E3S Web Conf.
Volume 95, 2019The 3rd International Conference on Power, Energy and Mechanical Engineering (ICPEME 2019)
|Number of page(s)||6|
|Published online||13 May 2019|
Status recognition for fused deposition modeling manufactured parts based on acoustic emission
The State Key Laboratory of Fluid Power Transmission and Control, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China
Fused deposition modelling (FDM), as one technology of additive manufacturing, fabricates parts always with curl and looseness defects which restrict its development to a great extent. In this paper, a method based on acoustic emission (AE) was proposed to recognise the status of the manufactured part in FDM process. Experiments were carried out to acquire the AE signal when the printing part was respectively in normal, looseness and curl state. The ensemble empirical mode decomposition (EEMD) was employed to the process of feature extraction and both the methods of Hidden-semi Markov model (HSMM) and support vector machine(SVM) were applied to recognise the three states of the normal, looseness and curl. The results reveal that the combination of EEMD and HSMM makes it more accurate to recognize these three states.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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