Course detail

Intelligent Manufacturing Systems

FSI-GISAcad. year: 2025/2026

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

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

Ing. Daniel Zuth, Ph.D.

Department

Institute of Production Machines, Systems and Robotics (ÚVSSR)

Entry knowledge

Basic knowledge of mathematics and fundamentals of computer science.

Rules for evaluation and completion of the course

The course consists of exercises and lectures. Exercise is completed by credit (awarded in the 13th week). To obtain it is required 100% participation in exercises and activity in exercises. Students will work out the individual work in the prescribed range and quality. Based on the quality of the work in the exercise, the student earns up to 30 points for the exam The work must be submitted in writing and checked and recognized by the teacher. The test is realized by written test, student can get up to 70 points from this test, where 30 points from exercises. The evaluation of the test result is given by the ECTS grading scale.
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

The aim of the course is to acquaint students with modern methods and tools for design of production systems and their control in the environment of automated production. The main focus is on tools and methods based on the application of knowledge systems and optimization approaches to solve design and control problems. Basic approaches related to artificial intelligence are also discussed.
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

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Chang, T., Wysk R.A., Wang, H.: Computer-Aided Manufacturing (EN)
Kusiak, A.: Intelligent Manufacturing Systems (EN)
Mařík V. a kol. Umělá inteligence, Akademia Praha 1-4 (CS)
Tomek G., Vávrová V. Řízení výroby, Grada Publishing 2000 (CS)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-VSR-P Master's 2 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

13 hod., optionally

Teacher / Lecturer

Ing. Daniel Zuth, Ph.D.

Syllabus

1.- 2. Fundamentals of artificial intelligence methods, basic approaches, differences from algorithmic approaches to problem-solving.
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

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Daniel Zuth, Ph.D.

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