Influence of the interaction between Si and Sc on the microstructure and tensile properties of as casted Al-Si-Sc alloys

Wang, Zengjie; Li, Xuan; Zhu, Chen; Xue, Junmin; Guo, Zhichao; Zhang, Yanan; Li, Xiang

doi:10.1016/j.jallcom.2022.167650

Cited by 13 publications

(4 citation statements)

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“…Scandium has also shown a certain refinement effect in Al-Si-Mg alloys, though not as effective as that of Ti. Its main mechanism consists of heterogeneous nucleation by the formation of the Al 3 Sc phase [83][84][85]. In an Al-7Si-0.25Mg alloy with 0.05% Sr and 0.36% Al-5Ti-1B, the addition of Sc in concentrations of 200-800 ppm resulted in a continuous increase of hardness and strength due to microstructural refinement [86].…”

Section: Grain Refinersmentioning

confidence: 99%

The Influence of Alloying Elements on the Microstructure and Properties of Al-Si-Based Casting Alloys: A Review

Callegari

Lima

Coelho

2023

Metals

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The excellent casting behavior and mechanical and corrosion properties of aluminum-silicon (Al-Si)-based alloys make them ideal for the manufacture of lightweight components with complex geometries. However, these properties depend directly on their microstructure, which, in its turn, is strongly affected by the composition of the alloy, among other factors. Several elements can be added to the material aiming to promote microstructural changes, e.g., grain refinement, optimization of phase morphology and distribution, and precipitation strengthening. Efforts are continuously put into such enhancements of cast Al alloys since they lead to quality improvements that allow for weight reduction and safety increase. Considering the technological relevance of the subject, this paper provides an overview of the research focused on the addition of alloying elements to these alloys, with a greater focus on Al-Si-based systems and the comprehension of the effects of these additions on their microstructure and properties.

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Section: Grain Refinersmentioning

confidence: 99%

The Influence of Alloying Elements on the Microstructure and Properties of Al-Si-Based Casting Alloys: A Review

Callegari

Lima

Coelho

2023

Metals

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“…Moreover, considering that the experimental points are distributed in the commercial hypoeutectic Al-Si-Mg-Sc space (see Supplementary Figures S5 and S6), the trained ANN models should be applicable to predict the mechanical properties over the entire hypoeutectic Al- x Si- y Mg- z Sc composition space. In order to test the predictive ability of the current models at unknown points in the range of hypoeutectic Al-Si-Mg alloys, the lasted experimental points of Al-6Si- x Sc ( x = 0.3, 0.6, 1 wt.%) alloys from the work of Wang et al [ 25 ]. were chosen as the predicted system.…”

Section: Resultsmentioning

confidence: 99%

“… Validation of the iterated machine learning model results. The 5 th iterative model is used to predict the experimental values in Al-6si- x Sc alloys [ 25 ]. The error ranges of predicted results are plotted with 2 standard deviations of the mean value of 1000 ANN model results, corresponding to the confidence interval of 95%.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, when the ratio of w (Si)/ w (Sc) is lower than 2.5 (i.e. Al-2Si-1Sc alloy [ 25 ]), one can observe the primary Al 3 Sc, which is coherent with the (Al) at low lattice misfit [ 26 ] and has a high resistance to coarsen [ 27 ] to refine the (Al) grains and AlSc 2 Si 2 phase via the peritectic reaction (L+Al 3 Sc→AlSi 2 Sc 2 ) [ 28 ]. While the primary AlSc 2 Si 2 may form at 2.5 < w (Si)/ w (Sc) < 4.5 [ 28 ] (see Supplementary Tables S1, S2, and Figure S1).…”

Section: Introductionmentioning

confidence: 99%

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Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Gao

Zhong

Liu

et al. 2023

Science and Technology of Advanced Materials

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Scandium is the best alloying element to improve the mechanical properties of industrial Al-Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc additions in different commercial Al-Si-Mg casting alloys with well-defined compositions. However, no attempt to optimize the contents of Si, Mg, and Sc has been made due to the great challenge of simultaneous screening in high-dimensional composition space with limited experimental data. In this paper, a novel alloy design strategy was proposed and successfully applied to accelerate the discovery of hypoeutectic Al-Si-Mg-Sc casting alloys over high-dimensional composition space. Firstly, high-throughput CALculation of PHAse Diagrams (CALPHAD) solidification simulations of ocean of hypoeutectic Al-Si-Mg-Sc casting alloys over a wide composition range were performed to establish the quantitative relation ‘composition-process-microstructure’. Secondly, the relation ‘microstructure-mechanical properties’ of Al-Si-Mg-Sc hypoeutectic casting alloys was acquired using the active learning technique supported by key experiments designed by CALPHAD and Bayesian optimization samplings. After a benchmark in A356-xSc alloys, such a strategy was utilized to design the high-performance hypoeutectic Al-xSi-yMg alloys with optimal Sc additions that were later experimentally validated. Finally, the present strategy was successfully extended to screen the optimal contents of Si, Mg, and Sc over high-dimensional hypoeutectic Al-xSi-yMg-zSc composition space. It is anticipated that the proposed strategy integrating active learning with high-throughput CALPHAD simulations and key experiments should be generally applicable to the efficient design of high-performance multi-component materials over high-dimensional composition space.

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Inverse design of aluminium alloys using multi-targeted regression

Bhat,

Barnard,

Birbilis

2024

J Mater Sci

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The traditional design process for aluminium alloys has primarily relied upon iterative alloy production and testing, which can be time intensive and expensive. Machine learning has recently been demonstrated to have promise in predicting alloy properties based on the inputs of alloy composition and alloy processing conditions. In the search for optimal alloy concentrations that meet desired properties, as the search space expands, the optimisation process can become more time intensive and computationally expensive, depending on the methodology used. We propose a faster workflow for inverse alloy design by using multi-target machine-learning models. We train a random forest regressor to predict the concentration of alloying elements and a random forest classifier to determine the processing condition. We further analysed the inverse model and validated findings against alloys reported in the literature.

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Influence of the interaction between Si and Sc on the microstructure and tensile properties of as casted Al-Si-Sc alloys

Cited by 13 publications

References 50 publications

The Influence of Alloying Elements on the Microstructure and Properties of Al-Si-Based Casting Alloys: A Review

The Influence of Alloying Elements on the Microstructure and Properties of Al-Si-Based Casting Alloys: A Review

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Inverse design of aluminium alloys using multi-targeted regression

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