|
| 增材制造金属材料的疲劳性能研究进展 |
| Research Progress on Fatigue Properties of Additive Manufactured Metal Materials |
| Received:August 26, 2023 |
| DOI：10.3969/j.issn.1674-6457.2024.01.001 |
| 中文关键词: 增材制造 金属材料 缺陷 疲劳寿命 疲劳裂纹 疲劳寿命评估 |
| 英文关键词: additive manufacturing metal materials defects fatigue life fatigue cracks fatigue life evaluation |
| 基金项目:天津市教委科研计划项目(2020KJ104) |
| Author Name | Affiliation | | DAI Junlin | Tianjin University of Technology and Education, Tianjin 300072, China | | WU Shipin | Tianjin University of Technology and Education, Tianjin 300072, China
Center for Advanced Joining Technology, Tianjin University, Tianjin 300350, China | | ZHANG Yu | Tianjin University of Technology and Education, Tianjin 300072, China | | WANG Xuejiao | Tianjin University of Technology and Education, Tianjin 300072, China | | MA Qiang | Tianjin Golden Bridge Welding Materials Group Co., Ltd., Tianjin 300399, China |
|
| Hits: 2465 |
| Download times: 677 |
| 中文摘要: |
| 金属增材制造作为前沿热点制造技术之一，近年来在各种重要工业领域的研究和应用日益广泛。利用增材制造技术制备金属材料的过程中，不可避免会造成材料表面粗糙、气孔、未熔合等缺陷，虽然工艺技术的改进可以在一定程度上减小缺陷程度，但至今仍无法完全消除这些缺陷。增材制造金属材料的过程中，缺陷部位通常会成为应力集中源诱发疲劳裂纹的形核，造成金属材料的疲劳寿命下降。首先从表面质量、内部缺陷及微观结构等方面阐述了增材制造金属材料疲劳性能的影响因素;其次从宏观与微观角度概括了疲劳裂纹萌生/扩展机理的研究现状与进展;总结了热处理、表面优化、电磁辅助以及超声辅助等疲劳延寿技术的研究进展;最后讨论了基于机器学习技术的疲劳寿命评估模型，同时展望了机器学习和人工智能技术在增材制造金属材料领域的应用前景，为推动增材制造金属材料的发展和应用提供了借鉴与参考价值。 |
| 英文摘要: |
| Metal additive manufacturing, also known as one of the prominent manufacturing technologies, has garnered significant attention and has been extensively investigated and used across diverse crucial industrial sectors in recent times. The additive manufacturing method inherently gives rise to various defects, including but not limited to surface roughness, porosity, and lack of fusion. Despite advancement in process technology, it remains unfeasible to entirely eradicate defects, but can reduce defects to a certain amount. During the additive manufacturing of metal materials, the defective parts usually become the source of stress concentration and induce fatigue crack nucleation, resulting in a decrease in the fatigue life of metal materials. The factors affecting the fatigue performance of metals produced by additive manufacturing were described firstly from the surface quality, internal defects, and microstructure. Secondly, the research progress of fatigue crack initiation and expansion mechanism was summarized from the macroscopic and microscopic perspectives. Then, the current progress of fatigue life-extension techniques such as heat treatment, surface optimization, electromagnetic-assisted and ultrasonic-assisted techniques were introduced. Finally, the fatigue life evaluation model utilizing machine learning technology was further examined, along with the potential application of machine learning and artificial intelligence technology in the domain of additive manufacturing of metal materials, providing experience and reference value for advancing the progress and utilization of metal additive manufacturing. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
渝公网安备 50010702501719号