Resin materials are susceptible to degradation due to external factors such as heat, water, and ultraviolet (UV) radiation, depending on their usage environment. These degradations significantly affect the fatigue strength of the materials. To rapidly and quantitatively evaluate the decrease in fatigue strength caused by environmental degradation, this study proposes an evaluation method of fatigue damage by infrared measurement.
In previous studies, Shiozawa, et al. have proposed an estimation method of the fatigue limit by measuring heat generation caused by plastic deformation using infrared thermography and analyzing changes in heat generation. In the case of metallic materials, it has been clarified that the second harmonic component included in actual temperature variations corresponds to heat generation due to fatigue damage. However, it remains unclear whether resin materials exhibit the same heat generation mechanism as metallic materials.
Therefore, this study aims to propose an infrared measurement to evaluate the impact of UV-induced degradation on the fatigue strength of epoxy resin. By extracting the second harmonic component from actual temperature variations, similar to the approach used for metallic materials, we investigated whether evaluation of fatigue damage is feasible for epoxy resin itself and for UV-induced degradation.
The second harmonic component of the epoxy resin exhibited a trend similar to the change in dissipated energy observed in metallic materials. Furthermore, this component was presumed to reflect degradation due to UV exposure and the resulting changes in fatigue strength. In UV-degraded materials, a decrease in the estimated fatigue limit was observed, suggesting that the decrease in fatigue limit can be captured using the second harmonic component.

This paper presents the measurement and analysis of a lateral-offset optical fiber Mach–Zehnder interferometer (MZI) as chloride ion concentration sensor using a near infrared light source (amplified spontaneous emission, wavelength = 1520-1620 nm). An 8-cm optical fiber MZI sensor was fabricated and fusion spliced using a lateral-offset process. We used this 8-cm lateral-offset op-tical fiber Mach–Zehnder interferometer (MZI) and measured chloride ions in samples of sodium chloride solutions with different weight concentrations ranging from 0.015% to 25% and then analyzed those interference spectra regarding normalized intensity, wavelength shift, and three ranges of the integral areas (1520-1580 nm, 1540-1600 nm, and 1520-1620 nm). The comparative spectral analysis results show that the lateral-offset optical fiber MZI sensor exhibited a linear decrease in the normalized intensity as well as wavelength shift when the concentration increased. The lateral-offset optical fiber MZI sensor displays a sine wave plot in the three ranges of integral areas when the concentration increased. Other than sensing parameters such as normalized intensity (adjusted R-squared = 0.98223) or wavelength shift (adjusted R-squared = 0.94209), the three ranges of integral areas (adjusted R-squared = 0.96425, 0.91621, and 0.9577, respectively) which possessed adjusted R-squared greater than 0.9, are also recommend as sensing parameters for this measurement and analysis of a lateral-offset optical fiber Mach–Zehnder interferometer (MZI) as chloride ion concentration sensor using a near infrared light source.

Deep Learning (DL) algorithms need extensive amounts of data for classification tasks, which can be costly in specialized fields like maritime monitoring. To address data scarcity, we propose a fine-tuning approach leveraging complementary Infrared (IR) and Synthetic Aperture Radar (SAR) datasets. We evaluated our method using the ISDD, HRSID, and FuSAR datasets, employing VGG16 as a shared backbone integrated with Faster R-CNN (for ship detection) and a three-layer classifier (for ship classification). Results showed significant improvements in IR ship detection (mAP: +20%, Recall: +17%) and modest but consistent gains in SAR ship tasks (F1-score: +3%, Recall: +1%, mAP:+1%). Our findings highlight the effectiveness of domain adaptation in improving DL performance under limited data conditions.

In recent years, chloride-induced deterioration of reinforced concrete (RC) structures has become increasingly severe, necessitating periodic inspections to mitigate its progression. As a potential solution, a non-destructive inspection technique using an Imaging-Type Two-Dimensional Fourier Spectroscopy system [Fig.1], developed at Kagawa University, has been proposed. This system enables non-destructive, non-contact, and two-dimensional acquisition of mid-infrared spectra from the surface of structures, facilitating efficient monitoring. However, in the mid-infrared region, a reliable method for detecting chloride-induced deterioration has not yet been established, as the characteristic wavelengths associated with chloride ions fixed in cement remain unclear. Therefore, this study aims to establish a quantitative method for chloride ion detection in concrete using mid-infrared spectroscopy.

First, cement paste specimens with varying chloride ion concentrations were prepared and measured using both the Imaging-Type Two-Dimensional Fourier Spectroscopy system and an FT-IR spectrometer. The spectral peaks correlated with chloride ion concentration were then analyzed. As a result, a peak wavelength around 10.6 µm was identified as exhibiting a clear correlation with chloride ion concentration. This trend was consistently observed using both measurement instruments. Fig.2 shows the spectra after baseline correction at the peak wavelength. These findings suggest that the chloride ion concentration in concrete can be estimated by analyzing the spectral peak near 10.6 µm. This study contributes to the development of a non-destructive chloride detection method, which is expected to enhance the durability assessment of RC structures affected by chloride-induced deterioration.

In this work, we report simulation-assisted analysis of a room-temperature (300 K) low-threshold avalanche photodiode (APD) based on a WSe₂ homojunction. Device simulations were conducted using a two-band model and the Chynoweth formalism for impact ionization, with material parameters extracted for few-layer and multi-layer homojunction WSe₂ structures. The simulated results accurately reproduce experimental dark and photocurrent characteristics, with an avalanche threshold voltage of approximately
~1.6 V-over 26 times lower than that of conventional InGaAs APDs. The structure exhibits ultra-low dark current (10–100 fA) and high sensitivity, enabling detection of optical signals as low as 7.7 × 10⁴ photons. The analyzed low voltage avalanche photodetector enables utilization in a wide range of applications.

Detecting barely visible impact damage (BVID) in carbon fibre-reinforced polymer (CFRP) materials is a key challenge in maintaining the safety and reliability of composite structures. This study presents the application of low-power vibrothermography to identify such defects. Using a Long-wave infrared (LWIR) camera, thermal data were captured from the CFRP specimens that inhibit BVID. How image-processing, specifically principal component analysis (PCA) and sparse principal component (SPCA) analysis can enhance thermal contrast and improve the accuracy of defect size is also explored. By combining low-energy excitation with advanced data analysis, this research aims to develop a more accessible and reliable approach to non-destructive testing (NDT) for composite materials.

This work presents a non-destructive methodology to estimate the residual porosity and mechanical properties of titanium foams produced via Hot Isostatic Pressing (HIP) followed by Solid-State Foaming (SSF). Pulsed laser spot thermography was employed to measure thermal diffusivity in compact and foamed Ti6Al4V-ELI samples derived from powders of different granulometries. A power-law correlation between thermal diffusivity and porosity was used to estimate post-foaming porosity, which was then used to predict elastic modulus, yield strength, and ultimate tensile strength. Results highlight the potential of thermal diffusivity as a reliable indicator of structural performance, offering a rapid and fully non-destructive route for evaluating metallic foams in biomedical and aerospace applications.

Fiber-to-fiber recycling must be promoted to achieve sustainability in the textile industry. Mixed fiber materials cause significant issues in recycling, making accurate sorting essential for recycling. Garments may contain internal materials called interlinings, which are wrapped in the outer fabric and are not visible from the garment surface. This study proposes a non-destructive method for detecting interlinings in garments using active infrared (IR) thermography. Numerical simulations showed that the presence, thickness, and material of the interlining affected the cooling behavior. Fourier analysis of the surface temperature curves revealed that an increased interlining thickness leads to slower cooling and a greater phase lag, enabling the identification of interlining characteristics from thermal responses.

The rapid growth of wind energy infrastructure over the past two to three decades has led to an urgent need for advanced non-destructive testing (NDT) methods—both for newly installed wind turbine blades (WTBs) and for ageing components nearing the end of their service life. Among emerging techniques, passive infrared thermography (IRT) offers a promising solution by enabling contactless, time-efficient inspection based on naturally occurring thermal variations. The effectiveness of passive IRT depends on the presence of sufficient thermal contrast to distinguish surface features, subsurface structures, and defects. To better understand the possibility of obtaining such contrast in composite structures such as WTBs, a controlled study was carried out on a blade section exposed to programmed temperature transients in a climate chamber. Infrared measurements were recorded, and the thermal behaviour of the specimen was simulated using finite element models (FEM) in COMSOL Multiphysics. Although direct validation is limited by measurement uncertainties and transient effects, the comparison provides insight into the capabilities and limitations of FEM in replicating real-world thermal behaviour. This paper focuses specifically on the challenges related to the modelling approach.

Defects are an inevitable occurrence in the manufacturing of composite components, leading to deviations from intended specifications and impacting performance. Nondestructive testing (NDT) techniques provide a noninvasive alternative to destructive methods for detecting and quantifying defects in CFRP materials. However, challenges remain in applying NDT effectively, particularly in sizing defects. This study evaluates the potential of simulation for predicting defect sizing accuracy using front and back step-heating thermography on thin CFRP materials. Finite-element-based software is used to simulate temperature distribution on the model surface, with defects consisting of voids and polyethylene inserts. The derivative and full-width half maximum (FWHM) methods are employed to measure defect size. For an accuracy threshold of 20% error, the minimum detectable void size in front heating is 4 mm in simulations and 6 mm in experiments. For polyethylene defects, the minimum detectable size is 6 mm in simulations, while no defect sizes meet the threshold in experiments. In back heating, both void and polyethylene defects have a minimum detectable size of 4 mm in both simulations and experiments. Collected thermal images are further analyzed using an artificial neural network (ANN). Robust principal component analysis (RCPA) summarizes the thermography data, and image segmentation is then applied to the resulting image. Initial results will be presented in the conference.