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Infrared Image Processing to Guide the Identification of Damage and Dissipative Mechanisms in 3D Layer-to-Layer Woven Composites

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Abstract

This article discusses techniques that aim at facilitating the identification of dissipative mechanisms activated in woven composites under cyclic loadings. The focus is put on the post-processing of thermal measurements acquired during heat build-up experiments, as these are usually used to identify the dissipation sources. The importance of motion compensation pre-processing is demonstrated as it is shown that the latter enhances the quality of the evaluated thermoelastic and dissipation fields. Two specific post-processing techniques are presented in this article. The first one analyzes temperature or thermoelastic fields and searches to detect thermal events associated with the creation of cracks. The second one is based on a Fourier decomposition of thermal fields and aims at highlighting an increased contribution of friction as a dissipation source.

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Data Availability Statement

The data that support the findings of this study are available from Safran Landing Systems but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Safran Landing Systems.

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Acknowledgements

The research presented in this article was funded by the Safran Group, France. Safran Tech-Composites Platform is acknowledged for the manufacturing of the samples used in this study. The authors would also like to thank the French ANRT Agency for its financial support (CIFRE n°2017/1456).

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Authors and Affiliations

  1. ENSTA Bretagne – Institut de Recherche Dupuy de Lôme (IRDL), UMR CNRS 6027, Brest, France

    Libor Navrátil, Vincent Le Saux, Nicolas Carrere & Yann Marco

  2. Safran Landing Systems, Vélizy, France

    Libor Navrátil & Sylvain Leclercq

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  1. Libor Navrátil
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  2. Vincent Le Saux
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Correspondence to Vincent Le Saux.

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Appendix

Appendix

1.1 Threshold Value Evaluation

As mentioned in Sect. 3.3.2, the results of the thresholding detection technique are strongly dependent on the fixed thresholding value. The overall objective is to set the thresholding value as low as possible in order to detect even low energy events that do not lead to high temperature elevations. However, there are two major constraints. The first limitation is avoiding the detection of regions exhibiting significant temperature increases that are not caused by thermal events. This concerns particularly high stress amplitudes that might induce temperature elevations exceeding the fixed threshold. The second challenge is limiting the diffusion-affected area. Indeed, when a thermal event occurs, it produces a temperature elevation that is rapidly diffused. This leads to a temperature elevation in the vicinity of the event that might also exceed the given threshold. However, as this excess is not linked to a formation of a new event but rather to the propagation of the original one, this affected area should not be included in the detection results. In order to verify these two aspects that might skew the detection results, a sensitivity study and mesoscale cooling observations were carried out.

The results of the sensitivity study presented in Fig. 30 were obtained on a specimen observed on a front face and tested with a classic heat build-up protocol (Fig. 13). Two variables were analysed: the number of events detected and their relative surface. The plot displays the results obtained for the entire heat build-up test for different thresholding values that vary from 0.05 to 0.5. Even though, the detected relative surface seems to stabilize at the value of 0.2 °C, the number of the detected events seems to stabilize between the values of 0.3 and 0.4. Since it is the relative surface that is more often used for further analyses and since the difference between the events counted at 0.3, and 0.4 °C is not critical, the thresholding value was set to 0.3 °C.

Fig. 30
figure 30

An example of the sensitivity of the results on the thresholding value

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In order to describe the diffusion effects at the mesoscale level, observations of the cooling period that follows the creation of a thermal event were carried out. The idea is to apply a sharp mechanical loading followed by a creep test that generates a thermal event that can be observed with an infrared camera. These observations are then used to quantify the diffusion rate (Fig. 31).

Fig. 31
figure 31

Illustration of the creep test

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The cooling period is schematically depicted in Fig. 32. It is possible to notice that the initial temperature elevation diffuses rapidly. Figure 33 confirms that a pixel localized in the middle of the affected area takes less than one second to reach thermal equilibrium.

Fig. 32
figure 32

Illustration of the cooling period following the development of a thermal event

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Fig. 33
figure 33

Illustration of temperature profiles obtained in the ROI during the cooling period

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Furthermore, it is possible to notice that the thermally affected area remains relatively small as the initial temperature increase does not tend to spread in the observed x-y plane. This is confirmed by Fig. 33 that shows the evolution of a temperature profile obtained along the x-direction. It is evident that the change in the width of the initial profile remains unimportant, especially at the macroscale level at which the detection process is normally realized. This would mean that the heat diffusion happens primarily in the direction that is perpendicular to the observed x-y plane.

Even though these afore-presented analyses are not exhaustive, they may serve as a rule of thumb when a thresholding value needs to be determined for a different material. The identified value of 0.3°C seems to detect the majority of the produced events without skewing the results with falsely identified pixels. Furthermore, the errors induced by diffusion effects were shown to be negligible.

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Navrátil, L., Le Saux, V., Leclercq, S. et al. Infrared Image Processing to Guide the Identification of Damage and Dissipative Mechanisms in 3D Layer-to-Layer Woven Composites. Appl Compos Mater 29, 1449–1477 (2022). https://doi.org/10.1007/s10443-022-10023-6

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