| Subject name (in Hungarian, in English) | Control and supervision of production systems | |||
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Control and Monitoring of Manufacturing Systems
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| Neptun code | BMEGEGT8566 | |||
| Type | study unit with contact hours | |||
| Course types and number of hours (weekly / semester) | course type: | lecture (theory) | exercise | laboratory excercise |
| number of hours (weekly): | 2 | 0 | 0 | |
| nature (connected / stand-alone): | - | - | - | |
| Type of assessments (quality evaluation) | exam | |||
| ECTS | 3 | |||
| Subject coordinator | name: | Dr. Monostori László István | ||
| post: | university professor | |||
| contact: | monostori@manuf.bme.hu | |||
| Host organization | Department of Manufacturing Science and Engineering | |||
| https://manuf.bme.hu/ | ||||
| Course homepage | https://manuf.bme.hu/?page_id=1640 | |||
| Course language | hungarian, english | |||
| Primary curriculum type | választható PhD tárgy | |||
| Direct prerequisites | Strong prerequisite | none | ||
| Weak prerequisite | ||||
| Parallel prerequisite | ||||
| Milestone prerequisite | at least obtained 0 ECTS | |||
| Excluding condition | none | |||
Aim
Basic tasks of control of production systems, starting and synchronizing parallel processes, logical model of control. Extended instruction system for NC controls. Tasks of the system management computer. Language tools for management. MAP, TOP. Production system and production cell monitoring solutions. Paradigms, implementation, and operating model of intelligent manufacturing systems. Use of artificial intelligence tools in production system management and monitoring.
Learning outcomes
Competences that can be acquired by completing the course
Knowledge
He has a comprehensive knowledge of the theory of manufacturing systems in terms of the tools and purpose of its applications. It compares the procedures needed to model the behavior of manufacturing systems and the more common control and monitoring methods. Knows the widely applicable problem-solving techniques required for research, scientific and expert work. Has the knowledge required to analyze the reliability of controlling and supervisory methods. He has an accurate knowledge of the basic production system management procedures and the monitoring methods used. He is aware of the time-varying characteristics of controlled and supervised features, process analysis. Identifies feature malfunctions and their dynamic, time-varying effects. He was informed about the domestic and international standards and requirements of control technology. He is aware of defining the requirements for different controls and monitoring systems. He is knowledgeable in the control of production processes, as well as in the definition of process oversight and the implementation of the necessary measurements.
Ability
Use methods of control technology and artificial intelligence, and information gathered during measurements. It explores the reliability, applicability and significance of the given production processes. It assembles management and supervision systems, designs experiments with different techniques. It develops calculation algorithms based on different procedures and methods. It ranks the different applicable control engineering models for a given task. It analyzes the errors and reliability of production processes, and their dynamic, time-varying effects. Defines the appropriate controls, sensors, and instrumentation requirements for controlling and monitoring manufacturing systems. It identifies problems that arise and suggests solutions. Use the appropriate chapters of numerical mathematical methods to solve problems. Able to manage software tools needed to analyze data and apply monitoring methods.
Attitude
He constantly monitors his work, results and conclusions. He is receptive to the application of the acquired knowledge using problem-solving techniques. It seeks to disseminate new professional and scientific results. It is open to formulating appropriate criticism or opinions, making decisions and drawing conclusions. It develops your ability to provide accurate and error-free problem solving, engineering precision and accuracy. It applies the principles of energy efficiency, sustainability and environmental awareness in the management and supervision of the implemented production system.
Independence and responsibility
Collaborates with faculty and fellow students to expand knowledge. Accepts well-founded professional and other critical remarks. In some situations, as part of a team, you work with your fellow students to solve tasks. With his knowledge, he makes a responsible, informed decision based on his analyzes. It is committed to enriching the management and supervision of the production system with new knowledge and scientific results. He is committed to the principles and methods of systematic thinking and problem solving.
Teaching methodology
The subject is taught in a task-oriented manner in the form of lectures. The lectures basically introduce the students to the information determined by the knowledge competence elements, using the technique of frontal education, during which the students get to know the background and significance of the theory and application possibilities of measurement technology. The independent sessions are related to the lectures, focusing on the sub-area of measurement technique designated by a lecturer, taking into account the students' area of interest if possible. The subject of the task can be experimental design, signal processing, data analysis, parametric analysis, literature search. The task can be individual or group work, the documentation can be a project report, presentation, software or calculation depending on the type of task.
Support materials
Textbook
Javier Silvestre-Blanes: Factory Automation, IntechOpen 2010 (ISBN 978-953-51-5911-7)
Lecture notes
Online material
https://manuf.bme.hu/?page_id=1640
https://doi.org/10.1016/j.procir.2013.06.127
Validity of the course description
| Start of validity: | 2020. February 10. |
| End of validity: | 2024. December 31. |
General rules
Assessment of learning outcomes is based on the mid-year written project report paper submitted at the end of the semester and the accompanying presentation. Summative academic performance evaluation: a complex, written way of evaluating the competence-type competence elements of a subject and knowledge in the form of a report, the report focuses on the application of the acquired knowledge, thus focusing on problem recognition and solution, ie asks for the necessary lexical knowledge during performance appraisal.
Assessment methods
Detailed description of mid-term assessments
| Mid-term assessment No. 1 | ||
| Type: | summative assessment | |
| Number: | 1 | |
| Purpose, description: | A summative performance evaluation is a demonstration of having knowledge of the lectures on the subject as well as the knowledge indicated in the literature. It is able to independently solve the calculations that arise and the structure of an ventilation system. The way of assessing the knowledge, ability, attitude, as well as the competence and responsibility type competence elements of the subject in the form of writing a closed dissertation during the semester. The uniform assessment principles are defined by the person in charge of the subject. | |
Detailed description of assessments performed during the examination period
Elements of the exam:
| Oral partial exam | ||
| Obligation: | mandatory (partial) exam unit, failing the unit results in fail (1) exam result | |
| Description: | Oral answers to two randomly selected questions from the topics covered in the lectures. Make a short, concise sketch before the oral answer. The basic causal relations, the application of the theory and its connection with the practice and its applicability are checked. The oral exam can be replaced by a high-quality solution of a task related to the research topic prepared during the semester. | |
The weight of mid-term assessments in signing or in final grading
| ID | Proportion |
|---|---|
| Mid-term assessment No. 1 | 100 % |
The condition for signing is that the score obtained in the mid-year assessments is at least 40%.
The weight of partial exams in grade
| Type: | Proportion |
|---|---|
| Oral partial exam | 100 % |
Determination of the grade
| Grade | ECTS | The grade expressed in percents |
|---|---|---|
| very good (5) | Excellent [A] | above 92 % |
| very good (5) | Very Good [B] | 85 % - 92 % |
| good (4) | Good [C] | 71 % - 85 % |
| satisfactory (3) | Satisfactory [D] | 56 % - 71 % |
| sufficient (2) | Pass [E] | 41 % - 56 % |
| insufficient (1) | Fail [F] | below 41 % |
The lower limit specified for each grade already belongs to that grade.
Attendance and participation requirements
Must be present at at least 70% (rounded down) of lectures.
Special rules for improving, retaken and replacement
The special rules for improving, retaken and replacement shall be interpreted and applied in conjunction with the general rules of the CoS (TVSZ).
| Need mid-term assessment to invidually complete? | ||
| yes | ||
| The way of retaking or improving a summary assessment for the first time: | ||
| each summative assessment can be retaken or improved | ||
| Is the retaking-improving of a summary assessment allowed, and if so, than which form: | ||
| retake or grade-improving exam possible for each assesment separately | ||
| Taking into account the previous result in case of improvement, retaken-improvement: | ||
| out of multiple results, the best one is to be taken into account | ||
Study work required to complete the course
| Activity | hours / semester |
|---|---|
| participation in contact classes | 28 |
| preparation for summary assessments | 16 |
| exam preparation | 21 |
| additional time required to complete the subject | 29 |
| altogether | 94 |
Validity of subject requirements
| Start of validity: | 2020. February 10. |
| End of validity: | 2024. December 31. |
Primary course
The primary (main) course of the subject in which it is advertised and to which the competencies are related:
Mechanical engineering sciences PhD programme
Link to the purpose and (special) compensations of the Regulation KKK
This course aims to improve the following competencies defined in the Regulation KKK:
Knowledge
Ability
Attitude
Independence and responsibility
Prerequisites for completing the course
|
Knowledge type competencies
(a set of prior knowledge, the existence of which is not obligatory, but greatly facilitates the successful completion of the subject) |
none |
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Ability type competencies
(a set of prior abilities and skills, the existence of which is not obligatory, but greatly contributes to the successful completion of the subject) |
engineering calculations |