期刊
MATERIALS AND MANUFACTURING PROCESSES
卷 20, 期 3, 页码 553-567出版社
TAYLOR & FRANCIS INC
DOI: 10.1081/AMP-200053580
关键词
genetic algorithms; metal design; multiobjective optimization; steel
Determining the optimal heat treatment regimen and the required weight percentages for the chemical composites to obtain the desired mechanical properties of steel is a challenging problem for the steel industry. To tackle what is in essence an optimization problem, several neural network-based models, which were developed in the early stage of this research work, are used to predict the mechanical properties of steel such as the tensile strength (TS), the reduction of area (ROA), and the elongation. Because these predictive models are generally data driven, such predictions should be treated carefully. In this research work, evolutionary multiobjective (EMO) optimization algorithms are exploited not only to obtain the targeted mechanical properties but also to consider the reliability of the predictions. To facilitate the implementation of a broad range of single-objective and multi-objective algorithms, a versatile Windows 2000 (R)-based application is developed. The obtained results from the single-objective and the multiobjective optimization algorithms are presented and compared, and it is shown that the EMO techniques can be effectively used to deal with such optimization problems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据