Innovative technologies in the automotive industry
Description: The discipline studies innovative technologies of Industry 4.0 in the automotive industry. There are considered technologies of “cobots” (Collaborative Robots) for partial relief of physical labor, technologies for complete robotization of the processes of manufacturing parts and assembling components, and “Internet of Things” technologies in the automotive industry. Students analyze reserves for increasing efficiency in the automotive industry, acquire skills in managing and optimizing production processes
Amount of credits: 5
Пререквизиты:
- Modern designs of aggregate and units of transport equipment
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 15 |
| Practical works | 30 |
| Laboratory works | |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 75 |
| SAW (Student autonomous work) | 30 |
| Form of final control | Exam |
| Final assessment method | exam |
Component: Component by selection
Cycle: Profiling disciplines
Goal
- Give an idea of the implementation of cyber-physical systems, robotization and the use of artificial intelligence tools in the automotive industry, automation tools for all processes in production within the framework of Industry 4.0
Objective
- Explore innovative Industry 4.0 technologies in the automotive industry
- Study technologies for complete robotization of the processes of manufacturing parts and assembling components
- Be able to analyze reserves for increasing efficiency in the automotive industry
- Acquire skills in managing and optimizing production processes
Learning outcome: knowledge and understanding
- knowledge of the main tools of innovative technologies "Industry 4.0": big data, Internet of things, virtual and augmented reality, 3D printing, quantum computing, blockchain and robotization.
Learning outcome: applying knowledge and understanding
- application of knowledge to implement processes for manufacturing cars, parts and assemblies
Learning outcome: formation of judgments
- analyze reserves for increasing efficiency in the automotive industry
Learning outcome: communicative abilities
- the ability to take technical, economic and administrative decisions; apply their knowledge in real life.
Learning outcome: learning skills or learning abilities
- acquire skills in managing and optimizing production processes
Teaching methods
During conducting training sessions, it is planned to use the following educational technologies: - Innovative technologies and analytical methods in the training of qualified specialistsin the automotive industry
Assessment of the student's knowledge
Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.
| Period | Type of task | Total |
|---|---|---|
| 1 rating | Task 1 | 0-100 |
| Task 2 | ||
| Testing | ||
| 2 rating | Task 3 | 0-100 |
| Task 4 | ||
| Testing | ||
| Total control | Exam | 0-100 |
The evaluating policy of learning outcomes by work type
| Type of task | 90-100 | 70-89 | 50-69 | 0-49 |
|---|---|---|---|---|
| Excellent | Good | Satisfactory | Unsatisfactory |
Evaluation form
The student's final grade in the course is calculated on a 100 point grading scale, it includes:
- 40% of the examination result;
- 60% of current control result.
The final grade is calculated by the formula:
| FG = 0,6 | MT1+MT2 | +0,4E |
| 2 |
Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;
E is a digital equivalent of the exam grade.
Final alphabetical grade and its equivalent in points:
The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:
| Alphabetical grade | Numerical value | Points (%) | Traditional grade |
|---|---|---|---|
| A | 4.0 | 95-100 | Excellent |
| A- | 3.67 | 90-94 | |
| B+ | 3.33 | 85-89 | Good |
| B | 3.0 | 80-84 | |
| B- | 2.67 | 75-79 | |
| C+ | 2.33 | 70-74 | |
| C | 2.0 | 65-69 | Satisfactory |
| C- | 1.67 | 60-64 | |
| D+ | 1.33 | 55-59 | |
| D | 1.0 | 50-54 | |
| FX | 0.5 | 25-49 | Unsatisfactory |
| F | 0 | 0-24 |
Topics of lectures
- Industry 4
- Industrial IoT platforms - Internet of Things and Services
- Big data and analytics
- Cloud Computing
- Additive Manufacturing
- Augmented Reality
- Digital cloning
- Machine learning
- Smart factory
- Predictive Maintenance
Key reading
- 1. Баймухамедов, М. Ф. Mechatronics / М. Ф. Баймухамедов . - Алматы : Бастау , 2019 - . Volume. 2 Мехатроника : textbook / Қ. Қ. Джаманбалин, М. К. Акгул ; Мин-во образования и науки РК. - Алматы : Бастау , 2019. - 256 p. - Библиогр.: 132 р. . - ISBN 978-601-7275-98-3 : 6272 2. Логический подход к искусственному интеллекту: От классич. логики к логическому программир. / пер. с фр. П. П. Пермяков ; ред. Г. П. Гаврилов. - М. : Мир, 1990. - 432 с 3. Искусственный интеллект. В 3-х книгах. - М. : Радио и связь. Кн.2 : Модели и методы : справочное издание / Ред. Д.А. Поспелов. - М. : Радио и связь, 1990. - 304 с.
Further reading
- 4. Акмаев, К. Х. Роботизация разборочных работ при ремонте машин. Вопросы нормирования [Текст] : учебное пособие / К.Х. Акмаев, Л.Е. Буздыганова, В.А. Тимошкин; Под ред. Л.В. Дехтеринского ; МАДИ. - М. : [б. и.], 1986. - 105 с.
