Lecturer: Vrabič Rok
Syllabus outline:
- The mechatronic system design process.
- Simulation of mechatronic systems.
- Hardware-in-the-loop and controller-in-the-loop approaches.
- Design of electronics for mechatronic systems.
- Integrated circuits.
- Printed circuit board (PCB) design and manufacture.
- Mechatronic system prototyping.
- Validation and testing of mechatronic prototypes.
Objectives and competences:
The course objectives are to introduce the principles, methods, techniques, and tools used in mechatronic system prototyping. The mechatronic system design process, from specification to prototype manufacturing, is detailed. Mechatronic system simulation is presented. Simulation methods are augmented with hardware-in-the-loop and controller-in-the-loop approaches. Electronic prototyping tools and techniques are introduced. Printed circuit board (PCB) design methodology is presented. Validation and testing of mechatronic prototypes is introduced.
Intended learning outcomes:
Knowledge and understanding:
To understand and be able to perform the mechatronic system design process from specification to prototyping. To be able to use simulation tools to guide the prototyping process. To have the ability to create a printed circuit board design and prototype for a mechatronic system.
Lecturer: Podržaj Primož
Syllabus outline:
• Sensor overview
• Sensor fusion, its challenges and advantages
• Digital image acquisition
• Basic point and neighbourhood processing
• Image processing software overview
• Most common image processing applications
Objectives and competences:
The course is divided into two parts. In the multisensory system part the students will first get an overview of various sensors and their capabilities. Then the benefits of sensor fusion will be discussed. As a result, the students will be able to couple various sensors and extract optimal performance of such a combination. The second part of the course is focused on machine vision. In this part, the students will get a basic understanding of a digital image and its acquisition. Image processing will then be discussed from a mathematical point of view. Consequently, the students will get the capability of designing algorithms for various machine vision tasks. After an overview of image processing software will be given, and some most common applications presented, the students will start working on a project. As a result, they will get the capability of designing a real-life machine vision application and also be able to assess all the potential risk involved in such a project. This will make the competent to execute such projects in future without too much difficulties.
Intended learning outcomes:
• to get an overview of existing sensors, their capabilites, advantages and weaknesses
• to get an understanding for the benefits of sensor fusion
• to get the basic understanding of digital image acquisition
• to develop necessary skilly for successful and efficient image processing application development
• to get an overview of possible image processing packages in various programming languages
• to assess the time needed for accomplishing the above-mentioned task and execute it in a real-life project
Lecturer: Vukašinovič Nikola, Pepelnjak Tomaž
Syllabus outline:
- Basic non-metal material processing and manufacturing technologies: wood, polymers, composites, ceramic.
- Engineering foundations of polymers and composites: viscoelasticity, adhesiveness, damping, tribology and conductivity.
- This is followed by a discussion of characteristic forms of machine elements and assemblies, made of non-metal materials.
- Plastic parts design rules, rib design, cold joints, shrinkage, quality limitations.
- Material joints: film joints, separable joints, welding of plastic, screw-in plastic, metal inserts.
- Inseparable joints. Snap fit design. Functions and calculations.
- Properties of symmetric and asymmetric beams.
- Shafts bindings and polymer bearings. Polymer gears.
- Injection moulding tool design.
- Sustainable product design from polymers.
- Engineering ceramic.
Objectives and competences:
Goals: To teach the students about designing typical elements from non-metal materials. Learning about the influence of the processing and manufacturing technologies on the design of a structural element. Understanding the manufacturability of detailed product designs. The calculation methods for typical structural elements: materials joints, inseparable joints, positioning elements, springs, folding elements and complex shapes.
Competences: The students are first acquainted with typical processing and manufacturing technologies. After that, they can commence the design of loaded products. They are able to determine the stress states in characteristic structural elements: materials joints, inseparable joints, positioning elements, springs, folding elements and complex shapes.
Intended learning outcomes:
Knowledge and understanding: The students obtain the fundamental knowledge for sizing typical polymeric machine elements and assemblies. They are able to understand the relations between the processing and manufacturing technology and the final product form, based on the design characteristics.
Usage: Direct use in the planning and execution of details in the scope of design and planning.
Reflection: In the phase of preparation for the execution of any project or design, it is important that all the data is prepared to reasonably ensure the quality of planning and design of products made of polymeric, or generally non-metal materials.
Transferrable skills: The students attain the ability to size elements and complex products made of non-metal materials. The purpose of the course is to ensure a comprehensive understanding of product design from manufacturing to testing.
Lecturer: Vrabič Rok
Syllabus outline:
- Markov decision process
- Value and policy iteration
- Q-learning
- Introduction to game theory
- Normal form games
- Extensive form games
- Network theory
- Random networks
- Network analysis
- Distributed system modelling
- Information-communication infrastructure for distributed systems
- Analysis of case studies
Objectives and competences:
The main objective of the course is to introduce the theory and practice of distributed systems, their modelling, and applications relevant for manufacturing systems. The course deals with decision making of a single agent through Markov decision process theory, multi-agent decision making through game theory, and multi-agent system modelling using network theory and analysis. The emphasis is given to information and communication structure for modern and future manufacturing systems. Several case studies are presented.
Intended learning outcomes:
Knowledge and understanding:
Understanding decision-making of a single agent, understanding decision-making, when an agent is faced with an environment that includes other agents, modelling and analysis of distributed systems using network theory, knowledge and understanding of modern and future information-communication structures.