Creep evaluation and temperature dependence in self-sensing micro carbon polymer-based composites for further development as an Internet of Things Sensor device

Abstract

This article investigates the dependency of temperature on electrical resistance (R) change in micro carbon fiber polymer composites (MCFPC), for further development as an Internet of Things sensor from previous research works. Three mixtures were prepared using Dow Corning’s Silastic 145 as base polymer and made vary fiber content weight percentages: fiber diameter to length ratio ∅⁄l 0.13 and carbon fiber content of 13%; ∅⁄l:0.66 and carbon fiber contents of 40% and 50%. Composites tested were submitted to temperature loading, with a constant strain of 0.0%, for assessment of R when a change in the composite’s temperature occurs. The composite response was observed to follow an Arrhenius function, for temperatures ranging from −10°C to 40°C. The apparent activation energy was calculated to evaluate further differences between carbon fiber contents and the sensitivity factor, [Formula: see text] due to temperature is determined. The specimens were also tested with a constant strain of 2.86% to assess creep. It was found that creep and R, over the period of time in the analysis, best fit a discrete latent variable model. The sensitivity factor change is determined in regard to stress relaxation, [Formula: see text]. The properties of MCFPC investigated here can be used to establish relationships between electrical resistance outputs and environmental loading conditions for this type of composites, enabling the possibility of deployment as part of a management system network for structural monitoring with real-time data acquisition.

A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics

“…A brief explanation of temperature dependence on this type of SCF polymer composites is presented next. For a more detailed explanation, see previously published work 16 for this same experimental campaign.…”

“…Their purpose is one of collecting data, and making it available, with the ultimate goal of facilitating the life of a citizen''. A recurring sentence utilized by Miguel Toma´s (see here Toma´s et al 16 ) to mention the importance of open technologies (data, source code, and electronics hardware) and solutions.…”

“…Alhashimi 12 in his thesis work defines the response of a sensor 13 to have linear, multilinear, static, and dynamic among others depending on the input-output expected, for instance Wang and Cheng work ''Piezoresistive effect of a carbon nanotube silicone-matrix composite''. 14 For cases of ideal sensors that follow a static linear model, it can be written as follows (see previously published research on this same experimental campaign): 15,16 it can be written as follows:…”

“…15 The behavior when specimens were subjected (as proposed) to a constant deformation state 6 ¼ 0 ð Þ, for prolonged periods of time was also analyzed, for evaluation of strain relaxation and strain sensitivity during relaxation in ''Creep evaluation and temperature dependence in self-sensing micro carbon polymer-based composites for further development as an Internet of Things Sensor device''. 16 In this study, a ML model, based on a backpropagation ANN algorithm, is developed within mind integration with smart devices for SHM. The main objective is to model calibration curves needed to establish a successful correlation between R measurements and strain states in a self-sensing carbon fiber composite during a steady state of deformation 6 ¼ 0 ð Þ.…”

“…Validation of the data for z = 3. where f MMF (t 0 ) is an experimentally determined function (MMF); a, b, c, and d are analytical constants determined during regression analysis from experimental data (see Table 11). Although S R t ð Þ (Equation ( 23)) does not equate temperature loading, it can be modified to do so, using results presented in Toma´s et al 16 One of the advantages of using ANN over traditional mathematical regression modeling is the ability to include variables with less obvious mathematical correlation in the model, with respect to the function output being optimized and modeled. For example, include the physical properties of each individual material in the base matrix in a SCF composite, as done in this work, and even include other external parameters, for example, the properties on surrounding materials where the self-sensing SCF composite is embedded.…”