采用熔化极气体保护电弧(Gas metal arc,GMA)作为热源,以H08Mn2Si焊丝作为填充材料,开展了多层单道薄壁构件堆积层尺寸特征研究。借助金相显微镜测量了堆积层尺寸,分析了堆积层尺寸特性并阐明其成形机制。结果表明,堆积层尺寸在前四层...采用熔化极气体保护电弧(Gas metal arc,GMA)作为热源,以H08Mn2Si焊丝作为填充材料,开展了多层单道薄壁构件堆积层尺寸特征研究。借助金相显微镜测量了堆积层尺寸,分析了堆积层尺寸特性并阐明其成形机制。结果表明,堆积层尺寸在前四层处于不稳定状态,波动较大。随堆积层数的增加,堆积层层高逐渐减小并趋于稳定,堆积层层宽先减小,随后逐渐增大并趋于稳定,层宽在第二个堆积层具有极小值。进一步设计了二次回归旋转组合试验方法,采集的试验数据作为训练样本,基于神经网络算法建立了堆积工艺参数(堆积电流、行走速度、堆积电压)与堆积层尺寸的非线性模型,经测试数据样本验证表明,模型预测精度较高,堆积层尺寸预测最大相对误差小于6.98%。根据堆积层尺寸预测模型,进行了封闭路径与非封闭路径薄壁构件的堆积成形,试验结果表明,该模型能够应用于薄壁构件GMA增材制造自适应分层切片过程。展开更多
Giant magnetostrictive actuators(GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model...Giant magnetostrictive actuators(GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations,including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function(E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 μm and decreases the root-mean-square error in displacement to within0.5 μm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data;it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.展开更多
在同向双螺杆挤出机中通过熔融接枝反应制备了EPM g GMA ,将其与PBT在转矩流变仪中熔融共混可以获得增韧的PBT工程塑料 .实验中EPM g GMA接枝率的测定采用红外工作曲线法 ,选用CCl4 做溶剂以避免溶剂对样品吸收峰的干扰 .随着EPM g GMA...在同向双螺杆挤出机中通过熔融接枝反应制备了EPM g GMA ,将其与PBT在转矩流变仪中熔融共混可以获得增韧的PBT工程塑料 .实验中EPM g GMA接枝率的测定采用红外工作曲线法 ,选用CCl4 做溶剂以避免溶剂对样品吸收峰的干扰 .随着EPM g GMA接枝率的增加 ,PBT EPM g GMA的缺口冲击强度相应提高 ,共混物中EPM g GMA的粒径尺寸减小 ,当EPM g GMA的接枝率为 4 7mL 1 0 0gEPM时 ,EPM g GMA的粒径尺寸可达 0 5 μm ,PBT EPM g GMA的缺口冲击强度达到 5 1 6kJ m2 ,是纯PBT的 3展开更多
文摘采用熔化极气体保护电弧(Gas metal arc,GMA)作为热源,以H08Mn2Si焊丝作为填充材料,开展了多层单道薄壁构件堆积层尺寸特征研究。借助金相显微镜测量了堆积层尺寸,分析了堆积层尺寸特性并阐明其成形机制。结果表明,堆积层尺寸在前四层处于不稳定状态,波动较大。随堆积层数的增加,堆积层层高逐渐减小并趋于稳定,堆积层层宽先减小,随后逐渐增大并趋于稳定,层宽在第二个堆积层具有极小值。进一步设计了二次回归旋转组合试验方法,采集的试验数据作为训练样本,基于神经网络算法建立了堆积工艺参数(堆积电流、行走速度、堆积电压)与堆积层尺寸的非线性模型,经测试数据样本验证表明,模型预测精度较高,堆积层尺寸预测最大相对误差小于6.98%。根据堆积层尺寸预测模型,进行了封闭路径与非封闭路径薄壁构件的堆积成形,试验结果表明,该模型能够应用于薄壁构件GMA增材制造自适应分层切片过程。
基金This work was supported by the Basic Technological Research Projects(Metrology)(Grant No.JSJL2020206B001).
文摘Giant magnetostrictive actuators(GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations,including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function(E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 μm and decreases the root-mean-square error in displacement to within0.5 μm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data;it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.
