Real-Time Detection and Monitoring of Oxide Layer Formation in 1045 Steel Using Infrared Thermography and Advanced...

Area of Science:

• Materials Science
• Metallurgy
• Computational Engineering

Background:

• High-temperature oxidation of 1045 steel impacts mechanical properties and phase stability.
• Monitoring oxide layer formation is crucial for quality control in steel manufacturing.
• Non-contact, real-time measurement techniques are needed for efficient industrial processes.

Purpose of the Study:

• To develop and validate an image processing algorithm for monitoring oxide scale formation on 1045 steel.
• To utilize infrared thermography for non-contact, real-time detection of oxide layers.
• To assess the algorithm's accuracy and effectiveness in a controlled high-temperature environment.

Main Methods:

• Controlled heating experiments of 1045 steel at 900 °C.
• Development of an image processing algorithm for region of interest (ROI) detection and segmentation.
• Application of infrared thermography for surface temperature monitoring and oxide scale visualization.
• Quantitative evaluation of the algorithm's performance using accuracy and Dice coefficient metrics.

Main Results:

• The developed image processing algorithm achieved high accuracy (96%) and Dice coefficient (96.15%) in detecting and segmenting oxide layers.
• Infrared thermography provided a non-contact, real-time method for observing oxide scale formation.
• Controlled experiments confirmed the algorithm's reliability in standardized data acquisition.

Conclusions:

• The image processing algorithm effectively monitors oxide scale formation in 1045 steel during high-temperature processes.
• Integration of thermography and machine learning enhances steel manufacturing by improving surface quality and material integrity.
• This advanced monitoring system offers potential for reducing material losses and improving product sustainability in the steel industry.