Projects

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.

The project aims to explore the adoption of open-source platforms for monitoring production lines and to develop a solution for monitoring Balsnack waffle production line. The objective is to extend the solution to other production lines after successful validation. The project created an integrated production monitoring solution that combines machine vision-based quality control with inputs from sensors. Machine vision facilitates visual quality control and product identification. Product identification will automatically provide the system with preset values that will be compared with inputs from sensors.

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.

The Demonstration Project is designed to test the various configurations of advanced AI and Robotics technologies in 5.0 ROBOTICS’ manufacturing processes. This project focuses on evaluating various platforms for AI-driven anomaly detection, real-time parallel simulations, depth-sensing cameras, and AR visualization solutions. These technologies aim to enhance human-robot cooperation, minimize human errors, and optimize production processes. The insights gained will inform our decisions for future large-scale investments.

Smart Industry Centre (SmartIC) was created at Tallinn University of Technology (TalTech) and Estonian University of Life Sciences (Institute of Technology) in 2017 to improve collaboration in research and development and use of distributed infrastructure in the field of Industry 4.0 - robotics, mechatronics, additive manufacturing, product quality control and related fields of IT (especially artificial intelligence). In 2018, Institute of Technology of University of Tartu joined in collaboration (mainly in the field of robotics). In 2017-2020 several new labs were opened (FMS and Robotics Lab, Industrial Virtual and Augmented Reality Lab, Additive Manufacturing Lab/ ProtoLab, Computer Tomography Lab for quality control, etc). Several new ERF and H2020 funded projects were initiated and launched in 2017-2020.

Monitoring, recording and analysis of the correct parameters of production equipment at the prototype level using AI.

New methods for producing mat type material from wood springs will be developed based on current equipment and by complementing this with collaborative robot. Possibilities to form the wood spring mat with suitable gripping method will be researched to find an optimal solution to produce this type of packaging material. The found trajectories and kinematics by using the collaborative robot (Omron) will be later used as input for developing next generation of production machine. The generated trajectories and kinematics information from robot programs can be also optimized by using AI tools (Machine Learning) to find more optimized solutions (for trajectory and weaving) considering production speed, travelled trajectories, movement dynamics, created mat density etc. as an input parameters.

The development project of an artificial intelligence-based management system Chemi-Pharm focuses on organizing and visualizing the company's daily production logistics through a smart artificial intelligence-based management system, which contributes to increasing the production throughput and efficiency of production processes.

The aim of the project is to update the existing subject "Production Digitalization" (6 ECTS), modernize the content of the subject according to the latest rapid developments in the field. New development tools based on virtual (VR) and augmented (AR) reality are introduced on a significant scale, and applications based on them in the areas of processing and production.

A reconfigurable robot system is being developed to check the quality and correct function of machine vision IoT modules. Variations of IoT modules and different are different visual quality control reliability is 80-87%. To make and fix the products, we have designed and manufactured a multi-position special jig, which is controlled by a robot. In order to identify products, a database of IoT modules was created, with the help of which algorithms identify and select the product. Also, quality assessment works based on the same logic. Checked products are divided into two: OK and defective. Defective products are separated and determined to be the actual defect.

The European Commission is promoting various initiatives within the scope of digital strategy. It aims at several focused areas such as increasing training in digital skills, modernising education across the EU, and harnessing digital technologies for learning. From the educational perspective, teaching engineering-related topics, such as robotics and automation, often requires access to a lab environment where many resources can be used, demonstrated, and tested. However, the current COVID-19 pandemic has forced more than 1.5 billion students to stay home and access teaching and education through the internet and other digital resources. Although the transition to online can be done without many issues for teaching theoretical classes, the current e-learning tools, such as Zoom and Teams, are not sufficient to replace practical classes and direct experience in real labs. Some online platforms provide engineering tools, for example, Autodesk TinkerCAD for 3D and circuit design, but they do not represent an immersive physical engineering lab, and the contents on many subjects are missing, e.g. robot control and automation. VirLaDEE aims at giving access to physical engineering labs through their digital twins that will be available in an innovative online platform. These virtual lab facilities will provide a playground that is complementary to the existing learning methods delivering quality and inclusive engineering education through state-of-the-art virtual technologies.

DIH-World aims to accelerate the uptake of advanced digital technologies by European manufacturing SMEs in all sectors and support them in building sustainable competitve advantages and reaching global markets strengthening the capacities of regional DIHs, particularly in underepresented regions across Europe. As intermediaries of successful local SME digital transformation, DIH World, aims at providing DIHs, acces to harmonised tools, well proven technologies, effective methodologies, sound knwoledge, smart investment sources, rich training assets and overall a vibrant innovation environment. The final aim is to accelerate the matureness of DIHs and the development of their collaboration capabilities, and avoid a DIH divide due to lack of access to technologies, skills, networks, investment and infrastructures with special enfasis in underrepresented regions; so they can capitalise and leverage on the European DIHs Networks their resources and facilities for the benefit of their local SMEs. This will be achieved thanks to the: DIH-World platform, that will provide a full coverage of the services needed by the DIHs and the SMEs willing to identify the right DIH for them, the DIH-Academy that will provide the tools to train DIHs and bring them to the next level, Open calls for experiments, that will provide sufficient technological support to SMEs and midcaps. As well as with a broad geographical coverage, with more than 26 countries to be covered in Europe including specific activities to involve regional and national actors in the DIH network.