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Strategic Study of CAE >> 2023, Volume 25, Issue 3 doi: 10.15302/J-SSCAE-2023.03.014

Intelligent Manufacturing for High-End New Materials: Opportunities and Directions

1. Shanghai Key Laboratory of Advanced High-Temperature Materials and Precision Forming, Shanghai 200240, China;

2. School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;

3. Beijing Advanced Innovation Centerfor Materials Genome Engineering, Beijing 100083, China;

4. Light Alloy Net Forming National Engineering Research Center, Shanghai 200240, China;

5. State Key Laboratory of Materials Processing and Die and Mould Technology, Wuhan 430074, China;

6. National Engineering Laboratory of Additive Manufacturing for Large Metallic Components, Beijing 100083, China

Funding project:Chinese Academy of Engineering project “Intelligent Research & Development, Manufacturing and Application of New Materials” (2021-JJZD-01); Key project of National Natural Science Foundation of China (52090042) Received: 2023-02-13 Revised: 2023-05-08 Available online: 2023-06-15

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Abstract

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.

 

References

[ 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 .
Xie M , Gan Y , Wang H . Research on new material power strategy by 2035 [J]. Strategic Study of CAE , 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 .
Chemical, Metallurgical and Materials Engineering Academic Division of Chinese Academy of Engineering, Chinese Materials Research Society . Development of advanced materials industry in China: Annual report 2021 [M]. Beijing : Chemical Industry Press , 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 .
Zhong Z H , Zang J Y , Yan J L , et al . Intelligent manufacturing promotes the comprehensive upgrading and innovative growth of China´s manufacturing industry [J]. Strategic Study of CAE , 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 .
Li Y Y . Opportunities and challenges for the development of new materials in China under the new situation [J]. Defense Industry Conversion in China , 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 .
Guo D M . High-performance precision manufacturing [J]. China Mechanical Engineering , 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 .
Sun B D , Wang J , Kang M D , et al . Investment casting technology and development trend of superalloy ultra limit components [J]. Acta Metallurgica Sinica , 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 .
Wang H M . Materials´ fundamental issues of laser additive manufacturing for high-performance large metallic components [J]. Acta Aeronautica et Astronautica Sinica , 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 .
Xie J X . Developing situation and prospects of materials processing technologies [J]. Chinese Journal of Mechanical Engineering , 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 .
Pan J S , Wang J , Gu J F . Study on the development strategy of high performance intelligent manufacturing in China [J]. Heat Treatment of Metals , 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 .
Su Y J , Fu H D , Bai Y , et al . Progress in materials genome engineering in China [J]. Acta Metallurgica Sinica , 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 .
Xie J X , Su Y J , Xue D Z , et al . Machine learning for materials research and development [J]. Acta Metallurgica Sinica , 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.

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