Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The s...Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The simulation experiments of accelerated thermal cycling test were performed by ANSYS software. Based on orthogonal array experiments, a back-propagation artificial neural network (BPNN) was used to establish the nonlinear multivariate relationship between thermo-mechanical fatigue reliability and control factors. Then, PSO was applied to obtaining the optimal levels of control factors by using the output of BPNN as the affinity measure. The results show that the control factors, such as print circuit board (PCB) size, PCB thickness, substrate size, substrate thickness, PCB coefficient of thermal expansion (CTE), substrate CTE, silicon die CTE, and solder joint CTE, have a great influence on thermo-mechanical fatigue reliability of PBGA solder joints. The ratio of signal to noise of ANN-PSO method is 51.77 dB and its error is 33.3% less than that of Taguchi method. Moreover, the running time of ANN-PSO method is only 2% of that of the BPNN. These conclusions are verified by the confirmative experiments.展开更多
对多旋翼电动垂直起降(electric vertical takeoff and landing,eVTOL)航空器推进系统进行了可靠性分析与分配。首先,针对多旋翼eVTOL航空器可靠性历史数据积累不足的问题,使用模糊贝叶斯网络(fuzzy Bayesian network,FBN)建立可靠性分...对多旋翼电动垂直起降(electric vertical takeoff and landing,eVTOL)航空器推进系统进行了可靠性分析与分配。首先,针对多旋翼eVTOL航空器可靠性历史数据积累不足的问题,使用模糊贝叶斯网络(fuzzy Bayesian network,FBN)建立可靠性分析模型,对其可靠性先验数据进行了补充,并进行可靠性后验推理,辅助定位推进系统关键环节。其次,基于FBN可靠性分析模型,提出一种改进电子设备可靠性咨询组(advisory group on reliability of electronic equipment,AGREE)可靠性分配方法,对不同构型多旋翼eVTOL推进系统进行可靠性分配。结果表明,FBN可靠性分析模型补充了推进系统可靠性数据,可有效识别系统薄弱环节。改进AGREE分配法的可靠性分配结果符合SC-VTOL-01中对eVTOL航空器的可靠性要求,同时该方法得到的可靠性分配结果更为合理,体现了不同构型、子系统、部件间的差异。展开更多
基金Project(60371046) supported by the National Natural Science Foundation of ChinaProject(9140C0301060C03001) supported by the National Defense Science and Technology Foundation of Key Laboratory, China
文摘Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The simulation experiments of accelerated thermal cycling test were performed by ANSYS software. Based on orthogonal array experiments, a back-propagation artificial neural network (BPNN) was used to establish the nonlinear multivariate relationship between thermo-mechanical fatigue reliability and control factors. Then, PSO was applied to obtaining the optimal levels of control factors by using the output of BPNN as the affinity measure. The results show that the control factors, such as print circuit board (PCB) size, PCB thickness, substrate size, substrate thickness, PCB coefficient of thermal expansion (CTE), substrate CTE, silicon die CTE, and solder joint CTE, have a great influence on thermo-mechanical fatigue reliability of PBGA solder joints. The ratio of signal to noise of ANN-PSO method is 51.77 dB and its error is 33.3% less than that of Taguchi method. Moreover, the running time of ANN-PSO method is only 2% of that of the BPNN. These conclusions are verified by the confirmative experiments.
文摘对多旋翼电动垂直起降(electric vertical takeoff and landing,eVTOL)航空器推进系统进行了可靠性分析与分配。首先,针对多旋翼eVTOL航空器可靠性历史数据积累不足的问题,使用模糊贝叶斯网络(fuzzy Bayesian network,FBN)建立可靠性分析模型,对其可靠性先验数据进行了补充,并进行可靠性后验推理,辅助定位推进系统关键环节。其次,基于FBN可靠性分析模型,提出一种改进电子设备可靠性咨询组(advisory group on reliability of electronic equipment,AGREE)可靠性分配方法,对不同构型多旋翼eVTOL推进系统进行可靠性分配。结果表明,FBN可靠性分析模型补充了推进系统可靠性数据,可有效识别系统薄弱环节。改进AGREE分配法的可靠性分配结果符合SC-VTOL-01中对eVTOL航空器的可靠性要求,同时该方法得到的可靠性分配结果更为合理,体现了不同构型、子系统、部件间的差异。
