Information Package / Course Catalogue
| Course Code | : CSE444 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 2 |
| Prt. | : 2 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 6 |
The aim of this course is to teach the integrated use of artificial intelligence, cyber-physical systems, and Internet of Things (IoT) technologies in manufacturing systems, and to demonstrate how industrial artificial intelligence can be designed and implemented in applications such as robotic automation, quality control, predictive maintenance, and process optimization.
This course is structured with a practical approach focusing on the integrated use of artificial intelligence and digital technologies in production processes. Students will learn a multi-stage process ranging from collecting and analyzing sensor data to designing decision support systems on industrial robots and production lines. The course covers topics such as robotic arm systems, quality control through image processing, predictive maintenance applications, production optimization, AI-based classification and prediction algorithms, real-time communication protocols (MQTT – Message Queuing Telemetry Transport, OPC UA – Open Platform Communications Unified Architecture), production data visualization, and digital twin concepts. In addition, students will develop industrial scenarios in simulation environments by integrating microcontrollers (ESP32, Raspberry Pi) and software in Artificial Intelligence of Things (AIoT)–based systems. The course adopts a project-based learning approach, aiming to produce solutions that are close to real-world challenges.
| Assoc. Prof. Ahmet Çağdaş SEÇKİN |
| 1. | Define industrial artificial intelligence and cyber-physical system architecture. |
| 2. | Collect and analyze data for real-world manufacturing problems. |
| 3. | Develop machine learning models for use in production processes. |
| 4. | Design interactive systems using industrial robots and sensors. |
| 5. | Produce AIoT-based solutions for real-time production scenarios. |
| 6. | Test robotic applications in simulation tools and/or laboratory environments. |
| 7. | Develop and present a project within a team. |
| 1. | Alur, R. (2015). Principles of cyber-physical systems. MIT press. |
| 2. | Stringham, G. (2009). Hardware/firmware interface design: best practices for improving embedded systems development. Newnes. |
| 3. | Jamali, J., Bahrami, B., Heidari, A., Allahverdizadeh, P., & Norouzi, F. (2020). Towards the Internet of Things. Springer International Publishing. |
| 4. | Lee, J., Davari, H., Singh, J., & Pandhare, V. (2024). Industrial AI: Data-driven applications in manufacturing, energy, and logistics. Springer |
| 5. | Kumar, R. (Ed.). (2023). Industrial AI: Practical applications and organizational strategies (AI and Big Data Handbooks). River Publishers. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Final Examination | 1 | %60 |
| Term Assignment | 1 | %40 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 0 | 2 | 28 |
| Lecture - Practice | 14 | 0 | 2 | 28 |
| Assignment | 14 | 0 | 1 | 14 |
| Term Project | 1 | 16 | 8 | 24 |
| Midterm Examination | 1 | 16 | 8 | 24 |
| Final Examination | 1 | 16 | 16 | 32 |
| TOTAL WORKLOAD (hours) | 150 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | |
OÇ-1 | 4 | 3 | 4 | 3 | 4 | 3 | 3 | 2 | 2 | 3 | 3 |
OÇ-2 | 4 | 5 | 5 | 4 | 5 | 4 | 4 | 3 | 2 | 3 | 4 |
OÇ-3 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 3 | 3 | 3 | 5 |
OÇ-4 | 4 | 4 | 5 | 5 | 5 | 4 | 4 | 3 | 3 | 3 | 5 |
OÇ-5 | 4 | 4 | 5 | 4 | 5 | 4 | 4 | 3 | 3 | 3 | 5 |
OÇ-6 | 4 | 4 | 4 | 4 | 5 | 5 | 4 | 3 | 3 | 3 | 5 |
OÇ-7 | 3 | 3 | 4 | 4 | 4 | 3 | 3 | 5 | 5 | 4 | 4 |