Parametric design-based multi-objective optimisation for high-pressure turbine disc. Issue 17 (2nd September 2017)

• Record Type: Journal Article
• Authors: Cui, Dongliang; Feng, Guoqi; Zhou, Ping; Zhang, Yajun
• Is Part Of: International journal of production research. Volume 55:Issue 17(2017)
• Page Start: 4847
• Page End: 4861
• Publication Date: 2017-09-02
• Subjects: Pareto optimisation -- genetic algorithm -- neural networks -- finite-element analysis -- parametric design -- virtual samples; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2016.1259669
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : 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.