Citation: Sarrionandia, X.; Nieves, J.;
Bravo, B.; Pastor-López, I.; Briñas,
P.G. An Objective Metallographic
Analysis Approach Based on
Advanced Image Processing
Techniques. J. Manuf. Mater. Process.
2023, 7, 17. https://doi.org/
10.3390/jmmp7010017
Academic Editor: Steven Y. Liang
Received: 18 November 2022
Revised: 21 December 2022
Accepted: 27 December 2022
Published: 4 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Manufacturing and
Materials Processing
Journal of
Article
An Objective Metallographic Analysis Approach Based on
Advanced Image Processing Techniques
Xabier Sarrionandia
1,2
, Javier Nieves
1,
* , Beñat Bravo
1
, Iker Pastor-López
2
and Pablo G. Bringas
2
1
AZTERLAN, Basque Research and Technology Alliance (BRTA), Aliendale Auzunea 6, 48200 Durango, Spain
2
Department of Mechanics, Design and Industrial Management, Faculty of Engineering, University of Deusto,
Unibertsitate Etorbidea 24, 48007 Bilbao, Spain
* Correspondence: jnieves@azterlan.es
Abstract:
Metallographic analyses of nodular iron casting methods are based on visual comparisons
according to measuring standards. Specifically, the microstructure is analyzed in a subjective manner
by comparing the extracted image from the microscope to pre-defined image templates. The achieved
classifications can be confused, due to the fact that the features extracted by a human being could
be interpreted differently depending on many variables, such as the conditions of the observer. In
particular, this kind of problem represents an uncertainty when classifying metallic properties, which
can influence the integrity of castings that play critical roles in safety devices or structures. Although
there are existing solutions working with extracted images and applying some computer vision
techniques to manage the measurements of the microstructure, those results are not too accurate. In
fact, they are not able to characterize all specific features of the image and, they cannot be adapted
to several characterization methods depending on the specific regulation or customer. Hence, in
order to solve this problem, we propose a framework to improve and automatize the evaluations by
combining classical machine vision techniques for feature extraction and deep learning technologies,
to objectively make classifications. To adapt to the real analysis environments, all included inputs in
our models were gathered directly from the historical repository of metallurgy from the Azterlan
Research Centre (labeled using expert knowledge from engineers). The proposed approach concludes
that these techniques (a classification under a pipeline of deep neural networks and the quality
classification using an ANN classifier) are viable to carry out the extraction and classification of
metallographic features with great accuracy and time, and it is possible to deploy software with the
models to work on real-time situations. Moreover, this method provides a direct way to classify the
metallurgical quality of the molten metal, allowing us to determine the possible behaviors of the final
produced parts.
Keywords: artificial vision; machine learning; deep learning; metallography; classification
1. Introduction
Metallurgy (as a process of production and transformation of materials) has allowed
society to evolve. More specifically, the foundry remains one of the central axes of the world
economy [
1
]. A huge number of parts are made in foundries all over the world to combine
and create a more complex system. Some of those parts are security components used in
several industries, e.g., brake calipers that help the braking systems of motorized vehicles,
the propellers that allow ships to move, the mechanisms that are in charge of moving the
flaps of the wings in an airplane, or the trigger and the firing system in a firearm.
The foundry, despite being a fundamental axis of society, is still at a lower level of
development in terms of digitalization and the application of advanced intelligent systems
compared to other industries of similar importance. In addition, current trends encourage
the production of ever smaller and more precise components. Thus, any tiny aspect or
characteristic of the process can influence the results of the final manufactured parts.
J. Manuf. Mater. Process. 2023, 7, 17. https://doi.org/10.3390/jmmp7010017 https://www.mdpi.com/journal/jmmp
