Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning

Wang, Yi; Liu, Guangchen; Lu, Zhao; Gao, Jianbao; Zhang, Lijun

doi:10.1016/j.jmst.2021.09.061

Cited by 42 publications

(14 citation statements)

References 57 publications

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“…The locations of high crack susceptibility regions were compared with those observed in the experimental crack susceptibility tests [Figure 6D]. The calculated results with back diffusion considered, with a cooling rate of either 20 °C/s or 100 °C/s, are in good agreement with the measured values, indicating that the influence of back diffusion on the crack sensitivity can be evaluated by [167] with permission from Elsevier.…”

Section: Common Applications Of Ct/ml Techniques In Different Aluminum Alloyssupporting

confidence: 53%

“…First, based on the reliable thermodynamic database of the target aluminum alloy, the equilibrium phase diagram and property diagrams with a wide range of components can be calculated by using thermodynamic software (e.g., Thermo-Calc [163] , Pandat [164] , FactSage [165] , and MTDTA [166] ), and the thermally stable phase region and phase transformation information are obtained. Moreover, the solid solubility of alloying elements in the matrix and the variation of phase fractions with temperature and composition are provided [167] . Second, the solidification process of a target aluminum alloy with different components can be simulated by the Scheil-Gulliver solidification mode, resulting in quantitative solidification behaviors such as the phase transition temperature, solidification sequence, and volume fraction of the precipitated phase [95] .…”

Section: To Be Strategically Situated: Synergy Between Ct and ML Approachesmentioning

confidence: 99%

“…Selection of suitable alloying elements and determination of the composition range are necessary steps to avoid the formation of harmful phases. Lu et al [167] developed a high-performance Al-Fe-Mg-Si alloy by selecting stable phase regions, screening alloy elements, and determining the atomic ratio. As shown in Figure 5A, the isothermal section calculation at 550 °C of the Al-Fe-Mg-Si system was used to design the Mg/Si atomic ratio in this system.…”

Section: Common Applications Of Ct/ml Techniques In Different Aluminum Alloysmentioning

confidence: 99%

“…(B) Vertical section of Al-1.05Mg-0.85Si-xFe (in wt%). Figure reproduced from Ref [167]. with permission from Elsevier.…”

mentioning

confidence: 99%

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Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Wang¹,

Liu²,

Gao³

et al. 2021

JMI

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Casting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying and post-solidification heat treatments are the most common measures to tune the microstructure for enhancing their mechanical properties. However, it is very challenging to achieve accurate and efficient development of novel casting aluminum alloys using the traditional trial-and-error method. With the rapid development of computer technology, the computational thermodynamics (CT) in the framework of the CALculation of PHAse Diagram approach, the data-driven machine learning (ML) technique, and also their combinations have been proved to be effective approaches for the design of casting aluminum alloys. In this review, the state-of-the-art computational alloy design approaches driven by CT and ML techniques, as well as their combinations, were comprehensively summarized. The current status of the thermodynamic database for aluminum alloys, as the core for CT, was also briefly introduced. After that, a variety of successful case studies on the design of different casting aluminum alloys driven by CT, ML, and their combinations were demonstrated, including common applications, CT-driven design of Sc-additional Al-Si-Mg series casting alloys, and design of Srmodified A356 alloys driven by combing CT and ML. Finally, the conclusions of this review were drawn, and perspectives for boosting the computational design approach driven by combining CT and ML techniques were pointed out.

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Section: Common Applications Of Ct/ml Techniques In Different Aluminum Alloyssupporting

confidence: 53%

Section: To Be Strategically Situated: Synergy Between Ct and ML Approachesmentioning

confidence: 99%

Section: Common Applications Of Ct/ml Techniques In Different Aluminum Alloysmentioning

confidence: 99%

“…(B) Vertical section of Al-1.05Mg-0.85Si-xFe (in wt%). Figure reproduced from Ref [167]. with permission from Elsevier.…”

mentioning

confidence: 99%

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Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Wang¹,

Liu²,

Gao³

et al. 2021

JMI

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“…但是, 由于数据量不足以及特征选择较片面, 现有的研究对于Al-Si-Mg系合金的设计仍存在局限性. 其中, Yang等 [20] 以固溶时效温度和时间 4种因素作为输入使用人工神经网络对A357合金的抗拉强度和伸长率进行了预测, 实验值与预测值的平均绝对误差分别为0.7%和1.85%, 并通过等值线图找到了抗拉强度和伸长率之间的关系. Yi等 [21] 使用 A356-xSr合金的计算热力学数据 (相变温分及热处理工艺, 优于同类合金使用Sr变质 [22] (Al-7.069Si-0.676Mg-0.02Sr) 341.8 MPa、 Sc变质 [3] (Al-7Si-0.6Mg-0.12Sc)324 MPa和复合稀土Re 变质 [5] (Al-6.75Si-0.63Mg-0.2Re)获得 349.1 MPa 的结果.…”

Section: N P R E S Sunclassified

Design of new Al-Si-Mg alloys by multi-modal mixed input simulation experiment

Duan¹,

Xiao-Long²,

Guo³

et al. 2023

Acta Phys. Sin.

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On the basis of a large amount of experimental data, it has been challenging to establish a data-driven non-linear law between mixing characteristics and mechanical properties for the proportioning and process design of new alloy compositions. This paper proposes a performance-oriented “composition-process-property” design strategy for Al-Si-Mg alloys based on a machine learning approach, aiming to adopt multimodal experimental data on the composition, melting and heat treatment processes of divergent grades of the same system as features, and a random forest algorithm was used to find the non-linear pattern between the features and the tensile strength. Afterward, this paper sets the composition and process parameters of some of the alloys in the dataset as the target null values and uses the chain equation multiple interpolation algorithms to predict the interpolation of the target missing data. The errors of both experimental and predicted values of tensile strength of the alloys predicted or guided by this strategy were kept within ±5%; among them, the composition ratio of Al-6.8Si-0.6Mg-0.05Sr and the heat treatment scheme of 540°C*10h+170°C*10h were experimentally confirmed to have a quality index Q<sub>DJR</sub> of 517.3 for comprehensive tensile properties, which is higher than that of similar alloys below a Q<sub>DJR</sub> value of 500. The result indicates that this strategy helps to enhance the long cycle time, high cost, and low efficiency of the traditional design method for Al-Si-Mg system alloys.

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Viscosity Calculation for Al–Si–Mg–Fe System through CALPHAD Method

Fu,

Luo,

Liu

et al. 2024

Adv Eng Mater

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Viscosity is a crucial parameter affecting the fluidity of metal melts, which directly influences the founding properties of Al alloys. However, obtaining viscosity measurement data is difficult for metal melts, and the viscosity prediction for ternary and multicomponent alloys would provide the significant data for the selection of process parameters. This study compares the applicability of the Hirai model, SDS model, and R‐K function for viscosity calculations in the sub‐binary systems of Al–Si–Mg–Fe alloys. R‐K function shows the best agreement with experimental data. However, the SDS model shows lower relative error than Hirai model, which would play an important role in those systems lacking experimental data to predict the viscosities. Ultimately, a database capable of predicting viscosity values for the entire composition and temperature range of the Al–Si–Mg–Fe system is established using the CALPHAD method. Viscosity parameters for Al–Si, Al–Mg, Al–Fe, Mg–Si, and Mg–Fe are evaluated and optimized through the R‐K derivation, corroborated with existing experimental data. Using the R‐K function, successful extrapolation and prediction of viscosity for the Al–Si–Mg–Fe ternary and quaternary systems are achieved, with a mean square error between predicted and experimental values of only 0.7%, demonstrating the successful application of the Al–Si–Mg–Fe database for viscosity prediction.

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Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning

Cited by 42 publications

References 57 publications

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Design of new Al-Si-Mg alloys by multi-modal mixed input simulation experiment

Viscosity Calculation for Al–Si–Mg–Fe System through CALPHAD Method

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