In order to increase the heavy-duty AGV load capacity to 60t, this paper proposes a design scheme of AGV frame structure based on the working demand of AGV workshop; based on the designed heavy-duty AGV frame structure, a finite element model of the frame structure is established, and the static simulation of the AGV frame is carried out to get the stress distribution and deformation under the full-loaded working condition. Through the dynamic performance analysis of the AGV frame, the 7th to 12th order modal frequencies and vibration pattern diagrams of the frame are obtained, and the results show that the dynamic performance of the AGV is good, and the frequencies of each order are greater than the road excitation, and no resonance damage will occur.
Fault diagnosis is an important part of the intelligent development of industrial robots. Aiming at the problem of lack of data in industrial robot fault diagnosis, this paper introduces a fault diagnosis method based on digital twin and data-driven fusion. The consistency between the model and the actual device is achieved by constructing a digital twin model of the industrial robot and mapping it to the actual industrial robot in real time. In order to solve the problem of lack of data, the fault injection technique was used to inject fault data into the digital twin model and combined with historical data to construct a training dataset. Through simulation experiments on real welding robot data, the machine learning fault diagnosis model was trained and evaluated for precision, recall and F-Score. The experimental results show that this method can effectively solve the problem of lack of fault data and train a reliable fault detection model, providing an effective solution for industrial robot fault diagnosis.
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