Advanced Electronic and Optoelectronic Sensors, Applications, Modelling and Industry 5.0 Perspectives

This review will focus on advances in electronic and optoelectronic technologies by through the analysis of a full research and industrial application scenario. Starting with the analysis of nanocomposite sensors, and electronic/optoelectronic/mechatronic systems, the review describes in detail the...

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Bibliographic Details
Main Author: Alessandro Massaro
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/13/7/4582
Description
Summary:This review will focus on advances in electronic and optoelectronic technologies by through the analysis of a full research and industrial application scenario. Starting with the analysis of nanocomposite sensors, and electronic/optoelectronic/mechatronic systems, the review describes in detail the principles and the models for finding possible implementations of Industry 5.0 applications. The study then addresses production processes and advanced detection systems integrating Artificial Intelligence (AI) algorithms. Specifically, the review introduces new research topics in Industry 5.0 about AI self-adaptive systems and processes in electronics, robotics and production management. The paper proposes also new Business Process Modelling and Notation (BPMN) Process Mining (PM) workflows, and a simulation of a complex Industry 5.0 manufacturing framework. The performed simulation estimates the diffusion heat parameters of a hypothesized production-line layout, describing the information flux of the whole framework. The simulation enhances the technological key elements, enabling an industrial upscale in the next digital revolution. The discussed models are usable in management engineering and informatics engineering, as they merge the perspectives of advanced sensors with Industry 5.0 requirements. The goal of the paper is to provide concepts, research topics and elements to design advanced production network in manufacturing industry.
ISSN:2076-3417