Department of Mechanical Engineering, Government Polytechnic Holenarasipura, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2019, 02(01), 077-083
Received on 13 May 2019; Revised 25 May 2019; accepted on 29 May 2019
Metal Matrix Composites (MMCs) are widely utilized in aerospace, automotive, and structural applications due to their superior mechanical properties, including high strength-to-weight ratio, excellent wear resistance, and enhanced thermal stability. However, the structural integrity of MMC components is crucial for ensuring their reliability and longevity, necessitating the development of effective damage detection and health monitoring techniques. This paper provides a comprehensive review of various methodologies used for damage assessment in MMCs, including traditional and advanced non-destructive evaluation (NDE) techniques such as ultrasonic testing, radiographic inspection, eddy current testing, and thermographic analysis. Additionally, acoustic emission (AE) analysis is examined as a real-time monitoring approach that captures transient stress waves generated by material degradation. With the advent of Industry 4.0, machine learning-based monitoring systems have gained significant attention for their ability to process large datasets and identify damage patterns with high accuracy. This study explores the integration of artificial intelligence (AI) and deep learning models in predictive maintenance frameworks, improving early fault detection and minimizing unexpected failures. A comparative analysis of these techniques is presented through figures, tables, and bar charts, illustrating their effectiveness in detecting defects such as cracks, delamination, voids, and fiber breakage. By evaluating the advantages and limitations of different monitoring strategies, this study aims to provide valuable insights into the optimization of damage detection systems for MMC structures. The findings contribute to the development of more efficient and reliable health monitoring solutions, ultimately enhancing the operational safety and performance of MMC-based components in critical applications.
Metal Matrix Composites (MMCs); Structural Health Monitoring (SHM); Non-Destructive Evaluation (NDE); Ultrasonic Testing (UT); X-ray Computed Tomography (XCT)
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Manju R B and Lakshmana H K. Damage detection and structural health monitoring of MMC components. World Journal of Advanced Research and Reviews, 2019, 2(1), 077-083. Article DOI: https://doi.org/10.30574/wjarr.2019.2.1.0125