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.
Keywords
- Microscopic residual stress
- Microstructure
- Diffraction
- Symbolic regression
- Genetic programming
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Notes
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Output has to be scaled linearly to the range of the diffraction measurement.
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Acknowledgments
This work has been supported by Madrid Regional Government-FEDER grants Y2018/NMT-4668 (Micro-Stress- MAP-CM) and MAT2017-83825-C4-1-R. Thanks are also due to the FLNR-JINP for the beam time allocated on FSD instrument and to the Centrum Výzkumu Ŕeź, in Prague, for the EBSD map and the micro-structural analysis.
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Millán, L., Kronberger, G., Hidalgo, J.I., Fernández, R., Garnica, O., González-Doncel, G. (2021). Estimation of Grain-Level Residual Stresses in a Quenched Cylindrical Sample of Aluminum Alloy AA5083 Using Genetic Programming. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_27
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DOI: https://doi.org/10.1007/978-3-030-72699-7_27
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