Inicio  /  Applied Sciences  /  Vol: 12 Par: 20 (2022)  /  Artículo
ARTÍCULO
TITULO

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

Jinyeong Yu    
Seung Jun Oh    
Seunghun Baek    
Jonghyun Kim and Taekyung Lee    

Resumen

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.

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