Fast scanning pattern selection in laser directed energy deposition via simulation data-driven based deep regression method
文献类型：期刊论文
| 作者 | Ma, Liang1,2; Kong, Xiangwei1,4,5; Cheng, Liu1; Liang, Jingjing2; Li, Jinguo2; Jin, Zhibo1; Jiao, Zhidong3; Zhang, Xue2; Sun, Cong1 |
| 刊名 | JOURNAL OF MANUFACTURING PROCESSES
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| 出版日期 | 2025-01-17 |
| 卷号 | 133页码:367-379 |
| 关键词 | Laser directed energy deposition 3D regression networks Deep learning Texture Scanning pattern selection Thermal field prediction |
| ISSN号 | 1526-6125 |
| DOI | 10.1016/j.jmapro.2024.11.072 |
| 通讯作者 | Kong, Xiangwei(shawnkongneu@163.com) ; Liang, Jingjing(jjliang@imr.ac.cn) ; Li, Jinguo(jgli@imr.ac.cn) |
| 英文摘要 | Thermal accumulation in the laser directed energy deposition (LDED) process is a crucial issue that can significantly affect the microstructure and texture of the deposited layer. In addition to the process parameters (including beam type, power, and scanning speed), the scanning strategy is also a significant factor that affects the temperature field. Most scanning strategies used in the past are heuristic methods due to the computation limit of the finite element model. In this paper, an optimal framework for the fast selection of scanning patterns for the LDED process is proposed. Firstly, a temperature field dataset is built based on the finite element simulation results of random scanning patterns. Secondly, a deep regression model based on skip-connected 3D convolutional autoencoder (SC-3DCAE) is developed and trained by the dataset for temperature field prediction. Afterward, optimal scanning patterns are quickly selected from randomly generated candidates using the validated SC-3DCAE model based on the thermal variance criterion. Finally, finite element simulations and a 50layer thin-wall deposition process are performed with the recommended optimal scanning pattern. The trained SC-3DCAE model shows high accuracy and fast prediction of the temperature field. The optimized scanning pattern achieves higher cooling rates and temperature gradients compared to the continuous deposition pattern. Smaller dendrite spacing and grain size are found in the samples with the optimized scanning pattern. |
| 资助项目 | National Science and Technology Major Project[2019-VII-0019-0161] ; National Key Research and Development Program of China[SQ2019YFB1704500] ; State Ministry of Science and Technology Innovation Fund of China[2018IM030200] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001370497500001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Science and Technology Major Project ; National Key Research and Development Program of China ; State Ministry of Science and Technology Innovation Fund of China |
| 源URL |  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Kong, Xiangwei; Liang, Jingjing; Li, Jinguo |
| 作者单位 | 1.Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang 110016, Peoples R China 3.CRRC Qingdao Sifang Co Ltd, Qingdao 266000, Peoples R China 4.Northeastern Univ, Key Lab Vibrat & Control Aeroprop Syst, Minist Educ, Shenyang 110819, Peoples R China 5.Northeastern Univ, Liaoning Prov Key Lab Multidisciplinary Design Opt, Shenyang 110819, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Liang,Kong, Xiangwei,Cheng, Liu,et al. Fast scanning pattern selection in laser directed energy deposition via simulation data-driven based deep regression method[J]. JOURNAL OF MANUFACTURING PROCESSES,2025,133:367-379. |
| APA | Ma, Liang.,Kong, Xiangwei.,Cheng, Liu.,Liang, Jingjing.,Li, Jinguo.,...&Sun, Cong.(2025).Fast scanning pattern selection in laser directed energy deposition via simulation data-driven based deep regression method.JOURNAL OF MANUFACTURING PROCESSES,133,367-379. |
| MLA | Ma, Liang,et al."Fast scanning pattern selection in laser directed energy deposition via simulation data-driven based deep regression method".JOURNAL OF MANUFACTURING PROCESSES 133(2025):367-379. |
入库方式： OAI收割
来源：金属研究所
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