Vibration Diagnosis and Optimization of Industrial Robot Based on TPA and EMD Methods

This paper proposedmethod that combined transmission path analysis(TPA)and empirical mode decomposition(EMD)envelope analysis to solve the vibration problemof an industrial robot.Firstly,the deconvolution filter timedomain TPA method is proposed to trace the source along with the time variation.Secondly,the TPA method positioned themain source of robotic vibration under typically different working conditions.Thirdly,independent vibration testing of the Rotate Vector(RV)reducer is conducted under different loads and speeds,which are key components of an industrial robot.The method of EMD and Hilbert envelope was used to extract the fault feature of the RV reducer.Finally,the structural problems of the RV reducer were summarized.The vibration performance of industrial robots was improved through the RV reducer optimization.From the whole industrial robot to the local RV Reducer and then to the internal microstructure of the reducer,the source of defect information is traced accurately.Experimental results showed that the TPA and EMD hybrid methods were more accurate and efficient than traditional time-frequency analysis methods to solve industrial robot vibration problems.

300M高强钢大型构件全流程锻造变形机理及工艺研究进展

高强钢大型构件是国防工业和国民经济中高端装备的关键承力构件,其质量直接关系着高端装备的使用性能及服役安全.高强钢大型构件往往结构尺寸大、形状复杂,锻造成形过程中变形道次多,使得在变形过程中材料的软化机制多且复杂、温度分布差异大、流动行为难控,进而使得其形性协调难控制.因此,有必要开展高强钢大型构件锻造中的变形机理及工艺研究.本文综述了本团队联合中国第二重型机械集团德阳万航模锻有限责任公司(下文简称“二重万航”),围绕高强钢大型构件全流程锻造变形机理及工艺所做的研究工作.在机理方面,讨论了高强钢大型构件全流程锻造微观演化机制及宏微观建模与模拟.在工艺方面,讨论了材料热加工性能评估方法、毛坯-预锻件联合优化设计方法及局部控温控流工艺.在应用方面,介绍了二重万航基于上述理论指导及技术支持,实现不同机型起落架外筒及活塞杆成功研制实例,突破了大型构件整体模锻技术所面临的难题.最后对大型构件锻造机理及工艺进行了总结和展望.

Review and discussion on fire behavior of bridge girders

This paper presents an overview on fire behavior of bridge girders mainly including prestressed concrete(PC) bridge girders and steel bridge girders. The typical fire accidents occurred on bridges are illustrated and, the seriousness of posing threats to bridge structures resulted from increasing traffic fires, specially intense hydrocarbon fires generated from petrol-chemicals, is highlighted. The current researches, embracing high-temperature properties of constituent materials, prestress state, measurement in fire tests, numerical methods, structural fire resistance, and so forth, taken on coping with problems existing in fire behavior and structural fire behavior in bridge girders are reviewed and discussed. Further, strategies for enhancing fire resistance of bridge girders followed with failure criterion and mode in types of bridge structures are provided. Future research area along with emerging trends in structural fire behavior of bridge girders is also recommended for mitigating fire hazards occurred on bridge girders. Herein, it can be attained a conclusion from review and discussion that prestressed concrete bridge girders with thin webs, specially T-shaped bridge girder, are prone to unstable under fire exposure conditions. High-strength concrete utilized in prestressed concrete bridge girders is vulnerable to spalling at elevated temperature. Steel-truss bridge girder present a more significant fragility to fire exposure compared than other steel bridge girders.

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components

Finite-element analysis(FEA)for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures.Conventional methods,such as FEA,provide high fidelity results but require the solution of large linear systems that can be computationally intensive.Instead,Deep Learning(DL)techniques can generate results significantly faster than conventional run-time analysis.This can prove extremely valuable in real-time structural assessment applications.Our proposed method uses deep neural networks in the form of convolutional neural networks(CNN)to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions.The CNN was designed and trained to use the geometry,boundary conditions,and load as input to predict the stress contours.The proposed technique’s performance was compared to finite-element simulations using a partial differential equation(PDE)solver.The trained DL model can predict the stress distributions with a mean absolute error of 0.9%and an absolute peak error of 0.46%for the von Mises stress distribution.This study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications.