EPJ D Highlight - Predicting the composition of a steel alloy

Experimental setup

Austenitic steel is a potential material for nuclear fusion reactors

Producing energy on Earth through nuclear fusion, the type of reaction that powers the Sun, has proven to be a major challenge. The extreme conditions needed for such a reaction require the walls of a nuclear fusion device to be made of a material with a particular set of mechanical properties, including being able to withstand incredibly high temperatures and be shock- and corrosion-resistant. Austenitic steel, a non-magnetic steel with a crystalline structure, is one of the materials considered for use in nuclear fusion devices.

In a new paper in EPJ D, Ivan Traparić and Milivoje Ivković from the Institute of Physics in Belgrade, Serbia, explore an effective way to predict the composition of austenitic steel. They found that laser-induced breakdown spectroscopy – a technique to determine the quantity of elements in a material – used in combination with a deep neural network – a machine learning technique – was most effective. This technique could be used to determine the elemental composition of a steel sample by those without access to certified steel samples.

The researchers used machine learning techniques in combination with laser-induced breakdown spectroscopy to speed up the process of identifying the elements that compose austenitic steel samples. They used a criterion called the Gini impurity test to select the most important data from their dataset, enabling them to minimise its complexity. They then used this simplified dataset to train their machine learning models to identify the composition of their steel samples.

The authors conclude that, when used in combination with laser-induced breakdown spectroscopy, a neural network was better at predicting the composition of austenitic steel than random forest, a machine learning technique that employs a collection of decision trees.

This was our first experience of publishing with EPJ Web of Conferences. We contacted the publisher in the middle of September, just one month prior to the Conference, but everything went through smoothly. We have had published MNPS Proceedings with different publishers in the past, and would like to tell that the EPJ Web of Conferences team was probably the best, very quick, helpful and interactive. Typically, we were getting responses from EPJ Web of Conferences team within less than an hour and have had help at every production stage.
We are very thankful to Solange Guenot, Web of Conferences Publishing Editor, and Isabelle Houlbert, Web of Conferences Production Editor, for their support. These ladies are top-level professionals, who made a great contribution to the success of this issue. We are fully satisfied with the publication of the Conference Proceedings and are looking forward to further cooperation. The publication was very fast, easy and of high quality. My colleagues and I strongly recommend EPJ Web of Conferences to anyone, who is interested in quick high-quality publication of conference proceedings.

On behalf of the Organizing and Program Committees and Editorial Team of MNPS-2019, Dr. Alexey B. Nadykto, Moscow State Technological University “STANKIN”, Moscow, Russia. EPJ Web of Conferences vol. 224 (2019)

ISSN: 2100-014X (Electronic Edition)

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