Journal
JOM
Volume 72, Issue 6, Pages 2363-2377Publisher
SPRINGER
DOI: 10.1007/s11837-020-04155-y
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Funding
- CCDC Army Research Laboratory Cooperative Research and Development [19-013-001]
- Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), Republic of Korea [20194030202450]
- Power Generation & Electricity Delivery Grant of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), Republic of Korea [20193310100030]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20193310100030] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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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