TY - GEN
T1 - Surface roughness prediction in additive manufacturing using machine learning
AU - Wu, Dazhong
AU - Wei, Yupeng
AU - Terpenny, Janis
N1 - Funding Information:
The research reported in this paper is partially supported by the University of Central Florida and the Digital Manufacturing and Design Innovation Institute. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the University of Central Florida and the Digital Manufacturing and Design Innovation Institute.
Publisher Copyright:
Copyright © 2018 ASME.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85054998273
UR - https://www.scopus.com/pages/publications/85054998273#tab=citedBy
U2 - 10.1115/MSEC2018-6501
DO - 10.1115/MSEC2018-6501
M3 - Conference contribution
AN - SCOPUS:85054998273
SN - 9780791851371
T3 - ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
BT - Manufacturing Equipment and Systems
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
Y2 - 18 June 2018 through 22 June 2018
ER -