Conventional Texture Research Articles

Traffic noise mitigation through texture-induced quiet pavement: Analytical modeling and field test

Tire-pavement noise is a major contributor to traffic noise pollution, impacting human health and well-being. The advent of 3D printing and prefabrication technologies makes it feasible to economically produce delicate concrete pavement textures for noise mitigation. However, relationships between the noise and texture characteristics (e.g., cavity shape, width, depth, and spacing) remain unknown. This study developed an analytical model for tire-pavement noise and calibrated/validated the model using On-board Sound Intensity tests on a purposely constructed road section with manufactured texture configurations. These textures were found to be capable of reducing the noise level by 8–15 dB(A) over conventional concrete textures, and the model prediction closely matched experimental data. Subsequently, the model was used to investigate the texture-noise relationships and identify those major influencing factors. This study contributes to tire-pavement noise modeling and offers insights for building quieter concrete pavements and enhancing the urban living environment.

Cleaner production of multi-scale surface textures using integrated ball-end and vibration-assisted milling

Surface texturing has emerged as a critical approach for enhancing the functional performance of engineered surfaces across various applications. However, existing methods often involve multi-step processes, excessive material waste, or the use of environmentally harmful chemicals. This study introduces a novel cleaner method for fabricating hierarchical multi-scale surface textures via integrated ball-end and vibration-assisted milling. The proposed technique synergistically combines millimeter-scale texturing through ball-end milling with micrometer-scale texturing via vibration assistance, enabling the creation of complex, multi-scale surface structures in a one-step process. A custom-designed 2-degree-of-freedom (2-DOF) flexible stage with a repeatability of ±0.1 μm was developed to precisely control two-direction vibrations during milling. Comprehensive modeling and experimental validation were conducted across various machining conditions and surface configurations. The results demonstrate that the proposed method achieves a 50% reduction in energy consumption and a great decrease in material waste compared to traditional texturing processes. Surfaces machined with two-direction vibration assistance exhibited the highest degree of precision, with chip adhesion reduced from 8.98% to 0.49% compared to non-vibration conditions. Contact angle measurements revealed that finer textures promoted increased hydrophilicity, with contact angles decreasing from 86.5° to 65.2° as groove pitch reduced from 400 μm to 200 μm under two-direction vibration. This innovative approach offers significant advancements in sustainable manufacturing techniques for functional surfaces, providing a cleaner, more efficient alternative to conventional multi-step texturing methods.

Grain size hardening versus texture softening: Mechanical anisotropy of an AZ31B magnesium alloy processed by equal-channel angular pressing

Microstructural evolution of hexagonally close-packed (hcp) alloys subjected to severe plastic deformation differs from that of alloys with cubic crystal structure. While grain refinement is the main objective in conventional ECAP texture changes in hcp materials can result in lower strength but improved ductility depending on the deformation direction. This paper addresses the question of how the processes of grain size hardening and texture softening contribute to the highly anisotropic mechanical behavior of a severely deformed AZ31B magnesium alloy. ECAP is performed at different temperatures starting at 250 °C for the first 4 passes and subsequently reduced to 200 °C (C5, C6) and 150 °C (C7, C8). The mechanical properties of the resulting material conditions are investigated with tensile tests in four different deformation directions. While the yield strength increases up to 280 MPa in the transverse direction (TD), an opposite trend of low strength and good ductility can be observed for deformation in the extrusion (ED) and normal directions (ND). Additional microstructural investigations using EBSD show that the grain size is reduced by 95% during ECAP. Moreover, texture measurements using XRD reveal a strong fiber texture, with a high number of basal planes parallel to the shear plane of ECAP deformation, which results in a significantly improved ductility and lower strength in ED and ND. The results are discussed in the light of the competition between grain size hardening and texture softening. They highlight the potential of ECAP for the improvement of strength and ductility for magnesium alloys.

Spatial frequency decomposition with bandpass filters for multiscale analyses and functional correlations

To address the essential problem in surface metrology of establishing functional correlations spatial, frequencies in topographic measurements are progressively decomposed into a large number of narrow bands. Bandpass filters and commercially available software are used. These bands can be analyzed with conventional surface texture parameters, like average roughness, Sa, or other parameters, for detailed, multiscale topographic characterizations. Earlier kinds of multiscale characterization, like relative area, required specialized software performing multiple triangular tiling exercises. Multiscale regression analyses can test strengths of functional correlations over a range of scales. Here, friction coefficients are regressed against standard surface texture parameters over the range of scales available in a measurement. Correlation strengths trend with the scales of the bandpass filters. Using bandpass frequency, i.e., wavelength or scale, decompositions, the R2 at 25 μm, exceeds 0.9 for Sa compared with an R2 of only 0.2 using the broader band of conventional roughness filtering. These improved, scale-specific functional correlations can facilitate scientific understandings and specifications of topographies in product and process design and in designs of quality assurance systems.

Evaluation of tribological performance in contact pairs by implementing the biomimetic surface textures with lubricant flow using CFD techniques

PurposeThis paper aims to investigate the tribological benefits of a biomimetic teardrop surface texture inspired by snakeskin compared to conventional surface textures with the help of geometrical and flow parameters using computational fluid dynamics techniques.Design/methodology/approachThe lubricant is assumed to be Newtonian, and the flow is laminar with constant viscosity and isothermal property. The governing equations, continuity and Navier–Stokes equation, are discretised by the finite volume method, and cavitation modelling is included. The discretisation for the momentum equations is carried out using the second-order difference method for the SIMPLEC algorithm of pressure–velocity coupling.FindingsThe results indicate that biomimetic teardrop surface texturing performs better than conventional shapes surface textures in improving tribological performance. Furthermore, the parallel texture orientation along with the flow generates a high-pressure distribution relative to other orientations. Surface texture area density also highly influences the load-carrying capacity, which is optimum at 29%. Zigzag pattern arrangement performs better compared to linear pattern arrangement of texturing.Originality/valueThe paper proposes that this unique biomimetic teardrop shape can give better tribological performance than conventional shapes.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0053/

Displacement Mapping as a Highly Flexible Surface Texturing Tool for Additively Photopolymerized Components.

Displacement mapping is a computer graphics technique that enables the design of components with regularly or randomly textured surfaces that can be quickly materialized on a three-dimensional (3D) printer when needed. This approach is, in principle, more flexible, faster, and more economical compared to conventional texturing methods, but the accuracy of the texture depends heavily on the parameters used. The purpose of this study is to demonstrate how to produce a surface-textured part using polygonal (mesh) modeling software and a photopolymerizable resin and to develop a universal methodology to predict the dimensional accuracy of the model file log combined with a resin 3D printer. The printed components were characterized on a scanning confocal microscope. In the setup used in this study, the mesh size had to be reduced to 10% of the smallest feature size, and the textured layer had to be heavily (×4.5) overexposed to achieve the desired accuracy. As a practical application, two functional stamps with a regular (honeycomb) and a random texture, respectively, were successfully manufactured. The insights gained will be of great benefit for quickly and cost-effectively producing components with innovative patterns and textures for a variety of hobby, industrial, and biomedical applications.

Survival time prediction in patients with high-grade serous ovarian cancer based on 18F-FDG PET/CT- derived inter-tumor heterogeneity metrics

BackgroundThe presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative 18F-FDG PET/CT derived inter-tumor heterogeneity metrics in determining progression-free survival (PFS) and overall survival (OS) in patients diagnosed with high-grade serous ovarian cancer (HGSOC). Additionally, the study investigated the potential correlation between model risk scores and the expression levels of p53 and Ki-67.MethodsA total of 292 patients diagnosed with HGSOC were retrospectively enrolled at Shengjing Hospital of China Medical University (median age: 54 ± 9.4 years). Quantitative inter-tumor heterogeneity metrics were calculated based on conventional measurements and texture features of primary and metastatic lesions in 18F-FDG PET/CT. Conventional models, heterogeneity models, and integrated models were then constructed to predict PFS and OS. Spearman’s correlation coefficient (ρ) was used to evaluate the correlation between immunohistochemical scores of p53 and Ki-67 and model risk scores.ResultsThe C-indices of the integrated models were the highest for both PFS and OS models. The C-indices of the training set and testing set of the integrated PFS model were 0.898 (95% confidence interval [CI]: 0.881–0.914) and 0.891 (95% CI: 0.860–0.921), respectively. For the integrated OS model, the C-indices of the training set and testing set were 0.894 (95% CI: 0.871–0.917) and 0.905 (95% CI: 0.873–0.936), respectively. The integrated PFS model showed the strongest correlation with the expression levels of p53 (ρ = 0.859, p < 0.001) and Ki-67 (ρ = 0.829, p < 0.001).ConclusionsThe models based on 18F-FDG PET/CT quantitative inter-tumor heterogeneity metrics exhibited good performance for predicting the PFS and OS of patients with HGSOC. p53 and Ki-67 expression levels were strongly correlated with the risk scores of the integrated predictive models.

MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma.

To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS). This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pathology, preoperative MRI, and clinical data available at our tertiary academic institution. Two radiologists independently evaluated 14 features on preoperative MRI. Texture features based on 3D segmentation were extracted from T2W-weighted MRI (T2WI) using commercially available texture software (TexRAD™, Feedback Medical Ltd., Great Britain). MRI conventional features, and clinical and MRI texture features were compared between LM and LMS groups. Dataset was randomly divided into training (86 cases) and testing (22 cases) cohorts (8:2 ratio); training cohort was further subdivided into training and validation sets using ten-fold cross-validation. Optimal radiomics model was selected out of 90 different machine learning pipelines and five models containing different combinations of MRI, clinical, and radiomics variables. 12/14 MRI conventional features and 2/2 clinical features were significantly different between LM and LMS groups. MRI conventional features had moderate to excellent inter-reader agreement for all but two features. Models combining MRI conventional and clinical features (AUC 0.956) and MRI conventional, clinical, and radiomics features (AUC 0.989) had better performance compared to models containing MRI conventional features alone (AUC 0.846 and 0.890) or radiomics features alone (0.929). While multiple MRI and clinical features differed between LM and LMS groups, the model combining MRI, clinical, and radiomic features had the best predictive ability but was only marginally better than a model utilizing conventional MRI and clinical data alone.

Image Inpainting Forgery Detection: A Review.

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images.

AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.

An Automated Grading System Based on Topological Features for the Evaluation of Corneal Fluorescein Staining in Dry Eye Disease

Background: Corneal fluorescein staining is a key biomarker in evaluating dry eye disease. However, subjective scales of corneal fluorescein staining are lacking in consistency and increase the difficulties of an accurate diagnosis for clinicians. This study aimed to propose an automatic machine learning-based method for corneal fluorescein staining evaluation by utilizing prior information about the spatial connection and distribution of the staining region. Methods: We proposed an end-to-end automatic machine learning-based classification model that consists of staining region identification, feature signature construction, and machine learning-based classification, which fully scrutinizes the multiscale topological features together with conventional texture and morphological features. The proposed model was evaluated using retrospective data from Beijing Tongren Hospital. Two masked ophthalmologists scored images independently using the Sjögren’s International Collaborative Clinical Alliance Ocular Staining Score scale. Results: A total of 382 images were enrolled in the study. A signature with six topological features, two textural features, and two morphological features was constructed after feature extraction and selection. Support vector machines showed the best classification performance (accuracy: 82.67%, area under the curve: 96.59%) with the designed signature. Meanwhile, topological features contributed more to the classification, compared with other features. According to the distribution and correlation with features and scores, topological features performed better than others. Conclusions: An automatic machine learning-based method was advanced for corneal fluorescein staining evaluation. The topological features in presenting the spatial connectivity and distribution of staining regions are essential for an efficient corneal fluorescein staining evaluation. This result implies the clinical application of topological features in dry-eye diagnosis and therapeutic effect evaluation.

Detailed evaluation of topographical effects of Hirtisation post-processing on electron beam powder bed fusion (PBF-EB) manufactured Ti-6Al-4V component

Metal additive manufacturing surface topographies are complex and challenging to characterise due to e.g. steep local slopes, re-entrant features, varying reflectivity and features of interest in vastly different scale ranges. Nevertheless, average height parameters such as Ra or Sa are commonly used as sole parameters for characterisation. In this paper, a novel method for selecting relevant parameters for evaluation is proposed and demonstrated using a case study where the smoothing effects after three processing steps of the electro chemical post-process Hirtisation of a metal AM surface are quantified. The method uses a combination of conventional areal texture parameters, multiscale analysis and statistics and can be used to efficiently achieve a detailed and more relevant surface topography characterisation. It was found that the three process steps have different effects on the surface topography regarding the types and sizes of features that were affected. In total, Sdq was reduced by 97 %, S5v was reduced by 81 % and Sa was reduced by 78 %. A surface texture with much lower average roughness, less deep pits and less steep slopes was produced, which is expected to be beneficial for improved fatigue properties.

Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion: A study from Poseidon, NW shelf Australia

Reservoir heterogeneities play a crucial role in governing reservoir performance and management. Traditionally, detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data, which is a time-intensive task. Many researchers have utilized a robust Grey-level co-occurrence matrix (GLCM)-based texture attributes to map reservoir heterogeneity. However, these attributes take seismic data as input and might not be sensitive to lateral lithology variation. To incorporate the lithology information, we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume (a rock property-based attribute) obtained from a deep convolution network-based impedance inversion. Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach, wherein seismic data is used as input. To evaluate the improvement, we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field, NW-shelf, Australia. This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach. In addition, we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization. This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines. Thus, the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity, which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.

Adsorptive Removal of Copper (Cu) in Sasirangan Liquid Waste by Utilization of Rice Husk as Activated Carbon

The business of sasirangan - the conventional texture of Banjar Tribe - has been one of the superb items of South Kalimantan. Sasirangan creation in the shading system utilizes many synthetic components containing weighty metals, and its waste possibly dirties the climate. One weighty metal squanders copper (Cu), poisonous to sea-going living beings and people. The treatment for sasirangan modern wastewater should be possible by an adsorption cycle that utilizations enacted carbon as an adsorbent. This study intends to decide the capacity of enacted carbon produced using rice husk to adsorb Cu from sasirangan fluid burn by dissecting the impact of contact time and adsorbent dose on the adsorption interaction. The bunch framework led the activity with substance and actual actuation. Synthetic initiation was finished by dousing the enacted carbon of rice husk with HCl answer for 24 hours. Simultaneously, actual enactment was done by consuming a heater at 500̊C for 2 hours. The adsorption treatment was given on sasirangan burn through examples with varieties in contact time (30, 60, and 120 minutes) and the adsorbent portion (2, 4, and 6 grams). The highest productivity of contained Cu decrease is 72.34% utilizing carbon with initiation. The ideal contact time expected in the adsorption cycle of weighty metal Cu in Sasirangan liquid waste is 120 minutes, with the ideal portion of 4 grams of actuated rice husk carbon adsorbent.

Characterising the broadband, wide-angle reflectance properties of black silicon surfaces for photovoltaic applications.

Black silicon nanotextures offer significant optical performance improvements when applied to crystalline silicon solar cells. Coupled with conventional pyramidal textures, to create so-called hybrid black silicon, these benefits are shown to be further enhanced. Presented here is a comprehensive analysis of different variations of this texture, coupled with typical anti-reflectance schemes such as coated pyramids, with a view to the significance of this on subsequent, real-world, solar energy generation. The study uses an angle-resolved spectrophometry system to characterise and compare the optical properties of these surface textures in terms of reflectance versus wavelength and incident angle, with and without encapsulant layers. This analysis, coupled with time-resolved, location specific irradiance data, leads to a new figure-of-merit, the weighted reflectivity, with which to compare surface textures for use in solar cells. Weighted reflectivity for an encapsulated solar cell surface, averaged over a year, for a Southampton, UK, location is calculated to be 7.6% for hybrid black silicon, compared to 10.6% for traditional random pyramids with a thin film anti-reflective coating.

Ductile behavior assisted by continuous dynamic recrystallization during high-temperature deformation of calcium-added magnesium alloy AZX612

Magnesium alloys containing Ca have an ignition point above the melting point and are known as non-inflammable magnesium alloys. Herein, the texture and microstructure formation of a Ca-added alloy, AZX612, during uniaxial tensile deformation at elevated temperatures is reported. The total elongation increases with temperature primarily due to prolonged unstable plastic deformation. The texture formed by room-temperature deformation is the conventional deformation texture of magnesium, which has 101̅0 along the tensile axis as the main component. At 573 K, the texture remains almost unchanged, even after straining up to 84%. Results of microstructural analysis show that fine dynamically recrystallized grains exhibit a weak texture at 573 K. Additionally, the deformed matrix exhibits a texture similar to that formed via room-temperature deformation. The significant elongation at high temperatures is attributable to the relaxation of strain concentration via continuous dynamic recrystallization.

Determination of Maximum Accuracy of Concrete Textures as Natural Targets for Movement Tracking Through DIC

The use of natural targets is one of the obstacles to the extensive use of digital image cross-correlation for measuring movements in civil structures. Long distance measurement through image and without access to the structure itself, brings results in an improvement in accessibility, being the procedure cheaper and safer than common methods that require direct access to the measuring point. One of the most used materials in construction is concrete. Therefore, it is important to analyze its performance when using image cross-correlation. In this work, we have made a series of concrete probes with different production characteristics to have a representative variety of concrete surfaces. With them, we have studied their floor error in a cross-correlation procedure using different illumination and blur conditions, to evaluate the influence of those parameters on the results. All results are compared to those obtained using the conventional texture for image cross-correlation techniques, that is a pseudo-speckle target. All experiments are done in laboratory conditions to control all variables involved and to avoid the influence of other variables linked to open air conditions, such as atmospheric disturbances. As a result, we have determined the best conditions to use the natural concrete texture and we have quantified that using this texture leads to a decrease in the accuracy of the results from two to three times the one obtained with a typical pseudo-speckle texture.

Prediction of sub-pyramid texturing as the next step towards high efficiency silicon heterojunction solar cells

The interfacial morphology of crystalline silicon/hydrogenated amorphous silicon (c-Si/a-Si:H) is a key success factor to approach the theoretical efficiency of Si-based solar cells, especially Si heterojunction technology. The unexpected crystalline silicon epitaxial growth and interfacial nanotwins formation remain a challenging issue for silicon heterojunction technology. Here, we design a hybrid interface by tuning pyramid apex-angle to improve c-Si/a-Si:H interfacial morphology in silicon solar cells. The pyramid apex-angle (slightly smaller than 70.53°) consists of hybrid (111)0.9/(011)0.1 c-Si planes, rather than pure (111) planes in conventional texture pyramid. Employing microsecond-long low-temperature (500 K) molecular dynamic simulations, the hybrid (111)/(011) plane prevents from both c-Si epitaxial growth and nanotwin formation. More importantly, given there is not any additional industrial preparation process, the hybrid c-Si plane could improve c-Si/a-Si:H interfacial morphology for a-Si passivated contacts technique, and wide-applied for all silicon-based solar cells as well.

The classification of wheat yellow rust disease based on a combination of textural and deep features.

Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models.

Enhanced interfacial joining strength of laser wobble joined 6061-T6 Al alloy/CFRTP joint via interfacial bionic textures pre-construction

The joining of metals and carbon fiber reinforced thermoplastics composite (CFRTP) is recently considered to be an effective and promising way to meet the lightweight requirements of manufacturing in the aviation field. Herein, novel bionic textures (fishbone-like and nacre-like), constructed by laser pretreatment, are proposed to enhance the hybrid joints of 6061 Al alloy and CFRTP by the laser wobble joining method. The effects of the proposed bionic textures on the hybrid joints of 6061/CFRTP were uncovered through a systematic experimental method and finite element analysis (FEA). The results indicated that the strength and toughness of the hybrid joints with novel bionic textures were both distinctly improved compared with that of conventional textures (groove-like and circular-like). This strengthening effect can be essentially attributed to the buffering of interfacial stress concentration and the deflection behavior of cracks. The observed interfacial gaps and fractured resin appearance both distributed near the sides of the textures demonstrate that the cracks propagate inside the resin on one side of the texture and propagate along the interface on the other side. A new reinforcement strategy for hybrid metal/CFRTP joints has been proposed: regulating interfacial bionic textures to inhibit the initiation and propagation of interfacial cracks.