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

Duan Zhi-Qiang; Pei Xiao-Long; Guo Qing-Wei; Hou Hua; Zhao Yu-Hong

doi:10.7498/aps.72.20221736

Abstract

On the basis of a large number of experimental data, it is a challenge 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 is 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 are kept within ±5%; The composition ratio of Al-6.8Si-0.6Mg-0.05Sr and the heat treatment scheme of 540 ℃×10 h+170 ℃×10 h are experimentally confirmed to have a quality index Q_DJR of 517.3 for comprehensive tensile properties, which is higher than that of similar alloys below a Q_DJR 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.

Keywords:

Authors and contacts

1.
School of Material Science and Engineering, North University of China, Taiyuan 030051, China
2.
Shanxi Engineering Reseach Center of Liquid Nonferrous Metals Forming, Taiyuan 030051, China
3.
College of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

Corresponding author: Zhao Yu-Hong, zhaoyuhong@nuc.edu.cn

Funds: Project supported by the Graduate Innovation Project of Shanxi Province, China (Grant No. 2021Y595)

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References

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[31]	谷海彤, 陈邵华, 吴晓强, 蔡妙妆, 崔卓, 曾小林 2017 广西科技大学学报 28 103 Gu H T, Chen S H, Wu X Q, Cai M Z, Cui Z, Zeng X L 2017 J. Guangxi Univ. Sci. Technol. 28 103
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Cited By

图 1 Al-Si-Mg系合金设计示意图

Figure 1. Schematic diagram of Al-Si-Mg alloys design.

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图 2 Al-Si-Mg合金“成分-工艺-性能”双向设计示意图

Figure 2. Schematic diagram of bidirectional prediction of Al-Si-Mg alloy composition-process-property.

DownLoad: Full-Size Img PowerPoint

图 3 链式方程多重插补示意图

Figure 3. Schematic diagram of chain equation multiple imputation.

DownLoad: Full-Size Img PowerPoint

图 4 斯皮尔曼相关系数矩阵图. 紫色代表正相关, 黄色代表负相关; 椭圆越扁, 数值越大; *号为显著性标记, 根据显著性水平变化进行设置, 小于0.05和小于0.01分别显示*和**

Figure 4. Spearman correlation coefficient matrix plot. Purple represents a positive correlation, and yellow represents a negative correlation; the flatter the ellipse, the larger the value; the * sign is a significant mark, which is set according to the change of the significance level, and it is displayed as * and ** when it is less than 0.05 and less than 0.01.

DownLoad: Full-Size Img PowerPoint

图 5 随机森林算法泛化能力测试结果　(a)随机森林模型训练集的预测精度; (b)随机森林模型测试集的预测精度; (c)线性回归模型训练集的预测精度; (d)线性回归模型测试集的预测精度

Figure 5. Random forest algorithm generalization ability test results: (a) The prediction accuracy of the random forest model training set; (b) the prediction accuracy of the random forest model test set; (c) the prediction accuracy of the linear regression model training set; (d) the prediction accuracy of the linear regression model test set.

DownLoad: Full-Size Img PowerPoint

图 6 新合金的成分及工艺的实验结果

Figure 6. Experimental results of the composition and process of the new alloy.

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图 7 性能预测结果　(a)变质剂K₂ZrF₆的含量对合金性能影响; (b)变质温度对合金性能的影响; (c)模具温度对合金性能的影响; (d)浇注温度对合金性能的影响

Figure 7. Performance prediction results: (a) The effect of the content of modifier K₂ZrF₆ on the properties of the alloy; (b) the effect of the modification temperature on the properties of the alloy; (c) the effect of the mold temperature on the properties of the alloy; (d) the effect of the pouring temperature on the properties of the alloy.

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图 8 基于控制变量法的合金性能预测结果　(a)固溶工艺对合金性能的影响; (b)时效工艺对合金性能的影响

Figure 8. Prediction Results of alloy properties based on controlled variable method: (a) Effect of solution process on alloy properties; (b) effect of aging process on alloy properties.

DownLoad: Full-Size Img PowerPoint

图 9 合金性能定量比较

Figure 9. Quantitative comparison of alloy properties.

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图 10 预测概率分布图

Figure 10. Prediction probability distribution map.

DownLoad: Full-Size Img PowerPoint

图 11 特征重要性分数

Figure 11. Feature importance.

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表 1 新合金的成分和工艺

Table 1. Composition and process of new alloys.

	Al	Si	Mg	Ti	Be	Sr	Solidtion temperature	Solidtion Time	Aging temperature	Aging Time
1#	Bal.	7.3	0.576	0.142	0.003	0.030	535	12	155	8
2#	Bal.	7.3	0.576	0.142	0.003	0.030	545	14	165	5
3#	Bal.	7.3	0.576	0.142	0.003	0.030	545	14	170	7
4#	Bal.	7.0	0.560	0.140	0	0.040	540	10	170	12
5#	Bal.	7.0	0.560	0.140	0	0.045	540	10	170	12
6#	Bal.	7.0	0.560	0.140	0	0.050	540	10	170	12
7#	Bal.	6.8	0.600	0.120	0	0.450	535	9	165	9
8#	Bal.	6.8	0.600	0.140	0	0.050	540	10	170	10

DownLoad: CSV

表 2 实验结果及误差对比

Table 2. Experimental results and error comparison.

	目标值/MPa	实验值/MPa	Error/%	Q_DJR
1#	345	327.46	–5.20	456.958
2#	345	329.42	–4.50	493.962
3#	345	328.15	–4.88	462.264
4#	345	321.19	–6.90	416.588
5#	345	330.74	–4.10	438.366
6#	345	331.53	–3.90	476.512
7#	345	340.55	–1.29	505.493
8#	345	349.47	+1.16	517.300

DownLoad: CSV

[1]	Ceschini L, Messieri S, Morri A, Seifeddine S, Toschi S, Zamani M 2020 Trans. Nonferrous Met. Soc. China 30 2861 Google Scholar
[2]	Fu J N, Yang Z, Deng Y L, Wu Y F, Lu J Q 2020 Mater. Charact. 159 110021 Google Scholar
[3]	Tzeng Y C, Wu C T, Bor H Y, Horng J L, Tsai M L, Lee S L 2014 Mat. Sci. Eng. A-Struct. 593 103 Google Scholar
[4]	Tzeng Y C, Nieh J K, Bor H Y, Lee S L 2018 Metals 8 194 Google Scholar
[5]	Jiang W M, Fan Z T, Dai Y C, Li C 2014 Mat. Sci. Eng. A-Struct. 597 237 Google Scholar
[6]	Wang X F, Guo M X, Cao L Y, Luo J R, Zhang J S, Zhuang L H 2015 Mat. Sci. Eng. A-Struct 621 8 Google Scholar
[7]	Jiang L T, Wu G H, Yang W s, Zhao Y G, Liu S S 2010 Trans. Nonferrous Met. Soc. China 20 2124 Google Scholar
[8]	Cheng W, Liu C Y, Huang H F, Zhang L, Zhang B, Shi L 2021 Mater. Charact. 178 111278 Google Scholar
[9]	Chen L W, Zhao Y H, Li M X, Li L M, Hou L F, Hou H 2021 Mat. Sci. Eng. A-Struct. 804 140793 Google Scholar
[10]	Chen L W, Zhao Y H, Hou H, Zhang T, Liang J Q, Li M X, Li J 2019 J. Alloys Compd. 778 359 Google Scholar
[11]	Wang C S, Fu H D, Jiang L, Xue D Z, Xie J X 2021 Acta Mater. 215 117118
[12]	Wang C S, Fu H D, Jiang L, Xue D Z, Xie J X 2019 npj Comput. Mater. 5 87
[13]	Su Y J, Fu H D, Bai Y, Jiang X, Xie J X 2020 Acta. Metall. Sin. 56 1313
[14]	Agrawal A, Choudhary A 2016 APL Mater. 4 053208 Google Scholar
[15]	Wang C H, Shen C G, Cui Q, Zhang C, Xu W 2020 J. Nucl. Mater. 529 151823 Google Scholar
[16]	Guo S, Yu J X, Liu X J, Wang C P, Jiang Q S 2019 Comp. Mater. Sci. 160 95 Google Scholar
[17]	刘彬, 汤爱涛, 潘复生, 黄光杰, 毛建军 2011 重庆大学学报 34 44 Google Scholar Liu B, Tang A T, Pan F S, Huang G J, Mao J J 2011 J. Chongqing Univ. 34 44 Google Scholar
[18]	Xu X N, Wang L Y, Zhu G M, Zeng X Q 2020 JOM 72 3935 Google Scholar
[19]	Chaudry U M, Hamad K, Abuhmed T 2021 Mater. Today Commun. 26 101897 Google Scholar
[20]	Yang X W, Zhu J C, Nong Z S, He D, Lai Z H, Liu Y, Liu F W 2013 Trans. Nonferrous Met. Soc. China 23 788 Google Scholar
[21]	Yi W, Liu G C, Lu Z, Gao J B, Zhang L J 2022 J. Mater. Sci. Technol. 112 277 Google Scholar
[22]	徐晓峰, 赵宇光, 张阳阳 2016 第十三届全国铸造年会暨2016中国铸造活动周成都, Octorber 26, 2016 第401页 Xu X F, Zhao Y G, Zhang Y Y 2016 The 13th National Foundry Annual Meeting and 2016 China Foundry Week (Chendu) p401 (in Chinese)
[23]	Yu L, Zhou R T, Chen R D, Lai K K 2020 Emerg. Mark. Financ. Tr 58 472
[24]	Luor D-C 2015 Intell. Data. Anal. 19 529 Google Scholar
[25]	van den Heuvel E, Zhan Z 2022 Am. Statist. 76 44 Google Scholar
[26]	Xiao C W, Ye J Q, Esteves R M, Rong C M 2016 Concurr. Comp-Pract. E. 28 3866 Google Scholar
[27]	Liaw A, Wiener M 2002 R News 2 18
[28]	Grömping U 2009 Am. Statist. 63 308 Google Scholar
[29]	van Buuren S, Boshuizen H C, Knook D L 1999 Stat. Med. 18 681 Google Scholar
[30]	黄子洋, 黄登一, 皮海亚 2016 科技尚品 91 172 Huang Z Y, Huang D Y, Pi H Y 2016 Premiere 91 172
[31]	谷海彤, 陈邵华, 吴晓强, 蔡妙妆, 崔卓, 曾小林 2017 广西科技大学学报 28 103 Gu H T, Chen S H, Wu X Q, Cai M Z, Cui Z, Zeng X L 2017 J. Guangxi Univ. Sci. Technol. 28 103
[32]	刘凤芹 2009 统计研究 26 71 Google Scholar Liu F Q 2009 Statist. Res. 26 71 Google Scholar
[33]	Hemphill, James F 2003 Am. Psychol. 58 78 Google Scholar
[34]	Drouzy M, Jacob S, Richard M 1980 Int. Cast Metals J. 5 43
[35]	胥晓强, 范建业, 董立新, 刘力菱, 吴成辉 2018 热加工工艺 47 38 Xu X Q, Fan J Y, Dong L X, Liu L L, Wu C H 2018 Hot Working Technol. 47 38
[36]	胥晓强, 董立新, 刘力菱, 吴成辉, 魏善涛 2018 特种铸造及有色合金 38 568 Xu X Q, Dong L X, Liu L L, Wu C H, Wei S T 2018 Spec. Cast. Nonferrous Alloys 38 568
[37]	胡兴业, 张永, 刘野, 乔昕, 刘洪汇 2015 铸造 64 1132 Google Scholar Hu X Y, Zhang Y, Liu Y, Qiao X, Liu H H 2015 Foundry 64 1132 Google Scholar
[38]	李云亮, 张文达, 杨晶, 范耀强, 党惊知 2016 热加工工艺 45 225 Li Y L, Zhang W D, Yang J, Fan Y Q, Dang J Z, 2016 Hot Working Technol. 45 225

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