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

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

Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications, often being the fundamental step in multi-agent systems. The increasing complexity of MAPF in complex and crowded environments, however, critically diminishes the effectiveness of existing solutions. In contrast to other studies that have either presented a general overview of the recent advancements in MAPF or extensively reviewed Deep Reinforcement Learning (DRL) within multi-agent system settings independently, our work presented in this review paper focuses on highlighting the integration of DRL-based approaches in MAPF. Moreover, we aim to bridge the current gap in evaluating MAPF solutions by addressing the lack of unified evaluation metrics and providing comprehensive clarification on these metrics. Finally, our paper discusses the potential of model-based DRL as a promising future direction and provides its required foundational understanding to address current challenges in MAPF. Our objective is to assist readers in gaining insight into the current research direction, providing unified metrics for comparing different MAPF algorithms and expanding their knowledge of model-based DRL to address the existing challenges in MAPF. 

pdf/learning-to-team-based-navigation-a-review-of-deep-b0yqcg7bdp.pdf

Learning to Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding

A Review of Strategies to Detect Fatigue and Sleep Problems in Aviation: Insights from Artificial Intelligence

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

Inspection of tunnel lining surface is an essential yet challenging task in tunnel construction. Timely identification of tunnel lining defects (e.g. water leakage, cracks and spalling, etc.) is critical for the quality control in the construction stage. Conventional methods rely on visual inspection and manual recording of the inspection results inside the congested tunnel environment, which is time-consuming and inefficient. In recent years, the advancements in AI and drone technologies show promising prospects in tunnel inspection. In this study, an AI-powered inspection system using edge-computing device on a specially configured drone is developed for efficient and accurate inspection of the completed tunnel lining surface. The proposed sensor-fusion method for defect detection and SAHI-based method to detect small cracks, spalling, and water leakages on the tunnel lining surface demonstrated satisfactory results in the pilot application in the construction of Trunk Road T2 and Cha Kwo Ling Tunnel in Hong Kong. This is the first reported onboard AI tunnel surface inspection system in the world that can be applied in other tunneling and infrastructure projects in the foreseeable future.

A Real-time Tunnel Surface Inspection System using Edge-AI on Drone

Deadlock is always a challenging problem for multi-agent pathfinding, especially when the system is in high scales in terms of number of agents and map size. Some recent studies showed that the agents can learn to resolve the deadlock problem through reinforcement learning. However, most of them are not designed for non-holonomic robots, which are commonly applied in warehouses. In particular, the rotation movement may cause the agents staying at the same locations for a long time, and the deadlock happens more frequently especially in dense environment. In this paper, an algorithm called MAPF-rot with a deadlock breaking scheme is proposed to tackle the deadlock problem arising from the rotation movement in the multi-agent pathfinding problem. Experiments are performed to demonstrate the efficiency of the proposed algorithm.

Multi-Agent Pathfinding for Deadlock Avoidance on Rotational Movements

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

A Real-time Tunnel Surface Inspection System using Edge-AI on Drone

Multi-Agent Pathfinding for Deadlock Avoidance on Rotational Movements