Mr. Sriram Ravisankar Padma Profile

Sriram Ravisankar Padma

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Publications (1)

Proceedings Article | 15 February 2024 Paper

Feasibility of in-situ health monitoring for composite structure with embedded piezoelectric sensor networks

Khanh Vo, Sriram Ravisankar Padma, Eric Ngin, Sridharan Vijay Shankar, Hua Li, Sridhar Idapalapati

Proceedings Volume 13069, 130691B (2024) https://doi.org/10.1117/12.3022258

KEYWORDS: Sensors, Composites, Structural health monitoring, Data modeling, Machine learning, Random forests, Artificial neural networks, Safety, Nondestructive evaluation

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Continuous Inspection and maintenance of high performing modern structural composite structures are essential to ensure the safety and efficiency of the any industry. Unlike conventional metals, the damage of fibre-reinforced polymer matrix composite materials that are commonly used in structural components is relatively difficult to detect, given the various micro-constituents present. Especially, carbon fiber-reinforced polymer and glass fiber-reinforced polymer laminate can produce no-visible surface damage while sustaining internal delamination and fiber failures upon experiencing Low-Velocity Impact (LVI) forces. Barely Visible Impact Damage (BVID) is one of important damages that is tedious to detect with non-destructive methods as the damage location and intensity is unknown to the operator unless detected using Non-Destructive Techniques (NDT). Therefore, Structural Health Monitoring (SHM) is an active system that provides constant surveillance of the component’s vitals in operating conditions, thereby reducing the structural Meant Time To Repair (MTTR). In this work, piezoelectric based sensors are embedded into a composite laminate with electrical cables for voltage detection under LVI impacts. Experiments were conducted with an array of sensors at various locations. The measured signals are analyzed for their amplitude with reference to the embedded location to determine the damage intensity and impact location. A Machine Learning (ML) model is developed to provide a predictive method for SHM of the composite structure for impact damage. Besides, mechanical tests are also conducted to prove the compatibility of the embedded sensors in the host structure in order to check for the knock down in the safety factor of the component due to the presence of a foreign object in the material system. The result from this study aims to develop a solution for a structural smart skin to increase the safety & reliability of composite components, assist repair technicians in reducing the time take to detect the damage location and the degree of repair required structural, as well as to enable the predictive maintenance tool for an efficient and environmentally conscious industry that reduces the material consumption and wastage during the repair stages.

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