Manufacturing Execution Systems Operator
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Manufacturing Execution Systems Operator
Course Details
Trade and Industry Vocational School
Programme of Manufacturing Execution Systems Operator
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 07831111 | Intelligent Manufacturing Systems | 2 | Autumn | 3 | 2+2+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | Lectures, Q&A, discussions, case analysis, group work, and simulation-based applications. |
| Mode of Delivery | Face to Face |
| Prerequisites | None; basic knowledge of artificial intelligence and manufacturing systems is recommended. |
| Coordinator | Lect. Mehmet AKSOY |
| Instructor(s) | Lect. Yasin BÜYÜKER |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Yasin BÜYÜKER | T-219 | [email protected] | 7408 | Tuesday 14:00/16:00 |
Course Content
The course reviews the fundamentals of artificial intelligence and machine learning. It examines the components, functions, and interactions of Intelligent Manufacturing Systems (IMS). Topics include intelligent design, process planning, production scheduling, quality management, transportation, and maintenance integration. The relationship between IMS and systems such as MES, ERP, and PCS is discussed. Students explore how intelligent systems monitor performance, analyze data, generate recommendations, and optimize processes through practical examples.
Objectives of the Course
The aim of this course is to provide students with an understanding of the conceptual structure, components, and industrial applications of Intelligent Manufacturing Systems (IMS). Students learn how to integrate artificial intelligence, machine learning, data analytics, and automation technologies into production processes to develop self-monitoring, self-learning, and self-optimizing manufacturing systems.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P4 | Effectively uses information technologies (software, programs, animation, etc.) related to their profession | 4 |
| P7 | Takes responsibility as a team member to resolve complex and unforeseen issues encountered in applications related to the field. | 4 |
| P11 | Has the ability to define and design manufacturing operations and processes. | 4 |
| P13 | It has the ability to integrate, operate, monitor and report manufacturing execution systems. | 4 |
| P15 | Possesses knowledge and skills in defining and designing intelligent production systems. | 4 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knows the concept of intelligent manufacturing, the intelligent manufacturing system and the components of this system. | P.4.26 | 1 |
| O2 | By recognizing manufacturing systems supported by current technologies, different solutions can be developed for current problems. | P.7.5 | 1 |
| O3 | Recognize different manufacturing systems. | P.11.8 | 1 |
| O4 | Recognize the intelligent systems used in various stages of the intelligent manufacturing system. | P.11.9 | 1 |
| O5 | Explains the integration of systems such as MES, ERP and PCS with intelligent manufacturing systems. | P.14.7 | 1 |
| O6 | Evaluate data analysis, prediction and optimization processes in intelligent manufacturing systems. | P.15.6 | 1,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Evolution of manufacturing systems: traditional, computer-integrated, and flexible manufacturing |
| 2 | Introduction to intelligent manufacturing systems: intelligence, AI, and learning concepts |
| 3 | Components and architecture of intelligent manufacturing systems |
| 4 | Key features and decision-making mechanisms of intelligent systems |
| 5 | Intelligent design and knowledge-based engineering approaches |
| 6 | Intelligent process planning and automatic optimization methods |
| 7 | Intelligent production scheduling and resource allocation applications |
| 8 | Intelligent quality control systems and learning from defects |
| 9 | Intelligent transportation, logistics, and warehouse systems |
| 10 | Intelligent maintenance systems and predictive maintenance applications |
| 11 | Integration of MES, ERP, and PCS systems with intelligent manufacturing |
| 12 | Reference model approaches for intelligent integrated manufacturing systems |
| 13 | Data analytics, recommendation, and alert mechanisms in intelligent systems |
| 14 | Industry 4.0 and beyond: autonomous manufacturing, digital twins, and cyber-physical systems |
Textbook or Material
| Resources | Automation, Production Systems, and Computer-Integrated Manufacturing, Mikell Groover |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | 1 | 20 (%) |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 50 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 4 | 56 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 10 | 10 |
| Quiz | 0 | 0 | 0 |
| Homework | 1 | 10 | 10 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 1 | 10 | 10 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 10 | 10 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 152 | ||
| Total Work Load / 30 | 5,07 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P4 | P7 | P11 | P14 | P15 |
|---|---|---|---|---|---|---|
| O1 | Knows the concept of intelligent manufacturing, the intelligent manufacturing system and the components of this system. | 4 | - | - | - | - |
| O2 | By recognizing manufacturing systems supported by current technologies, different solutions can be developed for current problems. | - | 4 | - | - | - |
| O3 | Recognize different manufacturing systems. | - | - | 4 | - | - |
| O4 | Recognize the intelligent systems used in various stages of the intelligent manufacturing system. | - | - | 4 | - | - |
| O5 | Explains the integration of systems such as MES, ERP and PCS with intelligent manufacturing systems. | - | - | - | 4 | - |
| O6 | Evaluate data analysis, prediction and optimization processes in intelligent manufacturing systems. | - | - | - | - | 4 |