Electronics in Advanced Research Industries. Alessandro Massaro

1 State of the Art and Technology Innovation

      The chapter is focused on the technological and scientific state of the art about information technology (IT) advances. Starting with Industry 4.0 enabling technologies, the scientific improvements transforming the production lines and machines in intelligent systems following the logic of Industry 5.0 are discussed. The new facilities and the new technologies are oriented on the design of flexible and dynamical production processes, taking into account the market demand which is increasingly unpredictable. Starting with the enabling technologies of Industry 4.0, the specifications of the hardware and software technologies for advances in Industry 5.0 manufacturing industries are introduced. Communication protocols able to improve sensing and actuation in production processes are also discussed. Moreover, the analysis describes the Internet of Things (IoT) protocols, IoT upgrade processes and technological improvements, where of particular interest in monitoring industrial processing is infrared thermography (IRT) for improving thermal measurements in the production environment. The chapter is also focused on the description of different levels of the company information system, where sensors monitoring production constitute the field layer. The discussion is then oriented to provide an overview about sensors communicating with the local network by protocols, and achieving intelligent and efficient sensing and actuation. All the analyzed topics are addressed for integration into an upgraded information infrastructure implementing advanced tools. The analysis is then moved to the production processes in industries by highlighting main interconnections and architectures interfacing different tools. The study also enhances the scientific approaches consolidated in Industry 4.0, by providing limits of the actual technologies and perspectives for future production upscaling. Furthermore, the chapter discusses mainly intelligent information infrastructure suitable for manufacturing industries. The chapter goal is to introduce technological elements such as artificial intelligence (AI), augmented reality (AR) and big data systems, providing knowledge gain (KG). Other important aspects are the horizontal and vertical integrations of the technologies, considering bus‐based networks and automatisms in data processing which is significant for the production advances. The chapter provides elements useful to comprehend how technologies can be implemented in flexible information architectures for innovative industrialization processes.

1.1 State of the Art of Flexible Technologies in Industry

      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]:

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.

      1.1.1 Sensors and Actuators Layer: I/O Layer

The I/O layer is the first layer related to the production field controlled by sensors. The process of machines can be changed by actuation commands provided by the processing layer. The actuation commands must ensure the production synchronization of the whole production lines managing different production steps. In this layer, IoT devices are very important for the accuracy and reliability of the performed measurements. The data sampling is essential for a correct monitoring procedure. When the sensors control different production process steps, it is fundamental to configure and to synchronize all the sensors of the same production line. The IoT technologies are defined for the specific production process to monitor. For example, if the process is fast, it is important to select an image vision technology having a high frame rate, or sensors having a sampling time “following” the production velocity. The technologies for industrial image vision converting light into electrons are charge‐coupled device (CCD), complementary metal oxide semiconductor (CMOS), indium antimonide (InSb) infrared (IR)