Comparative Analysis of Baseline Sensitivity Metrics in Coplanar Capacitive Sensors for Non-Destructive Evaluation

Non-Destructive Evaluation (NDE) techniques are pivotal in ensuring the integrity and safety of materials and structures. Among these, coplanar capacitive sensors (CCSs) have emerged as robust and versatile tools, offering adaptability to diverse applications. Despite the diversity of CCS designs, particularly in electrode geometries, a systematic comparative analysis of their performance remains underexplored. This study bridges this gap by evaluating five CCS configurations-square, rectangular, triangular, annular, and concentric-across conducting and non-conducting materials. Finite element simulations and experimental investigations were employed, focusing on three key sensitivity metrics: Signal-to-Noise Ratio (SNR), magnitude of signal variation relative to defect size, and rate of signal variation. The findings reveal strong agreement between simulations and experiments, validating the reliability of CCSs for detecting defect variations. Larger defects consistently elicit stronger responses, with concentric and annular configurations demonstrating superior performance for such cases. SNR analysis highlights the influence of sensor geometry and material properties, with non-circular-shaped electrodes achieving higher SNRs compared to circular. Rate-of-change analysis uncovers critical disparities in sensitivity, with triangular electrodes exhibiting the highest rates of change at the maxima and minima, while circular designs show lower rates of change. Additionally, spreading effects in non-conducting materials cause defects to appear larger, underscoring the importance of considering material behavior in defect characterization. This study underscores the indispensable role of electrode geometry in optimizing capacitive sensing for precise defect characterization. It establishes a foundation for integrating sensor-specific insights into advanced methodologies such as sensitivity-guided defect detection, image reconstruction, and machine learning-driven analysis, paving the way for transformative advancements in capacitive NDE technologies.