Machine Learning Techniques in Metallographic Analysis and Alloy Development

Strukturbild Swerim

Conventional metallographic analysis often requires manual assessments of field experts and/or a high level of parameter tuning, even when using available software. Artificial Intelligence (AI) and Machine Learning (ML) methods have been used to allow computers to develop algorithms that mimic the human brain.This workshop aims to provide an overview of AI and ML advances within the field of metallography and show examples of available industrial tools for image analysis and material development.

This webinar is a part of Swerim’s research program in Metallography and Microanalysis (META) and hence free of charge for companies that are members in META and participants from Swerim. Others: SEK 800.


Registration (no later than November 13). Link to registration.


09:00–09:10 Opening: Welcome and Introduction to Swerim MRC program
Shirin Nouhi/Fredrik Gustavsson, Swerim

09:10–09:35 Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
Ignacio Arganda-Carreras, University of Basque Country

09:35–10:00 Metals characterization using deep learning image analysis
Sammy Nordqvist, SciSpot: MIPAR representative

10:00–10:10 Short break

10:10–10:35 ZEN – Open Ecosystem for integrated Machine-Learning workflows
Sebastian Rhode, ZEISS

10:35–11:00 Apeer – Open Cloud-based Platform for Image-Processing and Machine-Learning
Simon Franchini, ZEISS

11:00–11:10 Short break

11:10–11:35 Effective classification of microstructures via the application of machine-learning to EBSD data
Patrick Trimby, Oxford Instruments

11:35–12:00 Machine Learning for Alloy Development
Josh Green, Citrine Informatics

12:00–12:25 Overview of ESTEP webinar: Impact and opportunities of Artificial Intelligence in the Steel Industry
Shirin Nouhi, Swerim

12:25–12:30 Closing of the webinar

2020-11-17 09:00 to 12:30
Last registration date
Free of charge for members in the research program META. Non-member: SEK 800