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Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures.

Substantial efforts have been invested in developing diverse methods for delamination identification. Among these, the vibration-based method has garnered considerable attention from researchers and has demonstrated commendable efficacy. In the present study, we have implemented a frequency-based methodology to effectively identify delamination in beams, employing a brief set of only three natural frequencies. The constrained mode model, an established model available in the literature, has been adopted as a forward model. Furthermore, the paper introduces a formulation of a system of nonlinear equations designed for inversion. The idea is based on nullifying the analytical expression of the determinant at the resonance condition. The analytical relationship between the frequencies and delamination parameters is written in the form of a nonlinear system of equations. A graphical technique has been used to solve the resulting nonlinear system of equations. The proposed method has been applied to beams subjected to three distinct boundary conditions for the sake of testing its adaptability across various structural configurations. The outcome of our study proves the successful identification of delamination parameters with a remarkable accuracy.

In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating guided waves in composite structures. The developed surrogate deep learning model takes as input full wavefield frames of propagating waves in a healthy plate, along with a binary image representing delamination, and predicts the frames of propagating waves in a plate, which contains single delamination. The evaluation of the surrogate model is ultrafast (less than 1 s). Therefore, unlike traditional forward solvers, the surrogate model can be employed efficiently in the inverse framework of damage identification. In this work, particle swarm optimisation is applied as a suitable tool to this end. The proposed method was tested on a synthetic dataset, thus showing that it is capable of estimating the delamination location and size with good accuracy. The test involved full wavefield data in the objective function of the inverse method, but it should be underlined as well that partial data with measurements can be implemented. This is extremely important for practical applications in structural health monitoring where only signals at a finite number of locations are available.

Identification of initial delamination is crucial to ensure the safety of the fiber-reinforced laminated composite structures. Amongst the identification approaches based on mode shapes, the concept of multiscale shear-strain gradient (MSG) has an explicit physical sense of characterizing delamination-induced singularity of shear strains; moreover, it is robust against noise interference owing to the merits of multiscale analysis. However, the capacity of the MSG for identifying initial delamination is insufficient because the delamination-induced singularity peak can be largely obscured by the global component of the MSG. Addressing this problem, this study proposes an enhanced approach for identifying initial delamination in fiber-reinforced composite laminates. In particular, the multiscale modulation filter (MMF) is proposed to modulate the MSG with the aim of focusing on damage features, by which a new concept of enhanced MSG (EMSG) is formulated to extract damage features. By taking advantage of the MMF with the optimal frequency translation parameters, the EMSG is concentrated in a narrow wavenumber band, which is dominated by the damage-induced singularity peak. As a consequence, the delamination-induced singularity peak in the EMSG can be isolated from the global component. The capacity of the approach for identifying initial delamination is experimentally validated on a carbon fiber reinforced polymer (CFRP) laminate, whose mode shapes are acquired via non-contact laser measurement. The experimental results reveal that the EMSG-based approach is capable of graphically characterizing the presence, location, and size of initial delamination in CFRP laminates.

This study focuses on the machinability of a novel eco-friendly biocomposite reinforced with 30 wt. % of treated date palm fibers (DPFs), designed to meet modern environmental requirements. This innovative material, fabricated through a combination of single-screw extrusion and compression molding, holds significant potential for applications in various sectors, including automotive interiors such as dashboards, where precise drilling is essential for component assembly and fixation. The research emphasizes optimizing drilling parameters to minimize delamination, a common issue in machining natural fiber-reinforced composites. It also represents a pioneering effort in analyzing the machining behavior of powdered fibers, underscoring the originality and relevance of the work. Drilling experiments evaluated the effects of three drill diameters (3.5 mm, 5 mm, and 8 mm), feed rates (50 to 200 mm/min), and spindle speeds (500 to 2000 rpm) on critical performance indicators, including delamination factor, circularity, and cylindricity. These parameters were measured using advanced tools such as the Alicona Infinite Focus microscope and a Three-dimensional Measuring Machine (TMM). The study employed the Response Surface Methodology (RSM) and a hybridization of NSGA-II (Non-dominated Sorting Genetic Algorithm II) + SQP (Sequential Quadratic Programming) with a desirability function approach to optimize the drilling process. The RSM results revealed that drill diameter significantly influences delamination, with optimal parameters identified as a 5 mm drill diameter, a 50 mm/min feed rate, and a spindle speed of 500 rpm. These conditions minimized delamination while ensuring excellent circularity and cylindricity. However, the Hybrid RSM-Fuzzy Logic-Multi-Objective Optimization results indicated that the optimal parameters are a 5 mm drill diameter, a 50 mm/min feed rate, and a spindle speed of 500 rpm with a remarkable 100% desirability factor.

In the recent years with greater emphasis on the environmental and sustainability aspects of engineering materials, natural fiber reinforced composites (NFRCs) are gaining more importance because of their numerous advantages. Several researchers have developed NFRCs using various natural fibers as well as matrix materials. However, real-world applications of NFRCs require some secondary operations in order to complete the assembly of the components or parts. Very few researchers have discussed issues related to the machinability of these NFRCs. In this paper, for the first time, a comprehensive literature review on machining of NFRCs is discussed with focus on drilling operation. The paper also reviews the studies on milling and turning of NFRCs. The distinct feature of this review is that it identifies the factors that affect the quality of the machined feature and provides general recommendations for the selection of process parameters so as to generate better quality holes during drilling. In addition, the review also discusses the challenges that hinder machining of NFRCs which is a significant contribution to the field of NFRCs.

Delamination in reinforced panels is one of the primary challenges facing the safety and reliability of aerospace structures. This article presents a sensitivity analysis of the fatigue behaviour during the compression of a composite aeronautical stiffened panel experiencing delamination. The main objective is to assess the impact of delamination size and depth on the lifecycle and structural integrity of the panel. Different dimensions and positions of delamination are considered to cover a comprehensive range of damage scenarios. The key feature of this sensitivity analysis is the adoption of a numerical procedure that is mesh- and load-step-independent, ensuring reliable results and providing valuable insight into the criticality of delamination and its impact on the fatigue behaviour during the compression of reinforced aeronautical panels. Sensitivity analyses are essential for enhancing the design process of aerospace structures, thereby contributing to the increased safety and reliability of structural components. In this regard, the use of robust and effective numerical procedures is of crucial significance. This may be seen as the real added value of this paper.

Carbon fiber reinforced polymer (CFRP) composites are widely used in many engineering applications such as aerospace, automotive, and defense industries due to their superior properties such as high specific strength, stiffness, and corrosion resistance. However, these materials require drilling, especially during assembly processes. Damage mechanisms arising during this process, such as delamination, high thrust force, and torque, negatively affect structural integrity and production quality. This study proposes a data-driven, multi-objective optimization approach to solve problems encountered during drilling in multi-walled carbon nanotube (MWCNT)-reinforced CFRP nanocomposites. The study considers the MWCNT reinforcement ratio, cutting speed, and feed rate as process parameters and examines their effects on thrust force, torque, and delamination factor. Second-degree polynomial regression-based prediction models were created using the experimental data obtained, and these models were included in the multi-objective optimization process. During the optimization phase, thrust force and torque values were simultaneously minimized, while the delamination factor was kept below the statistically determined constraint of Fd ≤ 1.054. Pareto-optimal solution sets were obtained using NSGA-II and MOPSO meta-heuristic algorithms in the solution process. The results indicate that suitable combinations of drilling parameters can be identified through Pareto-based optimization, allowing significant reductions in thrust force and torque while maintaining the delamination factor below the specified limit. The study presents a reliable optimization approach for the more efficient machining of CFRP nanocomposites.

Interlaminar failure caused by scratches is a common damage mode in automotive coatings and is considered the potential trigger for irreversible destruction, i.e., plowing. This work strives to numerically investigate the mechanisms responsible for the complex scratch behavior of an automotive coating system, considering the interfacial failure. A finite element model is developed by incorporating a large deformation cohesive zone model for scratch-induced debonding simulation, where the mass scaling technique is utilized to minimize computational burden while ensuring accuracy. The delamination phenomenon of the automotive coating is reproduced, and its effects on scratch damage behavior are analyzed. Accordingly, it is revealed that the interlaminar delamination would produce significant stress redistribution, which leads to brittle and ductile damage of the coating and consequently affects the formation of plowing. Eventually, parametric studies on the effects of interfacial properties are performed. They demonstrate that the shear strength and shear fracture energy dominate scratch-induced delamination.

Impact-echo/impact response testing is widely used to detect cracks, voids, and delamination, but transient signals and crowded spectra can complicate diagnosis. This study presents a nonlinear, harmonic-based framework that characterizes delamination using higher-order harmonics in the impact-free response, instead of the amplitude-dependent resonance–frequency shift. The delaminated region is formulated as a locally vibrating nonlinear plate/oscillator with polynomial material and geometric nonlinearities, predicting harmonic components whose levels depend on impact intensity and nonlinearity parameters. The approach is validated on a concrete slab containing an artificial delamination, excited by repeatable impacts, and measured with an accelerometer. Frequency-domain analysis shows that intact regions exhibit a distinct spectral pattern, whereas the delaminated region produces a clear fundamental component and, with modestly increased impacts, a strong second harmonic that serves as a defect signature; time series metrics corroborate nonlinearity. The results demonstrate a nondestructive technique that can localize and characterize delamination without driving the specimen into damaging strain. Looking ahead, the same harmonic signature principle can be extended to vibroacoustic/impact monitoring of lithium-ion batteries to flag mechanically induced internal defects (e.g., separator/electrode delamination) that can precipitate internal short circuits and elevate thermal runaway risk, improving quality control and in-service safety.