Title

Defects Classification of Laser Metal Deposition using Acoustic Emission Sensor

Abstract

Laser metal deposition (LMD) is an advanced additive manufacturing (AM) process used to build or repair metal parts layer by layer for a range of different applications. Any presence of deposition defects in the part produced causes change in the mechanical properties and might cause failure to the part. In this work, defects monitoring system was proposed to detect and classify defects in real time using an acoustic emission (AE) sensor and an unsupervised pattern recognition analysis. Time domain and frequency domain, and relevant descriptors were used in the classification process to improve the characterization and the discrimination of the defects sources. The methodology was found to be efficient in distinguishing two types of signals that represent two kinds of defects. A cluster analysis of AE data is achieved and the resulting clusters correlated with the defects sources during laser metal deposition.

Meeting Name

28th Annual International Solid Freeform Fabrication Symposium -- An Additive Manufacturing Conference, SFF 2017 (2017: Aug. 7--9, Austin, TX)

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Acoustic emission; Clustering analysis; Deposition defects; Laser metal deposition

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 The University of Texas at Austin, All rights reserved.

Publication Date

01 Aug 2017

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