Details - Tematika

Robots in Vehicle Manufacturing (GAJABAN-ROBOTJGY-1)

Basic data

Name and type of the study programme

Vehicle Engineering, undergraduate program

Curriculum

2023

Classes / consultation hours

2 + 0 + 2 (L+S+Labs)

Credits

4 credits

Theory – Practice

Theory: 50%, Practice: 50%

Recommended semester

Semester 5

Study mode

full-time

Prerequisites

–

Evaluation type

Mid-term evaluation

Course category

Szakirányon kötelező

Language

English

Instructors

Responsible instructor

Dr. Líska János

Responsible department

Innovatív Járművek és Anyagok Tanszék

Instructor(s)

Kelemen János

Checked by

Kelemen János

Course objectives

The aim of the course is to acquaint the student with the possibilities of using robots in the automotive industry. Get to know the types of robots and end effectors and understand how they works.

Course content

Lectures

Types of industrial robots, advantages and disadvantages of each type. Types of End Effectors and their uses. Conditions for the integration of industrial robots into a production system. Operation of production cells supported by industrial robots.

Labs

Acquired competences

Knowledge

–

Skills

–

Attitude

–

Autonomy and responsibilities

–

Additional professional competences

Students will be able to select the appropriate robot for a particular technical problem and design the associated end effector in advance. They will also be able to plan the conditions for integrating the robots into the production line.

Requirements, evaluation and grading

Mid-term study requirements

During the semester, one final examination will be written, which can be corrected or made up once. A satisfactory result in the final examination is a prerequisite for obtaining a satisfactory mid-semester grade. The mid-term mark is the final examination mark and the completion of the CAD design task.

Generative AI usage

2nd position: Use of GAI tools is permitted in a limited manner (e.g., for literature search support or specific tools). In this case, the course instructor is responsible for defining where and how GAI tools may be used in assignments. The course description must specify in detail how GAI tools may be used during the course.