Browsing by Author "Sfarra, Stefano"

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    Automatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model
    (Springer, 2024-09-16) Wang, Xin; Jiang, Guimin; Hu, Jue; Sfarra, Stefano; Mostacci, Miranda; Kouis, Dimitrios; Yang, Dazhi; Fernandes, Henrique; Avdelidis, Nicolas P.; Maldague, Xavier; Gai, Yonggang; Zhang, Hai
    In recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.
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    Enhanced infrared image processing for impacted carbon/glass fiber-reinforced composite evaluation
    (MDPI, 2017-12-26) Zhang, Hai; Avdelidis, Nicolas Peter; Osman, Ahmad; Ibarra-Castanedo, Clemente; Sfarra, Stefano; Fernandes, Henrique; Matikas, Theodore E.; Maldague, Xavier P. V.
    In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.
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    Exploring the potentialities of thermal asymmetries in composite wind turbine blade structures via numerical and thermographic methods: a thermophysical perspective
    (Springer , 2024) Figueiredo, Alisson A. A.; D’Alessandro, G.; Perilli, S.; Sfarra, Stefano; Fernandes, Henrique
    Using composite materials in turbine blades has become common in the wind power industry due to their mechanical properties and low mass. This work aims to investigate the effectiveness of the active infrared thermography technique as a non-destructive inspection tool to identify defects in composite material structures of turbine blades. Experiments were carried out by heating the sample and capturing thermographic images using a thermal camera in four different scenarios, changing the heating strategy. Such a preliminary experiments are prodromic to build, in future, the so-called optimal experiment design for thermal property estimation. The experimental results using two heaters arranged symmetrically on the sample detected the presence of the defect through temperature curves extracted from thermal images, where temperature asymmetries of 25% between the regions with and without defect occurred. Moreover, when only a larger heater was used in transmission mode, the defect was detected based on differences between normalized excess temperatures on the side with and without the defect in the order of 20%. Additionally, numerical simulations were carried out to present solutions for improving defect detection. It was demonstrated that active infrared thermography is an efficient technique for detecting flaws in composite material structures of turbine blades. This research contributes to advancing knowledge in inspecting composite materials.
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    Impact modelling and a posteriori non-destructive evaluation of homogeneous particleboards of sugarcane bagasse
    (2018-01-12) Zhang, Hai; Sfarra, Stefano; Sarasini, Fabrizio; Fiorelli, Juliano; Peeters, Jeroen; Avdelidis, Nicolas Peter; de Lucca Sartori, Diogo; Ibarra-Castanedo, Clemente; Perilli, Stefano; Mokhtari, Yacine; Tirillò, Jacopo; Maldague, Xavier P. V.
    With a view to gaining an in-depth assessment of the response of particleboards (PBs) to different in-service loading conditions, samples of high-density homogeneous PBs of sugarcane bagasse and castor oil polyurethane resin were manufactured and subjected to low velocity impacts using an instrumented drop weight impact tower and four different energy levels, namely 5, 10, 20 and 30 J. The prediction of the damage modes was assessed using Comsol Multiphysics ® . ®. In particular, the random distribution of the fibres and their lengths were reproduced through a robust model. The experimentally obtained dent depths due to the impactor were compared with the ones numerically simulated showing good agreement. The post-impact damage was evaluated by a simultaneous system of image acquisitions coming from two different sensors. In particular, thermograms were recorded during the heating up and cooling down phases, while the specklegrams were gathered one at room temperature (as reference) and the remaining during the cooling down phase. On one hand, the specklegrams were processed via a new software package named Ncorr v.1.2, which is an open-source subset-based 2D digital image correlation (DIC) package that combines modern DIC algorithms proposed in the literature with additional enhancements. On the other hand, the thermographic results linked to a square pulse were compared with those coming from the laser line thermography technique that heats a line-region on the surface of the sample instead of a spot. Surprisingly, both the vibrothermography and the line scanning thermography methods coupled with a robotized system show substantial advantages in the defect detection around the impacted zone.
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    Influence of thermal contrast and limitations of a deep-learning based estimation of early-stage tumour parameters in different breast shapes using simulated passive and dynamic thermography
    (Elsevier, 2025-04) Moraes, Mateus Felipe Benicio; Sfarra, Stefano; Fernandes, Henrique; Figueiredo, Alisson A. A.
    To enhance diagnostic sensitivity compared to passive thermography, thermal stress can be applied to the breast surface with the temperatures being measured in the thermal recovery phase, a process called dynamic thermography. This study aims to evaluate the limitations of both passive and dynamic thermography in estimating early-stage tumour parameters across different breast shapes and how to improve the results. Three breast models with thermoregulation were solved numerically using COMSOL Multiphysics®. A neural network developed in PyTorch was used to estimate breast tumour location and size. The estimates obtained using each approach were compared, and the effects of thermal contrast, noise, and tumour depth range were analysed. Dynamic thermography provided the most accurate estimates compared to passive thermography, with mean error reductions that reached up to 33.25%. Additionally, the number of estimates with errors higher than 10% was up to 48.42% lower. Tumour radius showed the lowest noise threshold, providing the highest estimations errors. Adding deeper tumours to the datasets caused mean error increases of up to 51.27%. Thus, this work contributes by comparing both types of thermography, analysing thermal aspects of the temperature data that influences the neural network's estimation process, and suggesting alternatives to improve its accuracy.
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    Non-destructive imaging of marqueteries based on a new infrared-terahertz fusion technique
    (Elsevier, 2022-06-29) Hu, Jue; Zhang, Hai; Sfarra, Stefano; Gargiulo, Gianfranco; Avdelidis, Nicolas Peter; Zhang, Mingli; Yang, Dazhi; Maldague, Xavier
    Detection of subsurface defects has hitherto been regarded as an important element in the course of preserving cultural heritage. To do so, non-destructive imaging approaches for viewing and determining the location of splitting inside the sample under test are required, which constitute the subject of the present study. Both active thermography and terahertz imaging have demonstrated their potential in providing non-destructive inspection on cultural heritage objects. Conventionally, active thermography has been used to retrieve details on the defects as well as morphological data from the surface and subsurface, whereas pulsed terahertz imaging has been applied to record the internal material distribution. Here, the feature extraction, selection and fusion framework is extended to design a fusion process to merge the information obtained by both active thermography and terahertz imaging; in this way, the technique naturally inherits the strengths of both aforementioned imaging technologies. The fusion technique is able to produce images with high-contrast defect information located at different depths. To demonstrate the efficacy of the suggested technique, an experiment has been conducted on an ancient marquetry.
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    Non-invasive inspection for a hand-bound book of the 19th century: numerical simulations and experimental analysis of infrared, terahertz, and ultrasonic methods
    (Elsevier, 2024-05-24) Jiang, Guimin; Zhu, Pengfei; Gai, Yonggang; Jiang, Tingyi; Yang, Dazhi; Sfarra, Stefano; Waschkies, Thomas; Osman, Ahmad; Fernandes, Henrique; Avdelidis, Nicolas P.; Maldague, Xavier; Zhang, Hai
    Due to fungal growth and mishandling in the book, there are various types of defects as they age such as foxing, tears, and creases. It is important to develop novel non-invasive inspection techniques and defect recognition algorithms. In this work, three non-invasive inspection techniques, including infrared thermography (IRT), terahertz time-domain spectroscopy (THz-TDS), and air-coupled ultrasound (ACU), were employed for the detection of defects in an ancient book cover. To improve the image quality and defect contrast, principal component analysis, fast Fourier transform, and partial least squares regression algorithms are used as the post-processing methods. Furthermore, the YOLOv7 network is deployed for defect automatic detection. Finite element analysis and finite-difference time-domain methods were employed for generating training dataset of YOLOv7 network. Experimental results demonstrate that IRT and THz-TDS has excellent detection capability for surface and subsurface defects, respectively. By employing YOLOv7 network with simulation datasets, defects can be effectively identified.
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    Optical and mechanical excitation thermography for impact response in basalt-carbon hybrid fiber-reinforced composite laminates
    (IEEE, 2017-08-24) Zhang, Hai; Sfarra, Stefano; Sarasini, Fabrizio; Ibarra-Castanedo, Clemente; Perilli, Stefano; Fernandes, Henrique; Duan, Yuxia; Peeters, Jeroen; Avdelidis, Nicolas Peter; Maldague, Xavier P. V.
    In this paper, optical and mechanical excitation thermography were used to investigate basalt fiber reinforced polymer (BFRP), carbon fiber reinforced polymer (CFRP) and basalt-carbon fiber hybrid specimens subjected to impact loading. Interestingly, two different hybrid structures including sandwich-like and intercalated stacking sequence were used. Pulsed phase thermography (PPT), principal component thermography (PCT) and partial least squares thermography (PLST) were used to process the thermographic data. X-ray computed tomography (CT) was used for validation. In addition, signal-to-noise ratio (SNR) analysis was used as a means of quantitatively comparing the thermographic results. Of particular interest, the depth information linked to Loadings in PLST was estimated for the first time. Finally, a reference was provided for taking advantage of different hybrids in view of special industrial applications.