| Code |
18160
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
|
| Workload |
PL(15H)/T(30H)/TP(15H)
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| Scientific area |
MECÂNICA COMPUTACIONAL
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Entry requirements |
N.A.
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Learning outcomes |
Promote the application of knowledge, interpretation, and comprehension skills to solve problems and design traditional automation projects for industrial processes, incorporating advanced automation concepts related to modern technologies such as IoT, AI, machine learning, robotic process automation (RPA), and low-code/no-code platforms to design and implement intelligent, scalable, and efficient automation
solutions.
Provide training on Automation and Programmable Logic Controllers (PLCs); involve hyperautomation technologies in programming; master PLC programming languages; model Discrete Event Systems (DES); develop Petri Nets; supervise industrial automation systems; and actively contribute in this field by fostering solutions both at a technical and project level.
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Syllabus |
1. Introduction to Automation
Automation Concepts
Programmable Logic Controllers (PLCs): Components, structure, operation, input/output interfaces, and interconnection.
Fundamentals of Hyperautomation: Definition, principles, and distinction from traditional automation. Integration of physical systems, digital workflows, and AI-driven decision-making.
Hyperautomation Technologies: IoT, RPA, AI, machine learning, and cloud computing.
2. PLC Programming and Technology Integration
PLC Programming Languages
Connecting PLCs with IoT devices for real-time monitoring and control
Using IoT sensors and AI models for predictive analysis
3. Discrete Event Systems (DES)
Modeling and Analysis of DES: PLCs, Petri nets, dynamics, and modeling
4. Supervision and Intelligent Control Systems
Supervised control of DES, controller synthesis, and process control
Intelligent Supervision: AI-based control techniques
Low-Code/No-Code Platforms
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Main Bibliography |
-Bornet, P., Barkin, I., & Wirtz, J. (2020). Intelligent Automation: Welcome to the World of Hyperautomation. Independently Published.
-Artigos de pesquisa relevantes e estudos de caso sobre aplicações de hiperautomação.
-P.D. Gaspar, S. Mariano, Apontamentos de Automação Industrial - Introdução à Automação, Universidade da Beira Interior, Covilhã, 2016.
-P.D. Gaspar, S. Mariano, Apontamentos de Automação Industrial - Redes de Comunicação Industriais, Universidade da Beira Interior, Covilhã, Edição de 2016.
-P.D. Gaspar, S. Mariano, Apontamentos de Automação Industrial - Introdução aos PLC Twido, Universidade da Beira Interior, Covilhã, Edição de 2016.
-P.D. Gaspar, S. Mariano, Apontamentos de Automação Industrial – Redes de Petri, Universidade da Beira Interior, Covilhã, Edição de 2016.
-J.A. Rehg, H.W. Kraebber, Computer-Integrated Manufacturing, 3rd ed., Prentice Hall, 2005.
-M.P. Groover, Automation, Production Systems, and Computer Integrated, 3rd ed., Prentice Hall., 2007.
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Teaching Methodologies and Assessment Criteria |
Theoretical Classes: Transmission of knowledge through lectures and discussions.
Practical Classes: Hands-on exercises in PLC programming, IoT integration, and AI-based solutions.
Laboratory Work: Implementation and testing of hyperautomation solutions.
Final Project: Development of a hyperautomation solution for a rea or simulated-world industrial scenario, integrating IoT, AI, and low-code platforms.
TAS: Analysis and Synthesis Work (15%): Research on hyperautomation technologies and applications.
Ex: Programming Exercises (20%): PLC programming and integration with IoT and AI.
PRO: Project (45%): Design and implementation of a hyperautomation solution.
FR: Evaluation Test (20%): Written exam covering industrial automation and hyperautomation concepts.
Final Grade (CF): CF = TAS + Ex + PRO + FR.
Improvements require a final exam, maintaining the same criteria and CF calculation.
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Language |
Portuguese. Tutorial support is available in English.
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