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.
Services
Service measurements
We offer you the possibility of characterizing components or materials and provide literature research and briefings on selected topics.
Feasibility studies
Projects
We are also happy to realize shorter development projects for you. The project manager that we provide you with takes care of your project.
Strategic partnerships
As a strategic partner we can also support you concerning exceptional questions as well as exceptional innovation projects.
Consulting
Trainings
Examinations
Microscopy
Surface analysis
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.
Material analysis
Areas of application
Correlative microscopy
Machine learning
Triboelectrical characterization
Damage analysis
Antimicrobial surfaces
News
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...
28.03-29.03.2023 – Onlinekurs: Deep Learning – Synthetische Erzeugung materialwissenschaftlicher Trainingsdaten
Für die Auswertung materialwissenschaftlicher und werkstoffkundlicher Daten wird Künstliche Intelligenz in Form des Maschinellen Lernens mit Hilfe von Deep Learning immer wichtiger. Während die Verarbeitung reiner Bilddaten mittels standardisierter Techniken und...
21.03-22.03.2023 – Onlinekurs: Deep Learning 2 – Fortgeschrittene Techniken und Anwendungen
In dieser Vertiefungsfortbildung bieten wir Ihnen einen praxisorientierten Einstieg in neuronale Netzwerke zur Analyse hybrider materialwissenschaftlichen Daten, d.h. Daten, die z.B. Bilddaten mit tabellarischen Daten kombinieren oder Bilddaten unterschiedlicher...
Network & Partners
