Course detail
Intelligent Manufacturing Systems
FSI-GIS-KAcad. year: 2023/2024
Advances in manufacturing and computing technology, and in particular in their interconnection, are bringing new approaches to product design and implementation in manufacturing processes and production systems. These are currently expressed in the Industry 4.0 concept, which implies that traditional tools for the necessary engineering activities are no longer sufficient for this development. Therefore, students are introduced to new approaches and methods: Manufacturing system such as intelligent system, basics of artificial intelligence, expert systems, neural networks, methods using knowledge bases. It is shown how to apply these methods and thus bring new quality for individual activities in the production system - product design and construction, technological preparation of production, group technology, production scheduling and management, production quality management.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
Attendance at obligatory lessons is checked and only substantial reasons of absence are accepted. Missed lessons can be substituted for via solution of extra exercises.
Aims
Students will acquire knowledge of selected methods for creating mathematical models of individual activities in production systems and basic methods of their solution. Emphasis is placed on acquiring knowledge and skills necessary for the algorithmization of the discussed methods. Furthermore, students will acquire basic knowledge in the application of artificial intelligence methods to production systems, especially expert systems and neural networks.
Study aids
Prerequisites and corequisites
Basic literature
Kusiak, A.: Intelligent Manufacturing Systems
Recommended reading
Classification of course in study plans
- Programme N-VSR-K Master's 2 year of study, winter semester, compulsory-optional
Type of course unit
Guided consultation in combined form of studies
Teacher / Lecturer
Syllabus
3. - 4. Classification methods, types of classifiers, choice of predictors, fuzzy logic.
5. - 6. Parameter optimization using evolutionary algorithms.
7. - 8. Neural networks, their basic principles, and applications in the area of production systems
9. - 10. Knowledge-based systems - methods of knowledge representation, basic methods.
11. - 12. Algorithms for travel planning.
13. Credit
Guided consultation
Teacher / Lecturer
Syllabus
1. Data classification, choice of predictors, comparison of methods
2. Fuzzy logic in a manufacturing system
3. IoT and cloud systems
4. Convolutional neural networks
5. Optimization using evolutionary algorithms
6. Introduction to expert systems,
7. Solving problems with expert systems, examples of applications.
8. Visualization of the production process, demonstration of SCADA/HMI
9. Visualization of the production process, SCADA/HMI demonstration
10. Work on the assigned project
11. Work on the assigned project
12. Evaluation of final project
13. Credit