Application Of Uncertainty MDO In Lightweight Design Of Driving Axle Housing

Development and application of lightweight design are effective measures to improve automobile performance and decrease fuel consumption and gas emission. Generally speaking, the process of lightweight design needs to meet a lot of performance indexes, so the multidisciplinary design optimization theory is introduced in, but the uncertainties which exist widely in the design, manufacture, and service process inevitably influence the effectiveness of the lightweight design scheme. So, the uncertainty-based multidisciplinary design optimization (UMDO) methods have been the significant trend of automobile components lightweight design.Uncertainty-based multidisciplinary design optimization is the combination of multidisciplinary design optimization process and uncertainty-based design optimization process, its main contents include:systematical analysis and optimization, disciplinary analysis and optimization, design of experiment, approximate model technology, modeling and propagation analysis of uncertainties, equivalent modeling technologies of constraint reliability, etc.. UMDO have two derivative problems, which are robust multidisciplinary design optimization (RMDO) and reliability-based multidisciplinary design optimization (RBMDO). In this paper, the driving axle housing is used as research object to be studied systematically on the lightweight design according to the UMDO.(1) The sparse grid method aiming at processing high dimensionality problems is introduced to the building of approximate model, and the determination method of sampling method and building process of approximate model using sparse grid method are stated. Then the Haupt function and NASA reducer design optimization examples are used to verify the efficiency and accuracy of sparse grid method in building high dimensionality approximate model.(2) The finite element model of driving axle housing is built. Then the results of driving axle housing static strength and fatigue tests are summarized, and the modal frequency measuring test is added in. Through the contrast of finite element and tests results, the accuracy of finite element model of driving axle housing is verified.(3) The systematical optimization model of lightweight design is built, and each discipline scope is defined according to the sensitivity analysis results of each design variables. Then the mathematical model of UMDO is built and uncertainty distribution model of each design variable is premised. Based on the results of disciplinary division, the UMDO model of system-level and discipline-level are built.(4) In order to obtain a more reasonable lightweight design scheme, the UMDO workflow based on collaborative optimization method is set up in the MDO software Optimus. At last, the robust and reliable lightweight design scheme of driving axle housing is obtained.(5) The UMDO workflow in this project can provide a reference for the other key parts optimization process which integrate multiple analysis softwares, DOE methods, approximate model technologies, etc..