Estimation of Grain-Level Residual Stresses in a Quenched Cylindrical Sample of Aluminum Alloy AA5083 Using Genetic Programming

Laura Millán, Gabriel Kronberger, J. Ignacio Hidalgo, Ricardo Fernández, Oscar Garnica, Gaspar González-Doncel

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

Residual stresses are originated during manufacturing processes of metallic materials, so its study is important to avoid catastrophic accidents during component service. There are two main types of residual stresses, according to the length scale; macroscopic and microscopic. While the determination of tmacroscopic ones is almost a routine analysis, determining the microscopic stress of individual grains remains a pending task. In this paper, we present an approach using genetic programming to obtain the micro residual stresses in grains of a quenched cylindrical sample of aluminium alloy AA5083. The microstructure of this alloy is formed by grains with different orientation and stress. To obtain the stress of each grain we estimate the values of the micro residual stresses for each crystallographic orientation using information from neutron and electron back-scattered diffraction experiments. This information includes orientation maps of a normal section to the cylinder axes (individual orientations) and the dimensions of each grain. We assume that the micro residual stresses of each grain can be expressed as a function based on these variables and use genetic programming to find this expression.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Proceedings
EditorsPedro A. Castillo, Juan Luis Jiménez Laredo
PublisherSpringer
Pages421-436
Number of pages16
ISBN (Print)9783030726980
DOIs
Publication statusPublished - 2021
Event24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021 - Virtual, Online
Duration: 7 Apr 20219 Apr 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12694 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021
CityVirtual, Online
Period07.04.202109.04.2021

Keywords

  • Diffraction
  • Genetic programming
  • Microscopic residual stress
  • Microstructure
  • Symbolic regression

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