CN112329275A - 一种激光金属增材沉积融合状态实时预测方法及系统 - Google Patents
一种激光金属增材沉积融合状态实时预测方法及系统 Download PDFInfo
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Cited By (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113059184A (zh) * | 2021-03-30 | 2021-07-02 | 南京航空航天大学 | 锭坯喷射成形工艺参数优化方法 |
CN113063507A (zh) * | 2021-03-26 | 2021-07-02 | 中国科学院物理研究所 | 基于卷积神经网络的超短脉冲宽度预测方法 |
CN113077078A (zh) * | 2021-03-19 | 2021-07-06 | 中国科学院物理研究所 | 基于深度学习的超短激光脉冲相位预测方法及其系统 |
CN113136578A (zh) * | 2021-04-20 | 2021-07-20 | 大连理工大学 | 一种基于离焦量预测的激光熔覆薄壁件高度控制方法 |
CN115415549A (zh) * | 2022-09-05 | 2022-12-02 | 苏州中科煜宸激光智能科技有限公司 | 基于非线性自回归神经网络的激光熔覆平整化控制系统与方法 |
TWI790668B (zh) * | 2021-07-02 | 2023-01-21 | 國立高雄科技大學 | 積層表面品質預測方法及其電腦程式產品 |
CN116070484A (zh) * | 2023-01-31 | 2023-05-05 | 南京林业大学 | 一种基于深度学习的在线实时预测构件状态的方法 |
CN116275124A (zh) * | 2023-05-11 | 2023-06-23 | 四川大学 | 基于无模型自适应迭代学习的激光增材制造分区控制方法 |
CN116408462A (zh) * | 2023-04-12 | 2023-07-11 | 四川大学 | 一种激光金属增材沉积内部空隙状态实时预测方法 |
CN117593255A (zh) * | 2023-11-07 | 2024-02-23 | 四川大学 | 一种基于时空信息融合的激光增材制造缺陷监控方法 |
CN118305332A (zh) * | 2024-06-07 | 2024-07-09 | 吉林大学 | 采用激光粉末床技术制备高强度不锈钢的控制方法 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108346151A (zh) * | 2018-03-12 | 2018-07-31 | 湖南大学 | 一种判断激光焊接熔透性的方法 |
US20180341248A1 (en) * | 2017-05-24 | 2018-11-29 | Relativity Space, Inc. | Real-time adaptive control of additive manufacturing processes using machine learning |
CN110472698A (zh) * | 2019-08-22 | 2019-11-19 | 四川大学 | 基于深度和迁移学习的金属增材成形熔深实时预测方法 |
CN110490866A (zh) * | 2019-08-22 | 2019-11-22 | 四川大学 | 基于深度特征融合的金属增材成形尺寸实时预测方法 |
CN112059386A (zh) * | 2020-09-08 | 2020-12-11 | 湘潭大学 | 一种控制熔丝沉积熔池状态的方法 |
CN112101432A (zh) * | 2020-09-04 | 2020-12-18 | 西北工业大学 | 一种基于深度学习的材料显微图像与性能双向预测方法 |
-
2021
- 2021-01-04 CN CN202110000530.6A patent/CN112329275B/zh active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20180341248A1 (en) * | 2017-05-24 | 2018-11-29 | Relativity Space, Inc. | Real-time adaptive control of additive manufacturing processes using machine learning |
CN108346151A (zh) * | 2018-03-12 | 2018-07-31 | 湖南大学 | 一种判断激光焊接熔透性的方法 |
CN110472698A (zh) * | 2019-08-22 | 2019-11-19 | 四川大学 | 基于深度和迁移学习的金属增材成形熔深实时预测方法 |
CN110490866A (zh) * | 2019-08-22 | 2019-11-22 | 四川大学 | 基于深度特征融合的金属增材成形尺寸实时预测方法 |
CN112101432A (zh) * | 2020-09-04 | 2020-12-18 | 西北工业大学 | 一种基于深度学习的材料显微图像与性能双向预测方法 |
CN112059386A (zh) * | 2020-09-08 | 2020-12-11 | 湘潭大学 | 一种控制熔丝沉积熔池状态的方法 |
Non-Patent Citations (4)
Title |
---|
H.L. WEI 等: "Prediction of spatiotemporal variations of deposit profiles and inter-track voids during laser directed energy deposition", 《ADDITIVE MANUFACTURING》 * |
XINBO QI 等: "Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives", 《ENGINEERING》 * |
向枭 等: "基于30CrNi2MoVA的激光熔化沉积工艺参数研究", 《机械》 * |
马琳杰 等: "一种基于PPCNN的金属激光熔化沉积熔池状态识别方法", 《内燃机与配件》 * |
Cited By (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113077078B (zh) * | 2021-03-19 | 2023-10-31 | 中国科学院物理研究所 | 基于深度学习的超短激光脉冲相位预测方法及其系统 |
CN113077078A (zh) * | 2021-03-19 | 2021-07-06 | 中国科学院物理研究所 | 基于深度学习的超短激光脉冲相位预测方法及其系统 |
CN113063507A (zh) * | 2021-03-26 | 2021-07-02 | 中国科学院物理研究所 | 基于卷积神经网络的超短脉冲宽度预测方法 |
CN113059184A (zh) * | 2021-03-30 | 2021-07-02 | 南京航空航天大学 | 锭坯喷射成形工艺参数优化方法 |
CN113136578A (zh) * | 2021-04-20 | 2021-07-20 | 大连理工大学 | 一种基于离焦量预测的激光熔覆薄壁件高度控制方法 |
TWI790668B (zh) * | 2021-07-02 | 2023-01-21 | 國立高雄科技大學 | 積層表面品質預測方法及其電腦程式產品 |
CN115415549A (zh) * | 2022-09-05 | 2022-12-02 | 苏州中科煜宸激光智能科技有限公司 | 基于非线性自回归神经网络的激光熔覆平整化控制系统与方法 |
CN115415549B (zh) * | 2022-09-05 | 2024-03-08 | 苏州中科煜宸激光智能科技有限公司 | 基于非线性自回归神经网络的激光熔覆平整化控制系统与方法 |
CN116070484A (zh) * | 2023-01-31 | 2023-05-05 | 南京林业大学 | 一种基于深度学习的在线实时预测构件状态的方法 |
CN116408462B (zh) * | 2023-04-12 | 2023-10-27 | 四川大学 | 一种激光金属增材沉积内部孔隙状态实时预测方法 |
CN116408462A (zh) * | 2023-04-12 | 2023-07-11 | 四川大学 | 一种激光金属增材沉积内部空隙状态实时预测方法 |
CN116275124B (zh) * | 2023-05-11 | 2023-08-01 | 四川大学 | 基于无模型自适应迭代学习的激光增材制造分区控制方法 |
CN116275124A (zh) * | 2023-05-11 | 2023-06-23 | 四川大学 | 基于无模型自适应迭代学习的激光增材制造分区控制方法 |
CN117593255A (zh) * | 2023-11-07 | 2024-02-23 | 四川大学 | 一种基于时空信息融合的激光增材制造缺陷监控方法 |
CN118305332A (zh) * | 2024-06-07 | 2024-07-09 | 吉林大学 | 采用激光粉末床技术制备高强度不锈钢的控制方法 |
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