Augmented Reality (AR) is transforming production systems, offering innovative solutions to challenges in manufacturing, assembly, and quality control. This technology overlays digital information onto the physical environment, enhancing the way we interact with and perceive the real world. The relevance of AR in production systems can be understood through several key aspects.

AR improves precision and efficiency in manufacturing. By projecting 3D models and instructions directly onto workpieces, workers can perform tasks with higher accuracy and less time. For example, in complex assembly processes, AR can guide workers through each step, reducing errors and training time. Boeing and Airbus are notable examples where AR has been successfully integrated into their assembly lines, showing significant improvements in time and cost efficiency.

AR enhances training and skill development. Traditional training methods in production systems can be costly and time-consuming. AR enables interactive, on-the-job training, where workers can learn in a simulated environment overlaid on the real one. This hands-on approach leads to better retention of information and skills, crucial in industries where precision and safety are paramount.

AR aids in maintenance and troubleshooting. It allows maintenance personnel to visualize the internal components of machinery without disassembly, identify problems, and receive step-by-step repair instructions. This application not only saves time but also reduces the likelihood of errors during maintenance. Companies like Siemens have utilized AR to enhance their maintenance procedures, demonstrating its potential in reducing downtime and improving operational efficiency.

AR facilitates remote collaboration. Experts can guide on-site workers through complex tasks from a distance, viewing the same AR overlay as the on-site worker. This capability is invaluable for global companies, where expert knowledge needs to be shared across different locations. It ensures consistency in quality and performance, irrespective of geographical boundaries.

AR contributes to customization and design in production. It enables designers and engineers to visualize and modify products in real-time, seeing how changes would function in the actual product. This immediate feedback accelerates the design process and supports the creation of more customized and user-centric products.

Total Hours

This course unit covers 100 hours, from which 28 hours lectures, 14 hours lab work, and 58 hours individual study and work.

General Objective

The primary objective of the course unit “Augmented Reality (AR) in Production Systems” is to enrich students’ theoretical and practical understanding of AR technology, with a special emphasis on its application in enhancing production systems. This course is designed to equip students with the knowledge and skills necessary to develop AR applications that provide visual aids and assembly instructions on the plant floor. Through this, the course aims to enable students to create intuitive, easily accessible AR tools that can significantly improve the efficiency, accuracy, and ease of various manufacturing and assembly processes. By merging in-depth theoretical insights with hands-on practical experience, the course prepares students to effectively harness AR technology in transforming modern production environments.

Specific Objectives / Learning Outcomes

The specific objectives of the course “Augmented Reality (AR) in Production Systems” are designed to provide students with a comprehensive skill set in AR technology application within industrial environments. These objectives are:

  • Development of AR Applications Using ARKit, ARCore, and Vuforia: Students will gain hands-on experience in creating AR applications using leading development platforms like ARKit for iOS, ARCore for Android, and Vuforia. This includes learning the nuances of each platform, understanding their strengths, and how to leverage them for creating robust AR solutions in production settings.
  • Augmented Reality UI Design: The course will focus on the principles of AR user interface (UI) design. Students will learn how to design intuitive and user-friendly AR interfaces, crucial for ensuring effective interaction between the user and the AR application in a production environment.
  • Environment Tracking Directly Within the Factory Floor: A key objective is to enable students to develop skills in environment tracking on the factory floor. This involves learning techniques to accurately overlay digital information onto the physical environment, ensuring that AR applications are seamlessly integrated into the production processes.
  • Development of 3D Model Targets Based on 3D Scans and CAD Files: Students will learn how to create 3D model targets from 3D scans and CAD files. This skill is essential for developing AR applications that can interact with real-world objects and machinery in a production setting, enhancing the precision and effectiveness of AR tools.
  • Using AR Technology as Input for Machine Learning Models: The course will also cover the integration of AR technology with Machine Learning (ML). Students will explore how AR data can serve as input for ML models, enhancing predictive maintenance, quality control, and other advanced applications in production systems.

Through these specific objectives, the course aims to empower students with a thorough understanding and practical ability in leveraging AR technology to optimize and innovate in production systems.

Professional Competencies

Professional competencies that students can expect to develop from the “Augmented Reality (AR) in Production Systems” course are:

  • Advanced AR Application Development for Industrial Use: Mastery in creating sophisticated AR applications tailored for production environments. This includes proficiency in using platforms like ARKit, ARCore, and Vuforia, along with skills in AR-specific UI design and environment mapping on factory floors.
  • Integration of AR with Traditional and Robotic Production Systems: Competence in integrating AR technology with both traditional and robotic manufacturing systems. This involves using AR for real-time monitoring, maintenance, and the enhancement of robotic manufacturing processes, including the use of AR for simulated training and operation analysis.
  • Expertise in 3D Modeling and Virtual Prototyping: Proficiency in developing detailed 3D models and virtual prototypes based on 3D scans and CAD files. This skill is crucial for designing, testing, and optimizing production system layouts and components before actual implementation.
  • Application of AR in Machine Learning and Predictive Maintenance: The ability to utilize AR as a data input source for machine learning models, particularly in predictive maintenance and performance optimization of manufacturing systems. This competency involves understanding how AR data can be effectively processed and analyzed to inform machine learning algorithms.
Cross Competencies

The following cross-competencies can be expected:

  • Interdisciplinary Collaboration and Communication: Given the interdisciplinary nature of AR in production systems, a key cross-competency is the ability to collaborate and communicate effectively across different fields, such as engineering, IT, design, and operations management. This includes understanding the language and needs of various stakeholders and being able to translate technical concepts into actionable insights.

  • Problem-Solving and Critical Thinking: Professionals will develop strong problem-solving skills, learning to approach complex production challenges with innovative AR solutions. This involves not just technical know-how but also critical thinking to assess situations, identify potential problems or improvements, and apply AR technology creatively and effectively.

  • Adaptability and Continuous Learning: The field of AR is rapidly evolving, necessitating a commitment to continuous learning and adaptability. This competency involves staying abreast of the latest developments in AR technology and its applications in production, as well as being flexible and open to adapting these new technologies and methods in their work.

  • Project Management and Organizational Skills: Implementing AR in production systems often involves complex projects with multiple stakeholders and components. Thus, developing strong project management and organizational skills is crucial. This includes planning, resource allocation, time management, and the ability to oversee a project from conception to implementation.

  • Technological Literacy and Data Analytics: As AR heavily relies on digital technology and data, a key cross-competency is technological literacy, including an understanding of data analytics. Professionals should be competent in interpreting and utilizing data gathered from AR applications for decision-making and process optimization.

  • Ethical Consideration and Responsibility: Understanding the ethical implications of implementing AR in production systems is crucial. This includes considering privacy, data security, and the impact of AR on employees, such as ergonomic considerations and job displacement concerns.

By developing these cross-competencies, professionals will be well-equipped to effectively implement and manage AR technology in production systems, ensuring they are not only technically proficient but also versatile, ethical, and forward-thinking in their approach.

Alignment to Social and Economic Expectations

The alignment of the “Augmented Reality (AR) in Production Systems” course with social and economic expectations can be articulated through several key aspects:

  • Economic Productivity and Efficiency: The primary economic expectation is to boost productivity and efficiency in production systems. AR technology can significantly streamline manufacturing processes, reduce errors, and shorten training times, thereby leading to cost savings and increased output. This aligns with the broader economic goal of enhancing competitiveness and innovation in the industrial sector.
  • Workforce Development and Employment Opportunities: Socially, the course aligns with the expectation of developing a skilled workforce adept in modern technologies. As AR becomes more prevalent in industry, there’s a growing demand for professionals skilled in these areas. The course prepares students to meet this demand, thereby enhancing employment opportunities and contributing to workforce modernization.
  • Enhancing Quality and Precision in Manufacturing: AR’s ability to improve the quality and precision of products aligns with economic goals of producing high-quality goods while minimizing waste and defects. This is particularly important in sectors where precision is crucial, such as aerospace, automotive, and medical devices.
  • Workplace Safety and Ergonomics: From a social perspective, the integration of AR in production systems can significantly enhance workplace safety and ergonomics. By providing real-time information and guidance, AR can help reduce workplace accidents and improve the overall working conditions, which is a key social expectation.
  • Sustainability and Resource Management: Economically and socially, there’s a growing emphasis on sustainable practices. AR can contribute to this by optimizing production processes, reducing material waste, and enabling more efficient use of resources. This aligns with the broader goal of sustainable industrial growth.
  • Adaptation to Technological Change and Innovation: The course aligns with the social expectation of adapting to rapid technological changes and fostering a culture of continuous innovation. By equipping students with cutting-edge skills in AR, it prepares them to be innovators and leaders in their field, driving forward technological advancements.
  • Global Competitiveness and Market Responsiveness: Economically, the course addresses the need for industries to remain globally competitive and responsive to market changes. Proficiency in AR technology can give businesses a significant edge, enabling them to quickly adapt to market demands and maintain a strong competitive position.

Assessment Methods

Theoretical Lectures Component:

  • Quizzes: Regular quizzes, both in-class and online, to test students’ understanding of AR concepts, technologies, and applications in production systems.
  • Written Assignments: Assignments requiring students to explore and critically analyze real-world AR applications in industrial settings, emphasizing problem-solving and application of theoretical knowledge.
  • Midterm and Final Exams: Comprehensive exams to assess overall understanding, including multiple-choice, short answer, and essay questions focused on AR in production systems.

Practical Laboratory Component:

  • Lab Reports: Detailed reports documenting lab experiments in AR application development, focusing on methodology, results, and analytical insights.
  • Oral Presentations: Presentations of lab projects assessing presentation skills, content clarity, and engagement with the audience, particularly focusing on AR solutions developed.

Assessment Criteria

Lectures Component:

  • Knowledge and Understanding: Ability to comprehend and apply core concepts and principles of AR in production systems.
  • Analytical and Problem-Solving Skills: Capacity to analyze complex production challenges and apply AR solutions effectively.
  • Communication Skills: Proficiency in conveying AR concepts and solutions clearly and engagingly.
  • Teamwork and Collaboration Skills: Ability to work effectively in teams, especially in group projects involving AR development.
  • Application of Technology: Proficiency in using AR development tools and understanding their application in industrial settings.

Laboratory Work Component:

  • Technical Skills: Competence in applying technical skills to develop practical AR solutions.
  • Quality of Work: Ability to produce high-quality, innovative AR applications.
  • Creativity and Innovation: Capacity for creative thinking and innovation in developing AR solutions.
  • Attention to Detail: Thoroughness in documenting and executing AR projects.
  • Time Management: Effectiveness in managing time to complete lab tasks and projects.

Quantitative Performance Indicators

For Lectures:

  • Attendance and Participation: Minimum 80% attendance and active participation in discussions.
  • Homework and Quizzes: At least 60% average score.
  • Midterm Exam: Minimum of 50% score.

For Lab Works:

  • Lab Attendance and Participation: Full attendance and active participation.
  • Lab Reports: Minimum of 60% score on each report.
  • Lab Assignments: At least 60% average score.
  • Lab Exams: Minimum of 50% score.

For Final Exam:

  • Minimum 70% of lecture-related questions answered correctly.
  • At least 50% score on basic concept questions, real-life case studies, technology questions, practical problem-solving, and critical evaluation questions.
  • Overall exam score of at least 50% to pass.

This comprehensive assessment approach ensures a balanced evaluation of both theoretical understanding and practical skills in AR applications in production systems.


Unit 1: Introduction to Augmented Reality (AR) in Production Systems (2 hours)

  • Overview of AR technology and its evolution
  • Distinction between AR, VR, and MR
  • Key components and technologies in AR
  • Role and impact of AR in modern production systems
  • Interactive session exploring basic AR applications

Unit 2: Fundamentals of AR Development Platforms (2 hours)

  • Introduction to ARKit, ARCore, and Vuforia
  • Comparing the features and capabilities of each platform
  • Basics of setting up an AR development environment
  • Demonstration of simple AR applications using these platforms

Unit 3: AR User Interface (UI) Design for Production Environments (2 hours)

  • Principles of AR UI design specific to production systems
  • User experience considerations in AR
  • Tools and techniques for effective AR UI design
  • Case studies and practical UI design exercise

Unit 4: AR for Environment Tracking and Mapping on Factory Floors (2 hours)

  • Techniques for environment tracking and spatial mapping
  • Integrating AR with factory floor layouts
  • Challenges and solutions in industrial AR deployment
  • Hands-on session with AR mapping tools

Unit 5: Creating 3D Models for AR using CAD and 3D Scanning (2 hours)

  • Basics of 3D modeling for AR applications
  • Converting CAD files to AR-compatible formats
  • Introduction to 3D scanning technologies
  • Workshop on creating 3D models from CAD and scans

Unit 6: Advanced AR Application Development (2 hours)

  • In-depth exploration of AR application development
  • Advanced features of ARKit, ARCore, and Vuforia
  • Developing interactive and complex AR applications
  • Group project to develop a custom AR application

Unit 7: AR in Robotic Systems and Automation (2 hours)

  • Integration of AR with robotic manufacturing systems
  • AR for monitoring and controlling robotic operations
  • Case studies of AR in automation
  • Practical demonstration of AR with a robotic system

Unit 8: Utilizing AR for Maintenance and Quality Control (2 hours)

  • AR applications in maintenance and troubleshooting
  • Enhancing quality control processes with AR
  • Interactive session with AR maintenance tools
  • Group activity on designing a quality control AR application

Unit 9: Data Handling and AR in the Context of Industry 4.0 (2 hours)

  • Role of AR in the data-driven Industry 4.0 landscape
  • Managing and interpreting data through AR interfaces
  • Integrating AR with IoT and big data
  • Workshop on AR and data visualization

Unit 10: AR for Training and Skill Development in Production Systems (2 hours)

  • AR in educational and training contexts
  • Designing AR training modules for production systems
  • Benefits and challenges of AR-based training
  • Hands-on session creating an AR training program

Unit 11: Collaborative AR Applications in Production Systems (2 hours)

  • Exploring collaborative features of AR
  • AR for team coordination and communication on the factory floor
  • Workshop on developing a collaborative AR tool

Unit 12: AR and Machine Learning: Enhancing Predictive Maintenance (2 hours)

  • Basics of integrating AR with machine learning
  • AR in predictive maintenance and analytics
  • Case study analysis and group discussion
  • Practical exercise on creating an AR predictive maintenance tool

Unit 13: Sustainability and Ethical Considerations in AR Deployment (2 hours)

  • Discussing sustainability aspects of AR in production
  • Ethical considerations and societal impact
  • Case studies on responsible AR implementation
  • Group discussion and critical thinking exercise

Unit 14: Future Trends and Innovations in AR for Production Systems (2 hours)

  • Exploring emerging trends and future potentials of AR in production
  • AR in the context of smart factories and digital twins
  • Guest lecture from an industry expert
  • Final project presentation and course wrap-up

Each unit is designed to provide both theoretical knowledge and practical skills, preparing students for effective application and innovation in AR within production systems.

Lab Work

Lab Unit 1: Introduction to Augmented Reality in Production Systems (2 hours)

  • Overview of AR in industrial settings.
  • Review of key AR technologies and their applications in production.
  • Interactive exploration of AR in factory environments.

Lab Unit 2: Getting Started with Augmented Reality and Tracking Methods (2 hours)

  • Basics of starting with AR development using platforms like ARKit, ARCore, and Vuforia.
  • Introduction to tracking methods in AR, focusing on their role in industrial applications.

Lab Unit 3: Industrial Robotics and AR Tracking (2 hours)

  • Exploring AR tracking in industrial environments, particularly tracking industrial robots.
  • Hands-on exercises in tracking and visualizing robotic movements using AR.

Lab Unit 4: AR and Machine Learning Integration for Production Systems (2 hours)

  • Incorporating Machine Learning models as inputs in AR applications.
  • Case studies on AR and ML enhancing production processes.

Lab Unit 5: CAD Model Tracking and Digital Twins (2 hours)

  • Techniques for tracking CAD models in AR.
  • Exploring the concept of digital twins in AR applications and their use in simulating real-world systems.

Lab Unit 6: Advanced AR Technologies – SLAM and Optics (2 hours)

  • Introduction to Simultaneous Localization and Mapping (SLAM) in AR.
  • Exploring the optics of AR displays and their calibration.
  • Practical exercises involving SLAM and optics in AR applications.

Lab Unit 7: Interactive and Applied AR for Industrial Applications (2 hours)

  • AR for remote assistance (ARRA) in production environments.
  • Interacting with AI-controlled equipment in AR scenarios.
  • Developing AR applications for industrial training, with a focus on Head-Mounted Displays (HMDs).

These lab units are designed to provide students with a comprehensive and practical understanding of AR technology and its application in industrial environments, particularly focusing on tracking, machine learning integration, digital twins, and interactive AR technologies.

Supporting Infrastructure

The supporting infrastructure for  “Augmented Reality (AR) in Production Systems” encompasses a range of resources and facilities to facilitate effective teaching and learning. This infrastructure is crucial for both the theoretical and practical components of the course. Here are the key elements:

  • Classroom and Lecture Facilities: Well-equipped classrooms with multimedia capabilities for lectures. This includes projectors, screens, and audio systems for presentations, as well as high-speed internet connectivity for accessing online resources and AR platforms.
  • Computer Labs: Dedicated computer labs with high-performance computers capable of running AR development software and applications. These computers have the necessary specifications to handle AR development tools like ARKit, ARCore, and Vuforia, as well as 3D design software.
  • AR Development Software and Tools: Licenses for AR development platforms (such as ARKit, ARCore, Vuforia) and 3D modeling software (like SolidWorks, AutoCAD). This also includes access to any relevant IDEs (Integrated Development Environments) for coding and application development.
  • AR Hardware: A range of AR hardware for practical demonstrations and student use, including AR headsets (like Microsoft HoloLens, Magic Leap), tablets, and smartphones with AR capabilities.
  • Simulation Environments: Access to virtual or augmented reality environments where students can test and deploy their AR applications. This include AR simulation software or virtual factory floor setups.
  • 3D Scanners and Printers: For creating and modifying physical models used in AR applications. 3D scanners to digitize real-world objects and printers to create prototypes or components.
  • Laboratory Space: Dedicated lab space where students can work on practical AR projects. This space is adaptable for different types of projects, including those that require interaction with physical components or machinery.
  • Library and Online Resources: Access to academic and industry publications, journals, and online databases focusing on AR technology, production systems, and related fields.
  • Collaborative Spaces: Areas designed for group work and collaboration, essential for project-based learning and team assignments in the course.

This infrastructure not only supports the delivery of course content but also provides a practical, hands-on learning environment where students can develop and refine their skills in augmented reality as applied to production systems.