This paper compares the results of multiaxial fatigue life estimation using machine learning methods and classical fatigue models. The fatigue life of PA38-T6 aluminum alloy under uniaxial, proportional, and non-proportional loading, including asynchronous loading, is studied. Machine learning methods are trained only on basic loadings, namely, axial, torsional, and 90° out-of-phase. The results obtained with the machine learning algorithms, dense neural networks, support vector regression (with linear and radial basis functions), decision tree, random forest, and XGBoost algorithms, are comparable to the results of classical models like Ellyin–Gołoś, Fatemi–Socie, and Ince–Glinka. The best results are achieved for dense neural networks. In that case, they are often slightly more accurate than those obtained using classical methods. 

Application of machine learning methods in multiaxial fatigue life prediction

Machine learning (ML) has recently enabled many modeling tasks in design, manufacturing, and condition monitoring due to its unparalleled learning ability using existing data. Data have become the limiting factor when implementing ML in industry. However, there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing. The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them. To establish the background for the subsequent analysis, crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition, management, analysis, and utilization. Thereafter, the concepts and frameworks established to evaluate data quality and imbalance, including data quality assessment, data readiness, information quality, data biases, fairness, and diversity, are further investigated. The root causes and types of data challenges, including human factors, complex systems, complicated relationships, lack of data quality, data heterogeneity, data imbalance, and data scarcity, are identified and summarized. Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed. This literature review focuses on two promising methods: data augmentation and active learning. The strengths, limitations, and applicability of the surveyed techniques are illustrated. The trends of data augmentation and active learning are discussed with respect to their applications, data types, and approaches. Based on this discussion, future directions for data quality improvement and data imbalance mitigation in this domain are identified. 

On the data quality and imbalance in machine learning-based design and manufacturing—A systematic review

Machine learning opens up a new avenue for advancing the development of phononic crystals and elastic metamaterials. Numerous learning models have been employed and developed to address various challenges in the field of phononic metamaterials. Here, we provide an overview of mainstream machine learning models applied to phononic metamaterials, discuss their capabilities as well as limitations, and explore potential directions for future research. 

Machine learning models in phononic metamaterials

Fractographic analysis poses a significant challenge for field researchers without specialized training in fractography. To address this issue, this study introduces a comprehensive integrated workflow that encapsulates the entire process from dataset preparation and data enhancement to leveraging the SegFormer model for deep learning-driven semantic segmentation. An extensive collection of fractography images is formulated and augmented to train the SegFormer model, enabling precise semantic segmentation of morphological fracture regions including cleavage, ductile, dimple, fatigue striations, and others. To accommodate the demanding SEM imaging conditions which frequently include distortions, noise, and aberrations, we developed a two-stage method with diverse data augmentation strategies. This method resulted in a robust model demonstrating exceptional performance, as evidenced by a high mean Intersection over Union (mIOU) score of 59.7 and other metrics. The findings validate the potential of deep learning techniques, particularly the SegFormer model's efficacy in morphological fractography image segmentation for the first time. Our work offers a cost-effective, and efficient alternative deep learning approach to traditional experimental fracture analysis, thereby expanding opportunities for a broader range of professionals in the engineering domain. 

Deep learning-based semantic segmentation for morphological fractography


 The hard rocks in the stratum can pose safety risks and hinder the progress of urban underground tunnel construction using shield and jacking methods, thereby reducing construction efficiency and increasing construction costs. This paper utilizes wavelet scale energy spectrum, wavelet packet theory and statistical methods to conduct research on the detection of special geological formations such as hard rocks and voids, as well as the analysis of their signal time-frequency characteristics based on the ground-penetrating radar (GPR) technique. On the basis of calibrating the permittivity of different types of rock blocks, we established a forward model for detecting hard rocks and voids, and the simulated signals were analyzed in the time and frequency domains. Subsequently, laboratory experiments were conducted to perform GPR tests on different types of hard rocks in natural and water-saturated states and voids, to explore the time-frequency characteristics, frequency band energy variations, and statistical patterns of typical single-trace signals. The results show that the granite detection signal contains more low-frequency components, the sandstone detection signal contains more medium-low frequency components, while the limestone detection signal contains more medium-high frequency components in their natural state; the signal from the karst cave has relatively more low-frequency components than the signal from the empty cavity. The geometric shape of the rock has no influence on the dominant frequency and time-frequency distribution of its reflection signal. Generally, rocks with higher rebound values (hardness) also exhibit larger variance and standard deviation in frequency band energy. The research has important theoretical significance and practical value for the measurement and assessment of special geological features such as hard rocks and voids in urban underground trenchless construction.

Geological detection of hard rocks by GPR and signal time-frequency characteristics analysis in urban underground trenchless construction

Fatigue fracture is one of the most common metallic component failure cases in manufacturing industries. The observation on fractography can provide direct evidence for failure analysis. In this study, an image semantic segmentation method based on fully convolutional networks (FCNs) was proposed to figure out the boundary between fatigue crack propagation and fast fracture regions from optical microscope (OM) fractography images. Furthermore, a novel morphing-based data augmentation method was adopted to enable few-shot learning of sample images. The proposed framework can successfully segment two categories, namely, the crack propagation and fast fracture regions, thus differentiating the boundary of two regions in one image. This artificial intelligence (AI)-assisted fatigue analysis architecture can complete the failure analysis procedure in 0.5 s and prove the feasibility of fatigue failure analysis. The segmentation accuracy of self-developed network achieves 95.4% for the fatigue crack propagation region, as well as 97.2% for the fast fracture region. Not only for semantic segmentation DNN, we also prove that our novel data augmentation method can applied at the instance segmentation DNN, such as mask regional convolutional neural network (mask R-CNN), one state-of-the-art deep learning network for instance segmentation, to achieve similar accuracy. 

Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

Topological phononic materials have been widely used in many fields, such as topological antennas, asymmetric waveguides, and noise insulation. However, due to the limitations of the manufacturing process, topological protection is vulnerable to some severe defects that may affect the application effect. Therefore, the quality inspection of topological materials is essential to ensure reliable results. Due to the low contrast and irregularity of defects and the similarity of topological phononics, they are difficult to recognize by traditional image processing algorithms, so manual detection is still mainstream at present. But manual detection requires experienced inspectors, which is expensive and time-consuming. In addition, topological materials are expensive to produce, and there is no large publicly available dataset, but deep learning usually relies on large datasets for training. To solve the above problems, we propose an automatic deep learning topology structure defect detection method (ADLTSDM) in this work, which could classify not only the structure of topological materials but also detect the defects of topological phononics based on a small dataset. ADLTSDM exploits the prior knowledge of the topological material structure and achieves an augmentation factor of more than 100 times through the random and fixed interval screenshot algorithm, thus enabling the training of deep neural networks with only two raw data. For defect detection, ADLTSDM has an accuracy of more than 97% and improves detection speed by more than 38% compared with manual detection. For structure classification, ADLTSDM can achieve an accuracy of over 99% and seven times faster speed compared with manual classification. Besides, the detection standard of ADLTSDM is unified, so the accuracy will not be affected by the experience of the inspectors, which has more potential in high-throughput industrial applications.

A defect detection method for topological phononic materials based on few-shot learning

As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However, digital instruments usually have multi-digit features, meaning that the numeric information on the screen is usually a multi-digit number greater than 10. Therefore, the accuracy of recognition with traditional algorithms such as threshold segmentation and template matching is low, and thus instrument monitoring still relies heavily on human labor at present. However, manual monitoring is costly and not suitable for risky experimental environments such as those involving radiation and contamination. The development of deep neural networks has opened up new possibilities for fully automated instrument monitoring; however, neural networks generally require large training datasets, costly data collection, and annotation. To solve the above problems, this paper proposes a new instrument monitoring method based on few-shot learning (FLIMM). FLIMM improves the average accuracy (ACC) of the model to 99% with only 16 original images via effective data augmentation method. Meanwhile, due to the controllability of simulated image generation, FLIMM can automatically generate annotation information for simulated numbers, which greatly reduces the cost of data collection and annotation.

/pdf/a-new-instrument-monitoring-method-based-on-few-shot-1aadys28.pdf

A New Instrument Monitoring Method Based on Few-Shot Learning

Substantial work has shown that the modulation of structural damage on probing guided ultrasonic waves can result in wave components corresponding to different frequencies, causing wave energy transfer from central frequency to other frequency bands. To make use of the damage-induced wave energy transfer in different frequency bands, a data-driven method which combines wavelet packet decomposition (WPD), principal component analysis (PCA) and support vector machine (SVM), is proposed in this study for structural damage identification in both metallic and composite materials. Firstly, WPD is employed to decompose the original signal into different frequency bands, based on which the wave energy at each frequency band can be determined. A wave energy distribution vector is constructed according to the energy proportion of each frequency band. Then, PCA is recalled conducting dimensionality reduction for the energy distribution vectors, in order to improve the computational efficiency for subsequent SVM classification. The compressed energy distribution vectors are used as the input to train an SVM-based classifier for identifying structural damage. To validate the proposed WPD-PCA-SVM method, experiments are performed on both aluminum plate and glass fiber reinforced polymer (GFRP) laminate. According to the experimental results, the embryonic fatigue crack in the (aluminum) plate and the anomaly in the GFRP laminate can be identified by the proposed method, with a detection accuracy of 92.86% for aluminum plate and 95.45% for GFRP laminate, respectively, demonstrating the effectiveness of the proposed method for damage detection in both metallic and composite materials.

Wavelet packet energy-based damage detection using guided ultrasonic waves and support vector machine

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Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

A defect detection method for topological phononic materials based on few-shot learning

A New Instrument Monitoring Method Based on Few-Shot Learning

Wavelet packet energy-based damage detection using guided ultrasonic waves and support vector machine