Monitoring of Laser Powder Bed Fusion process by bridging dissimilar process maps using deep learning-based domain adaptation on acoustic emissions

Advances in sensorization and identification of information embedded inside sensor signatures during manufacturing processes using Machine Learning (ML) algorithms for better decision-making have become critical enablers in building data-driven monitoring systems. In the Laser Powder Bed Fusion (LPBF) process, data-driven-based process monitoring is gaining popularity since it allows for real-time component quality verification. Real-time qualification of the additively manufactured parts has a significant advantage as the cost of conventional post-manufacturing inspection methods can be reduced. Also, corrective actions or build termination could be done to save machine time and resources. However, despite the successful development in addressing monitoring needs in LPBF processes, less research has been paid to the ML model's robustness in decision-making when dealing with variations in data distribution from the laser-material interaction owing to different process spaces. Inspired by the idea of domain adaptation in ML, in this work, we propose a deep learning-based unsupervised domain adaptation technique to tackle shifts in data distribution owing to different process parameter spaces. The temporal waveforms of acoustic emissions from the LPBF process zone corresponding to three regimes, namely Lack of Fusion, conduction, and keyhole, were acquired on two different 316 L stainless steel powder distributions (> 45 µm and < 45 µm) with two different parameter sets. Temporal and spectral analysis of the acoustic waveforms corresponding to the powder distributions treated with different laser parameters showed the presence of offset in the data distribution, which was subsequently treated with the proposed unsupervised domain adaptation technique to have an ML model that could be generalized. Furthermore, the prediction accuracy of the proposed methodology between the two distributions showed the feasibility of adapting to the newer environment unsupervisedly and improving the ML model's generalizability.