Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing
The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate...
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Published in | Applied sciences Vol. 14; no. 12; p. 5077 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.06.2024
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Abstract | The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed. |
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AbstractList | The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed. |
Audience | Academic |
Author | Pantoja-Pacheco, Yaquelin Verenice Jiménez-García, José Alfredo Cruz-Domínguez, Oscar Hernández-González, Salvador Ruelas-Santoyo, Edgar Augusto Ríos-Lira, Armando Javier |
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SubjectTerms | Alloys Artificial intelligence artificial neural network Chromium Corrosion Deep learning Fourier transforms Grain boundaries Image processing Literature reviews Localization Mechanical properties Methods Microstructure Neural networks pattern recognition Stainless steel Steel Support vector machines Temperature |
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Title | Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing |
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