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Surface Roughness Prediction in Additive Manufacturing Using Machine Learning

[+] Author Affiliations
Dazhong Wu

University of Central Florida, Orlando, FL

Yupeng Wei, Janis Terpenny

Pennsylvania State University, University Park, PA

Paper No. MSEC2018-6501, pp. V003T02A018; 6 pages
  • ASME 2018 13th International Manufacturing Science and Engineering Conference
  • Volume 3: Manufacturing Equipment and Systems
  • College Station, Texas, USA, June 18–22, 2018
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5137-1
  • Copyright © 2018 by ASME


To realize high quality, additively manufactured parts, real-time 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.

Copyright © 2018 by ASME



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