Enhancing Non-Destructive Examining of Composite Materials Using Lightweight Structures And Machine Learning

Composite materials and structures are becoming more efficient and cost-effective, and new special features are being developed to improve their specific properties. Their use in a variety of applications such as wind turbines, aeronautical, medical equipment, transportation, and other load-bearing structures is becoming increasingly important. It is necessary for non-destructive composite examining to be robust and trustworthy in order to limit the risk of damage as well as the cost of maintenance. In order to ensure the product quality throughout its life cycle, many non-destructive examining procedures based on a wide range of concepts have been created. In this paper, nondestructive examining was used to obtain vibration measurements of composites in high-frequency regimes, which were then analysed. Both end-to-end classifications of the data and classifications of data acquired by feature engineering with modal analysis employ Support Vector Machine (SVM) to carry out damage localization in their respective applications. Finally, the findings display a large number of plates made of various materials and with defects of varying magnitudes.