Surface Roughness Prediction In Additive Manufacturing Using Machine Learning

Keywords

Additive manufacturing; Machine learning; Process monitoring; Prognostics and health management; Surface roughness

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.

Publication Date

1-1-2018

Publication Title

ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018

Volume

3

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1115/MSEC2018-6501

Socpus ID

85054998273 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85054998273

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