Welcome to MECS
Material Engineering Center Saarland – Your bridge between material research and material engineering.
The Löhn Award 2019
Your bridge between material research and material engineering
The Material Engineering Center Saarland (MECS), centrally located on the university campus, was founded in 2009 by Prof. Dr.-Ing. Frank Mücklich and his team as a Steinbeis research center from the Materials Science and Engineering Department of Saarland University. The MECS is dedicated to a customized materials technology transfer in cooperation with regional, national and international companies.
Starting from compact feasibility studies for the rapid identification of the respective transfer potential, cooperation projects and also long-term strategic partnerships are developed. In addition, MECS offers various service measurements of material analysis and microstructure characterization.
The portfolio is completed by consultations on material development and digitalization including the use of machine learning. Customized training courses, failure analyses and expert reports are also part of the MECS offering.
We offer you the possibility of characterizing components or materials and provide literature research and briefings on selected topics.
We are also happy to realize shorter development projects for you. The project manager that we provide you with takes care of your project.
As a strategic partner we can also support you concerning exceptional questions as well as exceptional innovation projects.
In addition to the representation and analysis of surfaces, we also offer profile recordings to determine the layer system. We are not only at your side with the surface characterization, but can also change properties in a targeted manner.
Areas of application
17.04.2023 – 21.04.2023 – Online-Course: Deep Learning – Fundamentals and Applications
Artificial intelligence in machine learning using deep learning is becoming increasingly important for analyzing materials science data, especially image data. The training course offers a practice-oriented introduction to convolutional neural networks to...