Course detail

Intelligent Manufacturing Systems

FSI-GISAcad. year: 2021/2022

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)

Learning outcomes of the course unit

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.

Prerequisites

Basic knowledge of mathematics and fundamentals of computer science.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught in the form of lectures, which are an explanation of the basic principles and theory of the discipline. The exercise is focused on practical mastery of the subject matter covered in the lectures. Where possible, lectures will be organized for practitioners and field trips to companies dealing with activities related to the subject matter.

Assesment methods and criteria linked to learning outcomes

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.

Course curriculum

Not applicable.

Work placements

Not applicable.

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.

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

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)

Recommended reading

Not applicable.

eLearning

eLearning: currently opened course

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 hours, 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 hours, compulsory

Teacher / Lecturer

Ing. Daniel Zuth, Ph.D.

Syllabus

1. Introduction to expert systems,
2. Solving problems with expert systems, application examples.
3. Neural networks in the context of the production process
4. Convolutional neural networks
5. Data classification, choice of predictors, comparison of methods
6. Optimization using evolutionary algorithms
7. IoT and cloud systems
8. Fuzzy logic in production system
9. Production process visualization, SCADA / HMI demonstration
10. Production process visualization, SCADA / HMI demonstration
11. Algorithms for travel planning.
12. Evaluation of final theses
13. Credit

eLearning

eLearning: currently opened course