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Prediction of Secondary Dendrite Arm Spacing in Al Alloys Using Machine Learning

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Abstract

In this study, three machine learning (ML) models were developed to predict the secondary dendrite arm spacing (SDAS) and then predictions were validated experimentally. First, a three-layer artificial neural network (ANN) was built to predict the SDAS. Then, a linear regression model (LR) with backward selection method is applied to study the relationship of different elemental properties, processing parameters, and SDAS and make a prediction. A principle component analysis (PCA) further explores these relationships. The results show that the ANN model has the best performance compared with the LR and PCA models. Compared with the classical coarsening equation, the current SDAS predictions reveal a deviation from nearly linear relationship with the negative cubic root of cooling rate, which indicates there are other elemental properties that should be accounted for.

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Authors and Affiliations

  1. Department of Metallurgical and Materials Engineering, University of Alabama, Tuscaloosa, AL, 35487, USA

    Aqi Dong & Laurentiu Nastac

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  1. Aqi Dong
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  2. Laurentiu Nastac
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Correspondence to Laurentiu Nastac.

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Manuscript submitted January 25, 2021; accepted April 8, 2021.

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Dong, A., Nastac, L. Prediction of Secondary Dendrite Arm Spacing in Al Alloys Using Machine Learning. Metall Mater Trans B 52, 2395–2403 (2021). https://doi.org/10.1007/s11663-021-02183-w

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  • DOI: https://doi.org/10.1007/s11663-021-02183-w

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