期刊
JOURNAL OF MANUFACTURING PROCESSES
卷 75, 期 -, 页码 693-710出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jmapro.2021.12.061
关键词
Laser-based additive manufacturing; Machine learning; Defect detection; Product quality; Artificial intelligence
资金
- South Carolina Research Authority [30228]
- South Carolina Space Grant Consortium [521179-RP-SC007]
This paper provides a comprehensive overview of machine learning algorithms for defect detection in metal LBAM processes. It describes the algorithms, material types, defect types, dataset types, and algorithm accuracy for various LBAM technologies.
Laser-based additive manufacturing (LBAM), a series of additive manufacturing technologies, has unrivaled advantages due to its design freedom to manufacture complex parts with a wide range of applications. Although advancements in LBAM processes and materials have led to increased manufacturing capabilities, the printing process's repeatability, durability, and reliability still face significant challenges. Therefore, a defect detection system for the LBAM processes is essential, as it promises to guarantee product quality and increase the efficiency of the printing process. As a practical and widely applied technology, machine learning methods have been providing novel insights into the manufacturing process, which has proven advantages for defect detection in LBAM. This paper summarizes the machine learning algorithms for defect detection in the metal LBAM processes. To have a comprehensive and systematic summary, machine learning algorithm, material type, defect type, dataset type, and algorithm accuracy for various LBAM technologies are described.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据