Machine learning

DL Segmentation of lath-shaped bainite – LM*

DL Segmentation of lath-shaped bainite – SEM*

Benefits of Machine Learning (ML)

  • Offers promising new possibilities in microstructure analysis
  • Automation of time-consuming and labour-intensive standard tasks
  • Evaluation of large, high-dimensional data volumes
  • Objectivity and reproducibility
    Basis for improved analyses of complex microstructures that were previously not possible

How we approach ML

  • Combination of computer science know-how with material science domain expertise
  • Taking a holistic view of ML: also taking into account all the steps involved in obtaining a microstructure image
  • This sets us apart from pure data scientists, who often only feed microstructure images into an ML algorithm without taking domain knowledge into account
  • Pay particular attention to the assignment of the ground truth for the supervised ML
  • Controlling and understanding all material science related aspects that provide greater leverage for success than optimization of ML parameters

Contact

Dr.-Ing. Dominik Britz

Deputy Head MECS Saarbrücken

+49 681 302 70540
d.britz@mec-s.de
LinkedIn

Adrian Thome, M.Sc.

Our service offered

  • Customised ML-based solutions for automated microstructure analysis
    • Focus on objective ground truth, e.g. through correlative microscopy
    • Holistic approach: metallographic boundary conditions required for successful ML implementation
  • Customised training (ML basics, ML in materials science and engineering, ML in metallography and microstructural analysis)

Where our ML approaches are successfully applied

In addition to standard everyday metallographic tasks, such as grain size determination, phase analysis, layer thickness measurement or fracture surface analysis, our ML approaches are used in particular for complex microstructural analyses for which no conventional solutions were previously available.

Segmentation of lath-shaped bainite in multiphase steels

Microstructure classification in heat-treatable steels

Classification of non-metallic inclusions

Classification of the carbon-rich second phase in dual-phase steels

Segmentierung ehemaliger Austenitkörner in Stahl

Reference projects

Joint stock company of Dillinger Hüttenwerke

  • Strategic partnership:
    Establishment of ML methods in microstructure analysis
  • Key figures:
    • 4 doctoral projects
      > 30 student theses
      > 20 national and international conference papers
      > 20 publications

MTU Maintenance

  • Project objective: Joint R&D project for the segmentation of thermally sprayed coatings using deep learning
  • Business value: automated, objective microstructure quantification as a basis for improved process-structure property correlations

MTU Maintenance report

 

 

Further areas of application

Correlative Microscopy

A correlative characterisation of microstructures serves as a benchmark for automated microstructure recognition and as a starting point for machine learning approaches.

Triboelectric characterisation

In addition to our microstructure analysis, we are able to carry out various triboelectric measurements (e.g. electrical resistance and friction coefficient in the mating test) under controlled conditions.

Failure Analysis

In the field of failure analysis, we are not only able to identify and analyse fracture soles but also to fully characterise components on a macro and micro scale.

Antimicrobial surfaces

Our antimicrobial metallic surfaces have already been used in 3 research projects with ESA & NASA on the ISS.

Welding technology consulting

We assess the suitability of materials for welding and make suggestions for the use of materials, welding consumables and welding processes

You are interested in working with us?

Feel free to contact us! We look forward to talking to you and finding out how we can help you with your project.