Detection of grains in aluminium metal matrix composites using image fusion

Abstract

Aluminium metal matrix composites are lightweight, corrosion-resistant, and extremely durable. Because of their low mass density, stiffness, and high specific strength, aluminium alloys with ceramic-reinforced particles are more appealing in aircraft, transportation, and industrial applications. This piece of work illustrates an image fusion approach using discrete wavelet transform (DWT) for the detection of grains present in the hybrid composite to study the metallographic characterization. The fusion approach combines the same composite's images with different resolutions and intensities acquired by scanning electron microscope to produce an integrated image that is more suited for identifying grains and grain boundaries that are difficult to locate from images in other modalities. Some statistical evaluation measures are used to investigate the effectiveness and significance of the suggested fusion technique. The statistical measure’s indicate that the recommended methodology is commendable. According to the statistical analysis, the proposed fusion process successfully retains the maximal content of visual truth in material characterization, allowing for faster and more accurate metallographic characterization of hybrid composites.

Article Highlights

Enhanced Visualization: The DWT image fusion technique improves the visibility of grains and grain boundaries in hybrid composites, making them easier to identify than in individual images.

Improved Accuracy and Faster Analysis: The fused image enhances the accuracy of metallographic characterization, allowing for more precise analysis of the composite's microstructure. The fusion process streamlines the characterization process, leading to quicker analysis times compared to traditional methods.

Statistical Support: Statistical evaluation measures demonstrate the effectiveness and significance of the proposed fusion approach, confirming its ability to retain valuable information from the original images.