@inproceedings{40c78bb86daf420d86c36e0412286620,
title = "Surface roughness prediction in additive manufacturing using machine learning",
abstract = "To realize high quality, additively manufactured parts, realtime process monitoring and advanced predictive modeling tools are crucial for accelerating quality assurance and quality control in additive manufacturing. While previous research has demonstrated the effectiveness of physics-and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-Time monitoring and prediction of surface roughness in fused deposition modeling (FDM). This paper presents a new data-driven approach to surface roughness prediction in FDM. A real-Time monitoring system is developed to monitor the health condition of a 3D printer and FDM processes using multiple sensors. A predictive model is built by random forests (RFs). Experimental results have shown that the predictive model is capable of predicting the surface roughness of a printed part with very high accuracy.",
keywords = "Additive manufacturing, Machine learning, Process monitoring, Prognostics and health management, Surface roughness",
author = "Dazhong Wu and Yupeng Wei and Janis Terpenny",
note = "Publisher Copyright: Copyright {\textcopyright} 2018 ASME.; ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 ; Conference date: 18-06-2018 Through 22-06-2018",
year = "2018",
doi = "10.1115/MSEC2018-6501",
language = "英语",
isbn = "9780791851371",
series = "ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018",
publisher = "American Society of Mechanical Engineers (ASME)",
booktitle = "Manufacturing Equipment and Systems",
address = "美国",
}