Electronics in Advanced Research Industries. Alessandro Massaro

      Preface

Modern technologies in production systems open new approaches and concepts of industrial production. The digital Industry 4.0 upgrade provides new elements to control and manage production in all industry sectors. This upgrade allows to improve product quality, and in general the whole supply chain. The new digital technologies include hardware and software tools integrated in infrastructure oriented on the gain of digital knowledge. The fast dynamicity of the markets, the increase of the global competition between companies, and the unpredictable social and health events, imposes the need to think of a new concept of a production system based on full automatisms and self‐adaptive processes, predicting production failures and product defects. In this context, the Industry 4.0 facilities can be furthermore upscaled to an intelligent control and actuation system of the production, characterizing the new Industry 5.0 scenario. The new facilities which contribute to Industry 5.0 passage are mainly based on artificial intelligence (AI) implementations in production and information systems, accomplishing predictive maintenance, failure prediction, defect classification, efficient robotic control and actuation, design optimization, testing improvements, and in general technological advances due to the possibility to quickly process data in each production stage. This book analyzes innovative production approaches, and the integration aspects of the AI in different industrial digital technologies, by enhancing specific functionalities. In innovative production systems, AI is fully integrated in information systems and covers cybersecurity, quality processes, business intelligence and intelligent production management. The innovative production is also related to new services associated with the introduction in the market of new technologies such as for the telemedicine sector, and in general for industrial diagnostics, where AI is also adopted for the improvement of inspection services. The main advantage of AI is the self‐learning of the algorithms able to learn automatically from the same production data of companies. In an industrial upgrade, the implementation of sensor control and actuation based on intelligent feedback systems is especially important. In this scenario, AI algorithms can accomplish robotic movement, by automatically optimizing the machine parameter setting, by means of image and data processing. The correct use of AI is mainly based on the formulation of the algorithm, and on the dataset adopted to learn the related model. For each application there is an associated AI learning dataset which can be improved by big data systems. In particular, image processing and image segmentation approaches can be improved by AI, enhancing hidden information as defects, or adding new information about the performed production process. Another tool supporting the assembly in supply chains and the coordination of activities is augmented reality, which can be fully integrated in the information company infrastructure. An important step for a new concept of production is the upgrade of the information technology (IT) infrastructure automatically gaining the knowledge. Different IT architectures are proposed for different application fields to enhance technologies more suitable for a self‐adaptive production providing decision support systems. A particular interesting topic for the innovative IT is the Internet of Things. The design and the development of an advanced IT infrastructure is the primary action to add for the upgrade in Industry 5.0. The AI concept is extended to the logic condition implementation, acting on signal processing, and on the use of simply electronic circuits representing these logics. The discussed methodologies allow to comprehend how it is possible to move on a competitive production based on the concept of “flexible” production and on new products based on advanced technologies on a micro‐ and nanoscale. In this scenario, companies working in manufacturing can switch dynamically the production on the new products, thus converting the production in innovative components, machines, materials, sensors, or devices. Following this orientation, this book proposes important approaches to automatize efficiently the new production, by analyzing highly advanced production tools based on nanotechnology. In this direction useful methodologies are analyzed to implement the production of high technology devices, such as reverse engineering and rapid prototyping by showing different examples useful to comprehend the methodologies to apply for an innovative production based on scientific and industrial research development. Particular attention is paid to the procedures to follow to produce a new device, to increase the company capacity to accelerate the industrialization process starting a new innovative prototype fabrication, and to basic approaches for the design modeling and testing. The optimization of the pre‐industrialization process to perform is accompanied by a quick check of the basic properties of the new product to fabricate, and by the simultaneous support of the AI application improving analysis. In order to start and to develop a new research activity, also concerning advanced technologies, precise schemes must be followed. The discussed topics facilitate the understanding of the directions of the research for the production upscaling, just to apply the research activity. The last part of the book provides different elements useful for writing an industrial research project, and for the project management. This book deals with multidisciplinary topics including electronics, mechatronics, mechanics, and informatics. All the analyzed topics are useful to know; the key elements are indispensable and useful to move the production from Industry 4.0 to Industry 5.0.

      Alessandro Massaro Bari, 28 December 2021

      About the Author

      Professor Alessandro Massaro (ING/INF/01, FIS/01, FIS/03) carried out scientific research at the Polytechnic University of Marche, at CNR, and at Italian Institute of Technology (IIT) as Team Leader by activating laboratories for nanocomposite sensors for industrial robotics. He was head of the Research and Development section and scientific director of MIUR Research Institute Dyrecta Lab Srl. Actually, he carries out research activities in LUM Enterprise at LUM University ‐Libera Università Mediterranea‐ (Casamassima‐BA‐, Italy), he is in MIUR register as scientific expert in competitive Industrial Research and Social Development, and he is currently Member of the International Scientific Committee of Measurers IMEKO and IEEE Senior Member. He received an award from the National

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