An in-depth understanding of material flow behaviour is crucial for numerical simulation of plastic deformation processes. In present work, we use a Symbolic Regression method in combination with Genetic Programming for modelling flow stress curves. In contrast to classical regression methods that fit parameters to an equation of a given form, symbolic regression searches for both numerical parameters and the equation form simultaneously; therefore, no prior assumption on a flow model is required. This identification process is done by generating and adapting equations iteratively using a genetic algorithm. The constitutive model is derived for two aluminium wrought alloys: a conventional AA6082 and modified Cu-containing AA7000 alloy. The required dataset is created by performing a series of hot compression tests at temperatures between 350 °C and 500 °C and strain rates from 10−3 to 10 s−1 using a deformation dilatometer. The measured data, experimental set-up parameters as well as the material process history and its chemical composition are stored in a SQL database using a python™ script. To correct raw measured data, e.g. minimize the noise, an in-house Flow Stress Analysis Toolkit was used. The obtained results represent a data-driven free-form constitutive model and are compared to a physics-based model, which describes the flow stress in terms of internal state parameters (herein, mean dislocation density). We find that both models reproduce reasonably well the measured data, while for modeling using symbolic regression no prior knowledge on materials behavior was required.
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2 July 2019
PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING: ESAFORM 2019
8–10 May 2019
Vitoria-Gasteiz, Spain
Research Article|
July 02 2019
Prediction of stress-strain curves for aluminium alloys using symbolic regression
Evgeniya Kabliman;
Evgeniya Kabliman
a)
1)
LKR Leichtmetallkompetenzzentrum Ranshofen GmbH, Center for Low-Emission Transport, AIT Austrian Institute of Technology GmbH
, 5282 Ranshofen-Braunau am Inn, Austria
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Ana Helena Kolody;
Ana Helena Kolody
b)
1)
LKR Leichtmetallkompetenzzentrum Ranshofen GmbH, Center for Low-Emission Transport, AIT Austrian Institute of Technology GmbH
, 5282 Ranshofen-Braunau am Inn, Austria
b)Corresponding author: ana.kolody@ait.ac.at
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Michael Kommenda;
Michael Kommenda
c)
2)
Josef Ressel Centre for Symbolic Regression, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria
, Softwarepark 11, 4232 Hagenberg, Austria
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Gabriel Kronberger
Gabriel Kronberger
d)
2)
Josef Ressel Centre for Symbolic Regression, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria
, Softwarepark 11, 4232 Hagenberg, Austria
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AIP Conf. Proc. 2113, 180009 (2019)
Citation
Evgeniya Kabliman, Ana Helena Kolody, Michael Kommenda, Gabriel Kronberger; Prediction of stress-strain curves for aluminium alloys using symbolic regression. AIP Conf. Proc. 2 July 2019; 2113 (1): 180009. https://doi.org/10.1063/1.5112747
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