摆动焊接对异种热处理状态2219铝合金焊接接头性能的影响

针对2219异种热处理状态C1OS+CYS铝合金进行钨极氩弧焊接试验,对比研究了常规焊接与摆动焊接接头的力学性能、微观组织、硬度分布等.分析结果表明,相较常规接头,采用摆动焊接工艺的接头其力学性能有所提高,同时接头中下部PMZ、OAZ的硬度有所下降,宽度范围有所增加.应变分布结果表明,断裂前常规接头最大应变沿对角线分布...     

Effect of Isothermal Forging on the Microstructure and Mechanical Properties of Mg-8Gd-3Y-0.5Zr Alloy

Aiming at the problems of poor plastic forming ability， narrow forging temperature range， and strain rate sensitivity of rare earth magnesium alloys， a study on the microstructure and mechanical properties of Mg-8Gd-3Y-0.5Zr alloy with different isothermal forging processes is carried out. The microstructure and properties of the alloy in the as-cast， isothermal forged， and post-aging states after forging are studied with optical microscope (OM)， scanning electron microscope (SEM)， and tensile testing. The results show that significant dynamic recrystallization occurs during the isothermal forging process， a fine equiaxed grain structure is formed， and the mechanical properties of the alloy are greatly improved. When the isothermal forging temperature is 460 ºC and the strain rate is 0.02 s－1， the alloy structure performance is the best， the room temperature tensile yield strength (TYS) is 218 MPa， the ultimate tensile strength (UTS) is 299 MPa， and the fracture elongation (FE) is 19.2%. When the alloy is post-forging artificial aged， the α-Mg matrix is dispersed， the Mg5(Gd，Y) phase is precipitated， the UTS of the alloy is increased to 392 MPa， and the FE is reduced to 12.0%.     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.