轴向柱塞泵是液压系统中的核心元件,斜盘式轴向柱塞泵的寿命及可靠性直接取决于斜盘-滑靴副、缸体-配流盘副和缸体-柱塞副3种摩擦副的材料、配对情况及工艺参数的选择。使用MWF-10往复式摩擦磨损实验机进行实验,记录硬材料(38Cr Mo Al,2...轴向柱塞泵是液压系统中的核心元件,斜盘式轴向柱塞泵的寿命及可靠性直接取决于斜盘-滑靴副、缸体-配流盘副和缸体-柱塞副3种摩擦副的材料、配对情况及工艺参数的选择。使用MWF-10往复式摩擦磨损实验机进行实验,记录硬材料(38Cr Mo Al,20Cr Mn Ti,30Cr Mo VA)与软材料(HMn58-3,QAl9-4,QT500-7)所组成的摩擦副的磨损情况。基于正交实验法的分析,得到配对材料表面粗糙度和硬度对磨损量和摩擦因数的影响。当摩擦副配对材料为HMn58-3(Ra0.2)/20Cr Mn Ti(Ra0.2)时软材料的磨损量最小,硬材料表面粗糙度对摩擦因数和磨损量的影响最大。展开更多
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
文摘轴向柱塞泵是液压系统中的核心元件,斜盘式轴向柱塞泵的寿命及可靠性直接取决于斜盘-滑靴副、缸体-配流盘副和缸体-柱塞副3种摩擦副的材料、配对情况及工艺参数的选择。使用MWF-10往复式摩擦磨损实验机进行实验,记录硬材料(38Cr Mo Al,20Cr Mn Ti,30Cr Mo VA)与软材料(HMn58-3,QAl9-4,QT500-7)所组成的摩擦副的磨损情况。基于正交实验法的分析,得到配对材料表面粗糙度和硬度对磨损量和摩擦因数的影响。当摩擦副配对材料为HMn58-3(Ra0.2)/20Cr Mn Ti(Ra0.2)时软材料的磨损量最小,硬材料表面粗糙度对摩擦因数和磨损量的影响最大。
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.
