
Intelligent Manufacturing for High-End New Materials: Opportunities and Directions
Baode Sun, Da Shu, Huadong Fu, Donghong Wang, Liming Peng, Xinyun Wang, Yanyan Zhu, Huaming Wang, Wenjiang Ding, Jianxin Xie
Strategic Study of CAE ›› 2023, Vol. 25 ›› Issue (3) : 152-160.
Intelligent Manufacturing for High-End New Materials: Opportunities and Directions
Intelligent manufacturing is the main upgrading direction for China’s manufacturing industry and high-end new materials are core for high-end equipment and major engineering projects; therefore, promoting the integration of intelligent manufacturing and high-end new material manufacturing is crucial for enhancing the manufacturing capacity of high-end new materials and satisfying the demand of major equipment for high-end new materials. This study first analyzes the necessity of promoting intelligent manufacturing for high-end new materials. With increasing demands for high-performance manufacturing of high-end new materials, integration and lightweight manufacturing of complex components, and efficient and low-cost green manufacturing of high-end components, serious problems and huge challenges have been encountered by the traditional trial-and-error method for materials manufacturing. Meanwhile, grand opportunities are provided by a data-driven research and development mode for intelligent materials manufacturing. Taking materials forming and processing as an example, the common key technologies of intelligent materials manufacturing that need to be developed are systematically clarified, and countermeasures and suggestions to accelerate the development of intelligent manufacturing for high-end new materials, including key technology research and development, innovation system establishment, interdisciplinary talent cultivation, and achievement transfer, are also proposed, in order to support the upgrading and leapfrog development of China's materials industry.
high-end new materials / intelligent manufacturing / materials processing / integrated computation / big data / artificial intelligence
[1] |
谢曼 , 干勇 , 王慧 . 面向2035的新材料强国战略研究 [J]. 中国工程科学 , 2020 , 22 5 : 1 ‒ 9 .
|
[2] |
中国工程院化工、冶金与材料工程学部 , 中国材料研究学会 编. 中国新材料产业发展报告2021 [M]. 北京 : 化学工业出版社 , 2022 .
|
[3] |
钟志华 , 臧冀原 , 延建林 , 等 . 智能制造推动我国制造业全面创新升级 [J]. 中国工程科学 , 2020 , 22 6 : 136 ‒ 142 .
|
[4] |
李元元 . 新形势下我国新材料发展的机遇与挑战 [J]. 中国军转民 , 2022 1 : 22 ‒ 23 .
|
[5] |
郭东明 . 高性能精密制造 [J]. 中国机械工程 , 2018 , 29 7 : 757 ‒ 765 .
|
[6] |
孙宝德 , 王俊 , 康茂东 , 等 . 高温合金超限构件精密铸造技术及发展趋势 [J]. 金属学报 , 2022 , 58 4 : 412 ‒ 427 .
|
[7] |
王华明 . 高性能大型金属构件激光增材制造: 若干材料基础问题 [J]. 航空学报 , 2014 , 35 10 : 2690 ‒ 2698 .
|
[8] |
谢建新 . 材料加工技术的发展现状与展望 [J]. 机械工程学报 , 2003 , 39 9 : 29 ‒ 34 .
|
[9] |
潘健生 , 王婧 , 顾剑锋 . 我国高性能化智能制造发展战略研究 [J]. 金属热处理 , 2015 , 40 1 : 1 ‒ 6 .
|
[10] |
Wadley H N G, Vancheeswaran R. The intelligent processing of materials: An overview and case study [J]. JOM, 1998, 50(1): 19‒30.
|
[11] |
Wadley H N G, Eckhart W E. The intelligent processing of materials for design and manufacturing [J]. JOM, 1989, 41(10): 10‒16.
|
[12] |
Parrish P A, Barker W G. The basics of the intelligent processing of materials [J]. JOM, 1990, 42(7): 14‒16.
|
[13] |
宿彦京 , 付华栋 , 白洋 , 等 . 中国材料基因工程研究进展 [J]. 金属学报 , 2020 , 56 10 : 1313 ‒ 1323 .
|
[14] |
Wang H, Xiang X D, Zhang L T. On the data-driven materials innovation infrastructure [J]. Engineering, 2020, 6: 609‒611.
|
[15] |
Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the "fourth paradigm" of science in materials science [J]. APL Materials, 2016, 4: 053208.
|
[16] |
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 .
|
[17] |
Fang S F, Wang M P, Song M. An approach for the aging process optimization of Al-Zn-Mg-Cu series alloys [J]. Materials & Design, 2009, 30(7): 2460‒2467.
|
[18] |
Chen Y, Tian Y, Zhou Y, et al. Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy [J]. Journal of Alloys and Compounds, 2020, 844: 156159.
|
[19] |
Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning [J]. Nature Reviews Materials, 2021, 6: 655‒678.
|
/
〈 |
|
〉 |