Course detail

Intelligent Manufacturing Systems

FSI-GISAcad. year: 2017/2018

Progress in manufacturing and in computer technology and especially in their connection brings new approaches in the design of products and their realization in manufacturing processes and production systems. They are currently expressed in the concept of Industry 4.0, which implies that the traditional tools necessary activities in the engineering manufacture are insufficient for the future.The aim of the course is to familiarise students with new approaches and methods:
Manufacturing System as an intelligent system, basic knowledge of artificial intelligence, expert systems, neural networks, methods based on the use of knowledge bases in the manufacturing systems. It is shown how to apply these methods and thereby bring a new quality for each activity in the production and manufacturing system - design of products, process planning, group technology, design of the structure of the manufacturing system, scheduling and production control, management of production quality.

Learning outcomes of the course unit

Students will be made familiar with selected methods applied for creating and solution of mathematical models in different subsystems of a manufacturing system. Students will be provided with necessary information and will gain practical experience with algorithms used for these methods, as well as methods of artificial intelligence (expert systems, neural nets).


Students are expected to have: - basic knowledge of mathematical procedures applied in linear algebraic equations and unequations solution - knowledge of important subsystems of manufacturing system and their functions.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
According to the possibility of teaching can be organized lectures for students by practitioners and excursions to companies focused on activities related to the course content.

Assesment methods and criteria linked to learning outcomes

Course-unit credit is conditional on the following:
Participation in practicals and working out of semester work.

Examination: The exam verifies the acquired knowledge and is combined. It has a practical and theoretical part. The practical part examines the student's ability to apply acquired knowledge and methods on practical examples, in the theoretical part knowledge of the theoretical basis.
If a student solves less than half of the examples he has passed, he / she fails.

Language of instruction


Work placements

Not applicable.


The aim of the course is to familiarise students with new modern methods and tools used for design and control of manufacturing systems with respect to automated manufacturing. The emphasis is placed on methods based on application of knowledge base systems and optimisation procedures. Also discussed are the methods based on application of basic principles of AI.

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme M2I-P Master's

    branch M-VSR , 2. year of study, summer semester, 4 credits, compulsory-optional

Type of course unit



26 hours, optionally

Teacher / Lecturer


1. Production system like intelligent system (IVS), relationship with the concept of Industry 4.0
2. Basic method of artificial intelligence, basic access
3. Knowledge-based systems - knowledge representation, basic reasoning strategies in inference engine
4. Expert systems and their use in production systems, their structure, filling of knowledge base and knowledge evaluating
5. Neuron network, basic principles and applications inside production systems
6. Features in design and manufacturing
7. CAD as part of IVS
8. CAPP as part of IVS, variant and generic type of production process creating
9. Production planning and scheduling in IVS
10. Methods of group technology, cluster methods for product sorting, coding systems of workpieces.
11. Methods for selection production equipment and their layout
12. Methods for inventory space allocation and storage processes analysis
13. Methods applied for data retrieving and processing

Computer-assisted exercise

13 hours, compulsory

Teacher / Lecturer


1. Mathematical models and basic methods for their solution
2. Methods of linear programming
3. Knowledge representation as production rules
4. Basic reasoning strategies used in inference engines
5. Expert systems for analyzing production machines
6. Using neuron network as an accuracy detector for production machines
7. Using manufacturing features in process planning
8. Optimisation of production costs and methods finding of the best process plan
9. Methods of group technology
10. Methods for production equipment selection and layout
11. Heuristic scheduling of multiple resources
12. Methods for inventory space allocation
13. Course-unit credit awarding.