Mechanical Engineering

Machine design research addresses the challenges of modern mechanical engineering by developing systematic, model-based design methods and digital tools for product development. A central focus is the integration of modeling, simulation, and optimization into the design process to support performance-driven and resource-efficient solutions. Research themes include model-based design and manufacturing, simulation-driven engineering, axiomatic design theory, and the reuse of engineering data. These approaches aim to improve the quality of design decisions, shorten development cycles, and enable the creation of sustainable and innovative mechanical systems.

Machine Dynamics

Machine Dynamic Group bridges classical engineering methods with modern technologies. Our group is particularly interested in integrating multibody dynamics with computer vision, multiphysics modeling, and artificial intelligence to address complex engineering challenges. This interdisciplinary approach aims to advance the accuracy and efficiency of dynamic system simulations and improve real-world applications in automotive and mechanical systems.

We develop physics-based methods to analyze and control the dynamic behavior of mechanical systems. Their research focuses on multibody system dynamics, frictional contact, vibration, and vehicle dynamics, aiming to improve simulation accuracy and system performance.

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Tribology and design of machine elements

The research group focuses on tribology - friction, wear, lubrication and damage mechanisms - within mechanical systems. The group aims to enhance performance, reliability and sustainability of machine components, particularly in mechanical power transmission - within components such as gears, bearings and frictional joints. 

We integrate computational methods - including contact mechanics, finite element analysis, and machine learning - with experimental testing using over ten tribology and power transmission devices. The group continously develops custom tribology and power transmission testing devices and environments to better replicate real-world operating conditions. 

Through this multidisciplinary approach, the work contributes to the development of more energy-efficient, quieter, and longer-lasting machines, helping to reduce environmental impact.

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The Manufacturing Research Group at Tampere University develops advanced solutions for intelligent and sustainable manufacturing. Their work combines automation, optimization, simulation, and engineering design with technologies such as machining, robotics, AI, and digital modeling.

Key research areas include adaptive automation, hybrid manufacturing, integration of design and production, and the use of machine learning in process control and simulation. The team specializes in DED-Arc, ROS2-based systems, and multi-disciplinary optimization methods.

Led by Professor Eric Coatanea, the group collaborates with leading academic and industrial partners across Europe and beyond. With over €14 million in research funding, they actively contribute to numerous national and international projects such as ROBOT BOX and REINFORCE. Notable former projects where HYBRAM, COREMAN, and DIGITBrain.

The Mechatronics Research Group, led by Prof. Kari T. Koskinen at Tampere University, focuses on the smart design and control of complex machine systems. Key research areas include autonomous vehicles and robots, aircraft systems, and knowledge-based analytics for performance, reliability, and maintenance. The group integrates digital twins, AI, and simulation to improve machinery throughout its life cycle.

The group includes approximately 25 members—senior researchers, doctoral students, instructors, and assistants—and is part of the Mechanical Engineering study program, covering areas such as aircraft engineering, mechatronics, and reliability engineering. Their annual project volume is about €1 million.

Current projects include H2MAC (hydrogen-powered machines), Mastermine (autonomous mining systems), and AIRSHIP (autonomous flying ships), with funding from EU Horizon programs, Business Finland, and industry partners. The group was awarded the Tampere Innovation Award 2020 for the UNEXMIN underwater robot.

The Digital Design and Manufacturing Laboratory (D²M Lab) at Tampere University (ENS/TAU) pioneers research at the intersection of digital design and manufacturing engineering, artificial intelligence, and sustainable production. The lab investigates how nature-inspired design principles and data-driven intelligence can be integrated into bio-intelligent manufacturing systems to achieve circularity, adaptability, and resilience. Its overarching mission is to engineer digital manufacturing workflows capable of predicting and optimizing process–product quality while minimizing cost, energy use, and environmental impact. By merging materials science, perceptual intelligence, and computational intelligence, D²M aims to advance Europe’s transition toward regenerative, zero-waste industrial paradigms.

Under the leadership of Dr. Iñigo Flores Ituarte, Associate Professor and Academy Research Fellow in the research council of Finland, the D²M Lab develops Design-to-Manufacturing (D2M) workflows that connect perceptual intelligence (sensor technologies, IIoT), computational intelligence (digital twins, simulation), and artificial intelligence (machine learning, generative models) within a unified design–to-manufacturing framework. This integrative approach supports the creation of cognitive manufacturing systems that learn, adapt, and self-optimize in real time—transforming how products are designed, validated, and fabricated. The lab’s research portfolio spans advanced manufacturing processes, optimization-driven engineering design, and AI-enabled sustainability-by-design, driving the next generation of intelligent and environmentally responsible production systems.