Advancements in machine learning for material design and process optimization in the field of additive manufacturing

被引:0
|
作者
Hao-ran Zhou
Hao Yang
Huai-qian Li
Ying-chun Ma
Sen Yu
Jian Shi
Jing-chang Cheng
Peng Gao
Bo Yu
Zhi-quan Miao
Yan-peng Wei
机构
[1] Shenyang Research Institute of Foundry Co.,National Key Laboratory of Advanced Casting Technologies
[2] Ltd. CAM,Department of Mechanical Engineering
[3] Tsinghua University,School of Materials Science and Engineering
[4] Key Laboratory of Space Physics,undefined
[5] Nanyang Technological University,undefined
来源
China Foundry | 2024年 / 21卷
关键词
additive manufacturing; machine learning; material design; process optimization; intersection of disciplines; embedded machine learning; TG146.21; A;
D O I
暂无
中图分类号
学科分类号
摘要
Additive manufacturing technology is highly regarded due to its advantages, such as high precision and the ability to address complex geometric challenges. However, the development of additive manufacturing process is constrained by issues like unclear fundamental principles, complex experimental cycles, and high costs. Machine learning, as a novel artificial intelligence technology, has the potential to deeply engage in the development of additive manufacturing process, assisting engineers in learning and developing new techniques. This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing, particularly in model design and process development. Firstly, it introduces the background and significance of machine learning-assisted design in additive manufacturing process. It then further delves into the application of machine learning in additive manufacturing, focusing on model design and process guidance. Finally, it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.
引用
收藏
页码:101 / 115
页数:14
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