The basics of development of digital twins in Mechanical Engineering

Description: The discipline forms a general understanding of engineering and engineering education. The object and subject of engineering are being studied. Comprehension and understanding of the basic methods and techniques of augmented reality and their application at different stages of the development and management decision-making process

Amount of credits: 6

Course Workload:

Component: Component by selection

Cycle: Profiling disciplines

Goal

• During the study of the discipline, students get acquainted with the structure and stages of the educational process, types and forms of classes, practices, organization of intermediate and final control. Forms professional competencies in the field of virtualization and cloud technologies, development and operation of applied decision support systems and digital twins

Objective

• Formation of students' knowledge for end-to-end professional activities in industry in the fields of digital twins, automation, robotization and mechanization of production for various purposes using microprocessor technology, modern PLM, CAD/self-systems, IT technologies, programming of modern logic controllers based on artificial intelligence methods, neural network management and information processing, as well as skills and application skills the knowledge gained in practice

Learning outcome: knowledge and understanding

• The student receives knowledge about the construction of smart models and digital counterparts of technical, industrial, cyberphysical, anthropogenic, social and natural systems.

Learning outcome: applying knowledge and understanding

• The student uses knowledge in the development of the project, taking into account the analysis of alternative options for its implementation
• Uses methods for developing and justifying an action plan to solve a problematic situation

Learning outcome: formation of judgments

• Formation of basic knowledge for analyzing situations in the engineering sector and optimizing a new industrial product

Learning outcome: communicative abilities

• The ability to communicate effectively, avoid misunderstandings and conflicts, find solutions to difficult issues faster and work more productively

Learning outcome: learning skills or learning abilities

• Communication skills, leadership qualities, the ability to learn, motivation to study, the ability to think creatively and outside the box

Teaching methods

Dual education - the connection between theoretical training and practical skills in production

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.

The evaluating policy of learning outcomes by work type

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:

 

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:

Topics of lectures

• Topic 1
• Topic 2
• Topic 3
• Topic 4
• Topic 5
• Topic 6
• Topic 7
• Topic 8
• Topic 9

Key reading

• U.S. Manufacturing—Global Leadership Through Modeling and Simulation. – URL: http://www. compete.org/storage/images/uploads/File/PDF%20Files/HPC%20Global%20Leadership%20 030509.pdf (дата обращения: 08.10.2017)
• Samarasinghe, S. Neural Networks for Applied Sciences and Engineering / S. Samarasinghe. – Boca Raton, FL: Auerbach Publications, 2006. – 570 p.
• Инновационное проектирование цифрового производства в машиностроении : лабораторный практикум / С. Г. Селиванов, А. Ф. Шайхулова, С. Н. Поезжалова, А. И. Яхин. — Москва, Вологда : Инфра-Инженерия, 2022. — 240 c. — ISBN 978-5-9729-0921-6. — Текст : электронный // Цифровой образовательный ресурс IPR SMART : [сайт]. — URL: https://www.iprbookshop.ru/124212.html (дата обращения: 14.05.2025). — Режим доступа: для авторизир. пользователей