Machine Learning in Additive Manufacturing: A Review

被引：0
|
作者：
Lingbin Meng
Brandon McWilliams
William Jarosinski
Hye-Yeong Park
Yeon-Gil Jung
Jehyun Lee
Jing Zhang
机构：
[1] Indiana University-Purdue University Indianapolis,Department of Mechanical and Energy Engineering
[2] Aberdeen Proving Ground,CCDC Army Research Laboratory
[3] Praxair Surface Technologies,Department of Materials Science and Engineering
[4] Changwon National University,undefined
来源：
JOM | 2020年 / 72卷
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学科分类号：
摘要：
In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested.
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页码：2363 / 2377
页数：14
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