Surface roughness prediction in additive manufacturing using machine learning

Dazhong Wu, Yupeng Wei, Janis Terpenny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationManufacturing Equipment and Systems
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9780791851371
DOIs
StatePublished - Jan 1 2018
EventASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 - College Station, United States
Duration: Jun 18 2018Jun 22 2018

Publication series

NameASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
Volume3

Other

OtherASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
CountryUnited States
CityCollege Station
Period6/18/186/22/18

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Surface roughness prediction in additive manufacturing using machine learning'. Together they form a unique fingerprint.

  • Cite this

    Wu, D., Wei, Y., & Terpenny, J. (2018). Surface roughness prediction in additive manufacturing using machine learning. In Manufacturing Equipment and Systems (ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018; Vol. 3). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/MSEC2018-6501