Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0
Abstract
:1. Introduction
- The paper provides an overview of how students should learn advanced IoT sensor technology. According to this, firstly, we provide a review of how the Industry 4.0 and Operator 4.0 retrofitting solutions relate to different aspects of the IoT concept (see Section 2.1);
- The paper also presents how the IoT concept should be taught and what should be included in the curriculum of the graduate and post-graduate students (see Section 2.2);
- Subjects and Industry 4.0 instructional materials are defined according to the IoT layers and the ISA-95 standard (see Section 2.3);
- We propose an education framework that can demonstrate smart retrofitting solutions according to the goals of Industry 5.0;
- The concept of intelligent space is presented as a key element of monitoring operator activities (see Section 3);
- We highlight how the education of data science should be fit to the teaching of IoT technology;
- Based on this concept, a laboratory has been developed that is suitable for simulating industrial case studies and generating data for the validation of the related machine learning algorithms (see Section 4);
- The details of an education program that can support building the skills needed for building smart retrofitting, IoT sensors, and data science-based Industry 5.0 solutions are presented. We also present the developed digital twin of the laboratory and the strategy for utilizing it to improve the competencies of students (see Section 5);
- Finally, Section 6 summarizes the main findings and the applicability of the presented framework.
2. IoT Layerwise Presentation of Industry 4.0 and Operator 4.0 Retrofitting Solutions and Education Programs
2.1. IoT Layerwise Presentation of Industry 4.0 and Operator 4.0 Retrofitting Solutions
- Andon lights, where the color of the analog andon lights (which are not connected to the industrial network) can be identified with an RGB sensor;
- Accelerometers, where the actual state of the machines can be measured based on their vibrations (idle, changeover, machining);
- Power consumption measurements, where the usage of the machines can be measured directly.
2.2. IoT Education for Industry 4.0
2.2.1. Challenges in IoT Education
2.2.2. Overview of IoT Courses
2.2.3. Frameworks for Curriculum Development
2.2.4. Education Content Summary Based on the Five-Layer IoT Architect
2.2.5. Application-Oriented Educational Approach in IoT, Industry 4.0, and 5.0 Curriculum
2.3. Curriculum for Industry 4.0 and 5.0 Education
3. Intelligent-Space-Based Education of IoT Tools
4. Education on the Development of Real-Time Process-Monitoring Solutions
- Material storage;
- Milling of the sides of the material on the cantilever milling machine;
- Rough lathing;
- Smooth lathing;
- Drilling using a pillar drilling machine;
- Notch making on the cantilever milling machine;
- Finalization.
5. Teaching the Development of Digital Twins
- Analytical methods are not available or are too cumbersome to use;
- It would be dangerous and/or time-consuming to experiment in a real environment;
- The system is too complex;
- Experimenting with the real system would result in a loss of production.
6. Conclusions
- The education of the IoT layers;
- The proposed concept of intelligent space;
- The utilization of indoor positioning systems;
- The goal-oriented sensor development/selection;
- Data science to demonstrate how it can be used to process sensor data and calculate KPIs;
- The development of digital twins based on discrete event simulations;
- The concept of Operator 4.0, which puts the human workers into the focus of the production process and forms the bases of the development of Industry 5.0 solutions.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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IoT ID | Possible Applications | ||
---|---|---|---|
Machine States (Usage) | Machine Condition | Traceability | |
Production control based on real-time machine states | Preventive maintenance | Process mining-based simulation and real-time digital twin | |
OEE and changeover analyses | Real-time condition monitoring and diagnostics | Spaghetti diagram and location-based KPIs | |
Edge computing | Position calculation and preprocessing | ||
Ad hoc networks based on Bluetooth, WiFi, or other wireless communication channels | Radio-based: UWB, BLE, ZigBee, WiFi | ||
Non-radio-based: sound-based, light-based, vision-based | |||
Andon lights, accelerometer sensors, power consumption measurement | Vibration sensors, consumption monitoring | IPS, RFID, camera system |
IoT Layer IDs | IoT Layers | Layer Description | Proposed Subject |
---|---|---|---|
Business layer | Business models for business planning and strategy | Optimization, operations analysis, and artificial intelligence | |
Digital twin and process simulation | |||
Application layer | Graphical data representation based on ML and AI algorithms | Data science and machine learning | |
Middleware layer | Decision unit and data analytics | Process information systems | |
Network layer | Network technologies and edge computing | Production intelligence | |
Perception layer | Connect to physical object via sensors and actuators |
Mobile DAQ | Stationary DAQ | AGV | |
---|---|---|---|
Condition and state monitoring | Vibration Temperature Air quality Noise | Vibration Temperature Andon light state | Self-position (track) Self-position (Lidar) Temperature Air quality Noise |
Perception layer sensors | Accelerometer Thermometer Humidity sensor Dust sensor Microphone | Accelerometer Thermometer RGB sensor | Line-following sensor Motor encoders Lidar Thermometer Humidity sensor Dust sensor Microphone |
Network layer | BLE | WiFi | BLE or WiFi |
Middleware layer | Server | Edge computing on a Raspberry Pi or server | Edge computing on a Raspberry Pi |
Component | Tool | Competence, Knowledge |
---|---|---|
Process model | Siemens Plant Simulation, Simul8, open-source simulation tools | Modeling skills |
Communication protocol | Edge computing, Node-RED | Process informatics |
Data-collector, data logger | Raspberry Pi, Arduino | Sensor and electronics knowledge |
Database | MySQL, PostgreSQL | Database management skills |
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Ruppert, T.; Darányi, A.; Medvegy, T.; Csereklei, D.; Abonyi, J. Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0. Sensors 2023, 23, 283. https://doi.org/10.3390/s23010283
Ruppert T, Darányi A, Medvegy T, Csereklei D, Abonyi J. Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0. Sensors. 2023; 23(1):283. https://doi.org/10.3390/s23010283
Chicago/Turabian StyleRuppert, Tamás, András Darányi, Tibor Medvegy, Dániel Csereklei, and János Abonyi. 2023. "Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0" Sensors 23, no. 1: 283. https://doi.org/10.3390/s23010283
APA StyleRuppert, T., Darányi, A., Medvegy, T., Csereklei, D., & Abonyi, J. (2023). Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0. Sensors, 23(1), 283. https://doi.org/10.3390/s23010283