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A Novel Automatic Image Intensity Analysis Using Machine Learning Algorithms and Characterization of Tensile Fracture Surface of AA8011-B4C Nano-composite

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Abstract

The growths in e-mobility are the factor that impetus the research in lightweight material with improved mechanical properties, especially strength. The research concentrates on developing a novel metal matrix nano-composite of high-quality AA8011 aluminium alloy reinforced with varying weight percentages of nano-particles of B4C (0, 0.3, 0.6, 0.9, 1.2 and 1.5 wt%). The high-energy electromagnetic frequency stir casting technique was used to fabricate nano-composite, which enhanced the wetting of matrix and reinforcement. The rupture strength of nano-composite primarily hinges on the structure and formation of nano-composite. The morphology and distribution of nano-particles in metal matrix nano-composites (MMNC) were characterized by using Field Emission Scanning Electron Microscope. The yield strength, yield point, tensile strength, elongation, and reduction of area of MMNC under uniaxial tensile stresses were determined by Universal Testing Machine. The machine learning algorithms automatically characterized the fractured material texture in conjunction with the described techniques. The unique textural information extracted from each fractured sample distinguished the surfaces as agglomeration, brittle and ductile texture. The fractography analysis of MMNCs revealed the transition of composite from cup and cone to cleavage fracture, which ensured the property change of matrix by the addition of nano-reinforced particles.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Marian Engineering College, Trivandrum, Kerala, India

    J. Arun

  2. Department of Mechanical Engineering, Valia Koonambaikulathamma College of Engineering and Technology, Parippally, Trivandrum, Kerala, India

    T. G. Ansalam Raj

  3. Department of Mechanical Engineering, TKM College of Engineering and Technology, Kollam, Kerala, India

    K. E. Reby Roy

  4. Department of Mechanical Engineering, University College of Engineering, Nagercoil, Tamilnadu, India

    S. Suresh

  5. Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Tamil Nadu, Karungal, 629157, India

    C. Emmy Prema

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  1. J. Arun
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  2. T. G. Ansalam Raj
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Correspondence to S. Suresh.

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Arun, J., Raj, T.G.A., Roy, K.E.R. et al. A Novel Automatic Image Intensity Analysis Using Machine Learning Algorithms and Characterization of Tensile Fracture Surface of AA8011-B4C Nano-composite. Trans Indian Inst Met 75, 2891–2903 (2022). https://doi.org/10.1007/s12666-022-02654-x

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  • DOI: https://doi.org/10.1007/s12666-022-02654-x

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