The manufacturing industry is rapidly changing, creating a growing demand for more intelligent and adaptive systems. With recent developments in artificial intelligence, especially with the onset of large language models (LLMs)Large Language Models (LLMs) such as ChatGPT, new opportunities have emerged for companies to increase their productivity and maximize revenue. In a competitive environment, businesses must constantly innovate to stay ahead. To support innovative and competitive organizations, LLMs can analyze large amounts of data to identify trends and optimize processes. In addition, the industry faces a labor shortage, particularly in roles that require specialized skills. LLMs can fill this gap by providing real-time assistance and training. This knowledge transfer could help less experienced workers perform their tasks more effectively. Regulatory compliance is increasingly imperative in manufacturing, and LLMs can help ensure adherence to safetySafety in manufacturing standards and regulatory requirements. LLMs can address these and other challenges by using their capabilities in data processing, natural language understanding, and predictive analytics. In this chapter, we explain the fundamental concepts behind LLM techniques and how to use them in a smart manufacturing environment such as Industry X.0Industry X.0. We discuss the challenges and future trends of LLMs in different industrial fields. We also highlight the need for LLM frameworks that can guarantee data privacy, security, and ethical usage.

With the rapid development of artificial intelligence (AI) technologies, deep learning-based structural health monitoring (DeepSHM) methods have gained significant attention. However, their black box nature often limits interpretability and trust. The field of Explainable AI (XAI) aims to address this by enhancing model transparency and reliability through human-comprehensible explanations. This study investigates the use of XAI algorithms in interpreting a 1D convolutional neural network (1D CNN) developed for Lamb wave monitoring of bolt-loosening detection in multi-bolted double-layer aluminum plates under varying temperatures. Four existing XAI algorithms were employed, including Sensitivity Analysis, Deep Taylor, Gradient-weighted Class Activation Mapping (Grad CAM) and Guided Grad CAM. In addition, this paper introduces two new XAI methods, Smooth Simple Taylor and Deep Grad CAM as an enhancement of the Simple Taylor and Grad CAM methods, respectively. These six XAI algorithms were used to establish the relation between the 1D CNN model parameters and the input vector. The results were evaluated for their effectiveness in comparison to the physical insights of the input vector using two proposed methods, namely the Correlation Coefficient with Residual Signal and the Residual Signal Weighted Importance Score Ratio. The results of the evaluation methods, in conjunction with Infidelity, Sense sum, and Sanity check, were utilized to rank the performance of the six XAI algorithms. The rankings were consistent in both simulation and experiment data sets, and the newly proposed XAI algorithm, Smooth Simple Taylor, appeared to be the best in both data sets. Overall, this research establishes a novel approach to using XAI algorithms to enhance the explainability of AI in practical engineering applications.

Stroboscopic shearography techniques, including double pulse shearography, are able to image transient Lamb waves to support non-destructive testing of structures and materials. The amplitude of the signal measured with these techniques is known to depend on optical shear distance and direction but the experimental demonstrations presented in the literature are limited. We present improved experimental results that show the dependence of signal amplitude on shear distance. By carefully selecting the shear distance, we are able to visualize a defect with shearographic measurement of transient Lamb waves.

With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of AI models. In this work, the results of explainable artificial intelligence (XAI) algorithms are used to reduce the input size of a one-dimensional convolutional neural network (1D-CNN), hence simplifying the CNN structure. To select the most accurate XAI algorithm for this purpose, we propose a new evaluation method, feature sensitivity (FS). Utilizing XAI and FS, a reduced dimension 1D-CNN regression model (FS-X1D-CNN) is proposed to locate and predict the torque of loose bolts in a 16-bolt connected aluminum plate under varying temperature conditions. The results were compared with 1D CNN with raw input vector (RI-1D-CNN) and deep autoencoders-1D-CNN (DAE-1D-CNN). It is shown that FS-X1D-CNN achieves the highest prediction accuracy with 5.95 mm in localization and 0.54 Nm in torque prediction, and converges 10 times faster than RI-1D-CNN and 15 times faster than DAE-1D-CNN, while only using a single lamb wave signal path.

With the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due to the black box nature of deep learning, the trained models are usually difficult to interpret, which blocks their practical application. Therefore, it is of great importance to develop explainable artificial intelligence (XAI) methods to understand the internal decision-making mechanisms of damage classification in Deep SHM. In this paper, a novel XAI algorithm named Deep Gradient-weighted Class Activation Mapping (Deep Grad CAM) is proposed by combining the existing method Grad CAM with the convolutional neural network (CNN) deconvolution mechanism. In this paper, Deep Grad CAM is used to interpret a one-dimensional convolutional neural network trained to detect bolt loosening based on guided wave propagation. The interpretation performance of Deep Grad CAM is compared with Grad CAM, and their performances are quantified using Infidelity. The results show that the Infidelity of Deep Grad CAM is much smaller than that of Grad CAM, indicating significant improvements in explanation accuracy and reliability.

This book presents a guided wave-based structural health monitoring (GWSHM) system for aeronautical composite structures. Particular attention is paid to the development of a reliable and reproducible system with the capability to detect and localise barely visible impact damage (BVID) in carbon-fibre-reinforced polymer (CFRP) structures.

The authors introduce a novel sensor installation method that offers ease of application and replacement as well as excellent durability. Electromechanical Impedance (EMI) is also explored to assess the durability of the sensor installation methods in simulated aircraft operational conditions including thermal cycles, fatigue loading, and hot–wet conditions.

Damage characterisation using GWSHM is described and used to investigate damage in different CFRP structures. Key issues in guided wave-based damage identification are addressed, including wave mode and frequency selection, the influence of dynamic load, the validity of simulated damage, and the sensitivity of guided waves to impact damage in different CFRP materials.

The influence of temperature on guided wave propagation in anisotropic CFRP structures is described, and a novel baseline reconstruction approach for temperature compensation is presented. Finally, a multi-level hierarchical approach for the quantification of an ultrasonic GWSHM system is put forth.

During the service of composite airframes, damage initiates and accumulates due to the manufacturing imperfections, impact damage and cyclic loadings, leading to the degradation in its load-bearing capacity. The nature of the degradation process is complicated due to the multi-mode damage propagation and complexity in the structural details of airframes. In the condition-based health management of airframe structures, the degradation is expressed in the concept of remaining useful life (RUL). Online prognostic health management is an emerging field dedicated to the timely prediction of RUL using onboard sensors. This work presents a mechanics-informed approach to the prognosis of a typical airframe element, stiffened CFRP composite panel, under compression-compression fatigue. The fatigue degradation of axial stiffness is monitored by Lamb wave velocity and utilised for online RUL prediction via particle filter.

The research shows the link between the strain transfer properties of distributed optical fiber sensors and their probability of damage detection, which is crucial for a successful implementation in real structural health monitoring applications.

To move towards a condition-based maintenance practice for aircraft structures, design of reliable health management methodologies is required. Development of diagnostic methodologies is commonly realised on simplified sample structures with assumptions that methodologies can be adapted for application to realistic aircraft structures under in-service conditions. Yet such actual applications are not conducted. In this work, we study the development of diagnostic methodologies to training structures and their application to dissimilar testing structures. A heterogeneous population is considered, consisting of single-stiffener composite panels for methodology development and training and a multi-stiffener composite panel for application and testing. Characteristics as its composite material, lay-up, and temperature condition are constant while topologies and applied loads differ between the dissimilar structures. Damage in the structural panels is monitored on multiple diagnostic levels using a variety of structural health monitoring (SHM) techniques, including acoustic emission and distributed strain sensing. Specifically, we develop diagnostic methods for localising and monitoring disbond growth after impact using strain data collected during fatigue testing of multiple single-stiffener panels and apply these for disbond monitoring in an upscaled version of a multi-stiffener panel. In this manner, this study aids in the maturement and application of SHM methodologies to realistic aircraft structures.

Distributed Optical Fiber Sensors (DOFS) show several inherent benefits with respect to conventional strain-sensing technologies and represent a key technology for Structural Health Monitoring (SHM). Despite the solid motivation behind DOFS-based SHM systems, their implementation for real-time structural assessment is still unsatisfactory outside academia. One of the main reasons is the lack of rigorous methodologies for uncertainty quantification, which hinders the performance assessment of the monitoring system. The concept of Probability of Detection (POD) should function as the guiding light in this process, but precautions must be taken to apply this concept to SHM, as it has been originally developed for Non-Destructive Evaluation techniques. Although DOFS have been the object of numerous studies, a well-established methodology for their performance evaluation in terms of PODs is still missing. In the present work, the concept of Probability of Delamination Detection (POD²) is proposed for a DOFS network; Carbon Fiber-Reinforced Polymers (CFRP) Double-Cantilever Beam (DCB) specimens equipped with DOFS have been tested under static loading, and the strain patterns along with the relative observed delamination size have been collected to generate an adequate database for the POD analysis, suggesting a reference methodology to quantify the performance of DOFS for delamination detection.

In this paper we execute a complex test campaign to develop a novel methodology for the Remaining Useful Life (RUL) estimation of complex multi-stiffened composite aeronautical panels utilizing Machine Learning models trained with Structural Health Monitoring (SHM) data from hierarchically simpler elements, i.e., single-stiffened panels. Distributed Fiber Optical sensors (DFOS) are employed to monitor the panels’ behavior undergoing variable amplitude compression-compression fatigue after multiple impacts. A data processing methodology is first applied to the DFOS data, to both alleviate the effect of the variable loading conditions on the monitored strain and ease the computational burden. In this upscaling endeavor, an advanced strain-based Health Indicator (HI) based on Genetic algorithms, created and validated on the single-stiffened panel data, is utilized as the prognostic feature for the RUL estimations of the multi-stiffened panels. The HI displays favorable characteristics in terms of monotonicity and prognosability which are highly desirable for more accurate RUL estimations. For the prognostic task, standard machine learning models are trained using the historical degradation data of the single-stiffened panels and a similarity analysis is performed to enhance the accuracy when predicting the RUL of the multi-stiffened panels. Despite the increased structural complexity of the multi-stiffened panels, we demonstrate that the RUL is able to be predicted with reasonable accuracy. The present work paves the road for upscaling and applying prognostic methodologies to more complex structures beyond simple coupons or generic elements.

The application of structural health monitoring (SHM) in composite airframe structural elements under long-term realistic fatigue loading needs to consider the structural behavior on the global level, which is an intricate task. The overall structural stiffness is a key design parameter for composite structures and the stiffness degradation under fatigue loading is closely related to the damage accumulation and failure mechanism which can be used as an indicator for the structural degradation. Therefore, this paper investigates the use of guided waves in axial stiffness degradation estimation for stiffened carbon fiber reinforced polymer (CFRP) composite panels under post-buckling compression-compression (C-C) fatigue loads. Impacted or artificially debonded stiffened composite panels are tested under fatigue until failure and guided waves are acquired using a network of piezoelectric (PZT) sensors at fixed cycle intervals. The guided wave phase velocity along the loading direction is extracted to estimate the axial stiffness degradation with the consideration of mode conversion and failure of PZT sensors. The estimated stiffness of five stiffened composite panels matches well with the stiffness calculated from the load–displacement curves. The estimated stiffness is also assessed using prognostic performance metrics and shows good potential for being used as a health indicator for prognostic purposes.

Machine learning (ML) methods for the structural health monitoring (SHM) of composite structures rely on sufficient domain knowledge as they typically demand to extract damage-sensitive features from raw data before training the ML model. In practice, prior knowledge is not available in most cases. Deep learning (DL) methods, on the other hand, can obtain higher-level features from raw input data and have proven superior in several applications. This paper proposes a Convolutional Neural Network (CNN) based approach for the delamination prediction in CFRP double cantilever beam (DCB) specimens using raw local array strain measurements via distributed optical fiber sensors. The conventional CNN architecture is modified to perform regression, as the delamination size is a continuous value. 1D and 2D CNN architectures are deployed and compared and different techniques are exploited to encode 1D spatial strain pattern series as 2D images. Raw strain patterns collected during static testing are used to train the CNNs, while testing is performed on unseen raw fatigue strain patterns, showing the CNN ability to automatically extract discriminative features from the non-pre-processed static strain pattern-based signals that generalize to raw fatigue signals as well. This strategy has the potential to reduce fatigue testing expenditures while also shortening the time required to gather training data.

Despite the promising application of Distributed Optical Fiber Sensors (DOFS) in monitoring damage in composite structures, their implementation outside academia is still unsatisfactory due to the lack of a systematic approach to assessing their damage detection performance. The existing tool developed for non-destructive evaluation, Probability of Detection (POD) curves, needs to be adapted for structural health monitoring applications to account for spatial and temporal dependency. Damage detection performance with DOFS is deeply related to the inherent variability sources of the system, the strain transfer properties of the optical fiber, and the loading conditions, which determine the damage-induced strain on the structure. This paper establishes a systematic approach based on the Length at Detection (LaD) method to qualify DOFS for damage detection in composites under different scenarios. Specifically, this study considers two DOFS with different strain transfer properties for monitoring delamination in carbon fiber reinforced polymers double-cantilever beam specimens under mode I quasi-static and fatigue loading. The POD curves derived from the LaD method confirm that this methodology can quantify the change in the detection performance due to the DOFS type and the loading conditions. The study also proposes a practical solution to compare POD curves obtained with different sample sizes, by introducing the concept of virtual specimens to simulate the lower confidence bound convergence.