Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of ...Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.展开更多
【目的】在空间超冗余机械臂动力学建模中,其结构复杂、自由度多及刚性弱导致的动力学耦合问题十分突出,难以获得精准的动力学模型。针对此问题,提出了一种应用迭代WLS-TCS算法的空间超冗余机械臂地面动力学参数辨识方法,为获取机械臂...【目的】在空间超冗余机械臂动力学建模中,其结构复杂、自由度多及刚性弱导致的动力学耦合问题十分突出,难以获得精准的动力学模型。针对此问题,提出了一种应用迭代WLS-TCS算法的空间超冗余机械臂地面动力学参数辨识方法,为获取机械臂的高精度动力学模型和空间在轨动力学控制研究奠定基础。【方法】首先,采用一种基于终端交叉和转向的粒子群优化(Terminal Crossover and Steering-based Particle Swarm Optimization,TCS-PSO)算法来设计满足多约束条件的周期傅里叶级数,并将其作为最优的激励轨迹;其次,应用迭代加权最小二乘(Iterative Weighted Least Squares,IWLS)法获取最小参数集,通过迭代加权逐步剔除数据中的异常值,使得辨识结果更加鲁棒、准确。【结果】试验结果表明,在激励轨迹中,采用TCS优化方法获得的轨迹回归矩阵条件数更少,且能更好满足所给的约束条件。在参数辨识中,采用IWLS法辨识所得的结果对比递归最小二乘法,力矩残差均方根(Root Mean Square,RMS)值平均降低约2.22%;对比加权最小二乘法,力矩残差RMS值平均降低约4.85%。将获取的参数模型代入到零力控制试验中,实际效果符合预期。展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.51275415 and50905144)the Natural Science Basic Research Plan in Shanxi Province(No.2011JQ6004)the Program of the Ministry of Education of China for Introducing Talents of Discipline to Universities(No.B08040)
文摘Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.
文摘【目的】在空间超冗余机械臂动力学建模中,其结构复杂、自由度多及刚性弱导致的动力学耦合问题十分突出,难以获得精准的动力学模型。针对此问题,提出了一种应用迭代WLS-TCS算法的空间超冗余机械臂地面动力学参数辨识方法,为获取机械臂的高精度动力学模型和空间在轨动力学控制研究奠定基础。【方法】首先,采用一种基于终端交叉和转向的粒子群优化(Terminal Crossover and Steering-based Particle Swarm Optimization,TCS-PSO)算法来设计满足多约束条件的周期傅里叶级数,并将其作为最优的激励轨迹;其次,应用迭代加权最小二乘(Iterative Weighted Least Squares,IWLS)法获取最小参数集,通过迭代加权逐步剔除数据中的异常值,使得辨识结果更加鲁棒、准确。【结果】试验结果表明,在激励轨迹中,采用TCS优化方法获得的轨迹回归矩阵条件数更少,且能更好满足所给的约束条件。在参数辨识中,采用IWLS法辨识所得的结果对比递归最小二乘法,力矩残差均方根(Root Mean Square,RMS)值平均降低约2.22%;对比加权最小二乘法,力矩残差RMS值平均降低约4.85%。将获取的参数模型代入到零力控制试验中,实际效果符合预期。
基金Natural Science Foundation for Key Program of China(50735005,50935007)Natural Science Foundation(50905145)National Basic Research Program of China(2010CB731701)
