Agentic Reality Training in Skill Acquisition for Next-Generation Manufacturing.
Project Duration:
2025-09-01 – 2028-08-31
FUNDER:
Sweden's Innovation Agency (Vinnova)
PARTNERS:
AugmentedRealm, Hitachi Energy, Solme, Ekets Group, and Uppsala University (XRIA LAb).
The project focuses on capturing expert knowledge and delivering real-time, immersive guidance to new operators through XR headsets. Combining AI-based process analysis, multimodal data capture, and contextual XR interfaces, the system helps operators learn complex tasks directly on the shop floor—without requiring direct supervision. Key capabilities include voice-guided instructions, object recognition, and adaptive step-by-step support based on user actions and task context.
The goal is to accelerate training, reduce errors, and retain critical expertise in an increasingly automated industrial landscape. Designed for flexibility and scalability, ARTISAN offers a practical solution for improving workforce readiness across a range of manufacturing environments.
By integrating advanced AI and XR technologies, ARTISAN supports smarter, faster industrial learning and contributes to the digital transformation of Swedish industry.
AI-COMPETE
Coordinated Multi-Agent AI-Powered DEcision SupporT SystEm for Sustainable Manufacturing
Project Duration:
2025-09-01 – 2028-08-31
FUNDER:
Sweden's Innovation Agency (Vinnova)
PARTNERS:
University of Skövde, Volvo Penta, Scania, Daloc AB, Evoma AB and Uppsala University (XRIA Lab).
The project focuses on developing a multi-agent AI system that enables intelligent, real-time decision-making for production planning and energy optimization.
The system integrates reinforcement learning, digital twin simulations, and coordinated decision-making across multiple AI agents. These agents work together to align production schedules with renewable energy availability, reduce energy consumption, and resolve production bottlenecks—all while maintaining or improving productivity. A key ambition is to deliver a solution that is scalable, explainable, and easy to adopt—especially for small and medium-sized enterprises that often lack the resources for large infrastructure changes.
By combining advanced AI techniques with practical validation in real manufacturing settings, AI-COMPETE aims to accelerate the adoption of AI in the Swedish industrial sector and contribute to national and EU climate goals.
PUMA2025
Educating Machine Design with Extended Reality
Project Duration:
2025-09-01 – 2026-08-31
FUNDER:
Uppsala University
PARTNERS:
XRIA Lab and Division of Applied Mechanics.
This project develops an XR learning environment to teach machine design in the Mechanical Engineering program at Uppsala University. By integrating XR as a virtual lab, students can explore and interact with complex mechanical components—such as shafts, bearings, and gears—in realistic 3D contexts. The project aims to reduce cognitive load, improve understanding of design principles, and increase student engagement.
VFS2024
VFS: Verification for Collaboration project- Strategic partnership with Hitachi Energy
Project Duration:
2024-09-01 – 2025-08-31
FUNDER:
Uppsala Innovation Center
PARTNERS:
XRIA Lab, and Hitachi Energy.
The project aims to address the complex challenge of improving the training, service, and marketing of tap changers, critical components in transformers characterized by intricate mechanical parts and sophisticated thermal behavior leading to a system with multiphysics interactions. Tap changers' complexity makes them challenging to understand, maintain, and market effectively, leading to inefficiencies and higher costs. Considering the significance of transformers in transferring electricity, this collaboration will lead to the modernization of electricity transfer technology and have an impact toward green transition.
Immersive Technology for Educating Mechanical Engineers; HoloMech an Application in Mixed Reality.
Project Duration:
2023-09-01 – 2024-08-31
FUNDER:
Uppsala University
PARTNERS:
XRIA Lab and Division of Applied Mechanics.
This project developed HoloMech, a mixed-reality application for teaching Solid Mechanics in engineering education. By combining interactive 3D visualization and hands-on experiences with HoloLens, the project aimed to reduce cognitive load, improve students’ understanding of complex concepts, and increase engagement. HoloMech was implemented as a virtual lab in the Solid Mechanics course at Uppsala University and evaluated through both quantitative and qualitative methods.
Kaveh Amouzgar is an Assistant Professor in Industrial Engineering and Management at Uppsala University. His research focuses on the intersection of extended reality (XR), data analytics, and intelligent optimization for industrial applications, with a particular emphasis on developing AI-driven XR systems for human-centered manufacturing. He also teaches and supervises in areas related to XR, industrial analytics, simulation-based optimization, and digital transformation.
Jessica Lindblom is Associate Professor of Human‑Computer Interaction at Uppsala University, working within the Vi3 group at the Department of Information Technology. Her research bridges situated and embodied approaches to cognition and social interaction in both natural and artificial intelligence systems—especially in human‑robot interaction, UX, and distributed cognition. She applies these insights in professional settings such as manufacturing, agriculture, and transport. She is also recognized for her excellence in teaching and contributions as a pedagogical reviewer.
Matías Urenda Moris is an Associate Professor at Uppsala University, Sweden. He holds a B.Sc. degree in Automation Engineering, a M.Sc. degree in Manufacturing Management and a Ph.D. degree in Healthcare Engineering from the University of Skövde, Loughborough University, UK, and De Montfort University, UK, respectively. His main research area is Discrete Event Simulation for manufacturing and healthcare systems with emphasis on system modeling and analysis.
Thomas Schmitt received his M.Sc. degree in Industrial Engineering and Management from Uppsala University, Sweden. He is currently an industrial doctoral researcher employed by Scania in collaboration with Uppsala University, pursuing the Ph.D. degree in energy efficiency in production systems. His research interests include the economic and environmental performance analysis of production systems, particularly through simulation and data-driven modeling.
Featured
A novel XR-based real-time machine interaction system for Industry 4.0: Usability evaluation in a learning factory.
This study introduces an XR-based system that enables real-time interaction with industrial machines in a learning factory environment. By integrating extended reality with live machine data, the system supports operators in monitoring, control, and decision-making. The usability evaluation highlights its potential to improve efficiency, learning outcomes, and user experience in Industry 4.0 settings.
Read more!
HoloMech: An Extended Reality Tool for Supporting Student Motivation and Perceived Spatial Reasoning in Structural Mechanics
K. Amouzgar and M. Mousavi.
Virtual Reality, 2025 Read online
A novel XR-based real-time machine interaction system for Industry 4.0: Usability evaluation in a learning factory
K. Amouzgar and J. Willebrand.
Journal of Manufacturing Systems, 82:254–283, 2025 Read online
Augmented Reality for Machine Monitoring in Industrial Manufacturing: A Media Comparison in Terms of Efficiency, Effectiveness, and Satisfaction
T. Schmitt, P. Viklund, M. Sjölander, L. Hanson, M. U. Moris, and K. Amouzgar.
IEEE Access, 2025 Read online
Optimizing energy efficiency and productivity in industrial manufacturing: A simulation-based optimization approach with knowledge discovery
T. Schmitt, S. Olives Juan, K. Amouzgar, L. Hanson, et al.
Journal of Manufacturing Systems, pp. 748–765, 2025 Read online
Frequent and automatic monitoring of resource data via the Internet of Things
T. Schmitt, P. Sakaray, L. Hanson, M. Urenda Moris, and K. Amouzgar.
SPS2022, IOS Press, pp. 75–85, 2022 Read online
Augmented reality for machine monitoring in industrial manufacturing: framework and application development
T. Schmitt, P. Viklund, M. Sjölander, L. Hanson, K. Amouzgar, and M. U. Moris.
Procedia CIRP, 120:1327–1332, 2023 Read online
Education
We offer advanced courses integrating XR, project work, and optimization, open to Bachelor’s and Master’s students. Explore below:
Industrial Project with Extended Reality (10 credits, Advanced level)
In this course, students work on a full-scale XR project: designing, developing, and deploying an industrial application using VR/AR/MR. Topics include advanced project management (agile/extreme), ethical & inclusivity considerations, real-time data pipelines, XR tools & integration with industrial systems.
Course page
Project with Extended Reality (5 credits, Basic level)
An introduction to XR technologies where students undertake a hands-on project developing applications with VR/AR/MR. The course includes fundamentals of XR, real-time data acquisition, and collaborative project work with peers.
Course page
Production Systems Modeling and Optimization (5 credits, Advanced level)
This course covers mathematical and computational modeling of production systems, focusing on optimization methods, simulation, and decision-making to improve efficiency and performance in industrial settings.
Course page
Work With Us
PhD Position in Multimodal AI & XR
We are recruiting a fully funded PhD student to develop AI-driven XR solutions for knowledge transfer in manufacturing, focusing on multimodal data analytics, immersive learning, and operator support in collaboration with Swedish industry.
This is bold and this is strong. This is italic and this is emphasized.
This is superscript text and this is subscript text.
This is underlined and this is code: for (;;) { ... }. Finally, this is a link.
Heading Level 2
Heading Level 3
Heading Level 4
Heading Level 5
Heading Level 6
Blockquote
Fringilla nisl. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan faucibus. Vestibulum ante ipsum primis in faucibus lorem ipsum dolor sit amet nullam adipiscing eu felis.
Preformatted
i = 0;
while (!deck.isInOrder()) {
print 'Iteration ' + i;
deck.shuffle();
i++;
}
print 'It took ' + i + ' iterations to sort the deck.';
