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.
To greatly improve the design efficiency and the prediction of the welding properties of on-site solder joints in the welding workshop, an intelligent calculation algorithm based on CATIA. API for secondary development of the solder joint level is designed. This algorithm, based on different interference collision radiation radii, calculates the correlation accuracy between different solder joints and BIW products, and extracts the BIW product information data related to the solder joints, to realize the automatic generation technology of digital and intelligent welding attribute BOM. On this basis, we use Python to write the linear regression algorithm in machine learning to train the welding attribute parameters of the solder joints.
Accurate ergonomic risk assessment can provide favourable support for the improvement of workers' work tasks, and the traditional man-machine risk assessment method is inconvenient to operate, time-consuming and greatly affected by human subjective factors. In this paper, an ergonomic risk assessment system based on fuzzy theory is designed and developed, using the improved rapid whole body assessment (REBA) method based on fuzzy theory. The evaluation system uses a motion capture device to provide input and automate ergonomic evaluation, reducing time consumption. The reliability of the evaluation system was verified by the handling experiment, and the system evaluation results were compared and compared with the ergonomics expert and JACK software, and the results showed that the correlation coefficient between ergonomic experts and the system was r=0.947, and the correlation coefficient between JACK software and the system was r=0.856, which had significant correlation.
KEYWORDS: Transportation, Intelligence systems, Manufacturing, Control systems, Control systems design, Mobile robots, Computing systems, Automation, Telecommunications, Industry
According to the characteristics of the stamping line, this paper designs a 20T intelligent handling robot, which realizes the automatic loading and unloading and handling of stamping sheets. The communication between the stamping line and the handling robot is established, and the stamping line and the handling robot are controlled by using the WCS and the dispatching system to realize the automation of the stamping workshop. Experiments have shown that the use of intelligent handling robots has increased the efficiency of the stamping line by 25%, and the processing accuracy has increased by 33%, which has greatly improved the level of factory automation, provided strong technical support for the overall layout of intelligent factory automation, and promoted intelligent manufacturing. High-end, green and intelligent development of the industry.
Aiming at the switching production mode of 4+n multi car series in a domestic luxury independent brand welding workshop, a new module production mode is formulated by combining three production modes of order production, pull production and high module production. The MES system is used to schedule the production plan of the welding workshop, reorganize and form independent module orders for each line, so as to meet the demand of 4+n car series on-line production at the same time. At the same time, by using the characteristics of each line, the position of each sub assembly line is monitored in real time through PLC, and the current production model is identified, detected, and information converted at the key position. At the same time, the corresponding solutions to the planned and unexpected situations are formulated.
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