Course detail

Mechanisation and Automation

FSI-EMM Acad. year: 2026/2027 Summer semester

The course provides a comprehensive and integrated view of modern production systems in the context of digital transformation and Industry 4.0. Students will learn about the principles of Enterprise Resource Planning (ERP I and ERP II) systems, which are used for strategic production planning, supply chain management (SCM), and customer relationship management (CRM). An integral part of the course is the philosophy of Lean Manufacturing, whose methods, such as 5S, Kaizen, and Kanban, lead to the systematic elimination of waste. Graduates will learn to evaluate production data, work with KPI management indicators, and the basics of Business Intelligence (BI). The technical part emphasizes the basics of automation technologies: Programmable Logic Controllers (PLC I), their architecture and programming; Industrial Robotics (ROBOT I and II), including kinematics, programming, safety, and collaborative robots (Cobots). The course also covers complex machine automation, industrial network integration and visualization (HMI), and automated logistics systems such as automated warehouses (AS/RS) and crane handling. The conclusion is devoted to current trends, in particular the use of artificial intelligence (AI/ML) for predictive maintenance and adaptive control, and a practical demonstration of automation and mechanization in industrial practice. The course thus combines the managerial, data, and technical skills necessary for a modern engineer.

Supervisor

Ing. Miroslav Jopek, Ph.D.

Department

Institute of Manufacturing Technology

Learning outcomes of the course unit

Prerequisites

Prerequisite knowledge for the Mechanization and Automation course should include Fundamentals of Mechanical Engineering (reading technical drawings, knowledge of manufacturing processes), Fundamentals of Electrical Engineering (electrical circuits, principles of operation of sensors and actuators), and Computer Science. In the field of computer science, algorithmization and basic programming skills (loops, conditions, variables), orientation in computer networks, and advanced user knowledge of software are key. Newly required is also awareness of prompts and effective communication with artificial intelligence tools for quick research and problem-solving assistance. Furthermore, a basic understanding of business economics and logistics is necessary to understand the context of production management.

Planned learning activities and teaching methods

Assesment methods and criteria linked to learning outcomes

The course assessment is designed to verify both practical skills and theoretical knowledge acquired during the semester and is conditional on the fulfillment of three requirements. Regular and active participation in classes is mandatory, and obtaining a credit based on attendance is a prerequisite for admission to the final exam. The second condition for classification is the preparation, submission, and successful defense of a semester project, which serves as a practical application of knowledge and includes a functional model/demonstration and a technical report. At the end, there is a final exam (written and/or oral) that tests comprehensive theoretical knowledge of all topics covered in the lectures. The overall grade for the course is determined based on the results of the final exam, taking into account the evaluation of the semester project.

Language of instruction

Czech

Aims

After completing the course, students will understand information systems (ERP I/II), the principles of Lean Manufacturing, and methods of waste elimination (MUDA, Kanban). They will gain knowledge of PLC architecture and programming, robot kinematics and programming (including Cobots), and industrial network integration. They will be able to analyze data, evaluate KPI/OEE, and use Business Intelligence and artificial intelligence (AI/ML) tools to optimize processes. They will also learn about automated storage and retrieval systems (AS/RS) and crane handling. The aim is to teach students to integrate management, data, and technical systems and critically analyze practical examples of automation

Specification of controlled education, way of implementation and compensation for absences

The study programmes with the given course

Programme B-STR-P: Engineering, Bachelor's
branch STG: Manufacturing Technology, 6 credits, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Syllabus


  1. ERP I: Fundamentals and Production Planning -Definition of ERP (Enterprise Resource Planning) and its role in managing enterprise resources and production planning.

  2. ERP II: Extension to SCM and CRM -The concept of ERP II as the integration of the supply chain and customer relationships with manufacturing.

  3. Lean I: Philosophy and Waste (Muda) – Introduction to Lean Manufacturing and the systematic elimination of the seven types of waste.

  4. Lean II: Tools, 5S, Kaizen, and Kanban-Lean implementation tools, including workplace standardization, continuous improvement, and pull systems.

  5. Data Evaluation: Managerial KPI and BI-Analysis of manufacturing data, determination of KPIs (e.g., OEE), and the fundamentals of Business Intelligence for decision-making.

  6. PLC I: Architecture and Programming-Principles and architecture of PLCs (Programmable Logic Controllers) and basic programming languages according to the IEC 61131-3 standard.

  7. ROBOT I: Types, Kinematics, and Applications-Classification and kinematics of industrial robots and an overview of their manufacturing applications.

  8. Robot II: Programming, Safety, and Cobots-Methods of robot programming, safety zones, and the transition to Collaborative Robotics (Cobots).

  9. Machine Automation: Bus and HMI Integration-Complex automation of lines, industrial networks, and HMI (Human-Machine Interface) visualization panels.

  10. Automated Warehouses: AS/RS and Logistics-The function of Automated Storage and Retrieval Systems (AS/RS) and the management of internal logistics.

  11. Crane Handling: Classification and Safety-Principles and classification of cranes, including key safety and normative requirements.

  12. Utilization of Artificial Intelligence-Integration of AI/ML in industry for predictive maintenance, process optimization, and adaptive control.

  13. Practical Demonstration of Automation and Mechanization in Industrial Practice.

Exercise

26 hours, compulsory

Syllabus


  1. Introductory consultation on the methodological approach to project development.

  2. The aim of the semester project, its general content of the main parts and the structure of the outputs.

  3. Assignment of approved topics in mechanization and automation.

  4. The significance, content, and forms of research, searching, citation according to ISO 690, processing of literary sources, data analysis for AI/ML, and design of an artificial intelligence algorithm.

  5. Selection and choice of materials, consultation on assignments.

  6. Development of a technological procedure for the selected technology based on the principles of technological design.

  7. Project schedule and time management of work.

  8. Implementation of the AI/ML algorithm and selection of the control platform (e.g., Arduino, Rasberry), programming, and testing.

  9. Preparation of drawing documentation, control program, graphic processing of images, tables, and attachments.

  10. Individual review of partial results of the solution, review of drawing documentation.

  11. Ongoing consultations.

  12. Evaluation of semester work, including practical demonstrations.

  13. Awarding of credits.