Jul 2026· e-Journal of Nondestructive Testing· Vol 31· 0 citations
TL;DR
Results show that the proposed approach provides critical AI-based support, enabling inspectors to certify component acceptance or rejection with greater reliability and confidence, and facilitates next-generation NDT 5.0 systems and empowers inspectors in highly automated environments.
Abstract
Deploying AI models for Non-Destructive Testing (NDT) in production environments remains challenging due to the dynamic nature of industrial conditions. Over time, data drift caused by process variations alters input distributions; label noise arises from inconsistent or imperfect expert annotations; and unseen scenarios introduce new defect types or signal patterns not represented during training. Together, these factors lead to progressive performance degradation, reducing model reliability and operator trust. In an industry such as NDT, where inspection reliability directly impacts structural integrity and safety, model failure is simply not acceptable.
To address this challenge, this study introduces an innovative human-centric framework that synergistically integrates incremental and active learning strategies in complex NDT inspections. Incremental learning methodologies are specifically designed to enable models to continuously adapt to evolving data distributions while preserving previously acquired knowledge. At the same time, AI-assisted inspection strategies based on active learning allows NDT inspection to maintain high levels of automation with a central role of the operator in the loop.
Results show that the proposed approach provides critical AI-based support, enabling inspectors to certify component acceptance or rejection with greater reliability and confidence. This facilitates next-generation NDT 5.0 systems and empowers inspectors in highly automated environments.
A thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots explores significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation.
Muhammad Umar Elahi, Rana Talal Ahmad Khan, Muhammad Haris Yazdani et al.· Mathematics· 0 citations
Injection molding is a widely used process in the plastics industry, yet its quality depends heavily on precise adjustment of interdependent process parameters, which has traditionally relied on trial-and-error by skilled engineers. Such reliance is inefficient, time-consuming, and difficult to scale, particularly as the number of controllable parameters increases. This study investigates reinforcement learning (RL) as a means of supporting process optimization in injection molding by constructing an environmental model that reflects the severe class imbalance and the requirement for continuous control. The environmental model for RL-based agents was built using the Injection Molding AI Dataset, where weight balancing was applied to classifiers and anomaly detection methods were separately employed to improve reliability under imbalanced conditions. On this basis, two RL-based agents, Deep Q-Network (DQN) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG), were designed and evaluated for their ability to optimize key process parameters such as injection time, filling time, and screw positions. The comparative analysis showed that while DQN offered advantages in terms of lightweight architecture, stable training behavior, and ease of implementation in discretized settings, MADDPG achieved greater effectiveness in continuous action spaces with multiple interdependent variables. Importantly, the MADDPG-based agent consistently identified non-defective production conditions in a single step, within the simulated environment, suggesting promising industrial applicability in future injection molding systems.
Daeyoung Kang, Keonwoo Nam, Seongrae Kim et al.· International Journal of Pre...· 0 citations
To maintain high efficiency and reduce operational downtime in industrial manufacturing, effective Predictive Maintenance (PdM) for robotic manipulators is essential. Although combining Model-Agnostic Meta-Learning (MAML) with digital twin technology offers a solid basis for quickly identifying faults, conventional methods often face challenges regarding parameter sensitivity and generalizing to new domains. To mitigate these issues, we introduce an ensemble-based metalearning framework that combines MAML with majority voting and operational grouping. This methodology improves generalization, stabilizes performance across diverse conditions, and strengthens few-shot learning capabilities. We validated the framework using a synthetic vibration dataset generated via a digital twin to simulate various robotic arm faults. Our findings demonstrate that this method achieves 93.8% accuracy and 93.1% precision in the ten-shot regime, outperforming the MAML baseline by 11.1%, across a broad range of mechanical faults, showing strength in cross-domain few-shot (CDFS) scenarios. Comparisons with established frameworks - including Reptile, Protonet, and ANIL, confirm the effectiveness of our model. By employing ensemble learning, we attain greater robustness and classification accuracy, establishing the method as a viable solution for industrial PdM. Furthermore, the integration of digital twins bridges the gap between simulation and real-world deployment, reducing data dependency and enabling effective fault classification even in dynamic environments with limited labeled data.
Mainak Mallick, Seung-Kyum Choi· 2026 6th International Confe...· 0 citations
Industrial robotic systems are essential to modern manufacturing, but their reliability is threatened by progressive mechanical and electrical degradation. Traditional reactive and preventive maintenance strategies are inadequate for the complex, high-dimensional sensor environments of contemporary industrial robots. This study developed and evaluated AE-LSTM-ATT (Attention-Enhanced Hybrid LSTM Autoencoder), an LSTM encoder-decoder architecture with Bahdanau-style additive attention, for unsupervised anomaly detection in industrial robotic systems. The model was evaluated on the public Industrial Robot Anomaly Detection (IndRAD) dataset (robot joint positions, velocities, torques, and motor currents) against an attention-free LSTM Autoencoder and an Isolation Forest baseline, across five independent training runs and four synthetically injected anomaly types (spike, step, freeze-to-zero, and freeze-to-last-value). AE-LSTM-ATT and the attention-free baseline performed comparably on abrupt anomalies (spike, step; AUC-ROC (Area Under the Receiver Operating Characteristic Curve) = 1.000 for both). For the subtler, gradually manifesting anomaly types, attention provided a small, directionally positive advantage: for freeze-to-last-value, AE-LSTM-ATT achieved AUC-ROC = 0.518 ± 0.059 and F1-score = 0.682 ± 0.020 (95% CI, 5 seeds), versus 0.491 ± 0.046 and 0.670 ± 0.017 for the baseline; freeze-to-zero showed overlapping confidence intervals. An independent replication of this comparison with a paired significance test across five matched seeds (section "Ablation: Contribution of the Attention Mechanism") did not find the difference to be statistically significant, and the direction of the per-seed difference was not uniform. These results indicate that any attention-related benefit in this architecture is, at most, small and anomaly-type dependent, and is not established as statistically significant at the sample sizes evaluated here; attention showed no distinguishable advantage for abrupt, high-amplitude faults already near-perfectly separable by reconstruction error alone. The framework operates in an unsupervised paradigm requiring no labelled fault data, supporting deployment in settings where run-to-failure data are scarce. Overall, this study finds no evidence that attention-enhanced temporal autoencoders offer a universal advantage over simpler deep learning baselines for industrial robotic predictive maintenance; any benefit appears concentrated in detecting subtle, temporally extended fault signatures, is small in magnitude, and was not confirmed as statistically significant in the present sample.
A novel and robust Source-Free Domain Adaptation (SFDA) framework equipped with post-hoc explainability for bearing fault diagnosis is proposed, and a post-hoc visualization mechanism is incorporated to explain the model's decision-making process, enhancing the transparency and credibility of the diagnosis for practical industrial deployment.
chenghao yan, Dongsheng Liu, Tong Wu et al.· Measurement science and tech...· 0 citations
A data engine which gathers data and improves its performance while executing the task, and demonstrates the ability to learn and reduce the need for expensive verifications over time, while staying within the set error-rate.
Zebin Duan, Norbert Krüger, Juan Heredia et al.· 0 citations