Published

2018-01-01

System for the recognition of wear patterns on microstructures of carbon steels using a multilayer perceptron

Sistema para el reconocimiento de patrones de desgaste en microestructuras de aceros al carbón utilizando una perceptrón multicapa

DOI:

https://doi.org/10.15446/ing.investig.v25n2.60265

Keywords:

Artificial neural network, digital image processing, material defects (en)
Red neuronal artificial, procesamiento digital de imagen, defectos en material (es)

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Authors

  • Edgar Augusto Ruelas Santoyo Technological Institute of Irapuato
  • José Antonio Vázquez López Technological Institute of Celaya
  • Javier Yáñez Mendiola Center for Applied Innovation in Competitive Technologies
  • Roberto Baeza Serrato University of Guanajuato
  • José Alfredo Jiménez García Technological Institute of Celaya
  • Juan Sánchez Márquez University of Guanajuato

This paper describes the application of a recognition system wear patterns present in carbon steel, the system classifies the microstructure of the materials which have three conditions throughout life-time in thermoelectric plants. This approach employs the artificial neural network multilayer perceptron in conjunction with the digital image processing to recognize the different physical states of the materials used as conductors in conditions of high temperatures. The studied patterns in the microstructure are spheronization, decarburization and graphitization. The microstructure is revealed from microscope images obtained in the Testing Laboratory Equipment and Materials of the Federal Electricity Commission in Mexico (LAPEM-CFE). The proposed system compared to the human expert, obtained an accuracy of 96.83 % with a shorter analysis time and inspection cost.

Este artículo describe la aplicación de un sistema de reconocimiento de patrones de desgaste presente en aceros al carbón, el sistema clasifica la microestructura de los materiales los cuales presentan tres condiciones a lo largo de su vida útil en plantas termoeléctricas. El enfoque propuesto emplea la red neuronal artificial perceptrón multicapa, en conjunto con el procesamiento digital de imágenes para reconocer los diferentes estados físicos de los materiales utilizados como conductores en condiciones de altas temperaturas. La microestructura de las condiciones estudiadas son esferonización, descarborización y grafitización. La microestructura se revela a partir de imágenes de microscopio obtenidos en el Laboratorio de Pruebas de Equipos y Materiales de la Comisión Federal de Electricidad de México (CFE-LAPEM). El sistema propuesto, en comparación con el humano experto, obtuvo una exactitud promedio del 96.82 % con un menor tiempo de análisis y costo de inspección.

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System for the recognition of wear patterns on microstructures of carbon steels using a multilayer perceptron. (2018). Ingeniería E Investigación, 38(1), 113-120. https://doi.org/10.15446/ing.investig.v25n2.60265
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Copyright (c) 2018 Edgar Augusto Ruelas Santoyo, José Antonio Vázquez López, Javier Yáñez Mendiola, Roberto Baeza Serrato, José Alfredo Jiménez García, Juan Sánchez Márquez

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