Machine Learning Techniques in Metallographic Analysis and Alloy Development
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