以自行设计的连续驱动摩擦焊机推力缸支撑体为例,对其结构进行了设计.应用有限元分析软件对支撑体进行了参数化建模并校核了其刚度与强度.在静力学分析基础上,利用DOE(Design of Experiment)实验设计法对推力缸支撑体的结构参数进行了...以自行设计的连续驱动摩擦焊机推力缸支撑体为例,对其结构进行了设计.应用有限元分析软件对支撑体进行了参数化建模并校核了其刚度与强度.在静力学分析基础上,利用DOE(Design of Experiment)实验设计法对推力缸支撑体的结构参数进行了采样设计,通过灵敏度分析得出了对支撑体性能影响程度较大的设计变量.而后基于响应面模型及MOGA算法完成了支撑体结构优化设计,在保证支撑体满足使用要求的前提下,使其质量减少了16.56%,实现了支撑体轻量化设计.展开更多
基于ANSYS13.0 workbench的APDL参数化语言,以5 000 k N摩擦焊机的旋转夹具为研究对象,建立有限元模型,对旋转夹具进行拓扑优化分析和静力学分析,得到钢爪体的等效应力和位移云图,并以质量最小、最大等效位移、最大应力为约束条件,设计...基于ANSYS13.0 workbench的APDL参数化语言,以5 000 k N摩擦焊机的旋转夹具为研究对象,建立有限元模型,对旋转夹具进行拓扑优化分析和静力学分析,得到钢爪体的等效应力和位移云图,并以质量最小、最大等效位移、最大应力为约束条件,设计优化参数,求解最优参数组合,以达到既满足强度要求又用料最省。展开更多
Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing ...Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.展开更多
文摘以自行设计的连续驱动摩擦焊机推力缸支撑体为例,对其结构进行了设计.应用有限元分析软件对支撑体进行了参数化建模并校核了其刚度与强度.在静力学分析基础上,利用DOE(Design of Experiment)实验设计法对推力缸支撑体的结构参数进行了采样设计,通过灵敏度分析得出了对支撑体性能影响程度较大的设计变量.而后基于响应面模型及MOGA算法完成了支撑体结构优化设计,在保证支撑体满足使用要求的前提下,使其质量减少了16.56%,实现了支撑体轻量化设计.
文摘基于ANSYS13.0 workbench的APDL参数化语言,以5 000 k N摩擦焊机的旋转夹具为研究对象,建立有限元模型,对旋转夹具进行拓扑优化分析和静力学分析,得到钢爪体的等效应力和位移云图,并以质量最小、最大等效位移、最大应力为约束条件,设计优化参数,求解最优参数组合,以达到既满足强度要求又用料最省。
文摘Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.
