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.

In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems.

Laminated composites display exceptional weight-saving abilities that make them suited to advanced applications in aerospace, automobile, civil, and marine industries. However, the orthotropic nature of laminated composites means that they possess several damage modes that can lead to catastrophic failure. Therefore, machine learning-based Structural Health Monitoring (SHM) techniques have been used for damage detection. While Lamb waves have shown significant potential in the SHM of laminated composites, most of these techniques are focused on imaging-based methods and are limited to damage detection. Therefore, this study aims to localize the damage in laminated composites without the use of imaging methods, thus improving the computational efficiency of the proposed approach. Moreover, the machine learning models are generally black-box in nature, with no transparency of the reason for their decision making. Thus, this study also proposes the use of Shapley Additive Explanations (SHAP) to identify the important feature to localize the damage in laminated composites. The proposed approach is validated by the experimental simulation of the damage at nine different locations of a composite laminate. Multi-feature extraction is carried out by first applying the Hilbert transform on the envelope signal followed by statistical feature analysis. This study compares raw signal features, Hilbert transform features, and multi-feature extraction from the Hilbert transform to demonstrate the effectiveness of the proposed approach. The results demonstrate the effectiveness of an explainable K-Nearest Neighbor (KNN) model in locating the damage, with an $R^2$ value of 0.96, a Mean Square Error (MSE) value of 10.29, and a Mean Absolute Error (MAE) value of 0.5.

Metal–oxide–semiconductor field-effect transistors (MOSFETs) are critical in power electronic modules due to their high-power density and rapid switching capabilities. Therefore, effective thermal management is crucial for ensuring reliability and superior performance. This study used finite element analysis (FEA) to evaluate the electro-thermal behavior of MOSFETs with copper clip bonding, showing a significant improvement over aluminum wire bonding. The aluminum wire model reached a maximum temperature of 102.8 °C, while the copper clip reduced this to 74.6 °C. To further optimize the thermal performance, Latin Hypercube Sampling (LHS) generated diverse design points. The FEA results were used to select the Kriging regression model, chosen for its superior accuracy (MSE = 0.036, R² = 0.997, adjusted R² = 0.997). The Kriging model was integrated with a Genetic Algorithm (GA), further reducing the maximum temperature to 71.5 °C, a 4.20% improvement over the original copper clip design and a 43.8% reduction compared to aluminum wire bonding. This integration of Kriging and the GA to the MOSFET copper clip package led to a significant improvement in the heat dissipation and overall thermal performance of the MOSFET package, while also reducing the computational power requirements, providing a reliable and efficient solution for the optimization of MOSFET copper clip packages.

한국전산구조공학회 논문집 제38권 제2호(2025.4) 117 1. 서 론 Power Electronic Modules(PE 모듈)은 재생 에너지 시스템 부터 산업용 모터 구동 및 자동차 전자제품까지 다양한 전자 응용 분야에서 사용하는 기본 구성 요소이다(Zhang and Wang, 2019). 모스펫(MOSFET)과 같은 반도체 칩이 PE모듈의 한 종 류이며, 전기 에너지를 제어하고 변환하는 데 중추적인 역할 을 한다. 이러한 반도체에서 전기에 대한 저항으로 발생하는 열