Joint stiffness identification of industrial serial robots using 3D digital image correlation techniques

Industrial robots have been widely used in manufacturing for advantages of flexibility and high efficiency, while there exists a critical problem of low stiffness. Measuring the stiffnesses of joints accurately have a positive effect on optimizing the stiffness through compensation or posture adjustment. This study proposed a new method for stiffness identification of serial industrial robots using 3D digital image correlation (3D-DIC) techniques, which exhibits high accuracies. External forces are applied to the robot end and its 6-dimensional displacements are recorded with a 3D-DIC system. The values of joint stiffness are evaluated from the data of robot configurations, displacements and forces. The proposed method is implemented on the KUKA KR600-2830 robot experimentally and the average absolute value of relative error is 5.8%, which demonstrates that the proposed method provides much improved accuracy compared to the traditional method.

Torsional Vibration Attenuation Characteristics and Stiffness Identification of Flexible Coupling in Vehicle Power Train

Flexible coupling is one of the crucial components for vibration attenuation used in vehicle power train. Vibration attenuation characteristics and stiffness identification of flexible coupling are profoundly studied aiming at one vehicle power train. Firstly, the dynamics model of each crucial transmission component in power train is constructed. And the torsional vibration model of power train is established according to the concentrated mass method. The effects of coupling stiffness on vibration responses of power train are thoroughly analyzed based on system concentrated mass dynamics model. Secondly, the sensitivities of natural frequency and main forced vibration response parameters are calculated. The coupling stiffness is proved to be a sensitive parameter. Finally, taking the Geislinger coupling as an example, the damping and stiffness characteristics are acquired according to the parameter identification method based on the quantity of test data. The results provide the theory basis for the dynamics optimization of power train.