Detection of grains in aluminium metal matrix composites using image fusion

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Publicado en:SN Applied Sciences vol. 7, no. 6 (Jun 2025), p. 583
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Springer Nature B.V.
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Acceso en liña:Citation/Abstract
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245 1 |a Detection of grains in aluminium metal matrix composites using image fusion 
260 |b Springer Nature B.V.  |c Jun 2025 
513 |a Journal Article 
520 3 |a 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&#xa0;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<list list-type="bullet"><list-item></list-item>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.<list-item>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.</list-item><list-item>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.</list-item> 
653 |a Scanning electron microscopy 
653 |a Wavelet transforms 
653 |a Aluminum 
653 |a Grain boundaries 
653 |a Aluminum matrix composites 
653 |a Discrete Wavelet Transform 
653 |a Powder metallurgy 
653 |a Decomposition 
653 |a Image processing 
653 |a Boundaries 
653 |a Computer vision 
653 |a Grain size 
653 |a Automation 
653 |a Statistical analysis 
653 |a Corrosion resistance 
653 |a Energy consumption 
653 |a Industrial applications 
653 |a Fourier transforms 
653 |a Effectiveness 
653 |a Statistical methods 
653 |a Particle size 
653 |a Image acquisition 
653 |a Hybrid composites 
653 |a Aluminum base alloys 
653 |a Environmental 
773 0 |t SN Applied Sciences  |g vol. 7, no. 6 (Jun 2025), p. 583 
786 0 |d ProQuest  |t Science Database 
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