in the copper matrix.This article reviews and analyzes the effects of preparation techniques and the characteristics of ceramic particles,including ceramic particle content,size,morphology and interfacial bonding,on the diathermancy,electrical conductivity and mechanical behavior of copper matrix composites.The corresponding models and influencing mechanisms are also elaborated in depth.This review contributes to a deep understanding of the strengthening mechanisms and microstructural regulation of ceramic particle reinforced copper matrix composites.By more precise design and manipulation of composite microstructure,the comprehensive properties could be further improved to meet the growing demands of copper matrix composites in a wide range of application fields.
基金financially supported by the Fundamental Research Funds for the Central Universities (No.FRF-MP20-08)。
文摘To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization(PPO) has become an effective solution. Aiming at the multi-objective quality control problem of a company's cold-rolled products, based on industrial production data, we proposed a process parameter design and optimization method that combined multi-objective quality prediction and PPO. This method used the multi-output support vector regression(MSVR) method to simultaneously predict multiple quality indices. The MSVR prediction model was used as the effect verification model of the PPO results. It performed multi-process parameter collaborative design and realized the optimization of production process parameters for customized multi-objective quality requirements. The experimental results showed that, compared with the traditional single-objective quality prediction model based on support vector regression(SVR), the multi-objective prediction model could better take into account the coupling effect between process parameters and quality index, the MSVR model prediction accuracy was higher than that of the SVR, and the optimized process parameters were more capable and reflected the influence of metallurgical mechanism on the quality index,which were more in line with actual production process requirements.
