An intelligent process parameters optimization approach for directed energy deposition of nickel-based alloys using deep reinforcement learning

doi:10.1016/j.jmapro.2024.05.001

	An intelligent process parameters optimization approach for directed energy deposition of nickel-based alloys using deep reinforcement learning
	Shuai,Shi1 ; Xuewen,Liu1 ; Zhongan,Wang1 ; Hai,Chang1 ; Yingna,Wu1 ; Rui,Yang1,2 ; Zirong,Zhai1
	2023-06
发表期刊	JOURNAL OF MANUFACTURING PROCESSES (IF:6.1[JCR-2023],6.2[5-Year])
ISSN	1526-6125
EISSN	2212-4616
卷号	120 期号:30 June 2024 页码:1130-1140
发表状态	已发表
DOI	10.1016/j.jmapro.2024.05.001
摘要	Directed Energy Deposition (DED) is crucial in the ongoing industrial revolution, providing a unique ability to fabricate high-quality components with complex shapes. However, the determination of key process parameters, such as scan sequence, laser power, and scanning speed, often relies on offline trial-and-error or heuristic methods. These methods are not only suboptimal but also lack generalizability. A major challenge is the non-uniform temperature distribution during manufacturing, which affects the uniformity of the mechanical properties. To overcome these challenges, we have developed a framework based on Deep Reinforcement Learning (DRL). This framework dynamically adjusts process parameters to achieve an optimal control policy. Additionally, we introduce a cost-effective temperature simulation model of the deposition process. This model is particularly useful for researchers testing the proximal policy optimization algorithm. The experimental results demonstrate that DRL policies substantially improve temperature uniformity in Inconel 718, enhancing hardness variability with improvements of 31.8 % and 27.1 % in horizontal and vertical building directions, respectively. This research marks an important step toward achieving a highly intelligent and automated optimization of process parameters. It also proves to be robust and computationally efficient for future online implementation. © 2024
关键词	Cost effectiveness Deep learning Deposition Heuristic methods Nickel alloys Optimization Vickers hardness Deep reinforcement learning Directed energy Directed energy deposition Energy depositions Policy optimization Process parameters Proximal policy optimization Reinforcement learnings Temperature simulator Vickers hardness measurements
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收录类别	EI ; SCI
语种	英语
资助项目	CAS Interdisciplinary Innovation Team Project[JCTD- 2020-10]
WOS研究方向	Engineering
WOS类目	Engineering, Manufacturing
WOS记录号	WOS:001242384800001
出版者	Elsevier Ltd
EI入藏号	20242016087658
EI主题词	Reinforcement learning
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 548.2 Nickel Alloys ; 723.4 Artificial Intelligence ; 802.3 Chemical Operations ; 911.2 Industrial Economics ; 921.5 Optimization Techniques
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/373010
专题	创意与艺术学院_PI研究组(P)_武颖娜组物质科学与技术学院_硕士生信息科学与技术学院_硕士生创意与艺术学院_PI研究组(P)_翟梓融组创意与艺术学院_PI研究组(P)_杨锐组
通讯作者	Zirong,Zhai
作者单位	1.ShanghaiTech University 2.Chinese Academy of Sciences
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Shuai,Shi,Xuewen,Liu,Zhongan,Wang,et al. An intelligent process parameters optimization approach for directed energy deposition of nickel-based alloys using deep reinforcement learning[J]. JOURNAL OF MANUFACTURING PROCESSES,2023,120(30 June 2024):1130-1140.
APA	Shuai,Shi.,Xuewen,Liu.,Zhongan,Wang.,Hai,Chang.,Yingna,Wu.,...&Zirong,Zhai.(2023).An intelligent process parameters optimization approach for directed energy deposition of nickel-based alloys using deep reinforcement learning.JOURNAL OF MANUFACTURING PROCESSES,120(30 June 2024),1130-1140.
MLA	Shuai,Shi,et al."An intelligent process parameters optimization approach for directed energy deposition of nickel-based alloys using deep reinforcement learning".JOURNAL OF MANUFACTURING PROCESSES 120.30 June 2024(2023):1130-1140.