Artificial Intelligence in

Industrial Production 

MSc Studies

Industrial AI: Useful Online Links

Industrial AI: Useful Online Links

Points of Reference Industrial AI refers to the integration of artificial intelligence technologies in various industrial applications to automate and optimize production processes, improve quality and reduce costs. The application of AI in industries is becoming...

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How to Design an Experiential Course Unit?

How to Design an Experiential Course Unit?

General Steps to Design an Experiential Course Unit Experiential learning is a powerful approach to education that provides students with hands-on, practical experiences that reinforce the concepts and theories learned in the classroom. The design of an experiential...

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Experiential MSc Programs

Experiential MSc Programs

Experiential Learning Experiential learning is a pedagogical approach that involves learning through experience and reflection. The learning process is designed to be engaging, immersive, and relevant, enabling students to develop practical skills and a deep...

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Fields of Artificial Intelligence

Fields of Artificial Intelligence

Machine learning (ML) and artificial intelligence (AI) are related, but distinct fields. Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to learn from data and make...

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Ready to take on the future of the industry?
Enroll in this program and learn how to harness the power of AI to drive innovation and success!

As the director of the MSc program in Artificial Intelligence in Industrial Production, I want to extend a warm welcome to all aspiring AI professionals and tech enthusiasts. Our program is designed for individuals who share our passion for using AI and technology to drive innovation and make a positive impact in the world of industrial production. What defines our program different is our unique approach to AI education. We go beyond the theoretical aspect of AI and focus on providing students with a comprehensive understanding of the practical applications of AI in real-world industrial production. Our curriculum covers the latest technologies and advancements in the field, and students will gain hands-on experience with advanced and forefront tools and systems. Our program is dedicated to empowering students with the knowledge and skills they need to succeed in the complex field of AI. Our experienced instructors and industry partners provide students with a well-rounded education and valuable insights into the challenges and opportunities of the field. Joining our MSc program is not just about obtaining a degree, but embarking on a journey of personal and professional growth. Our flexible study options and supportive learning environment allow students to balance their education and work obligations. Our program also provides career counseling and job search assistance, ensuring that our graduates are well-equipped for careers in the industry. We invite you to join us in our mission to drive innovation and bring about positive change in the world of industrial production. If you are ready to take your passion for AI and technology to the next level, we look forward to welcoming you to our program.

Welcome to the MSc program in Artificial Intelligence in Industrial Production. This program is designed for individuals who are passionate about using AI and technology to drive innovation and improve efficiency in various industrial settings. With a focus on cutting-edge technologies and real-world applications, this program provides students with a comprehensive education in the field of AI in industrial production.

The MSc program in Artificial Intelligence in Industrial Production sets itself apart by offering students a unique perspective on AI and a comprehensive education that goes beyond just the theory of AI. Our program puts a strong emphasis on understanding the practical applications of AI in the field of industrial production, making sure that our graduates are equipped with the skills and knowledge necessary to drive innovation and stay ahead of the curve. With a curriculum that covers cutting-edge technologies like robotics, digital twins, security of industrial infrastructure, virtual reality, generative design, and 3D printing, our program ensures a continuous update on the latest developments in AI for industrial production. You’ll get an in-depth look at real-world AI models used in industry, which, where and how to apply them for a maximum benefit, and gain the hands-on experience needed to make an impact and thrive in this exciting field.

This MSc program is dedicated to empowering students with the knowledge and skills to become experts in the integration of AI in industrial production. Our curriculum goes beyond the sectorial aspect of AI, and focuses on the practical application of AI models in real-world industrial production. Our program teaches students the importance of understanding where and how to use AI in industrial production and the role it plays in the digitalization of production processes. Students will have a comprehensive understanding of the latest trends and advancements in AI for industrial production. With this approach, students will have a clear view of which AI models are relevant and how to adapt and use them in their respective fields, providing them with a competitive advantage in their careers.

Our program offers hands-on experience with the latest technologies in the field, ensuring that students receive a well-rounded education and are prepared for careers in the industry. The curriculum is designed to meet the needs and demands of the industry, and is taught by experienced professionals and leading experts in the field. The program includes a wide range of key courses, including:

Machine Learning and Data Analytics in Industrial Production Autonomous Mobile Robots
Design of Digital Twins Collaborative Robotic Systems
Lean and Agile Production Cognitive and Social Robotics
3D Printing Technologies Virtual Reality in Production Systems
3D Scanning Systems Augmented Reality in Production Systems
Digitalization of Supply Chains Green and Digital Transformation
Knowledge Bases in Design Generative Design and Topological Optimization
Security of Industrial Networks Integrated Project on AI in Industrial Production (4 semesters)

Throughout the program, students will have the opportunity to develop hands-on skills and gain practical experience through real-world projects and simulations. They will also work closely with industry partners, gaining invaluable insights into the challenges and opportunities of the field.

We foster a collaborative environment where students can work together on projects and develop problem-solving skills that are essential for success in the industry. Our program also provides support for career development, including career counseling, job search assistance, and access to a network of alumni and industry professionals.

We have flexible study options to suit your needs. Students can have a job during the day-time and attend the classes in the evening, from 17:00 to 21:00 o’clock.

Upon completion of the program, graduates will be well-equipped for careers in the rapidly-growing field of AI in industrial production, including positions in robotics, digital twins, virtual reality, and sustainable technologies. With a deep understanding of cutting-edge technologies and real-world applications, graduates will be well-positioned to drive innovation and bring about positive change in the industry.

If you are interested in using AI and technology to drive innovation and improve efficiency in various industrial settings, the MSc program in Artificial Intelligence in Industrial Production is the ideal choice for you. Join us today and start your journey towards a rewarding career in the field of AI and robotics!

Industrial AI typically refers to the application of artificial intelligence in the industry, such as goods movement and storage, supply chain management, manufacturing automation, and robotics. Unlike general artificial intelligence, which is a frontier research discipline to build computer systems that perform tasks that require human intelligence, industrial AI is more concerned with applying such technologies to address industrial pain points to create value for customers, productivity improvement, cost reduction, optimization, predictive analytics, and insight discovery.

Industrial artificial intelligence is often differentiated from other types of artificial intelligence because it focuses more on the application of artificial intelligence technologies than on the development of human or human-like systems. Data sets for industrial AI tend to be larger but potentially of lower quality than those for general AI. Industrial AI also has zero tolerance for false positives or negatives, delayed information, or unreliable predictions. Industrial AI is uniquely suited to process plants because the huge amount of data and rapidly changing circumstances are too complex for manual or even digital management.

Industrial AI is uniquely appropriate for process plants because the huge amount of data and quickly changing circumstances are too complex for manual or even digital management.

Why does industrial AI matter to process manufacturers? Process manufacturers are increasingly using AI-powered solutions to optimize operational efficiency, drive innovation, and improve profitability. Some of the use cases for industrial AI in process plants include:

  • Predictive analytics/predictive maintenance combines IoT data with deep learning to model large-scale networks, helping spot the earliest signs of anomalies anywhere in the plant, reduce unplanned downtime, and fine-tune maintenance scheduling.
  • Self-aware “smart” equipment that can independently measure performance to generate alerts when degradation reaches a critical point or performance is reduced for any reason.
  • Robotics and automation on the production floor can replace human involvement, thereby increasing efficiency and boosting production while improving human safety.
  • Faster root cause analysis that investigates understands and resolves process plant issues more swiftly to reduce bottlenecks in manufacturing flows.
  • Complex supply chain management that increases visibility into every step of the process, including tracking raw materials, inventory, warehouse management, logistics, and last-mile distribution.

One of the major pitfalls in the foundation of a professional training program is its conception out of context and following some “Procust’s bed” type templates. Before defining this study program we have analyzed various other offers in the educational market. The standard offer for “Artificial Intelligence in Industrial Production” might be synthesized in the following main topics:

Introduction to Artificial Intelligence Computer Vision Supply Chain Optimization
Machine Learning Natural Language Processing Operations Research
Deep Learning Industrial Internet of Things Artificial Intelligence in Manufacturing
Robotics Predictive Maintenance Artificial Intelligence in Logistics
Industrial Automation Quality Control Project

The peer MSc study program can be found here. You have the chance to compare our offer!

But going deeper into the analysis of the context, we see that the standard educational offer is not sufficiently embedded into the use cases of Industrial AI. Based on this finding, we have designed a disruptive master program that covers a wide range of topics related to the application of Artificial Intelligence in industrial production, and that is well thought out. Our proposal is more specialized and provides comprehensive coverage of the field.

Industrial Use Case Course Unit
Predictive analytics/predictive maintenance Machine learning and data analytics in industrial production
Design of digital twins
Self-aware “smart” equipment Design of digital twins
Robotics and automation Autonomous mobile robots
Collaborative robotic systems
Cognitive and social robotics
Faster root cause analysis Lean and agile production
3D printing technologies
3D scanning systems
Virtual reality in production systems
Knowledge bases in design
Generative design and topological optimization
Complex supply chain management Digitalization of supply chains
Green and digital transformation
Security of industrial networks
Augmented reality in production systems
Project work Integrative project (4 semesters)

We made a comparative analysis of the two possible curricula:

Both study programs cover the topic of Artificial Intelligence in Industrial Production. The standard proposal has a broader coverage of AI-related subjects, including Introduction to Artificial Intelligence, Computer Vision, Natural Language Processing, Deep Learning, and Predictive Maintenance. Our proposal is more focused on specific applications of AI in industrial production, such as Machine Learning and Data Analytics, Autonomous Mobile Robots, and Green and Digital Transformation. Additionally, our proposal also includes courses on cutting-edge technologies such as 3D Printing Technologies, Virtual Reality in Production Systems, and Augmented Reality in Production Systems. Ultimately, both programs offer a comprehensive education in AI for industrial production, but our proposal may be more suitable for students interested in exploring specific applications of AI in the field.

Our mission: To provide a comprehensive education in the field of Artificial Intelligence in Industrial Production, equipping students with the knowledge and skills necessary to drive responsible innovation and progress in industry.

Our vision: To be a leader in the education of AI professionals who are equipped to bring positive change to industrial production, balancing the power of AI with ethical considerations and a commitment to sustainability.

Our core values:

  • Innovation: Encouraging the development and application of new and cutting-edge technologies in the field of AI for industrial production.
  • Collaboration: Fostering a collaborative environment where students, faculty, industry partners, and other stakeholders work together to drive progress in the field.
  • Ethics: Ensuring that the use of AI in industrial production aligns with ethical principles and social responsibility.
  • Sustainability: Promoting the responsible and sustainable use of AI in industrial production, with a focus on minimizing negative impacts on the environment and society.
  • Inclusivity: Creating a diverse and inclusive learning environment that is accessible to students from all backgrounds and encourages their full participation and engagement.
  • Empowerment: Empowering students to be leaders and innovators in the field of AI for industrial production, equipping them with the skills, knowledge, and confidence they need to drive positive changes.

Artifical Intelligence is a multidisciplinary field that requires knowledge from multiple areas such as computer science, engineering, mathematics, statistics, and more. Therefore, for the use of AI in industrial production, it’s important to have a program that covers various aspects of AI and its applications, rather than just focusing on designing AI models. Our master program includes courses that cover the application of AI in various domains, such as industrial automation, production planning and scheduling, quality control, and supply chain optimization. Below you may see some of the strengths of this study program.

  1. Strong Curriculum: The program has a strong curriculum that covers a wide range of topics related to the field of Artificial Intelligence in industrial production. This will provide students with a comprehensive understanding of the field, preparing them for a successful career in the industry.
  2. Specialization: The program is specialized in the field of Artificial Intelligence in industrial production, which is a rapidly growing and in-demand field. It provides students with a comprehensive understanding of the latest technologies and trends in the field, preparing them for careers in this area.
  3. Flexibility: The program provides students with a good level of flexibility, allowing them to tailor their education to their specific interests and career goals. This approach will enable students to focus on the areas of AI that they find most interesting and relevant to their career aspirations.
  4. Coverage of the latest trends and technologies: The program includes courses on topics such as 3D printing technologies, 3D scanning systems, virtual reality in production systems, and augmented reality in production systems, which are all important and emerging technologies in the field. This gives students a cutting-edge education that will prepare them for the future of the industry.
  5. Relevance to industry: The program covers important topics such as Green and Digital Transformation and Security of industrial networks, which are critical to the field and the industry. This ensures that students will have the skills and knowledge to be successful in the workplace.
  6. Theoretical and practical aspects: The program includes a good balance of theoretical and practical aspects, with the inclusion of topics like generative design, topological optimization, and digitalization of supply chains. This allows students to not only understand the theories but also apply them in real-world situations.
  7. Experienced faculty: The professors in the commission are experts in the field of the topics included in this MSc program, and the program is designed by experienced professionals in the field of Artificial Intelligence for Industrial Production. This ensures that students will receive a high-quality education from experienced and knowledgeable instructors.
  8. Demand for AI talent in industrial production: The field of AI is growing rapidly and has a high demand for talent. Graduates from this program will be well-positioned to enter this field and contribute to the advancement of the industry.
  9. Interdisciplinary approach: The program takes an interdisciplinary approach, which combines the expertise of information technologies and engineering, making it an ideal program for students interested in both fields.
  10. Real-world projects: The program includes real-world projects, that will give students the opportunity to apply their knowledge and skills in a practical setting. This will help them understand the challenges and opportunities of the field and prepare them for the professional world.
  11. Learning by doing: The program offers a hands-on approach to learning, which will enable students to apply the knowledge and skills acquired in the classroom to real-world problems. This approach is essential in the field of AI, as it is a fast-paced and ever-changing field.

The MSc program in Artificial Intelligence in Industrial Production offers a comprehensive curriculum that covers a wide range of cutting-edge topics in the field of AI for industrial production. With a focus on real-world applications and hands-on experience, students will gain a deep understanding of the latest trends and advancements in this exciting field. From machine learning and data analytics to the design of digital twins, autonomous mobile robots, and more, the following list of courses provides a glimpse into the program’s curriculum and the comprehensive education that students will receive. The structure of our MSc program can be seen here.

Major course units

Machine Learning and Data Analytics in Industrial Production 3D Scanning Systems
Design of Digital Twins Virtual Reality in Production Systems
Autonomous Mobile Robots Knowledge Bases in Design
Collaborative Robotic Systems Generative Design and Topological Optimization
Cognitive and Social Robotics Digitalization of Supply Chains
Lean and Agile Production Green and Digital Transformation
3D Printing Technologies Security of Industrial Networks
Augmented Reality in Production Systems Integrated Project on AI in Industrial Production (4 semesters)

Machine Learning and Data Analytics in Industrial Production

This course covers a wide range of topics in the field of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. It also focuses on the practical applications of machine learning and data analytics in industry, with a specific emphasis on cloud AI. The course is designed to equip students with the skills and knowledge necessary to analyze complex data sets, make predictions, and use these insights to drive positive change in the industrial production sector. Read more.

This discipline is included in this master program to facilitate the analysis of data obtained from the various equipment and operations in the production processes, data collected in various ways and stored in the cloud. The technologies necessary for data collection and preparation are taken into account, as well as those that help with their analysis (correlations, regressions, classifications, groupings). Other forms of artificial intelligence, from the category of neuro-symbolic models, are also applied in this field, especially for operating with small data sets and for assisting in decision-making. Knowing what data to collect for each process is essential, as is the most appropriate machine or deep learning models for each process, and how to interpret the results. Also, the process of adopting artificial intelligence in the enterprise (step-by-step) must be known. Creating the ability to optimize processes by uncovering less visible aspects and superior understanding of process capability and maturity are expected outcomes.

Design of Digital Twins

The course is designed to provide students with a comprehensive understanding of the creation and implementation of digital replicas of real-world systems. The focus is on learning how to use digital twins to monitor and optimize physical systems, improving their performance, and making data-driven decisions. This course also covers the use of cloud AI in digital twin design, enabling students to understand the benefits of using the cloud to enhance the functionality and scalability of digital twins. The curriculum includes hands-on projects and case studies to give students practical experience in the design and implementation of digital twins. Read more.

A digital twin is a digital representation of an intended or actual physical product, system or process that serves as its indistinguishable digital counterpart for practical purposes such as simulation, integration, testing, monitoring and maintenance. Digital twins are essential to contextualize the collected data. They are based on I-IoT, virtual models (including 3D graphic models), simulations. Machine learning models are extremely useful in relation to predictive maintenance. But without understanding which data must be collected at the level of equipment and technological installations specific to industrial production, how they can be collected and under what conditions, it is impossible to apply artificial intelligence models for various analyses. The digital twin is a central element in concepts that deal with the ever-increasing challenges of shorter production cycles, individualized products, massive cost pressure and shorter development times. In order to develop and execute in these framework conditions high-quality products with adequate sustainability and efficiency, the traditional “trial-and-error” method of physical construction of models and prototypes is still valid only conditionally. On the digital twin, new ideas, concepts and innovations can be tested through simulations and virtual commissioning. If the digital twin is correctly composed in unitary concepts and continuously updated, not only simulation in the preliminary phase, but also controlled intervention during the production regime is possible. This “real-time capability” opens up new possibilities for use in smart factories. In addition to time and cost savings, in particular the flexibility of the production facilities as well as the load capacity of the facilities are in the foreground.

Autonomous Mobile Robots

This course is focused on exploring the implementation of autonomous mobile robots in industrial production settings. Students will gain hands-on experience in programming and operating these robots, as well as understanding the various challenges and limitations involved in deploying them in real-world environments. The course will delve into topics such as robot localization and navigation, perception, mapping, and control systems, as well as how these technologies can be integrated and optimized for use in industrial applications. With a strong emphasis on practical skills and real-world examples, students will gain a deep understanding of how to design, develop, and deploy autonomous mobile robots to drive innovation and improve efficiency in industrial production. Read more.

Autonomous mobile robots are present in industrial enterprises for logistics operations. Autonomous navigation in dynamic environments involves both a specific sensory architecture and navigation and trajectory planning algorithms. Data comes from integrated laser scanners, 3D cameras, accelerometers, gyroscopes, radar systems and more to help generate the most effective decisions for each situation. The artificial intelligence algorithms encountered in such systems are from the area of automatic learning, reinforced learning, fuzzy logic, but also those specific to the automatic mapping of the environment, etc. Aspects of optimizing the design of these robots using evolutionary algorithms are also needed, as well as agent-based modeling. Deep learning models (including based on neural networks) are also used for environment recognition. In factories based on Industry 4.0 technologies, mobile autonomous robots are key components, including for the application of advanced production concepts such as agile and lean production.

Collaborative Robotic Systems

This course provides a comprehensive education on the implementation of robotic systems that work in collaboration with humans. This course focuses on the latest technologies and best practices for integrating robotic systems into various industrial settings, such as manufacturing, and logistics. Students will learn about the key concepts and techniques for creating effective human-robot collaboration systems, including motion planning, control, and human-robot interaction. Through hands-on projects and real-world applications, students will gain a deep understanding of the design, programming, and deployment of collaborative robotic systems, preparing them for careers in this exciting field. Read more.

A cobot or collaborative robot is a robot intended for direct human-robot interaction in a shared space or where humans and robots are in close proximity. Cobots are still industrial robots in fact, but with certain design specificities and replace traditional industrial robots where human-robot contact is required during production. Artificial intelligence models are applied in many cases specific to cobotic applications, from multimodal human-robot interfaces, to optimizing the behavior of robot joints in contact with the operator, to optimizing human-robot synchronous operations or to intervention in unstructured spaces.

Cognitive and Social Robotics

The course focuses on the development of cognitive capabilities in social robots that can interact and cooperate with humans and other robots in various settings. Students will learn about the cognitive and social capabilities required for robots to function effectively in human environments, as well as the latest technologies and algorithms for achieving these capabilities. Topics covered include natural language processing, computer vision, decision-making, and social signal processing. With a hands-on approach, students will have the opportunity to apply their knowledge to real-world problems and gain practical experience in developing cognitive and social robots. Read more.

Cognitive robotics is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will enable it to learn and reason about how to behave in response to complex goals in a complex world. Social robotics is a subfield in cognitive robotics, applicable to use cases where human-robot interaction is deep. Currently, as a result of developments in personalized manufacturing, but also in the context of the development of resilient production systems, industrial robotics systems (especially collaborative ones, but not only) integrate components from the area of social robotics (see the production of Tesla, Toyota , etc.). This category also includes virtual assistants (robot-kiosks or chatbots based on NLP and QAM algorithms) to assist operators in small or customized series production. From the perspective of this discipline, the understanding of the various AI technologies incorporated in the architecture of a social robotic system will be approached, up to the level of communication with hardware elements, specific and general languages for the creation of human-robot dialogue, the way of integrating specialized libraries in this sense (including from the NLP area), etc., but also forms of interaction in the case of human-robot remote collaboration.

Lean and Agile Production

The course is focused on teaching the principles and practices of Lean and Agile methodologies for the optimization of industrial production processes. The course covers topics such as value stream mapping, inventory management, continuous improvement, and Lean product and process development. Students will learn how to apply Lean and Agile concepts to real-world production scenarios and how to effectively lead and implement change in a production environment. The objective is to equip participants with the skills necessary to increase efficiency, reduce waste, and improve overall production performance. Read more.

Justification: Lean manufacturing:. In the supply chain and production, lean principles analyze the steps in detail to understand where there is still non-value in order to reduce or eliminate it. In lean manufacturing all contributors to the production chain must operate “lean and mean” for the process to function properly. Thus, data on the operation of each operation (times, process parameters, etc.) are required. Agile manufacturing: While lean manufacturing focuses on eliminating areas of no value, agile manufacturing focuses more on the intelligent use of current resources and ensuring that the organization has the right data to implement changes in production. Accurate data is vital to making changes in production. By being agile or flexible, a company has the potential to adjust its manufacturing process – but more importantly and applicable to logistics, making changes based on evolving changes in the industry. To optimize agile / flexible production, the collected data can be analyzed with specific artificial intelligence models, especially evolutionary algorithms, but also machine learning models. The major objective pursued is to prepare the conditions for the generation of data on the streams of added value creation in the primary and secondary processes.

3D Printing Technologies

This course will focus on exploring the advancements and applications of 3D printing in various industries. The goal of this course is to provide students with a comprehensive understanding of the capabilities and limitations of 3D printing technology and how it can be utilized in various industries for efficient and cost-effective production. Students will learn about different types of 3D printing technologies, including Fused Deposition Modeling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS). The course will also cover the materials used in 3D printing and their properties, as well as design considerations for 3D printing and post-processing techniques. Students will also learn how AI can enhance the design, production, and optimization of 3D printed objects. In this area, topics include AI-powered design tools, cloud-based simulation, and machine learning algorithms for material selection and print optimization. The course will provide hands-on experience with state-of-the-art 3D printing software and hardware, and students will develop a deep understanding of how AI can revolutionize the field of 3D printing. Read more.

3D printing technologies help generate and collect data for the optimal design of mechanical products. Moreover, by combining 3D printing with generative design algorithms, it is possible to design innovative products and components, optimized in relation to the various materials used and loads to be supported. Machine learning models can be used to monitor and adjust the 3D printing process to correct errors in real time, considering the use of various innovative materials in the construction of products or prototypes. Without a deep understanding of the additive manufacturing technology process and the specifications of the materials used, developing machine learning models are useless. In the 3D printing process, artificial intelligence helps maintain the material properties of complex alloys such as titanium, carbon and other metals. The resulting models can be used for predictive maintenance. Machine learning even helps manufacturers improve spare parts and predict when to replace them. AI has other advantages when it comes to 3D printing, including the ability to analyze an object before the process begins and predict part quality. The use of machine learning algorithms also improves the fastening process and reduces waste in manufacturing. Machine learning in 3D printing is also applied to automatic monitoring of 3D printed parts. By integrating image processing and camera data, machine learning algorithms can detect defects during the printing process and help repair them without human intervention. AI technology can also help reduce the cost of reprinting parts. Another economic application of artificial intelligence in 3D printing is the guarantee of high-precision prints and quality of execution. The application of modern technologies for the rapid prototyping of mechanical components and products is an objective pursued by this discipline. By automating processes, AI can help eliminate human error and improve 3D printing performance. The fusion of AI and 3D printing has a number of benefits for manufacturing and quality control. It also helps accelerate the rise of Industry 4.0 and the Industrial Internet of Things.

3D Scanning Systems

The course focuses on 3D scanning technologies and the integration of artificial intelligence into these technologies. Students will learn about the various types of 3D scanning systems, including laser scanning and structured light scanning, and how they are used to capture and create digital representations of physical objects. They will also explore how AI algorithms can be applied to 3D scanning data to enhance the accuracy and efficiency of the scanning process, as well as to analyze and manipulate the resulting 3D models. Read more.

3D scanning systems are systems intended for the collection of massive data associated with various objects, in order to apply (reverse) reconstruction engineering. 3D laser scanning is also used to measure vibrations in dynamic systems in industrial production. Once this massive data is collected, machine learning models are used for preventive maintenance. 3D scanning is also used to collect data in quality control, which can then be used in artificial intelligence models for analytics (typically machine learning models). 3D robotic scanning can be deployed in in-line or near-line factories to collect high-resolution 3D measurement data. 3D data contains a depth of information beyond what 2D vision sensors capture. There are algorithms capable of reconstructing a 3D map from multiple 2D images, but the resulting 3D map tends to be less accurate.

Virtual Reality in Production Systems

The course is focused on exploring the use of VR technology in industrial production. Students will learn how VR can be used to enhance production processes, including prototyping, training, and design visualization. They will also study the potential of VR to improve collaboration and communication within production teams. The course will cover the technical aspects of VR and its integration with existing production systems, as well as the development of VR applications and solutions. Students will gain hands-on experience in using VR tools and software to design and implement virtual production environments. Additionally, the course will address the challenges and opportunities associated with the adoption of VR in industrial production and its impact on the future of work. Read more.

  Virtual reality in the context of this study program has a strong connection with the design of digital twins. Virtual reality systems are augmented with sensors to retrieve data or extract information from the virtual environment based on operators’ reactions. Among other things, without this data, systems used in industrial production such as exoskeletons or the ergonomic design of systems would be much more difficult to achieve.

Knowledge Bases in Design

The course is focused on the use of knowledge bases and expert systems in the field of product design. It covers topics such as the creation, representation and maintenance of design knowledge, as well as the use of these systems to support decision-making and improve the design process. The course also explores the application of artificial intelligence and machine learning techniques to the development of intelligent design systems. Students will learn about the benefits and limitations of using knowledge bases in design and will develop practical skills in the use of knowledge-based systems through hands-on projects and case studies. Read more.

 KBE (knowledge-base engineering) systems intend to capture product and process information to enable companies to replicate or model engineering processes. The model will then be used to automate parts of the process or all of it. The product model serves as a computer representation of the design process. In addition, models can contain physics-based analysis, databases, spreadsheets, legacy programs, and cost models, essentially other information outside of its environment. In this discipline students will understand how to create knowledge from various sources, how to validate it, how to represent it, how to generate knowledge-based inferences and how to create the ability to explain and justify the use of knowledge for various contexts. Students will come into contact with various software tools for engineering and knowledge base management in the context of product design (eg Protégé, PLTool, Prodigy, myPDDL).

Generative Design and Topological Optimization

The course is a cutting-edge educational program that focuses on the use of advanced algorithms and artificial intelligence to design more efficient and sustainable products and structures. In this course, students will learn about generative design, a process in which algorithms generate multiple design options based on input parameters, and topological optimization, which uses mathematical models to identify the optimal design from the generated options. The course will also explore the integration of AI in product design and the impact it has on sustainability, manufacturing, and the overall production process. The program provides hands-on training and real-world applications, allowing students to gain practical experience in the use of AI in the design process. Read more.

Generative design is a trend in optimal product design. Generative design is an iterative design process that involves a program that will generate a certain number of outputs that satisfy certain constraints and a designer that will fine-tune the feasible region by selecting a specific output or changing input values, ranges, and distribution. Evolutionary algorithms are applied in the composition of CAD systems for generative design. Specialists must know the process of applying these algorithms in constructive design and quantitative optimization. Generative design uses artificial intelligence and machine learning to transform tedious engineering design processes into a sophisticated yet natural interaction between computer and engineer. The main part of topology optimization and simulation is handled automatically by the computing unit. Additionally, feedback loops are shortened by lowering barriers to design. As a result, the engineer has more room to tackle challenges that require “common sense” or cannot be solved by computational systems.

Digitalization of Supply Chains

This course focuses on the digitalization of supply chains, exploring how new technologies, such as artificial intelligence and the Internet of Things, are revolutionizing the way goods and services are delivered to consumers. The course covers the key concepts and strategies for modernizing supply chains, including advanced data analytics, real-time tracking and monitoring, and the integration of digital technologies across the entire supply chain network. Through a combination of lectures, case studies, and hands-on exercises, students will gain a deeper understanding of how to design, implement, and manage digital supply chains that are efficient, secure, and sustainable. Read more.

Customer-supplier chains (internal and inter-company) are essential in value chains. The collection of data from all these processes allows the optimization of business systems through the use of artificial intelligence models, especially models from the category of automatic learning, but also those from the category of evolutionary algorithms, fuzzy logic, and those based on agents. The major objective pursued is the creation of capabilities on internal and external supply chains towards connected factories in preparing conditions for data generation on value-added streams in primary processes.

Green and Digital Transformation

This course explores how the integration of AI and other digital technologies can drive sustainable and efficient production processes while reducing environmental impact. The course covers topics such as energy-saving techniques, eco-friendly production methods, and the use of data analytics to optimize production processes, as well as the social and ethical implications of the transition to green and digital production. Students will learn how to analyze and design sustainable production systems, and how to evaluate the potential impact of different digital solutions on both the environment and the bottom line. Read more.

The aim is to prepare an industrial enterprise to know how to adapt its business models and organizational culture in order to use digital technology and digitization to create new directions of added value and meet the requirements of circular economy and orientation on new economic models driven by resilience, life cycle, servitization, shared resource use, etc.). A major objective pursued is to create the conditions to allow a company in the industrial zone to reach the level where the adoption of AI can bring added value.

Security of Industrial Networks

This course focuses on the security of industrial networks, including the protection of communication systems and data transmission in industrial environments. The course covers topics such as network architecture and protocols, threat analysis and risk assessment, as well as various methods for securing industrial networks, such as firewalls, access control, and encryption. The course also explores the integration of AI and machine learning techniques for real-time monitoring and threat detection in industrial networks. Upon completion, students will have a comprehensive understanding of the challenges and solutions for securing industrial networks and the role of AI in enhancing network security. Read more.

Justification: This discipline covers user-level topics such as: switching and monitoring; address allocation in the industrial network; routing and firewall functions; VLAN-separated production networks; Network Address Translation (NAT); Virtual Private Networks (VPNs); encrypted communication; authorization and access control; scalability through group and role concepts; how a PKI works; management of digital certificates. Machine learning models are used to improve security performance. Specialists also need to prepare themselves in understanding how to train models based on neural networks to deal with adversarial attacks that endanger the security of industrial networks, especially I-IoT.

Augmented Reality in Production Systems

This course focuses on the integration of Augmented Reality (AR) technology in industrial production systems. Participants will learn about the benefits of AR in enhancing productivity and quality, as well as its potential for creating a safer and more efficient production environment. Topics covered include the latest AR tools and applications, how to design and implement AR systems, and the role of AI in optimizing AR technology. By the end of the course, participants will have a solid understanding of AR in industrial production and its impact on the future of manufacturing. Read more.

Augmented reality (AR) is the process by which it is possible to superimpose a virtual reality on top of the real, concrete world observed with the naked eye. The applications of this technology can be found in many fields of activity, including in the field of industrial production, to assist operators in maintenance operations, in the use of equipment, in inspection operations, in assembly operations, etc. The driver for the adoption of AR in industrial manufacturing is the need for operators capable of agile operation in complex workloads. An industry where neglecting a small detail can be fatal (eg aircraft manufacturing), with AR an amazing 96% reduction in inspection time for already built components can be achieved. Inspection time is reduced from 36 hours to just 90 minutes. Of course, this is an extreme example, but a drop of at least 25% is typical for industrial production. In order to realize AR solutions in the industrial environment, there is a need for the development of complex, detailed 3D models of physical objects, there is a need for 3D simulation of the functioning of the systems and their integration in the AR technology architecture. In the context of this discipline, students will learn to be users of platforms that bring AI to AR for a range of benefits. For example, they will be able to use canonical AR SDKs (eg ARKit and ARCore) to place and manipulate objects in scenes, combine data from a device’s sensors to build the 3D world, track motion, render objects digital and to mediate interactions between digital and physical content. Also, Core ML and TensorFlow Lite are AI frameworks for mobile devices that provide control over the input and output of models and allow developers to input their own custom models, which are then trained to perform specific tasks for AR applications. The most common way to combine AR and AI models is to take images or sounds from a scene, run that data through a model, and use the model’s output to trigger effects within the scene. AI models are applied within AR and for use cases such as sound recognition, text recognition and translation, dynamic object position and orientation estimation, semantic segmentation and occlusion, object detection, scene or image labeling, etc.

Integrated Project on Artificial Intelligence in Industrial Production (4 semesters)

The project for the MSc program in Artificial Intelligence in Industrial Production is designed to be a comprehensive exploration of the application of AI technologies in industrial production processes. It is designed to be a 4-semester program that provides students with an in-depth understanding of how AI can be leveraged to improve productivity, efficiency, and competitiveness in manufacturing and other industrial processes. Students will have the opportunity to work on real-world projects, using state-of-the-art tools and technologies to develop and test AI-powered solutions to real industrial problems. Read mode.

The relevance of this project is based on the growing demand for advanced technologies in industrial production and the increasing importance of Artificial Intelligence (AI) in optimizing processes and enhancing efficiency. By undertaking a 4-semester project in AI in industrial production as part of a Master program, students will be able to acquire the necessary skills and knowledge to effectively apply AI in real-world industrial settings. The project would involve researching and developing AI models and applications that can improve various aspects of industrial production, such as predictive maintenance, quality control, energy management, and supply chain optimization. Additionally, the project would involve exploring the challenges and limitations of AI in industrial production and developing strategies to overcome these challenges.

Graduates of the MSc program in Artificial Intelligence in Industrial Production can expect to pursue a wide range of career opportunities in the field of AI and industry. Here are a few examples:

  1. AI Engineer: Graduates can work as AI engineers, using their skills and knowledge to design and implement AI solutions in various industrial processes and systems.
  2. Digital Twin Designer: Graduates can work as digital twin designers, using their skills in AI and industry to design and create digital replicas of industrial systems, processes, and products.
  3. Digital Twin Engineer: Graduates can work as digital twin engineers, using their knowledge of AI, generative design, agile and lean industry, and digital twins to develop and implement AI-powered digital twin solutions for various industrial applications.
  4. Virtual Reality Developer: Graduates can use their skills in virtual reality and augmented reality  to develop VR simulations of industrial systems and processes, creating digital twins that can be used for training, design, and optimization.
  5. Digital Transformation Specialist: Graduates can play a crucial role in driving digital transformation in various industries, using their skills in AI and industry to optimize processes, streamline supply chains, and create smarter and more efficient systems through the use of digital twins.
  6. Autonomous Systems Engineer: Graduates can specialize in autonomous systems, designing and developing autonomous mobile robots, collaborative robotic systems, and other AI-powered industrial systems.
  7. Industrial Data Scientist: Graduates can work as industrial data scientists, leveraging machine learning and data analytics, as well as knowledge bases in design to drive insights and optimization in industrial processes and systems.
  8. Green and Digital Transformation Manager: Graduates can take on leadership roles in driving green and digital transformation in various industries, using their knowledge of AI, agile and lean industry, security of industrial infrastructure, and sustainable technologies to create a better and more sustainable future.
  9. Advanced Manufacturing Engineer: Graduates can work as industrial engineers in manufacturing, leveraging their expertise in generative design, 3D printing, and 3D scanning to drive innovation and improvement in manufacturing processes.
  10. Design Optimization Specialist: Graduates can apply their skills of knowledge bases in design and generative design to optimize product design and manufacturing processes.
  11. Robotics Engineer: Graduates can work as robotics engineers, designing and developing AI-powered robotic systems for various industrial processes, such as assembly, inspection, and material handling.
  12. Collaborative Robotics Specialist: Graduates can specialize in collaborative robotics, designing and developing robotic applications with robots that can work alongside human operators in a safe and efficient manner.
  13. Cognitive Robotics Engineer: Graduates can work in the field of cognitive robotics, developing AI algorithms and systems that enable robots to understand and respond to their environment in real-time.

These are just a few examples of the career opportunities available to graduates of the MSc program in Artificial Intelligence in Industrial Production. With a strong background in AI and industry, graduates can expect to pursue careers in a wide range of roles that drive innovation and progress in the field.

The academic year starts on October 1. The master program runs for 4 semesters (2 academic years). The first 3 semesters have classes, labs, and project work. The last semester is dedicated to the preparation of the dissertation project. Classes are scheduled for the evening, from 17:00 to 21:00, such that students can have a job in the meantime. Classes are run on the university’s premises. Because this master program requires access to various hardware technologies, it cannot be organized in an online format.

The admission exam consists of an interview with the Admission Board, usually face-to-face. This is scheduled in July and in September. The dates for admission are shown on the web page of the faculty here. If the information is not translated into English, and if you are not a Romanian language speaker, please call +40-264-401611 to ask for support or write a message to the contact email from this website.

Candidate registration is done online / onsite. The online registration procedure is displayed on the website of the university. International students must contact the International Relations Office for more information.

For 2023 we organize admissions in September. Students must prepare in advance a file with specific documents. For international students please also consult this brochure.

The documents required to register for the admission exam are:

  • Documents completed electronically:
  • 1. Enrollment application – completed online. Based on it, the registration form will be generated, in the electronic file of the candidate.
  • Documents scanned in electronic format:
  • 1. Bachelor’s degree (or equivalent) or graduation certificate (only for graduation from the current year).
  • 2. Diploma supplement for previous promotions.
  • 3. Declaration on own responsibility regarding the number of years in which the candidate has benefited from the budget allowance for master’s studies in Romania (download from the platform).
  • 4. Birth certificate.
  • 5. Identity card.
  • 6. Marriage certificate (if applicable).
  • 7. Proof of payment of the registration fee (receipt or payment order) or supporting documents regarding the exemption from the fee.
  • 8. Medical certificate, issued by the family doctor or the territorial dispensary or by enterprise, as the case may be, to which they belong. Candidates with chronic conditions will present a medical certificate addressed by the county medical commissions for school and professional guidance, in which it will be mentioned in the manner express the degree of deficiencies, their location, according to the medical criteria of school guidance and professional. Failure to declare these conditions may attract consequences in force. The validity of the medical certificate is three months from the date of issuance according to the legislation in force.
  • 9. If applicable, documents proving the financing of the studies from the graduated university.
  • 10. Certificate of English Language Proficiency.

In the case of candidates presenting identity documents, studies, and medical certificates issued outside Romania, it is necessary to supplement the file with an authorized translation of them in Romanian, English, or French.

The tuition fee:

  • For Romanian citizens, there are allocated 20 study grants by the state.
  • For Romanian citizens that are in a position not accessing the grant, the annual fee is 3900 RON/year (about 780 €/year). This fee has a discount of 10% if it is paid once, at the beginning of the study program. The fee can also be paid at three equal rates, usually in October, January, and April of the respective academic year.
  • EU citizens pay the annual fee of 3900 RON/year (about 780 €/year).
  • Non-EU citizens pay the annual fee of 270 €/month (paid once for the first 9 months: 2430 €, and the remaining fee in rates, according to a schedule announced by the university each year). A 70 € is also required at the admission for the analysis of the application file.

Contact Us

We value your feedback and inquiries. If you have any questions, comments, or suggestions, please do not hesitate to reach out to us. Address: the Technical University of Cluj-Napoca, Department of Design Engineering and Robotics, B-dul Muncii 103-105, Cluj-Napoca, Romania Email: stelian.brad [at]

Alternatively, you can fill out the contact form here and one of our representatives will get back to you as soon as possible. We are always here to help and look forward to hearing from you!

Sincerely yours,

Prof. dr. Stelian Brad
program coordinator

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