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Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 18230

Special Issue Editors

Prof. Dr. Ahmad Osman

E-Mail Website
Guest Editor
1. Fraunhofer Institute for Nondestructive Testing IZFP, 66123 Saarbruecken, Germany
2. School of Engineering, University of Applied Sciences, 66123 Saarbruecken, Germany
Interests: applied artificial intelligence; NDE 4.0; deep learning; data interpretation and analysis; explainable AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Valérie Kaftandjian-Doudet

E-Mail Website
Guest Editor
INSA-Lyon, LVA, EA 677, CEDEX, 69621 Villeurbanne, France
Interests: image processing; data fusion; defect classification; applied artificial intelligence; NDE 4.0; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning (DL) is currently the most important discipline of Artificial Intelligence (AI). It enables machines to process data, learn patterns, and recognize and classify complex defects. By means of DL, problems whose mathematical descriptions are difficult or impossible can be processed automatically. In close combination with NDE sensors, process data, and robotics, the application of DL opens up new possibilities that will define the next generation of NDE systems. The NDE community is trending toward the NDE 4.0, and AI, especially DL, is a driver of the paradigm shift in this direction.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of:

  • Deep learning for NDE image reconstruction;
  • Deep learning for data processing (signal or image);
  • NDE 4.0 systems with AI as assisting technology;
  • Deep learning for NDE data interpretation, including defect recognition;
  • Interaction between NDE–personal and AI;
  • NDE big data applications, algorithms, and systems;
  • Cloud/edge/fog computing for NDE applications;
  • Trusted AI for NDE applications;
  • Qualification of AI methods for NDE applications: norms, benchmarks, metrics, etc.

Because of the significant relevance of this Special Issue on AI in NDE, the first 10 accepted papers will be published with full waivers. Up to 15 papers are expected to be accepted for this issue.

Prof. Dr. Ahmad Osman
Prof. Dr. Valérie Kaftandjian-Doudet
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • deep learning
  • nondestructive evaluation
  • defect recognition
  • NDE 4.0
  • NDE data

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Published Papers (11 papers)

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Research

28 pages, 26850 KiB  
Article
Deep Learning Utilization for In-Line Monitoring of an Additive Co-Extrusion Process Based on Evaluation of Laser Profiler Data
by Valentin Lang, Christian Thomas Ernst Herrmann, Mirco Fuchs and Steffen Ihlenfeldt
Appl. Sci. 2025, 15(4), 1727; https://doi.org/10.3390/app15041727 - 8 Feb 2025
Viewed by 698
Abstract
Additive manufacturing is gaining importance in a number of application areas, and there is an increased demand for mechanically resilient components. A way to improve the mechanical properties of parts made of thermoplastics is by using reinforcing material. The study demonstrates the development [...] Read more.
Additive manufacturing is gaining importance in a number of application areas, and there is an increased demand for mechanically resilient components. A way to improve the mechanical properties of parts made of thermoplastics is by using reinforcing material. The study demonstrates the development of a monitoring procedure for a fused filament fabrication-based co-extrusion process for manufacturing wire-reinforced thermoplastic components. Test components in two variants are produced, and data acquisition is carried out with a laser line scanner. The collected data are employed to train deep neural networks to classify the printed layers, aiming for the deep neural networks to be able to classify four different classes and identify layers with insufficient quality. A dedicated convolutional neural network is designed taking into account various factors such as layer architecture, data pre-processing and optimization methods. Several network architectures, including transfer learning (based on VGG16 and ResNet50), with and without fine-tuning, are compared in terms of their performance based on the F1 score. Both the transfer learning model with ResNet50 and the fine-tuning model achieve an F1 score of 84% and 83%, respectively, for the decisive class ‘wire bad’ classifying inadequate reinforcement. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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20 pages, 1764 KiB  
Article
A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment
by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng and Xiangchen Wang
Appl. Sci. 2025, 15(4), 1702; https://doi.org/10.3390/app15041702 - 7 Feb 2025
Cited by 1 | Viewed by 1069
Abstract
Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional [...] Read more.
Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. This model aims to enhance the reliability and accuracy of time-series fault prediction. It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. Then, the model employs an improved TCN layer and a BiLSTM layer for feature extraction and learning. The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. The BiLSTM layer further leverages its bidirectional learning mechanism to model the long-term dependencies in the data, enabling effective predictions of complex fault patterns. Finally, the model outputs the prediction results after iterative optimization. To evaluate the performance of the proposed model, simulation experiments were conducted to compare the TCN-BiLSTM model with mainstream prediction methods such as CNN, RNN, BiLSTM, and A-BiLSTM. The experimental results indicate that the TCN-BiLSTM model outperforms the comparison models in terms of prediction accuracy during both the modeling and forecasting stages, providing a feasible solution for time-series fault prediction. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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29 pages, 18502 KiB  
Article
Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
by Hengdi Wang and Jizhan Xie
Appl. Sci. 2025, 15(3), 1389; https://doi.org/10.3390/app15031389 - 29 Jan 2025
Cited by 1 | Viewed by 1062
Abstract
Compared to vibration sensors, microphones offer several advantages, including non-contact detection, high sensitivity, low cost, and ease of installation. To address the challenges posed by the complex components and significant interference in rolling bearing sound signals, we proposed a fault diagnosis method for [...] Read more.
Compared to vibration sensors, microphones offer several advantages, including non-contact detection, high sensitivity, low cost, and ease of installation. To address the challenges posed by the complex components and significant interference in rolling bearing sound signals, we proposed a fault diagnosis method for rolling bearing acoustic signals based on Secretary Bird Optimization Algorithm (SBOA)-optimized Feature Mode Decomposition (FMD). Initially, a microphone is utilized to collect sound data while the bearing operates, followed by the application of S-FMD (Secretary Bird Optimization Algorithm-optimized Feature Mode Decomposition) to decompose the sound signal and extract components that may contain fault information related to the bearing. The SBOA is employed to adaptively optimize four influencing parameters of FMD: mode number n, filter length L, frequency band cutting number K, and cycle period m. By minimizing envelope entropy as the objective function, we achieve FMD of the bearing sound signal with the assistance of the SBOA. Additionally, this paper introduces an Integrated Signal Evaluation Index (ISEI) to extract potential bearing failure characteristics from the filtered components. Simulation experiments and test results indicate that, compared to Empirical Mode Decomposition, Complementary Ensemble Empirical Mode Decomposition, fixed-parameter FMD, and adaptive variational mode decomposition methods, the proposed approach more effectively extracts weak characteristic information related to early faults in bearing sound signals. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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31 pages, 7014 KiB  
Article
Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
by Sangkeun Ko and Suan Lee
Appl. Sci. 2025, 15(3), 1257; https://doi.org/10.3390/app15031257 - 26 Jan 2025
Cited by 1 | Viewed by 670
Abstract
In time-series studies involving bearing sensor data, Gaussian noise and white noise techniques are commonly employed to evaluate model robustness. However, these conventional noise techniques are limited in their applicability to real-world industrial environments. This paper proposes three novel noise techniques—electrical interference noise, [...] Read more.
In time-series studies involving bearing sensor data, Gaussian noise and white noise techniques are commonly employed to evaluate model robustness. However, these conventional noise techniques are limited in their applicability to real-world industrial environments. This paper proposes three novel noise techniques—electrical interference noise, harmonic noise, and random shock noise—that more accurately reflect the complex noise encountered in industrial settings. Additionally, a new deep learning model, MultiPatchTST, is introduced, demonstrating robust performance under various noise conditions. Experimental results reveal that Gaussian noise has minimal impact on model performance, whereas the proposed noise techniques significantly affect performance, providing a more realistic evaluation of noise robustness. The proposed MultiPatchTST model achieves superior performance across all metrics in the presence of all four noise types, confirming its robustness and reliability. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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18 pages, 6973 KiB  
Article
Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images
by Qilin Jin, Qingbang Han, Jianhua Qian, Liujia Sun, Kao Ge and Jiayu Xia
Appl. Sci. 2025, 15(2), 597; https://doi.org/10.3390/app15020597 - 9 Jan 2025
Viewed by 742
Abstract
Drainage pipeline construction projects are vulnerable to a range of defects, such as branch concealed joints, variable diameter, two pipe mouth significances, foreign object insertion, pipeline rupture, and pipeline end disconnection, generated during long-term service in a complex environment. This paper proposes two [...] Read more.
Drainage pipeline construction projects are vulnerable to a range of defects, such as branch concealed joints, variable diameter, two pipe mouth significances, foreign object insertion, pipeline rupture, and pipeline end disconnection, generated during long-term service in a complex environment. This paper proposes two enhancements to multiple attention learning to detect and segment multiple defects. Firstly, we collected numerous samples of drainage pipeline sonar defect videos. Then, our multiple attention segmentation network was used for target segmentation. The test precision and accuracy of MAP@50 reach 96.0% and 90.9%, respectively, in the segmentation prediction. Compared to the coordinate attention and convolutional block attention module attention models, it had a significant precision advantage, and the weight file size is merely 7.0 MB, which is far smaller than the Yolov9 model segmentation weight size. The multiple attention method proposed in this paper was adopted for detection, instance segmentation, and pose detection in different public datasets, especially in the object detection of the coco128-seg dataset under the same condition. Map@50:95 has increased by 13.0% assisted by our multiple attention mechanism. The results indicated the memory efficiency and high precision of the integration of the multiple attention model on several public datasets. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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20 pages, 3218 KiB  
Article
Machine Learning-Based Lithium Battery State of Health Prediction Research
by Kun Li and Xinling Chen
Appl. Sci. 2025, 15(2), 516; https://doi.org/10.3390/app15020516 - 7 Jan 2025
Cited by 3 | Viewed by 2206
Abstract
To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), [...] Read more.
To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. Key features were extracted by analyzing the temperature, voltage, and current curves of the battery, and health factors with high correlation to SOH were selected as model inputs using the Pearson correlation coefficient. The PSO algorithm was employed to optimize model parameters, resulting in the construction of three predictive models: PSO-LSTM, PSO-CNN, and PSO-SVR. The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). Results indicate that the optimized models achieved significant improvements in prediction accuracy, with RMSE and MAE reduced by over 0.5%, a minimum reduction of 38% in MAPE, and R2 exceeding 0.8, demonstrating strong fitting capabilities and validating the effectiveness of the PSO strategy. Among the three models, PSO-LSTM exhibited the best predictive performance, achieving a minimum MAE of 0.67%, RMSE of 0.94%, MAPE of 45.82%, and R2 as high as 0.9298 across the three datasets. These findings suggest that the PSO-LSTM model provides a robust reference for accurate SOH prediction of lithium-ion batteries and shows promising potential for practical applications. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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22 pages, 9171 KiB  
Article
An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
by Jinwen Wang, Ting Chen, Xinke Xu, Longbiao Zhao, Dijian Yuan, Yu Du, Xiaowei Guo and Ning Chen
Appl. Sci. 2025, 15(1), 52; https://doi.org/10.3390/app15010052 - 25 Dec 2024
Cited by 1 | Viewed by 929
Abstract
In the process of steel strip production, the accuracy of defect detection remains a challenge due to the diversity of defect types, complex backgrounds, and noise interference. To improve the effectiveness of surface defect detection in steel strips, we propose an enhanced detection [...] Read more.
In the process of steel strip production, the accuracy of defect detection remains a challenge due to the diversity of defect types, complex backgrounds, and noise interference. To improve the effectiveness of surface defect detection in steel strips, we propose an enhanced detection model known as YOLOv8-BSPB. First, we propose a novel pooling layer module, SCRD, which replaces max pooling with average pooling. This module introduces the receptive field block (RFB) and deformable convolutional network version 4 (DCNv4) to obtain learnable offsets, allowing convolutional kernels to flexibly move and deform on the input feature map, thus, more effectively extracting multi-scale features. Second, we integrate a polarized self-attention (PSA) mechanism to improve the model’s feature representation and enhance its ability to focus on relevant information. Additionally, we incorporate the BAM attention mechanism after the C2f module to strengthen the model’s feature selection capabilities. A bidirectional feature pyramid network is introduced at the neck of the model to improve feature transmission efficiency. Finally, the WIoU loss function is employed to accelerate the model’s convergence speed and enhance regression accuracy. Experimental results on the NEU-DET dataset demonstrate that the improved model achieves a classification accuracy of 81.3%, an increase of 4.9% over the baseline, with a mean average precision of 86.9%. The model has a parameter count of 5.5 M and operates at 103.1 FPS. To validate the model’s effectiveness, we conducted tests on the Kaggle steel strip dataset and our custom dataset, where the average accuracy improved by 2.3% and 5.5%, respectively. The experimental results indicate that the model meets the requirements for real-time, lightweight, and portable deployment. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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24 pages, 3657 KiB  
Article
Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques
by Sonia Val, María Pilar Lambán, Javier Lucia and Jesús Royo
Appl. Sci. 2024, 14(24), 11840; https://doi.org/10.3390/app142411840 - 18 Dec 2024
Cited by 1 | Viewed by 998
Abstract
Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with [...] Read more.
Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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19 pages, 11591 KiB  
Article
Industrial Application of AI-Based Assistive Magnetic Particle Inspection
by Julien Baumeyer, Hermine Chatoux, Arnaud Pelletier and Patrick Marquié
Appl. Sci. 2024, 14(4), 1499; https://doi.org/10.3390/app14041499 - 12 Feb 2024
Cited by 1 | Viewed by 2339
Abstract
Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and [...] Read more.
Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and use chemical products in a confined environment. Those constraints tends to lower control performance and increase stress and fatigue. As a solution, we propose an AI-based assistive machine (called “PARADES”) inside the hazardous environment, remotely manipulated by a human operator, outside of the confined area, in cleaner and safer conditions. This paper focuses on the development of a complete industrial-grade AI machine, both in terms of hardware and software. The result is a standalone assistive AI-based vision system, plug-and-play and controller-friendly, which only needs the usual power supply 230 V plug that detects defects and measures defect length. In conclusion, the PARADES machines address for the first time the problem of occupational health in MPI with an industrial standalone machine which can work on several parts and be integrated into current production lines. Providing cleaner and healthier working conditions for operators will invariably lead to increased quality of detection. These results suggest that it would be beneficial to spread this kind of AI-based assistive technology in NDT, in particular MPI, but also in Fluorescent Penetrant Testing (FPT) or in visual inspection. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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13 pages, 5710 KiB  
Article
A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
by Ziang Wei, Ahmad Osman, Bernd Valeske and Xavier Maldague
Appl. Sci. 2023, 13(24), 13093; https://doi.org/10.3390/app132413093 - 8 Dec 2023
Cited by 3 | Viewed by 2020
Abstract
Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC [...] Read more.
Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC specimens is introduced. It was enriched with 3D positional information to advance research in this area. To ensure the labeling quality, a comparative analysis of two distinct data labeling methods was conducted. The first method is based on human domain expertise, while the second method relies on 3D CAD images. The analysis showed that the CAD-based labeling method noticeably enhanced the precision of defect dimension quantification. Additionally, a sophisticated deep learning model was employed on the data, which were preprocessed by different methods to predict both the two-dimensional coordinates and the depth of the identified defects. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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14 pages, 2845 KiB  
Article
Pulsed Thermography Dataset for Training Deep Learning Models
by Ziang Wei, Ahmad Osman, Bernd Valeske and Xavier Maldague
Appl. Sci. 2023, 13(5), 2901; https://doi.org/10.3390/app13052901 - 24 Feb 2023
Cited by 11 | Viewed by 3461
Abstract
Pulsed thermography is an indispensable tool in the field of non-destructive evaluation. However, the data generated by this technique can be challenging to analyze and require expertise to interpret. With the rapid progress in deep learning, image segmentation has become a well-established area [...] Read more.
Pulsed thermography is an indispensable tool in the field of non-destructive evaluation. However, the data generated by this technique can be challenging to analyze and require expertise to interpret. With the rapid progress in deep learning, image segmentation has become a well-established area of research. This has motivated efforts to apply deep learning methods to non-destructive evaluation data processing, including pulsed thermography. Despite this trend, there has been a lack of public pulsed thermography datasets available for the evaluation of various spatial-temporal deep learning models for segmentation tasks. This paper aims to address this gap by presenting the PVC-Infrared dataset for deep learning. In addition, we evaluated the performance of popular deep-learning-based instance segmentation models on this dataset. Furthermore, we examined the effect of the number of frames and data transformations on the performance of these models. The results of this study suggest that appropriate preprocessing techniques can significantly reduce the size of the data while maintaining the performance of deep learning models, thereby speeding up the data processing process. This highlights the potential for using deep learning methods to make non-destructive evaluation data analysis more efficient and accessible to a wider range of practitioners. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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