The Research On Technical Problems In The Integration Of CAD/CAE/MDO

With the transformation of China’s manufacturing industry to automation,digitalization and intellectualization,the competition focus of enterprise is how to quickly and efficiently complete the design of new products,which calls for the automation and intellectualization of product design.The integration of CAD/CAE/MDO,which is a very complicated and iterative process,involving the heterogeneous data conversion,system integration,and numerous domain knowledge,is a key part of product design.The automation of the process and the reuse of optimization resources,are the most important problems to be solved.This paper focuses on the following contents:(1)An automatic generation of simulation model method based on SIMS(Superset Inheritance Model for Simulation)is proposed.To solve the time-cost and experiencerelied work of model modification before simulation,this paper proposes a hybrid and quantitative criteria for simulation generation which takes features construction,geometry dimension and topology,design intent into consideration synthetically.The operators of geometry modification includes features-based retain and suppress,topology based split and wrap-around,and virtual topology based split and merge.The linkage between geometry and operators is based on attributes which helps to realize the automatic generation of the simulation model.(2)A surrogate based multi-objective optimization algorithm,RBF-MOGA,(Radial basis function and multi-objective genetic algorithm)is proposed and automatically implemented with the help of CAD/CAE integration for synchronous reduction in multi-objective.The hybrid RBF-MOGA algorithm utilizes Orthogonal array-Latin hypercube sampling(OA-LHS)as sampling method,metamodeling technique radial basis functions(RBF)to construct the response surface fitting parameters and simulation responses,and Pareto-ranking-based multi-objective genetic algorithm(MOGA)to make a trade-off among multi-objectives.Points are sequentially added to reach real pareto front during each iterative process.Data analysis and visualization is utilized to verify the accuracy and efficiency of the proposed algorithm and offer solid evident for decision making.An automated multi-objective optimization tool,based on CAD/CAE integration,is developed to improve the efficiency.(3)Optimization knowledge storage,retrieval and reuse based on OPTR(Optimization rational)and SGM(Semantic-based graph model),is proposed.Knowledge involved in optimization process including design parameters,simulation information,optimization variable parameters and objects,is classified and extracted as OPT-R,which is adopted to represent concepts of the data and data relationship.SGM,written Neo4 j,is utilized to store the concrete optimization cases which works as a database for knowledge management and cases reuse.Based on graphics database,SGM is utilized for keyword retrieval providing users with large numbers of cases and similarity evaluation based on OPT-R is implemented for exact retrieval.SGM is encapsulated into CAD/CAE/MDO process for knowledge reuse.Based on the proposed technologies,an automated and semantic-knowledge driven quick optimization prototype system,named as QuickMDO,is proposed,written in python and Qt.C++,NXOpenAPI and python are utilized to implement the function of integration of CAD/CAE/MDO,the generation of simulation model and encapsulation of optimization algorithm.The prototype system is utilized to implement the optimization of industrial products to shorten the design cycle,and the results verify the efficiency and accuracy.