机器学习在铝基复合材料组分和工艺设计中的应用

杜晓双; 曲囡; 张学习; 刘勇; 朱景川

机器学习在铝基复合材料组分和工艺设计中的应用

杜晓双¹,
曲囡¹,
张学习^1,2,
刘勇^1,2,,
朱景川^1,2,

1. 哈尔滨工业大学材料科学与工程学院, 黑龙江哈尔滨 150001;
2. 金属精密热加工国家级重点实验室, 黑龙江哈尔滨 150001

基金项目:

国家重点研发计划项目(2022YFB3705701)

详细信息

作者简介:
杜晓双,女,1999年生,研究方向:基于SiCp/Al界面调控的Al合金基体成分设计与组织性能。E-mail:13722511865@163.com

通讯作者:
刘勇,男,1975年生,教授,研究方向:合金相变与材料改性技术

朱景川,男,1963年生,教授,研究方向:梯度功能复合材料设计、制备与应用

中图分类号: TG146.21
计量
- 文章访问数: 239
- HTML全文浏览量: 30
- PDF下载量: 55
出版历程
- 收稿日期: 2023-08-01
- 网络出版日期: 2024-07-22

Application of Machine Learning in Composition and Process Design of Aluminum Matrix Composites

1. School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;
2. National Key Laboratory of Science and Technology on Precision Heat Processing of Metals, Harbin Institute of Technology, Harbin 150001, China

摘要

摘要: 铝基复合材料具有优异的力学性能,在航空、航天、国防、汽车等领域都得到一定的应用,机器学习在铝基复合材料组分和工艺设计中应用潜力巨大。首先介绍了机器学习的概念和常用算法,然后从性能预测、组分设计、工艺设计和材料体系与工艺一体化设计等4个方面详细论述了机器学习在铝基复合材料研发应用的进展,最后对机器学习在铝基复合材料领域未来的发展应用做了总结与展望。
- 机器学习 /
- 铝基复合材料 /
- 性能预测 /
- 组分设计 /
- 工艺设计
Abstract: Aluminum matrix composites have excellent mechanical properties and have been applied in aviation, aerospace, national defense, automobile and other fields. Machine learning has great potential in the composition and process design of aluminum matrix composites. In this paper, the concept and common algorithms of machine learning are introduced, and then the application progress of machine learning in the research and development of aluminum matrix composites is discussed in detail from perspectives of performance prediction, component design, process design, and integrated design of material system and process. Finally, the future development and application of machine learning in the field of aluminum matrix composites are summarized and prospected.
- machine learning /
- aluminum matrix composites /
- performance prediction /
- component design /
- process design

HTML全文

参考文献(35)

[1]	黄颖,李育川,雷波,等.粉末冶金颗粒增强铝基复合材料的制备及研究进展[J].功能材料, 2022, 53(10):10071-10086.
[2]	马宗义,肖伯律,张峻凡,等.航天装备牵引下的铝基复合材料研究进展与展望[J].金属学报, 2023, 59(4):457-466.
[3]	曹子林,张林慧,仲斌年,等.铝基复合材料的制备和研究现状[J].金属功能材料, 2023, 30(2):29-39.
[4]	KOLI D K, AGNIHOTRI G, PUROHIT R. Advanced aluminium matrix composites:the critical need of automotive and aerospace engineering fields[J]. Materials Today:Proceedings, 2015, 2(4-5):3032-3041.
[5]	DOKšANOVIĆ T, DžEBA I, MARKULAK D, et al. Applications of aluminium alloys in civil engineering[J]. Tehnicki Vjesnik-Technical Gazette, 2017, 24(5):1609-1618.
[6]	乔文明,李颖.铝基复合材料的制备及应用[J].热加工工艺, 2013, 42(4):126-128.
[7]	贺毅强,苏前航,郇昌宝,等. AlFeNiCrCoTi_0.5高熵合金颗粒增强6061铝基复合材料的制备与性能[J].材料工程, 2023, 51(3):67-77.
[8]	谢建新,宿彦京,薛德祯,等.机器学习在材料研发中的应用[J].金属学报, 2021, 57(11):1343-1361.
[9]	赵宝军.基于支持向量回归的铝合金收缩性和流动性预测模型的研究[D].南京:东南大学, 2019.
[10]	刘振东,潘嘉杰,刘全兵.机器学习在设计高性能锂电池正极材料与电解质中的应用[J].化学进展, 2023, 35(4):577-592.
[11]	夏钊,赵帆,刘新华,等.机器学习辅助预测铜铝复合材料界面结构与性能[J].中国有色金属学报, 2023, 33(1):88-99.
[12]	胡静怡,徐翔,季小妹,等.机器学习在非晶合金开发中的应用[J].工程科学学报, 2023, 45(9):1517-1527.
[13]	刘晓,王思迈,卢磊,等.机器学习预测混凝土材料耐久性的研究进展[J].硅酸盐学报, 2023, 51(8):2062-2073.
[14]	HASAN M S, WONG T, ROHATGI P K, et al. Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models[J]. Tribology International, 2022, 170:107527.
[15]	李孝晨.基于机器学习的RAFM钢与低活化高熵合金优化设计及其力学性能评价[D].合肥:中国科学技术大学, 2022.
[16]	MSEBAWI M S, LEMAN Z, SHAMSUDIN S, et al. Production of aluminum AA6061 hybrid nanocom-posite from waste metal using hot extrusion process:strength performance and prediction by RSM and random forest[J]. Materials, 2021, 14(20):6102.
[17]	寇雯博,董灏,邹岷强,等.混杂复合材料等效热传导性能预测的小波-机器学习混合方法[J].物理学报, 2021, 70(3):63-74.
[18]	戚兴怡,胡耀峰,王若愚,等.机器学习在新材料筛选方面的应用进展[J].化学学报, 2023, 81(2):158-174.
[19]	DINAHARAN I, PALANIVEL R, MURUGAN N, et al. Predicting the wear rate of AA6082 aluminum surface composites produced by friction stir processing via artificial neural network[J]. Multidiscipline Modeling in Materials and Structures, 2019, 16(2):409-423.
[20]	郑伟达,张惠然,胡红青,等.基于不同机器学习算法的钙钛矿材料性能预测[J].中国有色金属学报, 2019, 29(4):803-809.
[21]	周书蔚,杨冰,王超,等.机器学习法预测不同应力比6005A-T6铝合金疲劳裂纹扩展速率[J].中国有色金属学报, 2023, 33(8):2416-2427.
[22]	CHEN Z, ZHOU H B, YAN Z J, et al. Machining characteristics of 65vol.% SiCp/Al composite in micro-WEDM[J]. Ceramics International, 2021, 47(10):13533-13543.
[23]	王青林.基于机器学习算法的铸造高温合金材料性能预测[D].武汉:华中农业大学, 2022.
[24]	AYDIN F. Investigation of Elevated Temperature Wear Behavior of Al 2024-BN Composites using Statistical Techniques[J]. Journal of Materials Engineering and Performance, 2021, 30(11):8560-8578.
[25]	HARSHA N, SITA RAMA RAJU K, VENKATA RAMANA V S N, et al. Fabrication and prediction of tensile strength of Al-Al₂O₃ nano composites[J]. Materials Today:Proceedings, 2019, 18:2197-2200.
[26]	ALTINKÖK N. Modeling of the mechanical and physical properties of hybrid composites produced by gas pressure infiltration[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2018, 41(1):13.
[27]	HASAN M S, KORDIJAZI A, ROHATGI P K, et al. Triboinformatics approach for friction and wear prediction of Al-graphite composites using machine learning methods[J]. Journal of Tribology, 2022, 144(1):011701.
[28]	THANKACHAN T, SOORYA PRAKASH K, MALINI R, et al. Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks[J]. Applied Surface Science, 2019, 472:22-35.
[29]	KORDIJAZI A, BEHERA S, PATEL D, et al. Predictive analysis of wettability of Al-Si based multiphase alloys and aluminum matrix composites by machine learning and physical modeling[J]. Langmuir, 2021, 37(12):3766-3777.
[30]	TURKOGLU T, CELIK S. Process optimization for enhanced tribological properties of Al/MWCNT composites produced by powder metallurgy using artificial neural networks[J]. Surface Topography:Metrology and Properties, 2021, 9(4):045032.
[31]	SHEELWANT A, JADHAV P M, NARALA S K R. ANN-GA based parametric optimization of Al-TiB₂ metal matrix composite material processing technique[J]. Materials Today Communications, 2021, 27:102444.
[32]	BALACHANDHAR R, BALASUNDARAM R, RAVIC-HANDRAN M. Analysis of surface roughness of rock dust reinforced AA6061-Mg matrix composite in turning[J]. Journal of Magnesium and Alloys, 2021, 9(5):1669-1676.
[33]	ZUNGER A. Inverse design in search of materials with target functionalities[J]. Nature Reviews Chemistry, 2018, 2:121.
[34]	田宇泽.基于机器学习的电磁超材料逆向设计方法研究[D].武汉:华中师范大学, 2021.
[35]	陈水洲,王晓书,欧阳求保,等.数据驱动的铝基复合材料性能预测和逆向设计[J].上海大学学报(自然科学版), 2022, 28(3):512-522.