Course detail

Intelligent Manufacturing Systems

FSI-GIS-KAcad. year: 2015/2016

Progress in manufacturing and in computer technology opens new perspectives in the design of products, manufacturing processes and manufacturing systems. It requires application of new methods in this area. The aim of the course is to familiarise students with new methods, based on the knowledge base systems in the manufacturing systems. It will enable them to apply the methods of artificial intelligence and bring a new quality to all processes in manufacturing.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. Ing. Lubomír Vašek, CSc.

Department

Institute of Production Machines, Systems and Robotics (ÚVSSR)

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).

Prerequisites

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.

Co-requisites

Not applicable.

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.

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 has a written and an oral part. Final grade reflects student’s knowledge acquired in the course and his/her ability to apply this knowledge to practical problems solution. Successful solving at least half of the problems in the written part is necessary.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Chang, T., Wysk R.A., Wang, H.: Computer-Aided Manufacturing
Kusiak, A.: Intelligent Manufacturing Systems

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N2301-2 Master's

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

Type of course unit

 

Guided consultation

13 hod., optionally

Teacher / Lecturer

doc. Ing. Lubomír Vašek, CSc.

Syllabus

1. Production system like intelligent system (IVS)
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