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Updated: Mar 15, 2026

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Kinetic Oxidation Analysis in AISI 1045 Steel Using Infrared Thermography and Convolutional Neural Networks.

Oscar David Prieto-Sánchez1, Antony Morales-Cervantes1, Jorge Sergio Téllez-Martínez1

  • 1División de Estudios de Posgrado e Investigación, TecNM-Instituto Tecnológico de Morelia, Maestría en Ciencias en Ingeniería Electrónica (MCIE), Av. Tecnológico 1500, Morelia 58120, Mexico.

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Summary

This study introduces infrared thermography and deep learning (DL) for analyzing steel oxide layers. The novel method accurately monitors steelmaking processes, enhancing safety and quality control.

Keywords:
CNNinfrared thermographykinetic parameterssteel oxidation

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Area of Science:

  • Materials Science
  • Metallurgy
  • Artificial Intelligence

Background:

  • Steelmaking processes require advanced monitoring of surface oxide layers.
  • Current methods may lack precision or pose safety risks.

Purpose of the Study:

  • To develop and validate an integrated infrared thermography and deep learning approach for analyzing AISI 1045 steel surface oxide layers.
  • To enable precise, non-destructive monitoring of oxide formation and thickening during steel processing.

Main Methods:

  • Infrared thermography was used for real-time observation of steel surfaces heated between 200-700 °C.
  • A convolutional neural network (CNN), SegNet, was employed for semantic segmentation to identify oxide layers.
  • Quantitative analysis of pixelation changes and activation energy determined oxide evolution kinetics.

Main Results:

  • The SegNet model achieved 96.40% accuracy in identifying oxide presence.
  • Established relationships in oxide evolution kinetics and quantified activation energy.
  • Demonstrated feasibility of non-destructive, large-scale monitoring.

Conclusions:

  • The integrated approach offers a pioneering solution for advanced steelmaking process control.
  • This method can improve surface quality prediction and provide critical sub-process data.
  • Enhances industrial safety by enabling remote monitoring.