Prediction of microscopic residual stresses using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{MILLAN:2023:apples,
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author = "Laura Millan and Gabriel Kronberger and
Ricardo Fernandez and Gizo Bokuchava and Patrice Halodova and
Alberto Saez-Maderuelo and Gaspar Gonzalez-Doncel and
J. Ignacio Hidalgo",
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title = "Prediction of microscopic residual stresses using
genetic programming",
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journal = "Applications in Engineering Science",
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volume = "15",
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pages = "100141",
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year = "2023",
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ISSN = "2666-4968",
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DOI = "doi:10.1016/j.apples.2023.100141",
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URL = "https://www.sciencedirect.com/science/article/pii/S266649682300016X",
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keywords = "genetic algorithms, genetic programming, Material
science, Machine learning, Symbolic regression,
Residual stress, Neutron diffraction, Microstructure",
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abstract = "Metallurgical manufacturing processes commonly used in
the industry (rolling, extrusion, shaping, machining,
etc.) usually cause residual stress development which
can remain after thermal heat treatments. These
stresses can be detrimental for the in-service
performance of structural components, which makes their
study and understanding important. Residual stress
variations are usually determined at a macroscopic
scale (commonly, using diffraction methods). However,
stress variations at the microscopic scale of the
individual crystallites (grains), are also relevant.
Contrary to the macroscopic residual stresses,
microscopic residual stresses are difficult to quantify
using conventional procedures. We propose to use
machine learning to find equations that describe
microscopic residual stresses. Concretely, we show that
we are able to learn equations to reproduce the
diffraction profiles from microstructural
characteristics using genetic programming. We evaluate
the learned equations using real neutron diffraction
peaks as a reference, obtaining accurate results for
the most frequent grain orientations with runtimes of a
few minutes",
- }
Genetic Programming entries for
Laura Millan Garcia
Gabriel Kronberger
Ricardo Fernandez
Gizo Bokuchava
Patrice Halodova
Alberto Saez-Maderuelo
Gaspar Gonzalez Doncel
Jose Ignacio Hidalgo Perez
Citations