Complex Geometries Research Articles

Prediction Accuracy of Hyperelastic Material Models for Rubber Bumper under Compressive Load.

Different hyperelastic material models (Mooney-Rivlin, Yeoh, Gent, Arruda-Boyce and Ogden) are able to estimate Treloar's test data series containing uniaxial and biaxial tension and pure shear stress-strain characteristics of rubber. If the rubber behaviour is only determined for the specific load of the product, which, in the case of rubber bumpers, is the compression, the time needed for the laboratory test can be significantly decreased. The stress-strain characteristics of the uniaxial compression test of rubber samples were used to fit hyperelastic material models. Laboratory and numerical tests of a rubber bumper with a given compound and complex geometry were used to determine the accuracy of the material models. Designing rubber products requires special consideration of the numerical discretization process due to the nonlinear behaviours (material nonlinearity, large deformation, connections, etc.). Modelling considerations were presented for the finite element analysis of the rubber bumper. The results showed that if only uniaxial compression test data are available for the curve fitting of the material model, the Yeoh model performs the best in predicting the rubber product material response under compressive load and complex strain state.

Building-block-flow computational model for large-eddy simulation of external aerodynamic applications

Computational fluid dynamics is an essential tool for accelerating the discovery and adoption of transformative designs across multiple engineering disciplines. Despite its many successes, no single approach consistently achieves high accuracy for all flow phenomena of interest, primarily due to limitations in the modeling assumptions. Here, we introduce a closure model for wall-modeled large-eddy simulation to address this challenge. The model, referred to as the Building-block Flow Model (BFM), rests on the premise that a finite collection of simple flows encapsulates the essential missing physics necessary to predict more complex scenarios. The BFM is designed to: (1) predict multiple flow regimes, (2) unify the closure model at solid boundaries and the rest of the flow, (3) ensure consistency with numerical schemes and gridding strategies by accounting for numerical errors, (4) be directly applicable to arbitrary complex geometries, and (5) be scalable to model additional flow physics in the future. The BFM is utilized to predict key quantities in five cases, including an aircraft in landing configuration, demonstrating similar or superior capabilities compared to previous state-of-the-art models. The design of BFM opens up new opportunities for developing closure models that can accurately represent various flow physics across different scenarios.

Predicting maximum deflection of N-Edged thin-shelled hyperbolic-Paraboloid umbrella using machine learning techniques

Many thin-shelled hyperbolic paraboloid (hypar) umbrella forms have been built in the last 60 years as roof coverings. While the stresses in these forms remain relatively low, the deflections are a critical design parameter, and one that must be considered by architects and engineers in the conceptual design phase, meaning the design stage when the scale and form is given to the structure. To-date, there is no closed-form solution that can predict the maximum deflection of umbrellas due to their highly varying and complex geometry. The best option for predicting deflection is via finite element analysis, which is time-consuming for conceptual (preliminary) design purposes. In response, this paper uses machine learning via genetic programming (GP) and gene expression programming (GEP) to develop closed-form equations that predict the maximum corner deflection of N-edged hypar umbrellas – where N = 3, 4, 5, 6, 7, and 8. For a given a boundary condition and material, geometry is the most significant parameter influencing a shell's stiffness; thus, elastic finite element (FE) models use geometric properties as input variables (N, projected area, normalized rise, and shell thickness). The maximum corner deflection is recorded as an output and the FE analyses generate a large dataset of 53,754 results. It is observed that both GP and GEP can effectively parameterize the maximum deflection of N-edged hypar umbrellas, with GEP producing more concise, but relatively less accurate, equations than GP. While the formulations are trained using concrete material, a material factor multiplier transforms the results to other material properties within the assumption of elastic limits. The results of the study can be used to assist with conceptual design of hypar umbrellas and to validate complex FE models of hypar umbrellas. This research also illustrates the use of machine learning techniques as applied to the conceptual design of structures with highly varying geometries.

Controlling coating thickness distribution for a complex geometry with the help of simulation

This paper aims to develop a proper and valid simulation model for electroplating complex geometries. Since many variables influence the quality of the deposited coating and its thickness distribution, it is challenging to conduct efficient research only through experiments. In contrast, simulation can be an efficient way to optimize the electroplating experiments. Despite its potential, simulation has seen limited commercial use in the electroplating industry due to its inherent complexity and difficulty in achieving accurate precision for intricate geometries. The present study addresses the aspects that can enhance the electroplating simulation’s accuracy, which has been typically overlooked in the literature, such as the effect of current efficiency and its dependency on the current density, the input data for the electrode kinetics, the surface topology changes, and the differences between 2 and 3D simulations. The simulation model was validated by experimental results related to the coating thickness of Ni plating on a T-joint geometry. The results showed good agreement with the experimental ones, confirming the model’s ability to precisely predict the coating thickness and distribution and promote its broader utilization in the industry. Finally, the developed model was used to determine the optimal current density regime for achieving uniform coating thickness distribution on a T-joint sample.

Unveiling complex fault geometry and driving mechanisms: insights from a refined data processing and multiplet analysis of the 2010 Beni-Ilmane seismic sequence (NE Algeria)

SUMMARY This paper offers a comprehensive re-analysis of the Beni-Ilmane 2010 seismic sequence, using a data set that is 100 per cent larger than previous studies. This unprecedented sequence in Algeria features three main shocks with magnitudes Mw 5.4, 5.1 and 5.1. Our approach involves high-precision relocation, which includes the development of a new 1-D minimum velocity model, followed by a double-difference (DD) procedure and hierarchical clustering. We determined the focal mechanisms (FMs) for 128 key events and identified 21 multiplet groups using an average cross-correlation threshold of 0.8. Our analysis offers new insights into fault geometry and addresses ongoing debates, by proposing a seismotectonic model that reveals the activation of 14 fault segments during the sequence, in contrast to previous oversimplified models that suggested two or three faults. The computed stress field from the inversion of 128 FMs aligns with a tectonic loading force due to the convergence of the African and Eurasian plates. These findings highlight the complexity of the fault network in the study area and shed light on the role of strike-slip faults in shaping the thrust belt. We found a strong link between multiplet groups and fluid movement along the fault network. Analysis of the temporal history of these multiplet groups provides new insights into fluid dynamics timescales, with an estimated hydraulic diffusivity (D) of 0.36 m2 s−1 suggesting a fluid pressure diffusion process. The observed expansion of the aftershock area with the logarithm of time and the existence of repeating earthquakes indicates, for the first time, an aseismic slip mechanism that adds an additional layer to the driven processes. In conclusion, our results suggest that the underlying mechanisms governing the BI-2010 seismic sequence involve a complex interplay of tectonic loading, coseismic stress transfer, fluid dynamics and aseismic slip transients. We attempt to correlate our findings with various studies linking the structure, mechanics and fluid flow properties of fault zones and fault systems. The activation of smaller fault segments potentially averted a larger quake, resulting in three moderate main shocks and numerous aftershocks. This work not only enrich our understanding of seismic phenomena but also provides useful insights for seismic hazard assessment and risk mitigation strategies.

Material parameters affecting Li plating in Si/graphite composite electrodes

Silicon is a frequently used active material in the negative electrode of lithium-ion batteries which provides significant improvements in the energy density. Due to large volume changes during cycling, it is typically mixed with graphite. Understanding the interactions of composite materials during battery operation is key to optimizing battery performance and predicting aging phenomena. Even though lithium plating is a critical degradation process, which influences both lifetime and safety, a systematic analysis of electrochemical material parameters affecting plating in composite electrodes is missing. Therefore, a parameter study is performed using 3D microstructure-resolved simulations. We investigate the influence of critical material parameters such as open-circuit potential, electronic conductivity, chemical diffusion coefficient, and exchange current density in simple and interpretable model geometries. The simulations reveal the importance of chemical diffusion and exchange current density, as well as their ratio, in predicting plating. Finally, we apply the set of material parameters to a complex electrode geometry reconstructed from FIB/SEM tomography data of commercial anode material. The analysis shows that preferential plating on the electrode surface dominates all other factors.

Mononuclear nickel(II) and copper(II) coordination compounds with ligands based on acetyl(benzoyl)acetone S-methylisothiosemicarbazones and 8-quinolinecarboxaldehyde. Synthesis and crystal structure

Template condensation of S-methylisothiosemicarbazones of acetyl- or benzoylacetone with 8-quinolinecarboxaldehyde in the presence of nickel(II) and copper(II) ions gave four new mononuclear coordination compounds [NiL1]I (I), [CuL1I] (II), [NiL2]I (III) и [CuL2I] (IV). The chemical composition of the products was confirmed by elemental analysis, IR spectroscopy, and mass spectrometry, and the crystal structure of compounds I and II was determined by X-ray diffraction analysis (CCDC no. 2266386, 2266387). X-ray diffraction study revealed a square planar coordination environment of the central ion of the cationic Ni(II) complex and square pyramidal geometry for the molecular Cu(II) complex.

Energy Element Method for Three-Dimensional Vibration Analysis of Stiffened Plates with Complex Geometries

A novel numerical method, the energy element method, is proposed for the three-dimensional vibration analysis of stiffened plates with complex geometries. The problem is modeled in a cuboidal domain, and a cuboidal energy element is developed for the simulation of the structural components. To simulate the energy distribution of the structure, stiffened plates are treated as discrete energy systems, where variable stiffness is used to characterize their energy distribution in a cuboidal domain and a global admissible function is used to approximate their vibrational behavior. With extended interval integral, Gauss quadrature, variable stiffness, and Legendre polynomials used for numerical integration in the cuboidal domain, the cuboidal energy element can offer sufficient precision with sufficient Gauss points for simulating the strain energy of stiffened plates with complex geometries. The Gauss–Legendre quadrature will provide accurate integration results if the integral domain is cuboidal. Otherwise, small cuboidal energy elements are generated with denser Gauss points to capture the geometric boundaries. As the numerical model is constructed on a standard geometric domain, all energy functionals and computational procedures are standard. Three-dimensional vibration problems are investigated for variously shaped stiffened plates with straight or curvilinear stiffeners. The present results are compared with analytical, numerical, and experimental results published in the literature.

Achieving fine tailoring of elastocaloric properties of laser powder bed-fused NiTi alloy via laser beam manipulation

Laser powder bed fusion (LPBF) technology enables the development of NiTi alloys with complex geometries and tunable phase-transformation temperatures (PTTs). This technology is increasingly acknowledged as promising in the field of elastocaloric (eC) refrigeration. However, the mechanisms governing the manner in which this technology tunes the eC performance remain ambiguous. This study evaluated the fine-tuning of the eC properties by regulating Ni evaporation through laser manipulation. Our results demonstrate that although adjusting Ni loss via laser heat input can effectively control the PTTs, inappropriate combinations of laser parameters may result in lower than anticipated cooling capacity (ΔTad) and coefficient of performance (COPmat) of produced samples. An excessive heat input results in Ni evaporation and in grain coarsening through the remelting and combination of fine grains owing to overlapping molten pools. Lower Ni enhances the phase-transformation enthalpy (ΔHtr). However, larger grains increase the energy dissipation and thereby, counteracting ΔTad improvements. Theoretical analysis and experiments revealed that finer grains increase the misorientation angles. This hinders the dislocation motion and thereby, enhances the mechanical properties. Meanwhile, coarser grains can more conveniently promote PT and thereby, increase ΔHtr. Thus, based on the naturally controllable grain size heterogeneity in LPBF-manufactured NiTi alloys, we propose optimizing the eC properties by controlling the morphology of the molten pool. Thermal-history simulations could balance this relationship. Ultimately, we developed two NiTi alloys for both high-temperature (70 °C) and room-temperature (25 °C) refrigeration. This study has provided effective insights for customizing high-performance eC components such as multistage caloric cascade regenerators, using additive manufacturing.

Hybridizing Shear-Stiffening Gel and Chemically-Strengthened Ultrathin Glass Sheets for Flexible Impact-Resistant Armor.

Traditional anti-impact armors and shields are normally made of stiff and hard materials and therefore deficient in flexibility. This greatly limits their applications in protecting objects with complex geometries or significant deformability. Flexible armors can be developed with the application of hard platelets and soft materials, but the lower rigidity of the flexible armors renders them incapable of providing sufficient resistance against impact attacks. To address the inherent conflict between flexibility and impact resistance in traditional armors, here, a composite is developed by hybridizing a shear-stiffening gel as the matrix and chemically-strengthened ultrathin glass sheets (CSGS) as the reinforcement. The resulting laminate, termed PCCL, exhibits both high flexibility and high impact resistance. Specifically, at low strain rates, the high ductility of the gel combined with the high flexural strength of the CSGS enables the PCCL to undergo considerable deformation; at high strain rates, on the other hand, the shear stiffening behavior of the gel matrix endows the PCCL with excellent impact resistance manifested by its high performance in energy absorption and high rigidity. With the combination of high flexibility and high impact resistance, the PCCL is demonstrated to be an ideal armor for protecting curved vulnerable objects from impact attacks.

Online ultrasonic monitoring of FDM nozzle temperature based on metamaterial buffer rod

Abstract Fused deposition modeling (FDM) has been widely used for fabricating parts with complex geometries. However, during the printing process the nozzle temperature fluctuates which may cause nozzle blockage, inter-filament delamination, over-melting, etc. Therefore, it is of significance to develop an online and in-situ nozzle monitoring system which could overcome such drawbacks for quality assurance. In this work, a multifunctional system is designed by incorporating a traditional ultrasonic probe and a metamaterial buffer rod to online monitor the nozzle temperature and ensure a correct extrusion force and speed. A parametric simulation model is first constructed implicitly based on thermal-acoustic metamaterial structures for the buffer rod. Secondly, since the high-fidelity calculation is computationally expensive, an efficient surrogate model with kriging approach is constructed based on the dataset drawn from limited trials for rapid prediction. Thirdly, multidisciplinary optimization is performed by the NSGA-III algorithm considering the thermal insulation performance, ultrasonic attenuation and structure integrity. The rod is fabricated according to the best parameters decided. A cross-correlation algorithm is calibrated with thermocouple measurement after waveform recording at different temperatures. The online and in-situ temperature measurement can be realized during 3D printing successfully eventually. This will help to improve the printing quality with potential feedback strategies.

Generalized Background Vector Method for Tailored Three-Dimensional Nonperiodic Woven Composite Designs

In critical aerospace applications such as Ceramic Matrix Composite structures, novel three-dimensional nonperiodic textile composite preforms hold great promise for creating advanced composite structures with strategically aligned fiber tows. However, the inherently challenging nature of determining tow locations, a large-scale combinatorial problem, presents a significant obstacle in textile architecture design. This study generalizes the previously developed Background Vector Method (BVM) to address diverse design requirements and constraints, effectively circumventing combinatorial design complexities via a game theoretic approach. This approach allows for the creation of tunable designs for woven architectures with complex geometries, such as channels and tapering features, through simple control parameter adjustments. The method demonstrates exceptional computational efficiency, making it suitable for large-scale nonperiodic textile structures. Case studies including woven sandwich and airfoil structures highlight the generalized BVM’s versatility and effectiveness in addressing complex design challenges within the aerospace sector.

A Surrogate Model's Decision Tree Method Evaluation for Uncertainty Quantification on a Finite Element Structure via a Fuzzy-Random Approach

A novel additive manufacturing method (AM)) constructs a three-dimensional model from a computer-aided design by adding material layer by layer. This technique produces a lightweight end product with complex geometries and has gained recognition among industrial players. Nonetheless, the mechanical properties and geometry components are the uncertainties that prevail in its structures. An alternative approach using the Finite Element Method (FEM) to analyse these uncertainties demands extensive computational effort and time consumption. Therefore, a machine learning (ML) tool using the surrogate modelling technique offers an alternative way to provide and predict simulation outcomes. This study applies two surrogate modeling approaches, the decision tree (DT) and the Gaussian process regression (GPR) methods. Output data from a FEM simulation with uncertainty elements are obtained for the training purposes of the surrogate models. Both ML methods can predict simulation results with high precision. Both approaches obtained an excellent coefficient of determination value, R2 of 0.998, and Root Mean Square Error, RMSE of 0.012, successfully reducing time consumption and computational effort. The DT method shows better robustness when compared to the GPR method. A value change in the input parameter significantly impacts the surrogate model's prediction performance. An adequate quantity of data input for the training phase of both surrogate models exhibits the FEM results with the presence of uncertainty and robustness.

Supplementary treatment of FDM printed parts. Review

The application of additive technologies to the manufacture of polymer and composite products is now actively expanding. The FDM printing process is popular because of its ability to adapt to specific tasks and to bring products with complex geometries into production quickly and at minimal cost, and is seen as a technology that can compete with injection molding. However, due to the nature of the process, FDM printed parts are significantly inferior in quality to injection molded parts. Much attention is now being paid to research into supplementary treatment methods to improve the properties of FDM printing parts. However, there is no complete and clear description of such methods, nor are there any recommendations on the choice of treatment methods aimed at improving the specific parts properties. The aim of this review is to analyse the research in the field of post-treatment of parts in order to systematise their advantages and limitations, which will allow a more reasoned choice of a post-treatment method to improve specific properties of FDM printing parts. <br> The bibliography includes 120 references.

Geometric analysis and design of circular form tools

Abstract Supposing serial or quantity production of cylindrical workpieces having complex geometry the application of circular form tool is applicable. This type of cutting tool has complex geometry as a consequence the geometric and the manufacturing design take a lot of time and cost but it is economical since the cutting process is quite fast. In addition, the tool usage is feasible on conventional or CNC controlled machines and the process is independent from the workers’ facility. The tool profile is the conjugated pair of the workpiece profile. This tool geometry has high resharpening area that is advantageous comparing other form tools. The goal of this study is the manufacturing analysis and the geometric design of those tools. The geometric design process will be determined detailedly in constructional and mathematical way. After them a concrete tool generation process including CAD design will be shown.

Synthesis, structural elucidation, and antibacterial activities of novel copper(II), cobalt(II), and nickel(II) complexes with a bidentate Schiff base ligand against pathogenic bacteria

Novel Schiff base metal complexes diaqua(4-chloro-2-{[(4-(dimethylamino)phenyl)methylene]amino}phenolato)M(II) ion, (M = copper/cobalt/nickel) have been synthesized using the ligand 4-chloro-2-((4-(dimethylamino)benzylidene)amino)phenol, which was prepared by reacting 2-amino-4-chlorophenol with 4-(dimethylamino)benzaldehyde in a molar ratio of 1:1. The synthesized compounds were characterized based on elemental analysis, FTIR, UV–vis., 1H and 13C NMR, powder XRD, mass spectrometry and magnetic studies. Spectroscopic investigations demonstrated that the metal atom coordinates to a bidentate Schiff base ligand via the azomethine nitrogen and phenolic oxygen. The appropriate molecular geometry for each metal complex has been proposed through the analysis of spectroscopic and analytical data, Specifically, tetrahedral geometry has been proposed for Co(II), Ni(II), and square planar geometry for Cu(II) complex. Theoretical calculations employing Density Functional Theory (DFT) validated the experimental results, elucidating optimized geometries, frontier molecular orbitals, natural bond orbital distributions, molecular electrostatic potentials, non-linear optical properties, IR vibrational modes, and UV absorbance profiles. Additionally, the compounds' biological efficacy was validated through molecular docking against receptors for Gram(+ve) bacteria, Bacillus subtilis (PDB ID: 5H67), Staphylococcus aureus (PDB ID: 3TY7) and Gram(-ve) bacteria, Escherichia coli (PDB ID: 3T88), Proteus vulgaris (PDB ID: 5I39). The outcome revealed that Co(II) complex exhibited highest binding energy with Escherichia coli and Bacillus subtilis while the ligand with Proteus vulgaris (5I39) and Staphylococcus aureus (3TY7). Further the study was extended to the MD Simulation in water for 100 ns to evaluate the binding affinities and its stabilities by analyzing the deviation, fluctuations, and intermolecular interactions. The research presented here showcase the potential of employing a Schiff base ligand and its Co(II) complex in the synthesis of novel, highly potent antibacterial agents to combat emerging diseases, which holds considerable importance in the field of pharmaceutical science—however, in-vitro studies are needed to validate the results.

Experimental Investigations on the Fabrication of Low Alloy Steels Using Wire Arc Additive Method

Wire arc additive manufacturing (WAAM) has emerged as a transformational technology with the capacity to redefine the landscape of alloy steel component production. This method utilizes an electric arc to selectively fuse a continuous wire feed, progressively constructing intricate metal structures layer by layer. GMAW-based WAAM adaptability enables the creation of complex geometries and customized part designs, making it applicable across diverse industrial sectors. This paper investigates WAAM-specific applications in alloy steel fabrication, focusing on key process variables such as voltage, travel speed, and Gas mixture ratio. The experimental parameters for a single layer are examined with voltage ranging from 20 to 22, travel speed from 23, 25, and 27 mm/min, and Gas mixture ratio 1,5,9 mm/min. It utilizes materials like TM B9, a 1.2- diameter flux-cored wire. Additionally, the study considers the nominal composition (wt-%) of 9 Chromium, and 1 Molybdenum, with small additions of nitrogen and niobium to improve the creep resistance. Furthermore, the final findings of the study suggest that the optimal combination of process variables for achieving highquality weld beads in WAAM of alloy steel components can be determined through Response Surface Methodology (RSM). RSM is a statistical method for analysing and modeling the relationship between the response of interest and some independent variables. In this case, the response variable would be the quality of the weld bead, which encompasses factors such as bead geometry, integrity, and absence of defects.

X-ray polarisation in AGN circumnuclear media

Context. Cold gas and dust reprocess the central X-ray emission of active galactic nuclei (AGN), producing characteristic spectro-polarimetric features in the X-ray band. The recent launch of IXPE allows for observations of this X-ray polarisation signal, which encodes unique information on the parsec-scale circumnuclear medium of obscured AGN. However, the models for interpreting these polarimetric data are under-explored and do not reach the same level of sophistication as the corresponding spectral models. Aims. We aim at closing the gap between the spectral and spectro-polarimetric modelling of AGN circumnuclear media in the X-ray band by providing the tools for simulating X-ray polarisation in complex geometries of cold gas alongside X-ray spectra. Methods. We lay out the framework for X-ray polarisation in 3D radiative transfer simulations and provide an implementation to the 3D radiative transfer code SKIRT, focussing on (de)polarisation due to scattering and fluorescent re-emission. As an application, we explored the spectro-polarimetric properties of a 2D toroidal reprocessor of cold gas, modelling the circumnuclear medium of AGN. Results. For the 2D torus model, we find a complex behaviour of the polarisation angle with photon energy, which we interpret as a balance between the reprocessed photon flux originating from different sky regions, with a direct link to the torus geometry. We calculated a large grid of AGN torus models and demonstrated how spatially resolved X-ray polarisation maps could form a useful tool for interpreting the geometrical information that is encoded in IXPE observations. With this work, we release high-resolution AGN torus templates that simultaneously describe X-ray spectra and spectro-polarimetry for observational data fitting with XSPEC. Conclusions. The SKIRT code can now model X-ray polarisation simultaneously with X-ray spectra and provide synthetic spectro-polarimetric observations for complex 3D circumnuclear media, with all features of the established SKIRT framework available.

3D Printing for Traumatic Orthopedics: A Review of Current Trends and Opportunities

Three-dimensional (3D) printing enables the fabrication of functional scaffolding structures, prototypes, and implants with complex geometries suitable for various biomedical applications. As 3D printing continues to grow, there has been an increasing trend in reshaping the future of biomedical implants and prototypes in orthopedics, particularly in traumatic orthopedics. For example, 3D printing can be employed to create customized implant guides and prostheses to match the anatomical shape and structure of the defective part of the patient and resolve issues associated with clinical treatment. In addition, 3D printing can be used to print patient-specific prototypes, which is helpful for physicians to have a better 3D Printing for Traumatic Orthopedics: understanding of the anatomy and pAreRoepveierawtivoef Cduiarrgennot sTirsenodfsthaneddOepfepcotrttuonibtieesfixed. Keeping these points in viHewan, tehtisal. article reviews current trends and opportunities for 3D printing in traumatic orthopedics.

A model-based deep learning framework for damage classification and detection in polycarbonate infused with AEROSIL under dynamic loading conditions

Composite 3D printing is a significant engineering application owing to its robustness, ability to achieve complex geometries, and ease of use. Polycarbonate, particularly when infused with AEROSIL, is an interesting thermoplastic and potential candidate for 3D printing with enhanced properties. The primary objective of this research is to develop a new model-based deep-learning framework to classify and detect damage in this material under dynamic loading conditions. To achieve this, a FASTCAM high-speed camera was placed in front of the SHPB test setup to capture dynamic damage. The test results were then used as label inputs for training the advanced deep learning algorithms, focusing on dense image recognition techniques for detailed damage analysis. The study involved a series of fully convolutional networks (FCNs), evaluating semantic segmentation with U-Net and instance segmentation with state-of-the-art frameworks such as YOLOv8 and Mask R–CNN. A comparative analysis revealed that deep learning models outperform traditional methods, providing efficient and accurate damage classification and detection. The U-Net model demonstrated the ability to recognize cubes and bars but was limited in detecting minor damage regardless of size. YOLO-V8, which specializes in case segmentation, achieved remarkable performance in detecting significant damage but struggled to accurately identify minor damage. By leveraging deep learning techniques, this study enables an efficient and accurate damage assessment, which is crucial for ensuring the reliability and safety of composite structures in various industries.