Autonomous Interpretation of the Microstructure of Steels and Special Alloys

Abstract:

The main objective of presented research is an attempt of application of techniques taken from a dynamically developing field of image analysis based on Artificial Intelligence, particularly on Deep Learning, in classification of steel microstructures. Our research focused on developing and implementation of Deep Convolutional Neural Networks (DCNN) for classification of different types of steel microstructure photographs received from the light microscopy at the TU Bergakademie, Freiberg. First, brief presentation of the idea of the system based on DCNN is given. Next, the results of tests of developed classification system on 8 different types (classes) of microstructure of the following different steel grades: C15, C45, C60, C80, V33, X70 and carbide free steel. The DCNN based classification systems require numerous training data and the system accuracy strongly depend on the size of these data. Therefore, created data set of numerous micrograph images of different types of microstructure (33283 photographs) gave the opportunity to develop high precision classification systems and segmentation routines, reaching the accuracy of 99.8%. Presented results confirm, that DCNN can be a useful tool in microstructure classification.

• Periodical: Materials Science Forum (Volume 949)
• Pages: 24-31
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.949.24
• Online since: March 2019
• Authors: Bartłomiej Mulewicz*, Grzegorz Korpala, Jan Kusiak, Ulrich Prahl
• Keywords: Artificial Intelligence, Deep Learning, Microstructure Analysis

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