期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 55, 期 17, 页码 4847-4861出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2016.1259669
关键词
Pareto optimisation; genetic algorithm; neural networks; finite-element analysis; parametric design; virtual samples
Mass and radial deformation are of great importance for a high-pressure turbine disc (HPTD). However, computational cost of computer-aided engineering (CAE) is too high to optimise the mutually restricted objectives. A parameterisation-based method is proposed to speed the optimisation process of HPTD: 'body-flange'-based parametric template is used to generate CAE samples; noise-based virtual samples are implemented to enlarge the training set, a cost-effective neural network is used as fitness function of non-dominated sorting genetic algorithm-II for optimisation whose initial population is the combination of different sample sets. Experiment results show that the proposed data-driven framework reduces the engineering difficulty of multi-objective optimisation, and it has high popularisation value for optimisation of other complex products.
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