Toivo Tähemaa

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.

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.