Course detail
Intelligent Manufacturing Systems
FSI-GISAcad. year: 2010/2011
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
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
- 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
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
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
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Kusiak, A.: Intelligent Manufacturing Systems (EN)
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Exercise
Teacher / Lecturer
Syllabus
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.