Projects

Easy2reUse project will develop an intelligent reusable mask for healthcare workers, critical working groups and citizens. The mask will be sustainable, easy-to-breath, comfortable to wear long working hours, low lifetime cost, easy to clean, meeting the universal fit and standard requirements. User experiences of the mask will be closely studied in Finland and Spain. Cleaning and maintaining in the hospital environment and other environments are the essential part to make the mask reusable, fulfilling the cleanliness requirements. The market readiness research for reusable facemasks focuses on designing manufacturing processes that emphasize material sourcing, cost efficiency, and sustainability. A comprehensive understanding of economic feasibility, usability, and manufacturing efficiency is achieved by combining quantitative and qualitative analyses throughout the prototype development and testing phases. During these tests, facemask prototypes with integrated electronics are evaluated for functionality, durability, and compliance with current regulations and standards. Additionally, documentation for EU type-examination is prepared, and an internal quality control system is established and verified. The project explores best practices and challenges in production, regulatory compliance, and market entry by drawing insights from similar industries. Comparative case studies, life cycle costing (LCC), and assessments of market and technology readiness (MRL and TRL) guide the creation of scalable production plans. A preparedness plan is also developed for stockpiling, scale-up, and adoption by healthcare workers and the public during pandemics, applicable across European countries. The manufacturing process is designed to ensure production takes place in Europe, supporting regional supply chain resilience. Market readiness and cost-effectiveness are evaluated to create a comprehensive plan for rapid production and widespread market adoption.

The research project focuses on Industry 4.0/5.0 digital production technologies, which enable the development of new products also considering their production readiness to be significantly accelerated. The whole life cycle of the product is under consideration, from the creation of digital product models using 3D scanning, digital twins, and simulation technologies; rapid product prototyping through additive manufacturing technologies to integrate both mechanics and electronics; production based on the principles of lean manufacturing, quality control and product and production monitoring. As a result of the project, a prototyping development and demo centre/ experimental lab will be created in Virumaa College, which enables the development and production of complex and smart mechatronic products.

The Engineering Academy is a project initiated by the Ministry of Education and Research and funded by the European Social Fund, with the goal of improving the quality of engineering education and reducing the labor shortage in technical fields. The project is led by the Education and Youth Board and is joined by five higher education institutions. The Engineering Academy includes 22 engineering-related study programs, of which ten have been selected as priority focus programs for development. The project has three focus areas: • Increasing the number of applicants • Improving the quality of education and Increasing alignment with labor market needs • Reducing dropout rates The Technical University has set a goal to increase admissions in the field of engineering by 15% each year. To improve the quality of education, the action plan includes a significant expansion of project-based and problem-based learning, curriculum development, quality enhancement, and infrastructure upgrades. Additionally, lecturers’ training and the recruitment of teaching assistants are planned. To reduce dropout rates, individual support for students will be increased, both during the first year and when completing their final theses. First-year students will also be offered additional mathematics courses. The goal is to significantly reduce dropout rates and increase the number of graduates.

CERN is an international scientific centre that unites 24 member states and 10 associated states. Their mission is to conduct experiments in high and low energy particle physics and develop novel technologies and IT solutions for medicine, AI and quantum computing. Since 2023, Estonian research at CERN has been coordinated by the CERN consortium formed by the National Institute of Chemical Physics and Biophysics (KBFI), the University of Tartu (TÜ) and Tallinn University of Technology (TalTech) whose members participate in LHC CMS, WLCG, FCC, CLIC, iFast, AMBER, Cloud and CCC experiments. Cutting-edge research at CERN fosters internationalisation and advancement of Estonian science, technology and IT. CERN actively trains students, doctoral candidates, teachers and engineers, contributing to the next generation of Estonian scientists. This application presents the operational and infrastructure plan for CERN scientific infrastructure and consortium until the end of 2029.

Customization requirements in modern manufacturing demand a closer collaboration between operators and automated technologies, leading to a novel Human-Robot Collaboration (HRC) and interaction (HRI) paradigm aimed at augmenting human capabilities in the workplace. Digital Twin (DT) and Immersive technologies (XR) support the inclusion of the human operator in simulation-based interfaces intended for safe, efficient, multimodal, and adaptive HRI. The design and implementation of these interfaces are not yet adequately addressed. This project aims to define what is the current approach to the requirement definition for DT and XR by analyzing the potentials and challenges of the adoption of DT interfaces and other types of input methods in the HRC context, their allocation in the Human-Computer Interaction (HCI), and the state of current experimental research in this field as bringing the human back to the loop bring us the Industry 5.0 concept within industrial and healthcare domains.

For the development of the field of human-robot cooperation, a development and test laboratory for collaborative robotics and process-adaptive devices will be created based on the Virumaa Innovation Centre of Digitalisation and Green Technologies, Virumaa College, and Taltech's headquarters. In the created laboratory, it will be possible to study the psychological aspects of human-machine co-creation, workplace design, etc. In addition, adaptability of equipment/physical systems to production processes. All this in both real and digital (augmented reality) environments, based on the Industry X.0 concept.

This project focuses on the development and integration of Extended Reality and existing digital tools to support advanced engineering education in manufacturing. The purpose of the XREN is to bring back the results of research activities in the field of digital manufacturing to the engineering students. Since, process modelling, analysis, virtual and augmented reality, as well as the role of the human workers in the factories have been among the main research topics in manufacturing. Therefore, the project aims to experiment, test and validate learning approaches based on XR technologies, focusing the attention on: • Development of a VR environment to support learning activities in the area of manufacturing, namely in the design and analysis of manufacturing systems. • Development of an AR approach to support learning activities in the area of mechanical design and maintenance process. • Develop approaches and methodologies to analyze and evaluate the learning mechanisms based on these technologies.

The objective of the project is to conduct a feasibility study on welding in space in cooperation with the European Space Agency and the company Moliri OÜ. This includes the analysis of target materials and joints, a study of human user requirements and control methods, a literature review and comparative analysis of welding processes suitable for the space environment, and the preparation of a preliminary plan for testing, qualification, and validation of welding in space.

One of the challenges that this demonstration project sought to solve was the robotic production of acoustic panels to reduce the time needed to manufacture these. The demonstration project tested the robotic screwing process for acoustic panels of different configurations, taking into account the different dimensions of the panels, the number of screws to be installed and their installation. This demonstration project linked robotics (UR10 collaborative robot), automation (screw feeder, automatic screwdriver, intelligent jig), AI tools (digital twin workstation, machine vision tools, simulation of robot work trajectory). The solution took into account the company's specific production processes and the requirements for acoustic panel parameters, as well as the possibilities for implementing fully automated production in the company or for deployment in other companies in the sector. Results of the demonstration project: realization of the product assembly operation, development of the assembly cell concept, selection of a suitable robot for the assembly operation, selection of the tools, simulation and testing, data collection, analysis and verification of the results.

The novelty of the demo project is the joint handling of non-form-retaining textile materials and solid frame materials by intelligent and flexible robot/assembly robot stations. The active control method detects the performance of the robot’s working organs and checks the quality assurance data with the real ones. Through the analysis and pattern recognition of the collected data, artificial intelligence must be able to make changes to the drafting process. During testing, data will be collected and algorithms created for artificial intelligence to make decisions.

In the course of this project, the concept and prototype of a device for measuring the shadow line of real 3D objects was created and tested both in a test and real environment. The system is used to prepare the most suitable transport packaging for the safe packaging of the objects in question. At present, the production process contains too much manual labor and the level of automation needs improvement. The created system allows to reduce the volume of manual work and also to increase the quality of work (avoid errors and increase accuracy).

The project "Testing of the Applicability of an AI-based Optimization Model for Production Processes using a Digital Twin of the Factory" focuses on creating digital models of the factory and using AI to identify and mitigate bottlenecks in production processes. By using Siemens Plant Simualtion software, the project simulates the factory's actual process times to create a digital twin, enabling production optimization without disrupting real manufacturing. The project utilizes an AI optimization model based on machine learning and data analysis to improve production throughput and resource efficiency.