Artificial Intelligence and Data Analytics in Mechanical EngineeringSillabus

Fənn kodu

MM-0706

Kredit

6

Tələbənin iş yükü

180

Semestr

Yaz semestri

Akademik il

2025-2026

Fənnin tipi

Məcburi

Təhsil forması

Əyani

Tədris dili

İngilis

Auditoriyadaxili saatlar

Lecture:30 Seminar:15

Auditoriyadan kənar saatlar

Independent Study (TSİ):81 MRTSI:54

“Artificial Intelligence and Data Analytics in Mechanical Engineering Production” is an advanced Master’s level course designed to bridge the gap between traditional mechanical engineering and modern intelligent technologies. The primary objective of this discipline is to equip graduate students with the theoretical knowledge and practical skills required to implement data-driven methods, predictive modeling, and artificial intelligence within contemporary manufacturing environments. Throughout the curriculum, students explore advanced methodologies for collecting, processing, and analyzing complex engineering data derived from actual production lines, design cycles, and CAD/CAM/CAE system outputs. Special emphasis is placed on developing intelligent algorithms for the early detection of manufacturing defects, predicting structural or system failures, and optimizing technological performance indicators. To successfully enroll in this course, students are expected to possess foundational knowledge in mechanical engineering principles, strength of materials, automated control systems, and basic statistical analysis. This highly interdisciplinary course is academically interconnected with core engineering fields, ensuring that students can seamlessly apply data analytics to evaluate simulation results and enhance production efficiency. Ultimately, the course fosters a technical mindset focused on data-driven decision-making, enabling future engineers to accurately interpret analytical results and implement smart optimization models in the industrial sector.

• mühazirə
• seminar
• interaktiv müzakirə
• case study
• problem-based learning
• təqdimatlar
• komanda işi

Təlim nəticəsi əlavə edilməyib.

Mövzu əlavə edilməyib.

1. 1. Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
2. 2. Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Sebastopol, CA: O’Reilly Media.
3. 3. Groover, M. P. (2015). Automation, Production Systems, and Computer-Integrated Manufacturing (4th ed.). Pearson.
4. 4. Montgomery, D. C. (2019). Introduction to Statistical Quality Control (8th ed.). Wiley.
5. 5. Navidi, W. (2015). Statistics for Engineers and Scientists (4th ed.). McGraw-Hill Education.
6. 6. Smid, P. (2010). CNC Programming Handbook (3rd ed.). Industrial Press.
7. 7. McKinney, W. (2022). Python for Data Analysis (3rd ed.). O’Reilly Media.
8. 8. Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (3rd ed.). O’Reilly Media.
9. 9. Taulli, T. (2020). AI Basics: A Non-Technical Introduction (2nd ed.). Apress.
10. 10. Badarinath, P. V., Chierichetti, M., & Davoudi Kakhki, F. (2021). A machine learning approach as a surrogate for a finite element analysis. Sensors, 21(5), 1654.