Thermal fatigue degradation in glass fiber reinforced polymer (GFRP) composites presents a major challenge for structural reliability and sustainable asset management in aerospace, transportation, and energy systems. This study proposes a physics-based sustainability framework for evaluating non-destructive testing (NDT) methods through the integration of technical, environmental, and economic indicators into a Composite Sustainability Index (CSI). The framework is experimentally validated using lock-in infrared thermography for the detection of thermally induced fatigue defects in GFRP laminates with a symmetric ([0/90/+45/-45/0]s) lay-up configuration.,Specimens were subjected to controlled thermal cycling between 25 °C and 150 °C for up to 1500 cycles. Lock-in thermography inspections were conducted at excitation frequencies between 0.05 and 0.5 Hz. Experimental results demonstrated that frequencies of 0.1–0.2 Hz provided the optimal balance between thermal penetration depth and defect sensitivity, achieving CNR values of 6.5–7 for shallow subsurface defects (0.6–1.2 mm). Statistical validation confirmed high repeatability with a coefficient of variation below 5.5%.,The proposed sustainability framework incorporated energy intensity, life-cycle cost reduction, defect detectability, and environmental impact. Lock-in thermography achieved a CSI value of 0.82, outperforming ultrasonics (0.68) and radiography (0.60), while reducing inspection energy demand by up to 85%.,The results demonstrate that integrating thermographic physics with sustainability metrics enables a robust decision-making methodology for next-generation NDT systems. The proposed approach provides a sustainable, energy-efficient, and highly sensitive solution for structural health monitoring of composite structures.

One of the most important processes performed on hyperspectral images is their classification. In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image classification, each attempting to address the hyperspectral data's computational and processing challenges. Convolutional neural networks become less efficient at solving complex problems as the number of parameters and layers increases. As a result, a new architecture of convolutional neural networks is introduced in this paper, which improves network performance and significantly reduces computing time. The proposed method for reducing spectral bands employs sparse and low-rank representation feature extraction methods based on spectral and spatial information. This model expresses each pixel as a linear combination of dictionary atoms. In addition, the alternating direction multiplier method was used to solve the optimization problem. In this method, the two-dimensional convolutional neural network consists of convolutional, pooling, and fully connected layers. In addition, batch normalization and random elimination are used to prevent overfitting. This study's experiments were conducted using Indiana Pine, Pavia, and Washington DC Mall data sets. The results show that the proposed method has a high classification success rate and a shorter duration, and is less complex than existing models.

Selecting a proper method for inspecting particular materials is an imperative challenge in the nondestructive testing (NDT) field. In this regard, comparing different techniques to reach the best defect detection efficiency is of great importance. Digital shearography and active thermography are among the new NDT techniques providing many advantages such as full-field, noncontact, accurate and high-speed inspection. In this paper, both methods are employed to be compared in the detection of subsurface defects in carbon fiber-reinforced plastics (CFRPs). In this respect, several CFRP specimens with artificial defects, including delamination and cracks with various depths and sizes, were prepared. After conducting the relevant experiments, the results revealed that both methods are efficient in NDT of CFRPs. Nevertheless, active thermography indicated superior performance in the inspection of delamination by detecting deeper defects. Shearography showed a better performance in the case of cracks and short defects. However, shearography required more test preparation and a longer heating time.

In this paper, the applicability of Dynamic Active Microwave Thermography (DAMT) in nondestructive inspection of GFRP cylindrical shells has been investigated experimentally and numerically. In this regard, Cylindrical GFRP samples were prepared through a filament winding process. Two types of planar and linear defects were created inside the cylindrical samples. Holes and cracks in three different diameters and lengths were engraved on the inner side of the samples. Aiming to create temperature contrast between sound and defected areas a microwave excitation setup comprising a microwave horn antenna, a rotational element, and a Faraday’s cage was utilized. Thermal images were captured from the surface of the samples by means of an IR camera. The influences of different parameters including excitation power, heating time, and standoff distance on the temperature contrast were assessed. Besides, in order to examine the electrical field distribution, the interaction of the E-field with the sample, and the temperature distribution on the surface of the sample, the heating process was simulated numerically using the finite element method. The FEM analysis results indicate a proper agreement with the experimental test results.

This study investigates the impact of applied acceleration on the microstructure, mechanical properties, and residual stress distribution of AA5083-H321 aluminum alloy during automated tungsten inert gas (ATIG) welding. In this regard, a vibrational table was designed and fabricated to transfer the acceleration generated by the vibration motor to the weld pool. The vibrating motor operates at a sub-resonance frequency to apply controlled acceleration to the welding zone. Results demonstrate that increasing acceleration during welding, due to better ventilation in the weld pool and local plastic deformation during solidification, decreased residual stress by approximately 30

En este estudio, la efectividad de varias técnicas de procesamiento de imágenes, incluida la band ratio (BR), decorrelation stretch (DS), principal components analysis (PCA), minimum noise fraction (MNF), así como la composición de color falso ASTER RGB: 468, fue evaluada para la extracción de unidades geológicas a partir de imágenes de satélite ASTER en el margen sur de los montes Alborz en Irán. Además, se propuso un método basado en componentes principales de proporciones de bandas (BRs-PCs) para la discriminación de unidades geológicas a partir de imágenes ASTER. En este sentido, se utilizó un escenario de datos ASTER Level1T VNIR+SWIR del año 2004 y como referencia un mapa geológico escala 1:100000 del área de estudio. Los resultados indicaron la idoneidad de las técnicas convencionales de procesamiento de imágenes para la discriminación de unidades geológicas, especialmente la técnica PCA, que destacó claramente la piedra caliza, el basalto, la arenisca, la toba, el conglomerado y la dolomita de la imagen ASTER. El estudio también demostró la eficacia del método BRs-PCs para el mapeo geológico. Este enfoque consideró las ventajas de las técnicas PCA y BR, por lo tanto, proporcionó un resultado superior en comparación con cualquiera de estas técnicas solas, y también un mejor resultado en comparación con otras técnicas utilizadas en este estudio. Por lo tanto, puede ser útil para el mapeo geológico a lo largo de toda la montaña Alborz con condiciones litológicas y geomorfológicas similares.

In this article, ultrasonic welding of glass fiber-reinforced thermosetting polymers which were surface-treated by laser engraving, is investigated. Composite samples were prepared by hand lay-up method. In the next step, surface treatment of the composite samples was performed by means of a high-power laser confirming two grooved and circular patterns. Polymethylmethacrylate and polypropylene as the amorphous and semicrystalline thermoplastic intermediate layers were incorporated to create a joining between the two thermoset parts. Besides, the welding time was considered in the three different levels. In order to investigate the microstructure of the welding zone, scanning electron microscopy analysis were accomplished. Besides, lap-shear tests were performed so as to evaluate the mechanical performances of the welded parts. Morphological studies indicated that in the surface-treated samples, the penetration of the intermediate material into the welded parts is much higher than neat samples and this leads to the superior interaction between the coupling layer and the surface-treated parts. Confirming the morphological outcomes, the results of the lap-shear tests specified that the application of surface treatment using laser engraving has increased the laps-shear strength almost 5.5 times compared to the samples without preparation.

Ultrasonic welding (USW) of thermoplastic composites is considered as an interesting joint technique, which can be cost-effective due to its short welding time. The paper focuses on studying the effect of USW current on strength and quality of welding. In this paper, the welding of thermoplastic unidirectional prepregs glass/polyamide 6 composites laminate with stacking sequence of (0/90/+45/−45/−45/+45/90/0) is conducted by USW. A flat energy director is used to concentrate the interface heat. To examine the interface microstructure, the current-time graph was utilized during welding processes, and the relationship between such interface events, consumed current, weld strength, weldability, and weld quality is fully investigated. Based on the results, the highest welding strength and welding quality of thermoplastic composites can be obtained when the energy director and the first layer of the composite are completely melted and no disorder is observed in the fibers. In addition, the highest joint strength equals to 24.46 MPa, which is obtained after 1830 ms welding time.

In this study, the effectiveness of several image processing techniques, including the band ratio (BR), decorrelation stretch (DS), principal components analysis (PCA), minimum noise fraction (MNF), as well as the ASTER false color composition RGB: 468, was evaluated for the extraction of geological units from ASTER satellite imagery in southern margin of Alborz Mountain in Iran. In addition, a method based on Principal Components of Band Ratios (BRs-PCs) was proposed for discrimination of geological units from ASTER imagery. In this respect, a scene of ASTER Level1T VNIR+SWIR data of the year 2004 was acquired, and a geological map scale 1:100000 of the study area was used as the reference. The results indicated suitability of the conventional image processing techniques for discrimination of geological units, especially the PCA technique, which clearly highlighted Limestone, Basalt, Sandstone, Tuff, Conglomerate, and Dolomite from the ASTER image. The study also demonstrated effectiveness of the BRs-PCs method for geological mapping. This approach considered the advantages of both PCA and BR techniques, therefore, provided a superior result comparing to any of these techniques alone, and also better result comparing to other techniques used in this study. Thus, it may be useful for geological mapping along the whole Alborz Mountain with similar lithological and geomorphological conditions.

In the present paper, applicability of the continuous active infrared thermography in the non-destructive inspection of cylindrical 3D-printed parts has been investigated. In this regard, four cylindrical hollow parts with different dimensions and infills were printed using the Fused Deposition Modeling (FDM) method. In each of the samples, three voids with different dimensions were located. Continuous stimulation as well as flash excitation were employed in order to create thermal contrast. Finite Element Modeling (FEM) was exploited so as to numerically simulate the active thermography procedure. The results proved the capability of continuous excitation even in the detection of lower-sized defects. Moreover, the results indicated an appropriate verification between the FEM and experimental results.

In this paper, in an attempt to non-destructively evaluate the Glass Fiber Reinforced Plastic (GFRP) composites, active thermography with microwave excitation is employed. Different types of defects including holes and cracks with different sizes were located in GFRP samples as well as specimens produced from PVC foams. Active Microwave Thermography (AMT) tests were performed from two different distances and with two different power levels. Moreover, the enhancement of detectability by the application of water spraying on the surface of the samples was investigated. The results indicated that active microwave thermography has better performance in the detection of holes compared with cracks. The application of water spraying significantly enhanced the detectability of surface cracks in the GFRP composites and PVC foam samples. Finite Element Analysis was employed to numerically simulate the microwave heating of the GFRP samples and evaluate the temperature variations. Statistical analyses were carried out to investigate the effect of test parameters on the detectability of defects. The statistical analysis results indicated that the heating time has a superior influence on the temperature contrast and detectability.

In the present research, the applicability of active thermography using ultrasonic excitation in detecting delamination defects has been investigated experimentally. In this regard, a Glass Fiber Reinforced Plastic (GFRP) Sample was prepared via the hand layup method. PTFE sheet pieces were considered delamination defects located inside the sample. The delamination defects were considered in the three different sizes. Moreover, three delaminations with similar sizes were placed in three different depths. The ultrasonic excitations were conducted using a Piezoelectric PZT transducer. The excitation frequency was adjusted to a constant value. The ultrasonic thermography inspections were performed with three different excitation powers. The sample was constrained in the fixed-fixed mode. The thermography inspections were carried out with three various excitation times. Results indicated that the defect of a maximum size has been detected in all inspections. Moreover, in order to enhance the detectability a trade-off of excitation power and excitation time should be selected.

This study investigates the impact of CuO and TiO 2 nanoparticles (NPs) in polyvinyl chloride interface of ultrasonically welded glass fiber-reinforced plastic. Welding pressure, duration and holding time were optimized for superior mechanical bonding. Response surface methodology was employed to develop a regression model incorporating welding time, NP weight percentage and thermal conductivity. The results indicated that using a 2 bar pressure, a holding time of 0.5 s, 1 wt.% CuO, and a welding time of 0.45 s, the nanocomposite (NC) demonstrated its maximum breaking load of 2039 N. When the welding time was reduced to 0.4 s for the NC with 1 wt.% TiO 2 , a slightly lower breaking load of 2036 N was observed demonstrating NC strength and resilience. Therefore, uniform dispersion of 1 wt.% NPs in the polymer matrix increased the welding tensile strength up to 70% of the neat composite.

In the present study, the suitability of principal components analysis (PCA) based techniques was evaluated for identification of geological units from Landsat-9 satellite imagery. In this respect, a scene of Landsat-9 operational land imager 2 (OLI–2) data of the year 2023 was acquired and a geological map scale 1:100000 of the study area was used as the reference. The results indicated suitability of the PCA based techniques for discrimination of geological units from Landsat-9 image, especially the PCA of decorrelation stretch (DS) approach. The PCA-DS approach, which considered the advantages of both PCA and DS techniques, successfully identified all the geological units in the study area, including the Basalt, Sandstone, Dolomite, and Conglomerate. However, the performance of the PCA and DS techniques was also reasonable for this purpose. On the other hand, the study revealed weak performance of the minimum noise fraction (MNF) and PCA-MNF techniques for geological mapping using Landsat-9 imagery. In conclusion, the study demonstrated the advantage of the PCA-DS approach for geological mapping using Landsat-9 imagery; therefore, it may be useful in futures studies for geological mapping along the whole Alborz Mountain with similar lithological and geomorphological conditions.

Polymethyl methacrylate nanocomposite including CuO, TiO2 and Al2O3 nanoparticles (NPs) was studied as an interlayer to enhance properties of ultrasonically welded glass fiber reinforced plastic. Synthesized NPs were characterized by SEM and XRD methods. A multiple regression model based on response surface methodology by using the input parameters of welding time, NP's weight percentage and thermal conductivity according to analysis of variance was developed. Results showed that applying 2 bar pressure, 0.5 s holding time, 1 wt% CuO, and 0.4 s welding time, the maximum breaking load of 2585 N can be obtained, while with 1 wt% TiO2 and Al2O3 breaking loads of 2198 N and 2069 N can be expected, respectively. Therefore, using CuO, TiO2 and Al2O3 NPs, an increase in the weld strength by a factor of 1.5 can be realized.

Numerous uses of the hyperspectral remote sensing technology exist for identifying land cover and tracking its evolution. The classification of hyperspectral images must now take into account both spectral and spatial information due to recent advancements and the production of images with high spatial resolution. Convolutional neural networks (CNNs) have much employed in recent years to enhance the classification precision of hyperspectral images. The simultaneous use of spatial feature extraction methods in CNNs has not received significant attention in prior studies. In this study, a novel CNN architecture has been developed for classifying hyperspectral images. The weighted genetic (WG) algorithm is used in the proposed technique to minimize the hyperspectral image’s dimensions. The WG algorithm keeps every band in the image and gives each one weight between zero and one based on how much information it contains. Following the expectation maximization (EM) method to the collected features, the segmented objects are then categorized using the CNN algorithm. Three benchmark hyperspectral images, Pavia, DC Mall, and Indiana Pine, were used to assess the proposed approach. The trials’ findings demonstrate the proposed approach’s superiority over the multilayer perceptron (MLP) algorithm in the Pavia, DC Mall, and Indiana Pine images by 14, 16, and 8% in the overall accuracy parameter, respectively.

In this study, additive manufacturing of reinforced parts using metallic wire as both a reinforcement component and a shape memory stimulus through Fused Deposition Modeling (FDM) was investigated. A Shape Memory Polymer (SMP) restores its original shape and recovers it upon specific stimuli. This research employed chromium-nickel metal wire as a reinforcing component to enhance mechanical properties and introduce the capability for thermal stimulation of polylactic acid (PLA) via electrical current using the "in-situ impregnation" method within FDM process. Reinforced specimens were fabricated with wire with two diameters of 0.1 and 0.15 mm, along with two volume percentages of 5 and 10. Comprehensive evaluations encompassing mechanical (tensile and flexural) and thermal properties of the printed specimens were conducted. The outcomes revealed a significant enhancement in both tensile and flexural properties of the polymer matrix due to the embedding metallic wire, even under elevated temperatures during bending test. Furthermore, the thermal properties of the reinforced specimens were examined by subjecting them to various voltages, resulting in temperature ranging from 36.4 to 150.1°C. These findings highlight the ability to tailor a wide range of mechanical properties and shape recovery in the reinforced specimens by carefully selecting the wire volume fraction, voltage, and wire diameter, thus regulating the materials properties with specific application requirements.

Hyperspectral remote sensing technology has many applications in the fields of land cover classification and examination of their changes. It seems necessary to use both spectral and spatial information in the hyperspectral image classification due to recent developments and the availability of images at higher spatial resolution. In this study, a new approach for object-based classification of hyperspectral images is introduced. In the proposed approach, first nine spatial features, including mean, standard deviation, contrast, homogeneity, correlation, dissimilarity, energy, wavelet transform and Gabor filter, are extracted from the neighboring pixels of the hyperspectral image. Then, the dimensions of the obtained features are reduced using weighted genetic (WG) algorithm. Next, the hierarchical segmentation (HSEG) algorithm is applied to the reduced features. Then, for the objects obtained from segmentation, nine spatial features, area, perimeter, shape index, strength, maximum intensity, minimum intensity, entropy, relation and adjacency, are extracted. Finally, the classification is performed using the multilayer perceptron neural network (MLP) algorithm. The proposed approach was implemented on three hyperspectral images of Indiana Pine, Berlin and Telops. According to the experimental results, the proposed approach is superior to the MLP classification method. This increase in the overall accuracy is about 12% for the Indiana Pine image, about 11% for the Berlin image, and about 8% for the Telops image.

Abstract Ultrasonic welding is one of the most common methods for joining the polymer parts. It provides a rapid joining with a proper strength. Ultrasonic welding is also considered as a clean joining. This method is widely used in joining of thermoplastic parts. But, in order to weld the thermoset parts, a coupling layer shall be incorporated. This method occasionally is perfect because of voids and improper adhesion between to welded parts, leading to significant weld strength reduction. Consequently, application of a kind of NDT method could be useful for quality assurance of these parts. In this paper, active IR thermography is employed to nondestructively test of ultrasonically welded Glass Fiber Reinforced Plastics (GFRP). In order to study the weld microstructure, SEM analysis is performed. The results indicated that the IR thermography is capable to detect the existence of some defects in the welding zone. The Lap-shear tests indicate that the dimensions of the defect reduce the weld strength, significantly.

All plants need specific climatic and environmental conditions to survive, and the fig tree is no exception. In this research, we mapped the location of suitable lands for planting this crop in Estahban city using the functionality of a geospatial information system (GIS). For this purpose, the following six climatic elements were analysed: average, maximum, minimum temperature and average, maximum humidity and Rainfall, and five environmental parameters of slope, altitude, land use, soil type, and soil erosion. Due to their incompleteness, we reconstructed the climatic elements by the differences and ratios method. Then the maps related to the mentioned parameters were prepared in GIS and standardized and weighted using the fuzzy logic method and the criteria for planting fig trees. Then, we combined the maps with the help of fuzzy logic and obtained the zoning map of the lands suitable for planting fig trees. Based on the proposed output map, it was clear that most of the areas for planting figs were located in the central and middle parts of the city. In addition, we found that other places in the North-West sector had similar capability.