Defects Recognition in Selective Laser Melting with Acoustic Signals by SVM Based on Feature Reduction

Defects among the selective laser melting(SLM) part hinder the development of the SLM process. This work provides an approach to conduct the monitoring and defect diagnosis by support vector machines (SVM) model using extracted features from acoustic signals. After training and testing with the line...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 436; no. 1; pp. 12020 - 12025
Main Authors Ye, D S, Fuh, Y H J, Zhang, Y J, Hong, G S, Zhu, K P
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 23.10.2018
Subjects
Acoustics
Defects
Diagnosis
Discriminant analysis
Feature extraction
Laser beam melting
Monitoring
Principal components analysis
Rapid prototyping
Reduction
Signal processing
Support vector machines
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Summary:Defects among the selective laser melting(SLM) part hinder the development of the SLM process. This work provides an approach to conduct the monitoring and defect diagnosis by support vector machines (SVM) model using extracted features from acoustic signals. After training and testing with the linear SVM model, the result from the Fisher discriminant analysis (FDA) feature reduction performs optimal compared with those from the original features and the principal component analysis (PCA) feature reduction. The melted state monitoring and classification can be realized by simple discriminant model of SVM with extracted features after dimension reduction. The proposed method can be applied in the SLM process monitoring and defect diagnosis by acoustic signals with generalization.
ISSN:1757-8981
1757-899X
1757-899X
DOI:10.1088/1757-899X/436/1/012020