Syllabus

Introduction

Designing digital twins for industrial production provides students with a comprehensive understanding of the value of digital twin technology in the industrial sector. Digital twins enable organizations to create virtual representations of their physical assets and processes, providing real-time insights into the performance, condition and behavior of these assets. This technology has the potential to improve the efficiency, reliability, and sustainability of industrial processes, and is increasingly being adopted by a range of industries, including manufacturing, energy, and transportation. By taking a course unit on the design of digital twins, students will learn how to create digital twins that accurately reflect the real-world assets and processes they represent. They will also learn how to integrate data from a variety of sources, including sensors, machines, and control systems, to create a real-time, data-rich model of the physical environment. Additionally, students will learn how to use digital twins to analyze and optimize industrial processes, reduce downtime, and make data-driven decisions that drive process improvement and innovation.

Total Hours

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

General Objective

The general objective of the course unit is to equip students with the knowledge and skills needed to understand the concept of digital twins and how they can be applied in the industrial production environment. Students learn how to design, build, and maintain digital twins for different types of industrial equipment and processes, taking into account factors such as data sources, communication protocols, and cybersecurity. They would also gain practical experience in developing digital twins by working on a small project that involve designing, testing, and deploying these systems in real-world industrial environments.

Specific Objectives / Learning Outcomes

The specific objectives or learning outcomes of this course unit are:

  • Understanding the concept of digital twins in industrial production, including their benefits, challenges, and limitations.
  • Developing knowledge and skills in designing and building digital twins for industrial production processes, including data acquisition, model development, and simulation.
  • Understanding how to connect the digital twin with the physical production environment, including sensors, networks, and cloud systems.
  • Developing knowledge and skills in the application of AI, machine learning, and other advanced technologies in digital twins for industrial production processes.
  • Improving understanding of how to evaluate and validate the performance of digital twins in industrial production processes.
  • Understanding how to use digital twins to optimize production processes, improve quality, and increase efficiency.
  • Developing communication skills to effectively present and discuss digital twin design and implementation in industrial production.
  • Enhancing teamwork skills through collaboration with classmates in designing, building, and evaluating digital twins for industrial production processes.
Professional Competencies

The professional competencies that a student can gain from this course unit include:

  • Understanding the concept and application of digital twin technology in industrial production environments.
  • Knowledge of the various models and techniques used in creating digital twins, such as physics-based, data-driven, and hybrid models.
  • Ability to collect and integrate data from multiple sources, such as sensors, control systems, and cloud platforms.
  • Understanding of data visualization and analysis techniques, and how to use these to monitor and control the performance of industrial systems.
  • Knowledge of communication and networking technologies, such as Ethernet, OPC UA, and MQTT, and how these are used to connect digital twins to industrial systems.
  • Understanding of the integration of digital twins with other digitalization technologies, such as artificial intelligence, machine learning, and the Internet of Things.
  • Ability to design, implement, and validate digital twin solutions for industrial production systems.
  • Understanding of the ethical and privacy implications of using digital twins in industrial production, and how to ensure that these systems are secure and trustworthy.
Cross Competencies

The cross-competencies that could be developed through a course unit on the design of digital twins in industrial production include:

  • Problem-solving skills: The student will learn how to analyze complex industrial processes, identify areas of improvement, and develop innovative solutions to enhance production efficiency.
  • Interdisciplinary thinking: The student will gain exposure to various disciplines, such as computer science, mechanical engineering, and industrial design, and learn how to integrate knowledge from these fields to design effective digital twins.
  • Technical proficiency: The student will develop technical skills in areas such as data analytics, simulation, and programming, and will be able to apply these skills in a practical context.
  • Communication skills: The student will learn how to communicate complex technical concepts effectively to a diverse range of stakeholders, including engineers, managers, and other experts.
  • Collaboration skills: The student will learn how to work effectively in teams, and will be encouraged to collaborate with students from other MSc specializations, further enhancing their interdisciplinary thinking skills.
  • Leadership skills: The student will be encouraged to take ownership of the design and development of a digital twin project, and will develop leadership skills by managing a team, defining project goals and objectives, and overseeing the implementation of the project.
Alignment to Social and Economic Expectations

By learning how to design digital twins, students will be equipped with the necessary tools to optimize production processes, reduce downtime, and increase the overall quality of the final product. They will also be able to monitor and analyze the performance of the physical assets in real-time, allowing them to identify and address any issues promptly.

Moreover, this course helps students develop a strong understanding of the latest technologies and trends in the field, such as cloud computing, the Internet of Things (IoT), and artificial intelligence. This, in turn, will make them well-equipped to take on the challenges and opportunities that come with the ongoing digital transformation of the manufacturing industry.

Thus, the course on designing digital twins in industrial production aligns with the social and economic expectations by providing students with the knowledge and skills to help organizations in the manufacturing industry stay ahead of the curve, embrace innovation, and remain competitive in a rapidly changing market.

Evaluation

Assessment methods

For the lectures portion of the course unit on design of digital twins in industrial production, the following assessment methods are used:

  • Quizzes: In-class quizzes or online quizzes to test the students’ understanding of key concepts and theories covered in the lectures.
  • Written assignments: Individual assignments that require students to apply their knowledge and skills to solve a real-world problem or case study.
  • Midterm and Final Exams: These exams consist of multiple choice, short answer, and essay questions and assess the students’ overall understanding of the course material.

For the lab work portion of the course, the following assessment methods are used:

  • Lab reports: Students are required to write lab reports documenting their experiments, results, and analysis. These reports are graded on the quality of their writing, methodology, results, and analysis.
  • Oral presentations: Students are required to present their lab work to the class, which is assessed based on the quality of their presentation skills, content, and interaction with the audience.

Assessment criteria

For lectures, the assessment criteria for this course unit on the design of digital twins in industrial production are:

  • Knowledge and Understanding: Assessment of the student’s ability to comprehend and apply the concepts, theories, and principles of digital twin design in industrial production.
  • Analytical and Problem Solving Skills: Assessment of the student’s ability to analyze complex problems, evaluate different solutions, and make informed decisions related to digital twin design in industrial production.
  • Communication Skills: Assessment of the student’s ability to communicate their ideas, designs, and solutions in a clear, concise, and effective manner.
  • Teamwork and Collaboration Skills: Assessment of the student’s ability to work effectively in a team and collaborate with others to achieve a common goal.
  • Application of Technology: Assessment of the student’s ability to apply appropriate technologies, tools, and software to design and implement digital twin solutions in industrial production.

For lab work, the assessment criteria could include:

  • Technical Skills: Assessment of the student’s ability to use and apply the technical skills and knowledge acquired in the course to design, implement, and test digital twin solutions in industrial production.
  • Quality of Work: Assessment of the student’s ability to produce high-quality work that meets the requirements and standards set for digital twin design in industrial production.
  • Creativity and Innovation: Assessment of the student’s ability to think creatively and apply innovative solutions to design digital twins in industrial production.
  • Attention to Detail: Assessment of the student’s ability to pay close attention to details and ensure that the digital twin solutions are accurate, complete, and well-documented.
  • Time Management: Assessment of the student’s ability to manage their time effectively and deliver completed lab work within the specified timeframe.

Quantitative performance indicators to assess the minimum level of performance (mark 5 on a scale from 1 to 10)

Quantitative performance indicators to assess the minimum level of performance (mark 5 on a scale from 1 to 10) for the lectures:

  • Attendance and participation in class discussions – The student should attend at least 80% of the lectures and actively participate in class discussions.
  • Homework and Quizzes – The student should complete all homework assignments and quizzes with a minimum score of 60%.
  • Midterm Exam – The student should achieve a minimum score of 50% on the midterm exam.

Quantitative performance indicators to assess the minimum level of performance (mark 5 on a scale from 1 to 10) for the lab works:

  • Lab attendance and participation – The student should attend and participate in all scheduled lab sessions.
  • Lab reports – The student should submit all lab reports on time, with a minimum score of 60% on each report.
  • Lab assignments – The student should complete all lab assignments with a minimum score of 60%.
  • Lab exams – The student should achieve a minimum score of 50% on the lab exams.

Quantitative performance indicators for the final exam to assess the minimum level of performance:

  • Completion of a minimum number of lecture-related questions correctly – 70% of the total questions.
  • The student should be able to demonstrate an understanding of the basic concepts and theories related to digital twins and their applications in industrial production, with a minimum score of 50% on multiple-choice questions or short answer questions.
  • The student should be able to explain and analyze real-life case studies and their results, with a minimum score of 50% on case study analysis questions.
  • The student should be able to demonstrate a basic knowledge of the technologies, tools, and methodologies used in the design and implementation of digital twins, with a minimum score of 50% on matching or labeling questions.
  • The student should be able to apply the concepts and theories learned in the lectures to solve practical problems, with a minimum score of 50% on problem-solving questions.
  • The student should be able to critically evaluate the benefits and challenges of using digital twins in industrial production, with a minimum score of 50% on essay questions.
  • Evidence of the ability to apply learned concepts and theories to practical scenarios, as demonstrated by the number of correctly answered application-based questions.
  • Display of critical thinking skills, as evidenced by the number of correct answers to questions requiring analysis and synthesis of information.
  • Overall exam performance, measured in terms of the total number of correct answers and expressed as a percentage of the total exam score. A minimum score of 50% or above is set as the benchmark for a mark of 5.
Lectures

Unit 1: Introduction to Digital Twins (2 hours)

  • Simplistic Introduction to Digital Twin
  • 5-Dimension Digital Twin
  • Digital Twin Architecture
  • Applications of Digital Twins
  • Challenges of Digital Twins
  • Role of IoT and AI in Digital Twins

Unit 2: Strategic Integration of Digital Twins (2 hours)

  • Fusion of Big Data, Cloud, and Cyber-Physical Systems
  • Security in Digital Twins
  • Example of a Digital Twin for an Industrial Robot
  • Economic Benefits of Digital Twins
  • Selling Digital Twins to Factory Managers

Unit 3: Fundamentals of Digital Twin Design (2 hours)

  • Digital Models vs. Digital Shadows vs. Digital Twins
  • Digital Twins vs. Simulations
  • The Main Steps in Digital Twin Design
  • Digital Twin Modeling and Development

Unit 4: Advanced Communication and Storage Protocols for Digital Twins (2 hours)

  • Communication Protocols (MQTT, CoAP, DDS)
  • Data Storage Solutions (MongoDB, InfluxDB)

Unit 5: Functional Modeling for Digital Twins (2 hours)

  • Functional Decomposition
  • Functional Flow Block Diagram (FFBD)
  • IDEF0 Modeling
  • Behavioral Modeling
  • Functional Allocation
  • Functional Hazard Analysis (FHA)
  • System Context Diagrams
  • Value Stream Mapping (VSM)
  • Functional Mock-Up Interface (FMI)

Unit 6: Digital Twin Dynamic and Data-Driven Modeling (2 hours)

  • Dynamic Modeling of Digital Twins
  • Data-Driven Modeling of Digital Twins
  • Technology Inputs: Overview of Modeling Tools
  • Technology Inputs: Overview of Modeling Languages and Standards
  • Technology Inputs: Overview of Simulation Tools
  • Applications of Dynamic and Data-Driven Digital Twins

Unit 7: Analytics-Driven Insights for Dynamic System Performance (2 hours)

  • Dynamic System Modeling Through Transfer Functions
  • Data-Driven Performance Analytics for Dynamic Systems
  • Stability and Robustness Analysis
  • Exploring Failure Dynamics and Diagnostics
      Lab Work

      Lab 1 & Lab 2: Introduction to Digital Twins

      • Software Installation and Configuration
      • Exploration of Digital Twin Environments
      • Introduction to RobotStudio and Basic Programming
      • Setting Up Digital Twin Communication with MQTT and CSV Integration
      • Hands-On Test Environment and Validation

      Lab 3 & Lab 4: Creating a Digital Twin Shop-Floor

      • Developing a Digital Twin of a Shop-Floor Using RobotStudio
      • Cyber-Physical Fusion Techniques for Digital Twin Applications
      • Real-World Data Integration into Digital Twins
      • Validation of Digital Twin Models with Real-World Data

      Lab 5 & Lab 6: Develop a Digital Twin for Conceptualization of a Robotic Cell

      • Creating a Simple Robotic Cell Using RobotStudio
      • Introduction to RAPID Programming for Robotic Control
      • Development and Configuration of Smart Components in RobotStudio
      • Connecting ABB Robots to the Cloud for Data Collection
      • Virtual Commissioning and Analysis of Robotic Cells

      Lab 7 & Lab 8: Using Python for Creating a Digital Twin

      • Introduction to Python for Digital Twin Modeling
      • Modeling a Transistor as a Digital Switch
      • Developing the Analytical Model
      • Creating the Program in Python
      • Analyzing the Results

      Lab 9 & Lab 10: Create a Digital Twin for a Li-ion Battery to Predict its Behavior Using Machine Learning Models

      • Introduction to Li-ion Batteries and Digital Twins
      • Modeling Battery Degradation with Empirical Data
      • Building a Hybrid Model Combining Empirical and Machine Learning Approaches
      • Neural Network for Residual Prediction
      • Sensitivity Analysis and Model Evaluation Metrics

      Lab 11 & Lab 12: Modeling Dynamic Systems Using Transfer Functions

      • Overview and Components of the Robot Axis System
      • Control Loop Interconnections and Feedback Mechanisms
      • Transfer Functions for Dynamic System Analysis
      • System Simulation and Input Response Types (Step, Ramp, Sinusoidal, Disturbances)
      • Effects of PID Parameter Adjustments on System Stability and Performance

      Lab 13 & Lab 14: Digital Twin for PID Controller Tuning (3D Printer Application)

      • Introduction to PID Controllers and Their Role in 3D Printing
      • Transfer Function Modeling of Heating Systems in 3D Printers
      • Developing and Simulating a Digital Twin of a PID-Controlled System
      • Manual and Automated PID Tuning Methods
      • Advanced Optimization Techniques (e.g., Particle Swarm Optimization, Differential Evolution, Neuro-fuzzy)
      Supporting Infrastructure

      To run the activity for this course unit, students will have the possibility to work in our labs with the following technologies:

      • Access to UTCloud to experiment digital twins concepts using I-IoT.
      • Develop virtual prototypes with Visual Components/Python, RoboDK/Python/Fusion 360, and RobotStudio/Smart Components.
      • Work with robotic cells: ABB [RAPID/RobotStudio/Python/nodeRed], Kuka [Python/Siemens/nodeRed], UR [RoboDK/Python], Dobo Magician [Python], and other robotic arms.
      • Work with Xcos (Dassault Systems) to model digital twins.
      • Use Python programming language and related libraries for digital twins.