Multi-objective Optimization of web profile of railway wheel using Bi-directional Evolutionary Structural Optimization

Document Type : Research Paper

Authors

1 School of Mechanical Engineering, College of Engineering, University of Tehran, Iran.

2 Mechanical Engineering Department, Islamic Azad University-Karaj Branch, Karaj, Iran.

Abstract

In this paper, multi-objective optimization of railway wheel web profile using bidirectional evolutionary structural optimization (BESO) algorithm is investigated. Using a finite element software, static analysis of the wheel based on a standard load case, and its modal analysis for finding the fundamental natural frequency is performed. The von Mises stress and critical frequency as the problem objectives are combined using different weight factors in order to find the sensitivity number in the method, which specifies which elements to be omitted and which to be added. The iterative process is continued until convergence to an a priori specified material volume. The resulted web profiles show that when the stress is important, material removal is from the middle part of the web, while for frequency as the important objective, the removal is from near the rim part of the web. The suggested profile, corresponding to equal weight factor for the objectives, has a better volume and stress state compared to a standard web profile, and has a more uniform stress distribution. However, higher natural frequency, compared to that of the standard profile, are obtained for larger frequency weight factors, although with a bigger volume. In the end, considering manufacturability of the wheel, the jagged profile resulted from BESO is replaced with a fitted smooth curve and performing the finite element analysis on it. It is seen that there is an improvement in the obtained objectives for the smoothened profile, with no significant change in volume.

Keywords

Main Subjects

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Volume 48, Issue 2
December 2017
Pages 307-318
  • Receive Date: 09 July 2017
  • Revise Date: 14 September 2017
  • Accept Date: 20 September 2017