An intelligent process parameters optimization approach for directed energy deposition of nickel-based alloys using deep reinforcement learning
文献类型：期刊论文
| 作者 | Shi, Shuai1; Liu, Xuewen1; Wang, Zhongan1; Chang, Hai1; Wu, Yingna1; Yang, Rui1,2; Zhai, Zirong1 |
| 刊名 | JOURNAL OF MANUFACTURING PROCESSES
 |
| 出版日期 | 2024-06-30 |
| 卷号 | 120页码:1130-1140 |
| 关键词 | Directed energy deposition Temperature simulator Deep reinforcement learning Proximal policy optimization Vickers hardness measurement |
| ISSN号 | 1526-6125 |
| DOI | 10.1016/j.jmapro.2024.05.001 |
| 通讯作者 | Zhai, Zirong() |
| 英文摘要 | 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 nonuniform 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. |
| 资助项目 | Double First -Class Initiative Fund ; ShanghaiTech University ; CAS Interdisciplinary Innovation Team Project[JCTD- 2020-10] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001242384800001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | Double First -Class Initiative Fund ; ShanghaiTech University ; CAS Interdisciplinary Innovation Team Project |
| 源URL |  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Zhai, Zirong |
| 作者单位 | 1.ShanghaiTech Univ, Ctr Adapt Syst Engn, 393 Huaxia Middle Rd, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Inst Met Res, 72 Wenhua Rd, Shenyang 110016, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Shuai,Liu, Xuewen,Wang, Zhongan,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,2024,120:1130-1140. |
| APA | Shi, Shuai.,Liu, Xuewen.,Wang, Zhongan.,Chang, Hai.,Wu, Yingna.,...&Zhai, Zirong.(2024).An intelligent process parameters optimization approach for directed energy deposition of nickel-based alloys using deep reinforcement learning.JOURNAL OF MANUFACTURING PROCESSES,120,1130-1140. |
| MLA | Shi, Shuai,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(2024):1130-1140. |
入库方式： OAI收割
来源：金属研究所
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