Oftentimes, the complexity in manufacturing composite materials leads to corresponding structures which although they may have the same design specifications they are not identical. Thus, composite parts manufactured in the same production line present differences in their dynamics which combined with additional uncertainties due to different operating conditions may lead to the complete concealment of effects caused by small, incipient, damages making their detection highly challenging. This damage detection problem in nominally identical composite structures is pursued in this study through a novel data-based response-only methodology that is founded on the autoregressive with exogenous (ARX) excitation parametric representation of the transmittance function between vibration measurements at two different locations on the structure. This is a statistical time series methodology within which two schemes are formulated. In the first, a single-reference transmittance model representing the healthy structure is employed, while multiple transmittance models from a sample of available healthy structures are used in the second. The model residual signal constitutes for both schemes the damage detection characteristic quantity that is used in appropriate hypothesis testing procedures with the likelihood ratio test. The methodology is experimentally assessed via damage detection for a population of composite beams which are manufactured in the same production line representing the half of the tail of a twin-boom unmanned aerial vehicle. The damage detection results demonstrate the superiority of the multiple transmittance models based scheme that may effectively detect damages under significant manufacturing variability and varying boundary conditions.

We demonstrate a sensing platform for composite manufacturing (RTM-6) process based on silicon photonics, being controlled by novel Process Monitoring Optimization Control (PMOC) system. The photonic multi-sensor is based on bragg grating components, allowing measurements of temperature, pressure and refractive index, and is packaged employing a ball lens fiber-to-chip interface. We present results of the packaged temperature photonic sensor regarding bandwidth, linearity and thermo-optic efficiency, being controlled by our PMOC system. We experimentally achieve 0.074 nm/C with R^2 = 0.995 linearity for temperature up to 180°C (RTM-6 compatible) with 1 kHz data acquisition and 0.2°C accuracy.