Multi-objective Optimization Task Scheduling Of 3D Printing Based On Improved Genetic Algorithm

As the 3D printing market grows stronger,3D printing cloud service platforms combined with Internet technology are gradually increasing,and large-scale 3D printing manufacturing has become an inevitable trend in the development of 3D printing technology.Different from the traditional manufacturing model,3D printing manufacturing has the characteristics of one-time molding,customization,differentiation,and small batch.Therefore,task scheduling is particularly important for the clustered 3D printing manufacturing system.A good task scheduling method can not only guide the smooth progress of printing and manufacturing activities,but also reduce printing time and printing costs,and ensure the profit and competitiveness of manufacturing enterprises.In order to find a reasonable task scheduling method,this paper analyzes the process of 3D printing task scheduling,and establishes a 3D printing multi-objective optimization task scheduling problem model with the shortest printing time and the lowest printing cost as the optimization goal.The characteristics of 3D printing task scheduling are genetic the algorithm is improved,and the improved genetic algorithm is used to obtain the optimal solution of the optimization problem,and finally a 3D printing multi-objective optimization task scheduling method based on the improved genetic algorithm is obtained,and a 3D printing task scheduling experimental test platform is designed and experimentally verified.The specific work done by the subject research is as follows:(1)Compare the difference between the traditional 3D printing task scheduling method and the 3D printing multi-objective optimization task scheduling method,and highlight the superiority of the latter.Through the study of the overall process of 3D printing multi-objective optimization task scheduling,the optimization problem model is established from the three aspects of related problem definition,constraint conditions,and objective function.In order to simplify the problem,the n-norm weighting method is used to transform the multi-objective optimization problem into Combination target optimization problem.(2)Aiming at the characteristics of the multi-objective optimization scheduling problem of 3D printing tasks and the shortcomings of the traditional genetic algorithm,a method to improve the genetic algorithm is proposed.On the one hand,it introduces a concentration balance mechanism and suppression conditions in the offspring selection process,and on the other hand,it is iterative in the process,three rounds of iteration were transformed into one round of iteration.Design and apply the improved genetic algorithm to solve the 3D printing multi-objective optimization task scheduling problem,and conduct a simulation experiment to verify the superiority of the improved genetic algorithm.(3)Clarify the basic functions of the 3D printing task scheduling experiment test platform,design the overall architecture including the system processing layer,the scheduling decision layer,and the plan execution layer,and study the information packaging and storage of 3D printing resources,and the remote intelligence of heterogeneous 3D printers Access,3D printer intelligent control and status monitoring and other key technologies,write 3D printing task scheduling experiment management system software,and finally verify the design based on improved genetic algorithm 3D printing multi-objective optimization task on the built experimental test platform through example manufacturing experiments Effectiveness of scheduling methods.