Measurement of viscoelastic material properties of adhesives for SHM sensors under harsh environmental conditions

Abstract

This paper addresses the influence of the viscoelastic material properties of adhesives on the functionality of SHM (Structural Health Monitoring) sensor applications. Adhesives behave viscoelastically and show a strong temperature and time dependency of their mechanical properties. Creep processes increase the deformation under mechanical load and relaxation can decrease the stress in the adhesives (polymer) with time. These processes are most effective in the temperature range of glass transition (Tg), at which the material behavior switches between the glassy and rubbery state and all material parameters change drastically. Additional the viscoelastic material properties are influenced by environmental loading like moisture as it behaves like a plasticizer in the epoxy matrix. In this study the moisture influence on the viscoelastic behavior of structural adhesives was investigated by DMA (Dynamic Mechanical Analysis) methodology for different moisture loading conditions. Water immersion (DI) and relative humidity (RH.) at different temperature levels showed the strong temperature dependence of the absorption process. For testing under RH (Relative Humidity) conditions a novel DMA-RH equipment was used. It allows online modulus measurement at temperatures and RH levels between 0% r.h. and 90% r.h. Commercially available two component epoxy adhesives (unfilled, high filled) with high potential of acceptance in avionic applications were studied. For the unfilled adhesive a significant material property change due to moisture absorption occurred. The modulus decreased down to about 25 percent of the dry state modulus E' and kept constant for further conditioning. As expected a Tg reduction for "wet" samples was visible. Additional the time dependence increased proportional to the square root of conditioning time. The high filled adhesive showed much lower diffusion constants at room temperature and the modulus change was not that remarkable. For 85°C/85% r.h conditioning the modulus decrease to about 40% of the initial room temperature value

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

“…These approaches combine, for instance, the use of principal component analysis (PCA) and machine learning (ML) [48] or PCA and self-organizing maps (SOM) [49]. Other approaches consider different elements, such as the influence of the viscoelastic material properties of the adhesives used by sensors on SHM applications [50], the influence of temperature and surface wetting on the ultrasonic waves used for damage detection [51], and the relationship between feature extraction and data fusion. Proposed solutions include the use of sensor data fusion, PCA, and self-organizing maps to compensate for the undesirable effect of temperature on damage detection and classification [49], the use of optimal baseline subtraction [52], and the effect of elevated temperatures on the adhesive layers of piezoelectric transducers (PZTs).…”

Preface

“…There are many damage metrics used to evaluate the integrity of mechanical structures (Boehme et al, 2010). In this context, one of the most used statistical models found in the literature is the RMSD (Root Mean Square damage) and its definition is given by Eq.…”

Desarrollo de un sistema de monitorización de la integridad estructural para aplicaciones en ingeniería mediante técnicas de reducción de la dimensionalidad

“…These approaches combine, for instance, the use of principal component analysis (PCA) and machine learning (ML) [48] or PCA and self-organizing maps (SOM) [49]. Other approaches consider different elements, such as the influence of the viscoelastic material properties of the adhesives used by sensors on SHM applications [50], the influence of temperature and surface wetting on the ultrasonic waves used for damage detection [51], and the relationship between feature extraction and data fusion. Proposed solutions include the use of sensor data fusion, PCA, and self-organizing maps to compensate for the undesirable effect of temperature on damage detection and classification [49], the use of optimal baseline subtraction [52], and the effect of elevated temperatures on the adhesive layers of piezoelectric transducers (PZTs).…”