Metal Additive Manufacturing (AM) is a pillar of the Industry 4.0, with many attractive advantages compared to traditional subtractive fabrication technologies. However, there are many quality issues that can be an obstacle for mass production. The in-situ camera-based monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build, can be an effective solution to this problem. In this context, the use of Computer Vision and Machine Learning algorithms have a very important role. Nonetheless, they are up to this date limited by the scarcity of data for the training, as well as by the difficulty of accessing and integrating the AM process data throughout the fabrication. To tackle this problem, this paper proposes a system for in-situ monitoring that analyses images from an off-axis camera mounted on top of the machine to detect the arising defects in real-time, with automated generation of synthetic images based on Generative Adversarial Network (GAN) for dataset augmentation purposes. The computing functionalities are embedded into a holistic distributed AM platform allowing the collection, integration and storage of data at all stages of the AM pipeline.

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters.

Despite the many advantages and increasing adoption of Electron Beam Powder Bed Fusion (PBF-EB) additive manufacturing by industry, current PBF-EB systems remain largely unstable and prone to unpredictable anomalous behaviours. Additionally, although featuring in-situ process monitoring, PBF-EB systems show limited capabilities in terms of timely identification of process failures, which may result into considerable wastage of production time and materials. These aspects are commonly recognized as barriers for the industrial breakthrough of PBF-EB technologies. On top of these considerations, in our research we aim at introducing real-time anomaly detection capabilities into the PBF-EB process. To do so, we build our case-study on top of a Arcam EBM A2X system, one of the most diffused PBF-EB machines in industry, and make access to the most relevant variables made available by this machine during the layering process. Thus, seeking a proficient interpretation of such data, we introduce a deep learning autoencoder-based anomaly detection framework. We demonstrate that this framework is able not only to early identify anomalous patterns from such data in real-time during the process with a F1 score around 90%, but also to anticipate the failure of the current job by 6 h, on average, and in one case by almost 20 h. This avoids waste of production time and opens the way to a more controllable PBF-EB process.

Recent technological transitions present in the manufacturing sector are transforming the way operators work, greatly impacting their mental workload and presenting new challenges for the cognitive ergonomics field. In an effort to design and implement human-centered management strategies, it is increasingly necessary to effectively explore and evaluate the various aspects related to mental workload. Despite the maturity of the field, in the existing literature a consensus on defining mental workload remains elusive. To bridge this gap, this paper introduces a comprehensive and holistic definition, drawing inspiration from the ISO 10075 standard. This novel taxonomic framework distinguishes various constructs related to mental workload, clarifying their causes and the consequences they induce. Additionally, an extensive literature analysis was conducted to develop a taxonomy of mental workload assessment tools, outlining their operational principles along with their strengths and weaknesses. This taxonomy can be used as a pragmatic guide for selecting appropriate tools based on specific contexts. Moreover, the existing literature was extensively investigated to ascertain the nature of the correlation-whether positive or negative-between the metrics of these tools and the range of the effects of mental workload fluctuations. This methodological approach addresses the common issue of misinterpreting results from mental workload measurements. In essence, the research seeks to enrich the understanding of mental workload concepts and offers a critical evaluation of its measurement tools. The findings are intended to assist practitioners in the informed selection of measurement tools and to refine data interpretation, thereby facilitating better management of mental workload in manufacturing environments.