Predicting the Effect of Processing Parameters on Caliber-Rolled Mg Alloys through Machine Learning

The multi-pass caliber rolling (MPCR) of Mg alloy has attracted much attention due to its engineering and manufacturing advantages. The MPCR process induces a unique microhardness variation, which has only been predicted using a finite element analysis thus far. This study employed machine learning...

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Bibliographic Details
Published in:Applied sciences 2022-10, Vol.12 (20), p.10646
Main Authors: Yu, Jinyeong, Oh, Seung Jun, Baek, Seunghun, Kim, Jonghyun, Lee, Taekyung
Format: Article
Language:English
Subjects:
Alloys
artificial neural network
caliber rolling
Datasets
Deformation
Finite element analysis
Finite element method
generative adversarial networks
hardness
Learning algorithms
Machine learning
magnesium
Mechanical properties
Microhardness
Neural networks
Predictions
Process parameters
Steel
Variation
Yield stress
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Summary:The multi-pass caliber rolling (MPCR) of Mg alloy has attracted much attention due to its engineering and manufacturing advantages. The MPCR process induces a unique microhardness variation, which has only been predicted using a finite element analysis thus far. This study employed machine learning as an alternative method of microhardness prediction for the first time. For this purpose, two machine-learning approaches were evaluated: the artificial neural network (ANN) approach and that aided by generative adversarial networks (GANs). These approaches predicted microhardness variation in the most difficult case (i.e., after the final-pass MPCR deformation). The machine-learning approaches provided a good prediction for the center area of the cross-section, because the prediction was relatively easy due to the small deviation in microhardness. In contrast, the ANN failed to anticipate the shifted hardness variation in the side sections, leading to a low predictability. Such an issue was effectively improved by integrating the GAN with the ANN.
ISSN:2076-3417
2076-3417
DOI:10.3390/app122010646