A novel approach of online monitoring for laser powder bed fusion defects: Air-borne Acoustic Emission and Deep Transfer Learning

Abstract

Defects in laser powder bed fusion (L-PBF) are a serious constraint on the application of the technology. Developing a real-time monitoring technology to guide the production process of parts can solve this problem. Currently, the quality of L-PBF with different scanning strategies varies greatly. Therefore, the online monitoring methods for a specific scanning strategy cannot be effectively generalized to other scanning strategies. This is a rarely investigated topic in L-PBF, and defects monitoring mechanism research is insufficient to effectively support the study of L-PBF defects monitoring technology utilizing advanced sensing technologies. Towards this end, we explored the acoustic source generation mechanism, analyzed the acoustic monitoring principle of L-PBF defects, and hence, we propose a novel online monitoring method for L-PBF defects based on air-borne acoustic emission (ABAE) and deep transfer learning (DTL). The method uses time-frequency spectrograms of acoustic signals as the input to the network, and a method of deep transfer learning with multi-source domains knowledge fusion (DTL-MDKF) is proposed to realize the classification of defects. The proposed method was compared with the traditional transfer learning method based on single-source domain knowledge. The results showed that the classification accuracy of the proposed method for L-PBF defects is 98.2 %. In addition, the feature mining capability of the DTL-MDKF is demonstrated by visualizing the features of transfer learning models with different knowledge. Looking at the results, the proposed method can be considered a promising L-PBF defects online monitoring method for complex and changeable working conditions.