科创中国

Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures.However,new materials and processing techniques inevitably lead to higher costs.Also,material substitution and processing lightweighting have to be realized through body structural profiles and locations.In the huge conventional workload of lightweight optimization,model modifications involve heavy manual work,and it always leads to a large number of iteration calculations.As a new technique in carbody lightweighting,the implicit parameterization is used to optimize the carbody structure to improve the materials utilization rate in this paper.The implicit parameterized structural modeling enables the use of automatic modification and rapid multidisciplinary design optimization(MDO)in carbody structure,which is impossible in the traditional structure finite element method(FEM)without parameterization.The structural SFE parameterized model is built in accordance with the car structural FE model in concept development stage,and it is validated by some structural performance data.The validated SFE structural parameterized model can be used to generate rapidly and automatically FE model and evaluate different design variables group in the integrated MDO loop.The lightweighting result of body-in-white(BIW)after the optimization rounds reveals that the implicit parameterized model makes automatic MDO feasible and can significantly improve the computational efficiency of carbody structural lightweighting.This paper proposes the integrated method of implicit parameterized model and MDO,which has the obvious practical advantage and industrial significance in the carbody structural lightweighting design.

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

针对轨道车辆铝型材焊接接头缺乏标准化管理、重复建模等问题,设计开发了一个3D铝型材焊接接头库,实现了三维接头模型及其数据信息的有效管理.首先在Pro/E系统中创建了具有参数化功能的样板模型;然后利用MicrosoftAccess创建了数据库,用ADO数据库访问接口技术解决了与Pro/E系统之间的通信;最后在VisualC++编译环境下,调用Pro/Toolkit应用程序提供的相关函数,编制了菜单和用户操作界面.研究结果表明,该铝型材焊接接头库的建立方便了车体建模,还可以允许用户按需求扩展焊接接头库,实现了系列化设计,提高了设计效率.