Research On Modeling And Scheduling Of Reconfigurable Production Line Based On Digital Twin

With the rapid development of the global economy,the demand of personalized market is growing day by day.If enterprises want to stand out in the competitive market,they must strengthen the adaptability of the manufacturing system.Reconfigurable manufacturing system is an advanced production and manufacturing mode.It has become an important means for enterprises to improve their manufacturing capability with rapid transformation capability and low reconfiguration cost.Reconfigurable production line is one of the main categories of reconfigurable manufacturing system.Applying digital twinning technology to production line reconfiguration can solve the problems of incomplete data acquisition,low service level,and opaque reconstruction process of traditional reconfiguration schemes.Combined with the above analysis,this paper conducts research on the modeling and scheduling of reconfigurable production lines based on digital twin technology.The main work is as follows:First of all,in view of the lack of reconfigurability of the digital twin architecture,the reconstruction negotiation mechanism is introduced into the five-dimensional twin model to model the equipment,which is used to realize the independent reconstruction of the production line.An improved genetic algorithm is proposed to optimize the equipment layout.The algorithm has a chromosome repair mechanism,which avoids a large number of repeated random operations in the population initialization and crossover mutation stages when the traditional genetic algorithm is used to solve the equipment layout problem.The experiment in an example shows that the proposed algorithm can accelerate the training speed and has better convergence.Secondly,in view of the problems of low utilization rate of historical data and lack of real-time response ability of meta-heuristic algorithm in solving job shop scheduling problems,a deep reinforcement learning algorithm with selective elimination mechanism is proposed.This algorithm uses historical data for training,and can conduct offline learning.Because the traditional deep reinforcement learning algorithm has the problem of premature loss of effective historical data during training,so the selection and elimination mechanism of genetic algorithm is introduced into the sample elimination of experience pool.The benchmark data set is used for verification.Compared with other traditional algorithms,the proposed algorithm has better scheduling results and convergence,and can make full use of historical data.Finally,combined with the improved five-dimensional twin model and equipment layout optimization strategy proposed above,the digital twin workshop is built,and the deep reinforcement learning scheduling algorithm is used as the driving strategy for production line reconstruction.Based on the B/S architecture,the reconfigurable production line digital twin simulation system is designed.The system has the functions of production line operation and scheduling visualization,real-time interaction,dynamic management,and so on.It proves that the application of digital twin technology can solve the problems of low service level and opaque reconstruction process in traditional production line reconfiguration simulation.