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Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. This work aims at enriching existing approaches in this field. We propose a method for real-time detection of process instabilities that can lead to defects. Hard X-ray radiography is used for the ground truth observations of the sub-surface events that are critical for the quality. A deep artificial neural network is applied to reveal the unique signatures of those events in wavelet spectrograms from the laser back-reflection and acoustic emission signals. The autonomous classification of the revealed signatures is tested on real-life data, while the real-time performance is reached by means of parallel computing. The confidence of the quality classification ranges between 71% and 99%, with a temporal resolution down to 2 ms and a computation time per classification task as low as 2 ms. This approach is a new paradigm in the digitization of industrial processes and can be exploited to provide feedbacks in a closed-loop quality control system. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

The defective regimes in metal-based Laser Powder Bed Fusion (LPBF) processes can be minimized by deploying in-situ monitoring strategies comprising Machine learning (ML) algorithms and sensing techniques. So far, algorithms trained for monitoring a particular material type cannot be re-used to monitor another material in Additive Manufacturing (AM). This is a topic rarely researched in AM. Inspired by the idea of transfer learning in ML, we demonstrate the knowledge learned by the two native Deep Learning (DL) networks, namely VGG and ResNets, on four LPBF process mechanisms such as balling, Lack of Fusion (LoF) pores, conduction mode, and keyhole pores in stainless steel (316L) can be transferred to bronze (CuSn8). In this work, the spectrograms computed using Wavelet Transforms (WT) on Acoustic Emissions (AE) during the LBPF process of stainless steel and bronze are used for training the two DL networks. Either network is first trained for classification by spectrograms representing four mechanisms during the processing of stainless steel. The trained model is then re-trained using transfer learning with spectrograms from bronze data for a similar classification task. The accuracy of the two networks during transfer learning shows that it is effectively possible to learn transferable features from one material to another with minimum network training time and dataset collection. [Display omitted] • In situ monitoring for Metal-based LPBF is presented by combining airborne acoustic signatures with machine learning. • Spectrograms computed from acoustic emissions are used to train the VGG-16 and ResNet-18 for classifying mechanisms. • The classification model trained on one material is selectively retrained for classification on another material. • Accuracies based on transfer learning shows that it is possible to transfer features from stainless steel to bronze. • The performance of the two CNN architectures during transfer learning is compared and discussed. [ABSTRACT FROM AUTHOR]