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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 reallife 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. The introduction of laser technology in metal welding of metals is dated back to the late 1960s 1,2 when it immediately showed advantages as compared to traditional arc welding 3. The attractions of this technique are in the non-contact processing, the absence of tool wear, high aspect ratio of the melt pool, better material fusion, possibility to process refractory materials, low running costs and high processing speed 3,4. Today, laser welding is a key technology in many fields e.g. automotive 5 and aerospace 3,6 industries, naval and heavy machinery production 7 , medicine and micromechanics 3. Unfortunately, the potential of this technology is not fully exploited, particularly in applications that require the guarantee of high weld quality. The reason is the non-linear nature of light-matter interactions, which complicates the reproducibility of the weld quality in mass production 8-10. The complex dynamics of the process, especially in keyhole welding regime, and its instabilities can cause various defects at the joint 3,10-12. A defect type of particular interest is porosity, which is a hidden threat for the mechanical properties of the workpieces 3,9-11. Obviously, an adequate, robust and low cost quality monitoring system is of great desire. The major challenge in developing such technique is in the difficulties to inspect directly the sub-surface behavior of the process zone in real-life conditions 13. Multiple approaches have been proposed, which are mostly based on mathematical modeling aiming to reconstruct the under surface dynamics using inspections of the surface via measurements of temperature 11,12,14,15 , optical 16,17 and/or acoustic 18,19 emissions (AE). However, those approaches face three main problems. Firstly, modeling often suffers inaccuracies originating from the deviations of the model assumptions from the real parameters' values. More complicated assumptions can be used to imp...

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The design of reliable wearable technologies for real-time and long-term monitoring presents a major challenge. Self-awareness is a promising solution that enables the system to monitor itself in interaction with the environment and to manage its resources more efficiently. In this work, we aim to utilize the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring. Specifically, we consider cognitive workload detection during manual labor as a case study to illustrate the impact of our proposed technique in wearable technologies. Our multimodal machine-learning algorithm is able to detect cognitive workload during manual labor with a performance of 81.75%. By adopting the notion of self-awareness, we achieve an improvement of 27.6% in energy consumption, with less than 6% of performance loss.

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Despite extensive research efforts in the field of laser welding, the imperfect repeatability of the weld quality still represents an open topic. Indeed, the inherent complexity of the underlying physical phenomena prevents the implementation of an effective controller using conventional regulators. To close this gap, we propose the application of Reinforcement Learning for closed-loop adaptive control of welding processes. The presented system is able to autonomously learn a control law that achieves a predefined weld quality independently from the starting conditions and without prior knowledge of the process dynamics. Specifically, our control unit influences the welding process by modulating the laser power and uses optical and acoustic emission signals as sensory input. The algorithm consists of three elements: a smart agent interacting with the process, a feedback network for quality monitoring, and an encoder that retains only the quality critic events from the sensory input. Based on the data representation provided by the encoder, the smart agent decides the output laser power accordingly. The corresponding input signals are then analyzed by the feedback network to determine the resulting process quality. Depending on the distance to the targeted quality, a reward is given to the agent. The latter is designed to learn from its experience by taking the actions that maximize not just its immediate reward, but the sum of all the rewards that it will receive from that moment on. Two learning schemes were tested for the agent, namely Q-Learning and Policy Gradient. The required training time to reach the targeted quality was 20 min for the former technique and 33 min for the latter.

Quality monitoring in Additive Manufacturing (AM) is currently mostly based on temperature measurements of the process zone or on layer/piecewise high-resolution surface imaging. To this aim, various sensors, such as pyrometers, photodiodes, and matrix CCD detectors, have been tested. These standard temperature measurements, however, do not provide a comprehensive description of the process dynamics, as they are just limited to surface observations. Furthermore, they are often used for post-factum inspection, i.e., after the piece is partially or even completely produced. No robust and low-cost methods are so far known to monitor the quality of laser processes in real-time. To close this gap, we propose an innovative approach for online quality monitoring of additive manufacturing employing acoustic emissions (AE). In fact, AE signals can provide in-depth information about the process, e.g., melting, resolidification, delamination, and cracking of the workpiece. Moreover, the sintering or melting of the metal powder has several unique acoustic signatures that can be detected and interpreted in terms of quality. In our approach, the correlation of the acoustic signals to the quality of the produced pieces is made by Artificial Intelligence (AI) methods. Specifically, AI in the form of Machine Learning is used to perform a data-driven extraction and recognition of the unique acoustic signatures from different sintering or melting events. In this contribution, we present a summary of our results in the fields of selective laser melting and laser welding, which have similar underlying mechanisms. At first, we discuss how, by using AE, we can classify different types of defects and porosity content in both processes. Afterward, with the aid of high-speed X-ray imaging, we demonstrate the real-time performance of our approach in the classification of transient events/regimes that are critical for the final quality -in particular conduction, stable keyhole, unstable keyhole, pore formation, and blowout. Finally, we present the future possibilities in terms of control of AM processes based on AI.