Читать книгу Electronics in Advanced Research Industries - Alessandro Massaro - Страница 13

Industry 4.0 introduced digital technologies improving industry productivity and different facilities supporting processes. The main enabling technologies introduced by Industry 4.0 are [1–3]:

 Three‐dimensional (3D) printers connected to production software.

 AR oriented on production processes.

 Simulation tools able to optimize production processes by simulating production of different interconnected machines of different production lines.

 Horizontal integration of supply chain elements, such as human resources, supplies, products, transports, logistics, etc., and vertical integration of different production functions including product design, production processes, production quality, and end to end combination of horizontal and vertical functions.

 Cloud computing, cloud data storage, and data management in open data and big data systems.

 Cybersecurity improving security during network operations and in open systems, managing network interconnections.

These main facilities enable smart manufacturing (SM) and computer integrated manufacturing (CIM) industry processes in the fourth industrial revolution. In this scenario of enabling technologies, the information network architecture of companies plays a fundamental rule in production upgrade and in production engineering. The information digitalization is the first step for Industry 4.0 implementation, where the production machines transfer data in the local area network (LAN) and in general in the cloud environment. A particular function in Industry 4.0 improvement is the production monitoring, automated by IoT sensors [4], reading in real time the operation conditions of the whole production lines and allowing intelligent manufacturing. The control performed by sensors is more efficient for in‐line monitoring procedures, where all sensors are synchronized in order to provide the best production setting of the whole supply chain. All the phases of the supply chain are important to trace. The main parts to trace in the production processes are: (i) warehouse, (ii) production lines, and (iii) logistics. In all these parts, robotics in general improves the processes, by increasing production volumes and by assisting human work. This kind of “joint collaboration” decreases the production errors and consequently the waste materials and related costs. Other technologies such as AR [5, 6] are used for human resources training during production processing, by increasing the worker skills and supporting workers to follow interactively and continuously the production. Augmented reality aided manufacturing (ARAM) is another important topic supporting production quality [5] by means of the programming of machines, robots and production tools, by managing logistics, and by checking assembled products in the whole supply chain. AR is adopted also in manufacturing as a dynamic authoring tool monitoring simultaneously the production activities of several workstations [6], for telerobotics controlling robots from a distance, for waste reduction in production activities, for assembly support, for remote maintenance, and for computer‐aided design (CAD) applications [7]. In the Industry 4.0 scenario, AI can furthermore improve the industry production efficiency. AI algorithms are mainly indicated for machine predictive maintenance [8, 9] and for assisted production, where machine working operations are properly and automatically set in order to avoid failures [10], by decreasing or stopping machine in cases of alerting conditions. IoT sensors are very important for control and actuation thus enabling totally automated processes. A broad use of IoT sensing is related to image vision [11, 12] including IRT [13], and temperature and humidity sensors. Moreover, accelerometers provide supplementary information about anomalous vibrations indicating a possible system failure, and other sensors can be applied depending on the manufacturing process to be controlled. IoT signals are processed by AI algorithms to predict the machine status in self learning modality: by analyzing historical data, the AI algorithms create the training models to test for prediction. The AI improvements represent mainly the passage from Industry 4.0 to Industry 5.0 facilities adapting automatically the production with high level efficiency, and optimizing the production processes which are previously simulated. The flexibility of the production is due to the correct choice of the sensor network architecture, of protocols and the possibility to optimize the different layers of the whole communication system of the company. A correct design of the information system allows the disposal of a modular network open to vertical and horizontal integrations introducing innovative tools and algorithms addressing the automatic production control. The layers where it is possible to operate for a flexible production are the input/output (I/O) layer, the user interface layer, the gateway layer, the IoT middleware, the processing layer, and the application layer.