Status recognition for fused deposition modeling manufactured parts based on acoustic emission

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 pa...

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Bibliographic Details
Main Authors: Li, Feng, Yu, Zhonghua, Shen, Xuanwei, Zhang, Hao
Format: Conference Proceeding
Language:English
Subjects:
Acoustic emission
Deposition
Feature extraction
Fused deposition modeling
Markov chains
Rapid prototyping
Signal processing
Support vector machines
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Summary: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.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/20199501005