Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlighting advanced methodologies such as hybrid PINNs, k-space PINNs, Theory-Constrained PINNs, optimal PINNs, and disjointed PINNs. Key applications, including structural health monitoring (SHM), structural analysis, stress-strain and failure analysis, and multi-scale modeling, are explored to illustrate how PINNs optimize material configurations and enhance structural reliability. Additionally, this review examines the challenges associated with deploying PINNs and identifies future directions to further advance their capabilities. By bridging the gap between classical physics-based models and data-driven techniques, this review advances the understanding of PINN methodologies for laminated composites and underscores their transformative role in addressing modeling complexities and solving real-world problems.

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of laminated composite structures. Their favorable strength-to-weight ratio renders them ideal for the automotive, marine, and aerospace industries. Among various non-destructive testing (NDT) methods, ultrasonic techniques have emerged as robust tools for detecting and characterizing internal flaws in composites, including delaminations, matrix cracks, and fiber breakages. This review concentrates on recent developments in ultrasonic NDT techniques for the SHM of laminated composite structures, with a special focus on guided wave methods. We delve into the fundamental principles of ultrasonic testing in composites and review cutting-edge techniques such as phased array ultrasonics, laser ultrasonics, and nonlinear ultrasonic methods. The review also discusses emerging trends in data analysis, particularly the integration of machine learning and artificial intelligence for enhanced defect detection and characterization through guided waves. This review outlines the current and anticipated trends in ultrasonic NDT for SHM in composites, aiming to aid researchers and practitioners in developing more effective monitoring strategies for laminated composite structures.

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.

Due to their precision, compact size, and high torque transfer, Rotate vector (RV) reducers are becoming more popular in industrial robots. However, repetitive operations and varying speed conditions mean that these components are prone to mechanical failure. Therefore, it is important to develop effective health monitoring (HM) strategies. Traditional approaches for HM, including those using vibration and acoustic emission sensors, encounter such challenges as noise interference, data inconsistency, and high computational costs. Deep learning-based techniques, which use current electrical data embedded within industrial robots, address these issues, offering a more efficient solution. This research provides transfer learning (TL) models for the HM of RV reducers, which eliminate the need to train models from scratch. Fine-tuning pre-trained architectures on operational data for the three different reducers of health conditions, which are healthy, faulty, and faulty aged, improves fault classification across different motion profiles and variable speed conditions. Four TL models, EfficientNet, MobileNet, GoogleNet, and ResNET50v2, are considered. The classification accuracy and generalization capabilities of the suggested models were assessed across diverse circumstances, including low speed, high speed, and speed fluctuations. Compared to the other models, the proposed EfficientNet model showed the most promising results, achieving a testing accuracy and an F1-score of 98.33% each, which makes it best suited for the HM of robotic reducers.

Soft robotics offers enhanced flexibility, adaptability, and safety compared with traditional rigid-body systems, making it particularly suitable for applications involving human interaction and unstructured environments. Among the various actuation mechanisms developed for soft robots, shape memory alloy (SMA)-textile actuators have emerged as a promising solution owing to their lightweight, silent operation, and high energy density. This study investigates the actuation performance and deflection behavior of a soft knitted gripper fabricated using SMA-based textile actuators configured in a P-loop pattern. To model and predict the deformation behavior of the gripper, an equivalent unit cell (EUC) model was implemented in ABAQUS. This model was employed to analyze and compare the deflection capabilities of the soft knitted gripper against the EUC model. Geometric nonlinearity was incorporated to simulate large deformations resulting from the shape memory effect under electrical excitation. The analysis focused on the bending behavior of the P-loop structure, which is critical for effective gripping and manipulation tasks. Experimental validation was performed by applying varying voltages to the gripper and measuring the resulting deflection angles. Results indicate that increasing the applied voltage significantly enhanced bending, with maximum deflection angles up to 180°, demonstrating the actuator's strong performance. Numerical simulations from the EUC model closely aligned with experimental results, with only a 5% deviation in maximum bending angle, thereby confirming the accuracy and reliability of the proposed modeling approach. This study demonstrates that the EUC model is exceptionally proficient in estimating the actuation behavior of SMA-textile-based soft grippers. The findings provide significant insights for the design and enhancement of next-generation soft actuators and underscore the potential of SMA-textile composites in modern soft robotic systems and biomedical applications requiring conformal, compliant actuation.