Optimization analysis of assembly system based on digital twin and deep learning
Aiming at the problems of the slow response of equipment in the operation of traditional automated assembly systems and lack of close connection with data and reliance on manual experience for equipment maintenance, a new optimization method for assembly systems is proposed. First, a digital twin established to monitor the operating status of the equipment in real time. Secondly, the artificial neural network is trained through deep learning technology based on historical assembly big data. Then the trained artificial neural network is used to compare the next step based on the collected real time data. Work status is predicted, so that it has the ability of self prediction and self perception, and finally realizes intelligence.
Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering