In Situ Monitoring of Optical Emission Spectra for Microscopic Pores in Metal Additive Manufacturing

Abstract

Quality assurance techniques are increasingly demanded in additive manufacturing. Going beyond most of the existing research that focuses on the melt pool temperature monitoring, we develop a new method that monitors the in-situ optical emission spectra signals. Optical emission spectra signals have been showing a potential capability of detecting microscopic pores. The concept is to extract features from the optical emission spectra via deep auto-encoders, and then cluster the features into two quality groups to consider both unlabelled and labelled samples in a semi-supervised manner. The method is integrated with multitask learning to make it adaptable for the samples collected from multiple processes. Both a simulation example and a case study are performed to demonstrate the effectiveness of the proposed method.

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

“…In [ 63 ], the authors developed a semi-supervised clustering-based method to automatically detect spectra patterns that are sensitive to a high density of pores, i.e., the high number of microscopic pores within a unit space. This is equivalent to clustering the spectra into two groups—one relates to high-quality products with a low pores density, and the other relates to low-quality products with a high pores density.…”

Deep Learning for In-Situ Layer Quality Monitoring during Laser-Based Directed Energy Deposition (LB-DED) Additive Manufacturing Process

“…Research has also focused on image-based methods for in situ monitoring during metal AM applications [61]. Zhao et al created a real-time approach to defect detection through the use of high-speed synchrotron hard X-ray imaging [62].…”