One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

Nowadays, there is a fresh push towards putting more attention on sustainability issues without affecting productivity as main target in Industrial Cyber-Physical Systems (ICPS). In this direction, this work proposes a procedure and presents a data-driven insight method in order to predict the remaining useful life (RUL) and to classify faults by a condition base-monitoring (CbM). Therefore, by using a framework that combines both outputs, a maintenance stop can be scheduled near to the failure, thus improving its sustainability, without affecting productivity. A fuzzy decision-making strategy supported on generated insights is developed in order to extend the useful life of electromechanical devices. A case study is presented in order to assess the proposed methodology using a dataset of bearing faults. Experimental results and its comparison with previous reported works corroborate a good trade-off solution offered by the proposed procedure considering productivity and sustainability for bearing faults detection.

Pilot lines have become a key element in modern industry. The demands for resilience, human-centricity, and sustainability in Industry 5.0, combined with technological innovations, highlight the critical role of these lines in the new industrial paradigm. Pilot lines facilitate the validation of processes and the reduction in uncertainties, making them essential tools for future industries. This article examines the current state of pilot lines and evaluates their alignment with Industry 5.0 principles. As part of this analysis, the GAMHE 5.0 pilot line is presented as a line designed with a focus on automation and robotics, meeting the requirements of the industries of the future. This article also details a series of case studies conducted on the pilot line, demonstrating its flexibility and adaptability. Finally, the advantages and disadvantages of small-scale lines are analyzed within the context of Industry 5.0, emphasizing their importance in balancing technology, human factors, and sustainability.

Self-reconfiguration in manufacturing systems refers to the ability to autonomously execute changes in the production process to deal with variations in demand and production requirements while ensuring a high responsiveness level. Some advantages of these systems are their improved efficiency, flexibility, adaptability, and cost-effectiveness. Different approaches can be used for designing self-reconfigurable manufacturing systems, including computer simulation, data-driven methods, and artificial intelligence-based methods. To assess an artificial intelligence-based solution focused on self-reconfiguration of manufacturing enterprises, a pilot line was selected for implementing an automated machine learning method for finding and setting optimal parametrizations and a fuzzy system-inspired reconfigurator for improving the performance of the pilot line. Additionally, a deep learning segmentation model was integrated into the pilot line as part of a visual inspection module, enabling a more efficient management of the production line workflow. The results obtained demonstrate the potential of self-reconfigurable manufacturing systems to improve the efficiency and effectiveness of production processes.