Design, optimization, and real-time structural health monitoring using embedded 3d-printed piezoresistive sensors in composite structures

Abstract

Structural Health Monitoring (SHM) is an essential strategy for ensuring the safety and longevity of critical infrastructure and advanced composite structures. While embedded sensors offer a transformative approach to real-time monitoring by enabling in-situ damage detection, their widespread adoption is hindered by challenges at the sensor-material interface, unoptimized geometric designs, and a critical lack of system-level validation in complex load-bearing components. This thesis presents a comprehensive ”Experiment- Model-Optimize-Validate” framework for the development, optimization, and system-level integration of additively manufactured piezoresistive strain sensors. Through a systematic review of the state-of-the-art, the predominance of carbon-based piezoresistive sensors manufactured via Fused Deposition Modeling (FDM) was established, alongside the critical need to balance sensor sensitivity with the mechanical integrity of the host structure. To address this inherent trade-off, a rigorous computational and experimental methodology was developed. A Design of Experiments (DoE) evaluated the influence of geometric parameters (trace width, inter-trace distance, thickness, and end-loops) on sensor performance. Utilizing Gaussian Process Regression (GPR) and a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, optimal geometries were identified. Experimental validation demonstrated that fully embedded configurations achieved exceptional piezoresistive sensitivity (Gauge Factor ≈ 59), while a geometrically scaled variant successfully restored the host structure’s stiffness to near-native levels (≈ 2.18 GPa) without significant loss of sensitivity. To bridge the gap between laboratory-scale coupon testing and field-ready deployment, the optimized sensing architectures were integrated into a geometrically complex aerospace pylon structure using multi-material FDM. Sensor placement was strategically guided by Finite Element Analysis (FEA) to target critical strain hotspots, and a redundant sensing network was implemented to ensure fault tolerance. System-level testing, validated by full-field Digital Image Correlation (DIC), proved that the embedded sensors exhibited high-fidelity temporal synchronization with mechanical deformation, capturing transient structural events with negligible viscoelastic lag. Furthermore, the redundant architecture successfully maintained monitoring capabilities and diagnosed asymmetrical loading anomalies. Ultimately, this research provides a highly scalable and robust methodology for transitioning 3D-printed embedded sensors from isolated component optimization to integrated, self-sensing structural systems, advancing the technological readiness of real-time SHM in aerospace and civil engineering applications.