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

Abstract

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. © Published under licence by IOP Publishing Ltd.