Process identification of laser powder bed fusion anomalies based on process-monitored melt pool radiation intensity data

Abstract

Abstract Laser powder bed fusion (LPBF)) has great application prospects in aerospace and other fields, but low process stability and difficult quality assurance in the process of Laser Powder Bed Fusion are the key problems that restrict its extensive development at present. One of the important ways to solve this problem is to realize online monitoring of molten pool and closed-loop quality control. In this paper, the photodiode sensor is used to collect the radiation signal of the molten pool in real time, and the characteristics of the radiation intensity signal of the molten pool in time domain and frequency domain are extracted. The most appropriate modeling features are selected by comparing and analyzing different features, and XGBoost classical integration algorithm is used to model, and the relationship between the radiation intensity signal of the molten pool and process parameters is established, which provides a new method for abnormal process identification and quality control.