• Design of manufacturing systems.
• The problem of resource requirements.
• The problem of resources layout.
• The problem of material flow.
• The problem of information flow.
• The problem of queue capacity.
• Complex design problems.
• Operation of manufacturing systems.
• Methods and tools for the operation of the manufacturing systems.
• The task – resource assignment problem.
• Decision-making for the operation of manufacturing systems.

LEARNING OUTCOMES

The learning outcomes expected by the end of the course are:

• Knowledge: Students will learn basic concepts of designing production systems and the associated main challenges. They will also familiarize with methods and tools for the design and operation of production systems.
• Skills: Students will develop skills in setting up technology programs for assigning work to system resources as well as developing and using decision making systems for the operation of production systems.

Το άρθρο Special Topics in Manufacturing Systems εμφανίστηκε πρώτα στο MEAD.

Introduction to Industrial Automation – Sensors and Actuators – Introduction to switching theory, switching algebra and design of switching systems -Electrical automation: elements and systems -Pneumatic automation: valves, pneumatic circuits design -Electropneumatic automation: sequence charts, ladder diagrams, analysis and synthesis of circuits- Programmable Logic Controllers (PLC):programming and applications, fuzzy logic based PLC – Communication of sensors and actuators with computers, A/D, D/A, Applications to Industrial Automation – Design of Industrial Automation Systems.

LEARNING OUTCOMES

The course constitutes a first, comprehensive, introduction to the basic notions of the theory and practice of industrial automation systems. Upon successful completion of the course the student will be in position to:

• Recognize and study industrial automation systems.
• Study and design sensor and actuator systems.
• Analyze and designs basic circuits of electrical automation.
• Study and design pneumatic and hydraulic automation circuits.
• Understand the analysis and synthesis of electropneumatic automation circuits.
• Design common PLC systems and based on Fuzzy Logic.
• Programming PLC.
• Design and programming computer’s communication systems with A/D, D/A.

Το άρθρο Industrial Automation εμφανίστηκε πρώτα στο MEAD.

1. System analysis in frequency domain
2. Discrete-time signals and systems
3. Discrete control systems
4. Introduction to state space
5. Analysis in state space
6. Design of control systems in state space
7. Advanced control topics
8. Applications in Matlab/Simuling environment

LEARNING OUTCOMES

This course provides basic theory and practice for analog and digital signals and systems as well as for the design of digital automatic control. With the successful completion of the course the student will be in position to:

• Analyze systems and set control specifications in frequency domain
• Investigate open and closed loop system stability in frequency domain using Bode and Nyquist diagrams
• Discretize analog systems and perform proper signal sampling and filtering
• Develop discrete models of dynamical systems using z-transform
• Develop pulse (discrete) transfer functions
• Develop multiple-input multiple-output (MIMO) state space dynamical models
• Study the system response in state space
• Work in the z-domain, state space, s-plane and frequency domain interchangeably
• Comprehend and properly use the canonical forms and the principles of controllability and observability
• Develop digital control systems in time domain and in state space based on state feedback control
• Comprehend the principles of observer based advanced control systems
• Apply the above in Matlab /Simulink environment

Το άρθρο Systems and Automatic Control ΙΙ εμφανίστηκε πρώτα στο MEAD.

Το άρθρο Dynamic of rotating machines εμφανίστηκε πρώτα στο MEAD.

• Introduction – What is Artificial Intelligence
• Description of problems Search Algorithms – Blank and heuristic search algorithms
• Representation of knowledge – Propositional and predicate logic – Structured representations of knowledge (Semantic networks, frameworks, conceptual dependencies, scripts) – Rules – Fuzzy Logic
• Knowledge systems – Types of reasoning in knowledge systems – Expert systems (ES) – Characteristics, structure  and development  of expert systems
• Machine learning
• Genetic algorithms
• Applications of knowledge systems of discrete and fuzzy logic in the design and for machine fault diagnosis
• Intelligent control and intelligent control techniques
• Fuzzy Controllers – Design and Applications of fuzzy Controllers
• Introduction to NN-based control and applications

LEARNING OUTCOMES

The course is a lecture course and is offered by the CAD/CAM sector in the 9th semester of study.

Objectives of the course:

1. Gain familiarity with the basic concepts of Artificial Intelligence
2. Understand the algorithms, methods and techniques of systematic problem formulation and solving
3. Familiarize with ways of representing discrete and fuzzy logic
4. Understand how to apply methods of solving and representing knowledge to solve problems from the field of mechanical engineering (design, production processes, robotics, automatic control, etc.)

Upon successful completion of the course the student will be:

• familiar with the basic concepts of Artificial Intelligence and the methods of exploring and representing knowledge to solve problems
• able to apply the above to solve problems arising from the field of mechanical and aeronautical engineering

Το άρθρο Applications of Artificial Intelligence εμφανίστηκε πρώτα στο MEAD.