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  • Review
  • Open Access

4 April 2023

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

1
LUM Enterprise Srl, S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, Italy
2
Dipartimento di Management, Finanza e Tecnologia, Università LUM “Giuseppe Degennaro”, S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, Italy
Appl. Sci.2023, 13(7), 4582;https://doi.org/10.3390/app13074582
This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes
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Abstract

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.

1. Introduction

Technological advances in industry are in different production sectors of the supply chain. Today, many optical, electronic, and mechatronic technologies can be applied to the industrial scenario, improving the Industry 4.0 framework based on the digital transformation process. Some recently advanced technologies are in metastructures oriented to optical computing [1], nanoparticles in metasurface applications [2], Epsilon–Near-zero (ENZ) metamaterials [3,4], graphene-based transistors for biomedical applications [5], plasmonic devices [6,7], and deep learning that supports decision-making, as seen in COVID-19 risk management processes [8,9]. These technologies could be integrated into industrial processes or considered as innovative products for manufacturing industries. An upscale in production was gained by Industry 4.0 and by innovative facilities of Industry 5.0. The Industry 5.0 era has been characterized by a full digitalization and transformation process which contributed to fully changing organizational and production processes. This change is mainly focused on the implementation of Artificial Intelligence (AI) algorithms, which tailor production and processes in a self-adaptive modality. Specifically, the new advances in optoelectronic and mechatronic technologies show a possible evolution in the management of industrial processes, thus optimizing resources according to the dynamic market. Today, a strategic market plan should be designed first in the short term, to follow better the market’s trends and trajectories. The goal of this proposed short review is then to define production models based on advanced technology discussed in the paper, and offer an approach to designing a possible future framework which includes Industry 5.0 facilities that can start an analysis of the scientific state of the art profiled here to serve actual industry technology needs, and to orient further research. Figure 1 illustrates a block diagram sketching a basic Industry 5.0 framework, which will be discussed in this paper. The diagram, deduced from the analysis of the state of the art and from emergent topics in research, consists of the following blocks or grouped modules:
Figure 1. Industry 5.0 framework of the topics of this review structured in five main different blocks.
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A sensing module composed of innovative optoelectronic sensors (block 1), industrial mechatronic sensors (block 2), and detection algorithms (block 3);
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Control and actuation facilities (block 4) which address production, quality, safety, process management, raw materials management, and Process Mining (PM);
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An AI engine managing production line actions (blocks 1, 2, 3, 4, 5);
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An Advanced robotic industrial platform (block 5).
The proposed auto-consistent framework of Figure 1 highlights the relationships between all mentioned blocks. Particular attention is given to new advanced sensors such as optoelectronic sensors suitable for high-velocity data transfer, industrial mechatronic sensors supporting the control and actuation mechanisms of advanced production machines and industrial robots, and engineered intelligent processes requiring AI decision-making procedures. The proposed work is structured as follows:
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Discussion of the adopted methodology of the searching approach of the state of the art;
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Discussion of the blocks of the framework of Figure 1, which will by provide a perspective of the implementation of each framework in a future Industry 5.0 scenario for each topic;
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Description of a complex model of an Industry 5.0 framework defined by the topics found in the state of the art (framework constructed by supposing possible evolutions/implementations of the analyzed technologies and by hypothesizing a production line in the manufacturing sector);
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Simulation of the information flux of the designed framework by means of the estimation of the diffusion heat parameters;
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Design of Business Process Modeling and Notation (BPMN) workflows applied to this example of the Industry 5.0 framework, which will be useful for enhancing the different technological levels that will upgrade the production;
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A BPMN model explaining, with more details, the processes of the robotic and machine control exploiting predictive maintenance and quality assessment processes designed with the Industry 5.0 framework.

Methodology

The method of searching for works in the literature is based on the scheme of Figure 2, and is defined by the following main steps:
Figure 2. Block diagram of the adopted methodology followed to construct the proposed work.
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The topics found in the literature are related to the requirements of the industry research projects (regional, national and European research topics) outlining the following macro-topics: Mechatronic Systems, Industry 4.0, Digital Transformation, Agriculture 4.0, Internet of Things (IoT), Human & Machine Interfaces (HMI), Quantum Computing, Energy, Edge Computing, Artificial Intelligence, Dynamic Business and Strategic Marketing, Additive Manufacturing, Key Enabling Technologies (KET), Photonics, Micro-Technologies, Nano-Technologies, Advanced Materials, Smart Materials, Technologies and Advanced Production Systems, Innovative Management Frameworks, Change Management, Product Quality Assessment, etc.;
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Different keywords matching with specific industrial application fields (papers found in literature) are extracted, such as: Micro-Sensors, Nano-Sensors, Optoelectronic Sensors, Image Vision Techniques, Leakage Detection, Sensing and Actuation AI Systems, Nanocomposite Sensors, Defect Detection, Defect Prediction, Embedded Electronic Devices and Systems for the Automatic Control of Assembly Processes, Embedded Microelectronics, Integrated Systems for Applications of Remote Control, Multi-Sector Environment, Soft Robotics, Infrared Thermography, Multi-Spectral Analysis, AI Control and Actuation, etc.;
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New research topics following the correlations between the different extracted keywords are defined, such as: rapid prototyping, reverse engineering, Industry 5.0, process mining applied to industrial production processes and on quality assessment, electronic assisted production management, etc.;
The proposed models integrate innovative Industry 5.0 topics found in litterature, are designed by the draw.io open source tool (BPMN standard), and are simulated by the Cytoscape tool (simulation of a complex Industry 5.0 framework which estimates the diffusion heat parameter).
The next sessions discuss the papers selected from literature about the topics and the keywords matching with the framework of Figure 1. For each topic found in these works, possible implementations in Industry 5.0 scenarios are proposed, and innovative aspects of perspectives in possible implementations are discussed.

2. Sensing Field

Many works found in literature address the sensing field in industrial environments (blocks 1, 2, and 3 of Figure 1). Innovative sensors such as optoelectronic sensors are a part of the sensing field and of innovative electronic/mechatronic systems that are interconnected to detection algorithms (see Figure 1). Specifically, Complementary Metal-Oxide Semiconductor (CMOS) and laser-based technologies [10,11,12] are typically applied to implement and to improve image vision techniques detecting colors, temperatures, and defects in manufacturing production processes. Looking forward-, advanced systems could include cameras integrated to other sensor systems, switching automatically in cases of defective cameras and detecting intelligently specific defect regions. For example, the scanning of small regions could be performed by means of intelligent algorithms controlling zooming. Nanocomposite optoelectronic sensors [13,14,15,16,17,18,19,20,21,22,23,24,25] are characterized by a fast detection response and by a high sensitivity, and are suitable for the detection of gases, energy-harvesting applications, pressure sensing, detection of notches and surface defects such as cracks, and three dimensional (3D) object morphologies and colors. High sensitivity and a fast response rate are very important in workpiece processing requiring a micrometric manipulation and very low processing tolerances (high-precision workstations), and for fully optical systems requiring a working frequency in the THz band. Advanced solutions could be implemented in worker security systems that detect small quantities of gases or liquids, thus enabling automatically alerting systems and security procedures. Concerning the robotic handling process, a high sensitivity pressure response allows the use of nanocomposite sensors for soft handling or for production systems characterized simultaneously by both hard and soft pressure forces; an intelligent system could calibrate the robotic handling by reading simultaneously strong and soft pressure forces to accurately control the product’s handling, and to detect possible millimeter-sized defects on the surface. The possibility of controlling the chemical composition of polymeric materials would allow tuning of the sensitivity of the optical response, which would allow the design of sensors which have different pressure working ranges that can adapt to the system of control. The control logic to be implemented could be improved by an auto-adaptive feedback control system which could intelligently drive robotic motors. An advanced nanocomposite sensor system could include both touch and no-touching sensors that detect 3D object morphology and other parameters such as colors and temperatures.
The research topics give particular attention to the leakage detection of oil, water and gases, including pipeline and electrical leakages [26,27,28,29,30,31,32,33,34,35,36,37,38]. Advanced solutions could include the adoption of AI tools for the pollution and leakage detections (as for production processes using liquids), and the integration of different technologies interconnected to renewable energy routing systems (sustainable production systems).
Finally, optoelectronic and fiberoptic systems are applied in manufacturing processes by controlling cutting machines, assessing the quality of surfaces of processed workpieces, and in additive manufacturing processes [39,40,41,42,43,44]. Advanced Industry 5.0 solutions could be addressed on reverse engineering approaches that automatically optimize laser cutting operations, that also integrate image vision techniques, or AI data driven systems that enable self-adaptive manufacturing processes. Specifically, additive manufacturing processes could be controlled intelligently with an AI self-tuning the machine parameters.
Table 1 reports more details about the optoelectronic systems and their possible implementation in an Industry 5.0 framework.
Table 1. State of the art: some advanced optoelectronic sensors adaptable for industrial applications in Industry 5.0 scenario.
Our selected state of the art discussion of electronic and mechatronic sensors is about optomechatronic systems [45,46,47,48], printed and flexible electronics [49,50,51,52], gas sensors [53,54], sensor network systems [55,56,57,58,59,60], and other sensors applied in different production processes such as food and agriculture; and manufacturing processes such as drilling, milling and cutting [61,62,63,64,65,66,67,68,69,70,71]. Perspectives in Industry 5.0 frameworks involve the possibility of integrating different technologies into a unique platform, and adopting spatially adaptable electronic components for specific production plants. Furthermore, the evolution of production processes requires advanced diagnostic to be implemented by AI predicting risks and by applying PM models, auto-calibration of the manufacturing machine parameters, and auto-adaptive control systems monitoring tool wear. Table 2 lists more information about some electronic and mechatronic sensors and their possible implementation in an Industry 5.0 framework.
Table 2. State of the art: some electronic and mechatronic sensors adaptable for industrial applications in Industry 5.0 scenario.
Concerning the industry sensing field, the analyzed state of the art finally addresses the important topics of algorithms supporting leakage detection [72,73,74,75,76,77,78,79,80,81], the digital transformation process of Industry 4.0 [82,83,84,85,86,87,88,89,90,91,92,93,94,95,96], and the data processing regarding energetic consumption in production processes [97,98,99,100,101,102,103]. Possible improvements to integrate into an Industry 5.0 framework relate to automated AI decision-making processes which enable interventions and procedures, production simulation setting machine parameters, and auto-calibration of the machines used in manufacturing processes. In the proposed scenario, an important role is assigned to big data systems interconnected to AI engines and to data-fusion approaches. The AI-based detection algorithms could provide an important contribution to managing complex production systems by estimating many variables such as energy required for the machined production, product defects, machine failure features, and more. Some efficient AI algorithms are Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). Other useful algorithms are Discrete Fourier Transform (DFT), and K-Means clustering algorithm (machine learning unsupervised algorithm). Two possible upgraded systems are in the management of energy used in production, and in energy digital twins using quantum computing. A possible future professional skill would be an energy manager that optimizes electrical consumption in industrial processes. Machine learning algorithms are also suitable for defect classification and defect prediction. Table 3 discusses some topics about detection algorithms and their possible implementation in an Industry 5.0 framework.
Table 3. State of the art: detection algorithms and possible implementation of processes in an Industry 5.0 scenario.

3. Supply Chain Processes and Advances

The research topics in industrial production mainly address an intelligent way to manage raw materials [104,105,106,107,108,109,110,111], new models of AI decision-making in processes controlling production [112,113,114,115,116,117,118,119,120,121,122,123], and new procedures/methods oriented to control and actuation actions [124,125,126,127,128,129,130,131,132]. The topics are indicated in block 4 of Figure 1. Possible implementations of the analyzed topics in Industry 5.0 perspectives are mainly in process automation and in processes controlled by AI decision model engines that improve production, security, and quality. Other important aspects of industry upgrades are in the integration of different technologies and in intelligent machine reconfiguration systems. The integration of AI in the decision-making processes allow to the implementation of PM models; AI data driven by production phases is a new advance for process switching and process management, which includes new organizational models. Image vision techniques based on infrared thermography technologies are important for improving inline monitoring processes controlling defects and automatically reconfiguring machines and robots. Table 4 has the works found regarding the state of the art around raw materials management, PM models, control and actuation processes, and provides possible implementations in an Industry 5.0 framework.
Table 4. State of the art: raw materials management advanced approaches, process mining, control/actuation methods and possible implementations of processes in Industry 5.0 scenarios.

4. Advanced Robotic Industrial Platforms

Many developments in research fields are in robotic platforms [133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151] (see block 5 of Figure 1). Collaborative robot (Cobot), soft robot, robot agents, and industrial robots are today controlled by innovative sensors and techniques (as for writing 3D processes in additive manufacturing). Neural networks, reinforcement learning methods, and AI algorithms are possible new solutions for improving the handling and object manipulation of robots. Possible projections in Industry 5.0 scenarios are in synchronization of the whole supply chain’s machinery, in self-adaptive robot control systems, in the use of quantum computing to increase AI efficiency, and in the digital twin models assisting production and marketing strategies. AI data processing could be processed into an Industry 5.0 framework by edge computing systems. Advanced technological platforms are also platforms that accurately control motion tracking, information systems suitable for integration of heterogeneous data from different technologies such as the Enterprise Service Bus (ESB), and the Supervisory Control and Data Acquisition (SCADA) system. All the systems could be integrated into a unique one controlling and synchronizing a whole production. In Table 5, we discuss the works found in the state of the art about advanced robotic platforms and possible improvements in an Industry 5.0 framework.
Table 5. State of the art: advanced robotic systems and technological platforms, and Industry 5.0 perspectives.

5. Discussion: Industry 5.0 Framework and Complex Systems Simulations

This section designed and simulated an advanced Industry 5.0 framework integrating different advanced technologies. The discussion is completed by a description of the limitations and perspectives related the analyzed layout, by through a description of examples of applications matching with the Industry 5.0 scenario.

5.1. Industry 5.0 Workflow Design and Process Simulation

The works selected from the state of the art literature allow us to define a possible framework of how Industry 5.0 could involve a lot of previously mentioned technologies. The hypotheses of possible improvements of processes in Industry 5.0 frameworks allowed us to design the production layout, while including the main important variables found in the literature. A modern framework is characterized by a large number of variables, so we structured a complex system characterized by a “capillary” information flux between all the variables named nodes. An approach used to simulate complex models [103] is the calculation of the diffusion heat parameter [152,153,154]. The diffusion heat parameter is a probabilistic definition of the information flux distribution flowing between all nodes. Diffusion is typically a phenomenon associated to “particles” moving in an environment. In the specified case, the environment is modelled by nodes, and edges connecting all the nodes of the complex model. The probability of the distribution of the diffusion heat parameter is the position distribution of particles, which simulates the information flux, after a time t. In the analyzed model, a parameter of t = 0.038 s is enough for the transient calculation of the simulated complex Industry 5.0 manufacturing network. The hypothesized network is assumed to be a single production line consisting of three production machines and a robot, connected in a series configuration as illustrated in Figure 3a. The raw materials are processed by a first machine (Machine 1 node indicated by M1), then the workpiece is handled by a robot (Robot 1 node indicated by R1), sent to be processed by a second machine (Machine 2 node indicated by M2), which realizes a semi-product, which is then processed by a final, third machine (Machine 3 node indicated by M3), which produces the final product. As illustrated in Figure 3a, the machines define a first operating level named Production Line Level which is interconnected to a second one associated to sensing and actuation processes (Sensing and Actuator Level made up of sensors S and actuators A). A third level, named Industry 5.0 Level, represents the upgrade from Industry 4.0 to the Industry 5.0 scenario, and is consists of the following main elements (nodes): Quantum Computer, Big Data, External Big Data, AI Edge Computing, and an Actuator Engine (all the elements suggested for an upscale in production). The simulated network is better modelled by the graph of Figure 3b, which indicates nodes and edges. The simulated nodes are:
Figure 3. (a) Simplified Industry 5.0 framework differentiating three main levels of the processing of a manufacturing workpiece: production line level, sensing and actuation level, and Industry 5.0 level. (b) Cytoscape complex model simulating the Industry 5.0 framework.
  • Machine 1 (M1): the first machine, processing raw materials loaded at the input of the production line.
  • Sensor M1_1: sensor monitoring Machine 1.
  • Sensor M1_2: sensor monitoring Machine 1.
  • Actuator M1: actuator that relays the sensing and actuation processing of from Machine 1.
  • Power Meter_M1: power meter reading the electrical power of Machine 1.
  • Robot 1 (R1): robot handling the workpiece processed by Machine 1.
  • Sensor R1_1: first sensor monitoring Robot 1.
  • Sensor R1_2: second sensor monitoring Robot 1.
  • Actuator R1: actuator which relays sensing and actuation processing from Robot 1 (includes sensing of from both the sensors Sensor R1_1 and Sensor R1_2).
  • Machine 2 (M2): second machine processing the workpiece after robotic manipulation.
  • Sensor M2_1: sensor monitoring Machine 2.
  • Sensor M2_2: sensor monitoring Machine 2.
  • Actuator M2: actuator relaying sensing and actuation processing from Machine 2.
  • Power Meter_M2: power meter reading the electrical power of Machine 2.
  • Semi-Product (SP): semi-product output of Machine 2.
  • Image Vision SP: camera implementing image vision algorithms to detect the defects of the Semi-Product.
  • Machine 3 (M3): third machine processing the semi-product.
  • Sensor M3_1: sensor monitoring Machine 3.
  • Sensor M3_2: sensor monitoring Machine 3.
  • Actuator M3: actuator relaying the sensing and actuation processing of Machine 3.
  • Power Meter_M3: power meter reading electrical power of Machine 3.
  • Product (P): final product of the whole production line (output of Machine 3).
  • Image Vision Product: camera implementing image vision algorithms for detecting defects of in the final product;
  • Big Data: internal big data collecting all production data (data from the whole production line).
  • AI Edge Computing: edge computing nodes processing all data collected into the internal big data system by AI algorithms; the AI algorithms are optimized by quantum calculus (Quantum Computer).
  • Quantum Computer: the quantum computer processing data collected in Big Data and External Big Data.
  • External Big Data: dataset collected from the cloud by other third parties related to this specific production (external backend systems).
  • Actuator Engine L1: engine synchronizing all the actuators: Actuator M1, Actuator R1, Actuator M2, Actuator M3 (with synchronization supported by the AI algorithms).
The complex network of Figure 3b is characterized by the parameters listed in Table 6.
Table 6. Summary statistics concerning the parameters of the complex network of Figure 3b.
The complexity of the operations of the network of Figure 3b can be simplified by the workflow of Figure 4, which sketches all the operations of the simulated Industry 5.0 framework and facilitates comprehension of the information flux trajectories. The workflow is sketched by a standard BPMN (standard ISO/IEC 19510:2013 [155]), typically adopted for process mapping in industrial applications such as predictive maintenance [156] and security [157]. The BPMN workflow of the analyzed model is structured into three pools (representing the three levels indicated in Figure 3a), and highlighted in red are the data processes of the Industry 5.0 level, which upgraded production processes. The BPMNs model integrating AI decision-making procedures are named BPMN Process Mining (PM) models. The adopted BPMN tool was illustrated with the open-source program, draw.io [158].
Figure 4. BPMN process modelling a simplification of the complex network of Figure 3b.
Figure 5 illustrates the result of the simulation of the complex Industry 5.0 network of Figure 3b: the calculation of the diffusion heat parameters of the whole information flux is summarized by the heat map in Figure 5. In this map, it is possible to observe that the elements that majorly upgrade the industrial production processes are: Big Data, Actuator Engine L1, Ai Edge Computing, and Quantum Computer, which are all the elements highlighted in red in the BPMN workflow of Figure 4, which confirms these nodes belong to the Industry 5.0 level (upgrading level). The simulation was performed by the open source Cytoscape tool [159].
Figure 5. Heat map: diffusion heat simulation of the complex Industry 5.0 model enhancing central elements of the industry upgrade (Big Data, Actuator Engine L1, AI Edge Computing, Quantum Computer).
The BPMN approach is then adopted to “explode” in detail the processes involving robot/machine control, predictive maintenance, and quality assessment. By hypothesizing a production line constituted by a single robot that handles a workpiece that will be processed successively by a machine (simplification of the production line layout of Figure 3a), the BPMN workflow of Figure 6 illustrates the interaction between the three processes that integrate the AI facilities (highlighted in red). The main process is represented by the first pool, which describes the robot and machine control flux performed by sensors, and actuation actions performed by the AI by adjusting by according to feedback systems from the robot and machine parameters during the continuous production. Digital production data are transferred to a data analysis engine able to activate predictive maintenance interventions by adopting AI based decisions (AI predicting product defects and machine failure) and processing simultaneously the electrical power data of machines. An anomalous electric load could represent a machine start failure. The data, stored in a big data system, are also processed for quality assessment processes. Also, in this case, the AI algorithms could improve the whole production process by using its quality prediction.
Figure 6. BPMN model “exploding” Industry 5.0 processes and correlations between robot/machine control, predictive maintenance, and quality assessment.
The proposed approach is based on the Industry 5.0 framework design and simulation. The framework conceptualization was extracted by analyzing the state of the art concerning the specific application field and technologies associated to an industry in the high-tech manufacturing sector. The limitations of the framework are mainly in the interoperability between all sub-processes. Table 7 lists some important limitations and the associated perspectives as possible solutions.
Table 7. Framework limitations and perspectives to overcome these limitations.

5.2. Examples of Applications Matching with Industry 5.0 Framework

Table 8 indicates some examples of potential application fields and possible solutions for integrating the associated technologies into an Industry 5.0 scenario.
Table 8. Examples of application fields matching with the Industry 5.0 framework.

6. Conclusions

The goal of this paper was to provide an overview of possible technologies and approaches that could potentially be adopted to construct an advanced production framework which included the main important variables and topics found in the cited literature. Some technologies analyzed in the state of the art were selected to construct an Industry 5.0 framework, while defining possible variables used to model and simulate a high-tech manufacturing layout. Specifically, we defined a large number of variables that allowed us to design a complex Industry 5.0 production layout by creating a manufacturing production line composed of three machines and a robot. A simulation of the diffusion heat parameter has defined the information flux of the whole complex network, and we highlighted some innovative elements, such as edge computing, quantum computing, big data and AI data processing, which improved production processes and quality. Finally, the paper discussed an approach to model Industry 5.0 processes by means of graphs constructed using nodes and edges and BPMN workflows, and described in detail the interaction between these processes. The processes highlighted in the Industry 5.0 model are predictive maintenance, machine control and actuation, and quality assessment. The selected literature is useful for constructing industrial research projects. The discussed models could support scientists in finding possible solutions for the design of Industry 5.0 frameworks using some tools suitable for designing a complex network and defining information fluxes of the main processes, thereby improving production.

Funding

This research received no external funding.

Conflicts of Interest

The author declares no conflict of interest.

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