摘要：Passive variable stiffness joints have unique advantages over active variable stiffness joints and are currently eliciting increased *** passive variable stiffness joints rely mainly on sensors and special control algorithms,resulting in a bandwidth-limited response speed of the *** propose a new passive power-source-free stiffness-self-adjustable mechanism that can be used as the elbow joint of a robot *** new mechanism does not require special stiffness regulating motors or sensors and can realize large-range self-adaptive adjustment of stiffness in a purely mechanical *** variable stiffness mechanism can automatically adjust joint stiffness in accordance with the magnitude of the payload,and this adjustment is a successful imitation of the stiffness adjustment characteristics of the human *** response speed is high because sensors and control algorithms are not *** variable stiffness principle is explained,and the design of the variable stiffness mechanism is analyzed.A prototype is fabricated,and the associated hardware is set up to validate the analytical stiffness model and design experimentally.

摘要：In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental *** experimental data of residual stress and microhardness were measured in the same *** residual stress and microhardness laws were investigated and *** neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap *** experimental data were divided as training-testing sets in *** energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as *** prediction performances with different network configuration of developed ANN models were compared and *** developed ANN model with network configuration of 4-7-6-2 showed the best predict *** predicted values showed a good agreement with the experimental *** addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were *** can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.