Browsing by Author "Deng, Kailun"

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    A cyclic self-enhancement technique for complex defect profile reconstruction based on thermographic evaluation
    (Springer, 2025-05) Liu, Haochen; Wang, Shuozhi; Zhao, Yifan; Deng, Kailun; Chen, Zhenmao
    Although machine Learning has demonstrated exceptional applicability in thermographic inspection, precise defect reconstruction is still challenging, especially for complex defect profiles with limited defect sample diversity. Thus, this paper proposes a self-enhancement defect reconstruction technique based on cycle-consistent generative adversarial network (Cycle-GAN) that accurately characterises complex defect profiles and generates reliable artificial thermal images for dataset augmentation, enhancing defect characterisation. By using a synthetic dataset from simulation and experiments, the network overcomes the limited samples problem by learning the diversity of complex defects from finite element modelling and obtaining the thermography uncertainty patterns from practical experiments. Then, an iterative strategy with a self-enhancement capability optimises the characterisation accuracy and data generation performance. The designed loss function structure with cycle consistency and identity loss constrains the GAN’s transfer variation to guarantee augmented data quality and defect reconstruction accuracy simultaneously, while the self-enhancement results significantly improve accuracy in thermal images and defect profile reconstruction. The experimental results demonstrate the feasibility of the proposed method by attaining high accuracy with optimal loss norm for defect profile reconstruction with a Recall score over 0.92. The scalability investigation of different materials and defect types is also discussed, highlighting its capability for diverse thermography quantification and automated inspection scenarios.
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    Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials
    (Elsevier, 2024-03-14) Deng, Kailun; Liu, Haochen; Cao, Jun; Yang, Lichao; Du, Weixiang; Xu, Yigeng; Zhao, Yifan; This work was partially supported by the Royal Academy of Engineering Industrial Fellowship [#grant IF2223B-110], and partially supported by the Science and Technology Department of Gansu Province Science and Technology Project Funding, 22YF7GA072.
    Most existing machine learning approaches for analysing thermograms mainly focus on either thermal images or pixel-wise temporal profiles of specimens. To fully leverage useful information in thermograms, this article presents a novel spatiotemporal-based deep learning model incorporating an attention mechanism. Using captured thermal image sequences, the model aims to better characterise barely visible impact damages (BVID) in composite materials caused by different impact energy levels. This model establishes the relationship between patterns of BVID in thermography and their corresponding impact energy levels by learning from spatial and temporal information simultaneously. Validation of the model using 100 composite specimens subjected to five different low-velocity impact forces demonstrates its superior performance with a classification accuracy of over 95%. The proposed approach can contribute to Structural Health Monitoring (SHM) community by enabling cause analysis of impact incidents based on predicting the potential impact energy levels. This enables more targeted predictive maintenance, which is especially significant in the aviation industry, where any impact incidents can have catastrophic consequences.
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    Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network
    (Springer, 2022-07-25) Liu, Haochen; Li, Wenhan; Yang, Lichao; Deng, Kailun; Zhao, Yifan
    Quantitative defect and damage reconstruction play a critical role in industrial quality management. Accurate defect characterisation in Infrared Thermography (IRT), as one of the widely used Non-Destructive Testing (NDT) techniques, always demands adequate pre-knowledge which poses a challenge to automatic decision-making in maintenance. This paper presents an automatic and accurate defect profile reconstruction method, taking advantage of deep learning Neural Networks (NN). Initially, a fast Finite Element Modelling (FEM) simulation of IRT is introduced for defective specimen simulation. Mask Region-based Convolution NN (Mask-RCNN) is proposed to detect and segment the defect using a single thermal frame. A dataset with a single-type-shape defect is tested to validate the feasibility. Then, a dataset with three mixed shapes of defect is inspected to evaluate the method’s capability on the defect profile reconstruction, where an accuracy over 90% on Intersection over Union (IoU) is achieved. The results are compared with several state-of-the-art of post-processing methods in IRT to demonstrate the superiority at detailed defect corners and edges. This research lays solid evidence that AI deep learning algorithms can be utilised to provide accurate defect profile reconstruction in thermography NDT, which will contribute to the research community in material degradation analysis and structural health monitoring.
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    Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection
    (Springer, 2023-01-31) Deng, Kailun; Liu, Haochen; Yang, Lichao; Addepalli, Sri; Zhao, Yifan
    With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.
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    dataset for 'A Cyclic Self-enhancement Technique for Complex Defect Profile Reconstruction based on Thermographic Evaluation'
    (Cranfield University, 2023-04-05 13:01) Liu, Haochen; Wang, Shuozhi; Deng, Kailun; Zhao, Yifan
    The dataset for this research is the synthetic dataset combined FEM simulation and experimental results of a Pulsed Thermography (PT) inspection for flat-bottom hole defects in metal materials.
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    A fiber-guided motorized rotation laser scanning thermography technique for impact damage crack inspection in composites
    (IEEE, 2023-04-11) Liu, Haochen; Tinsley, Lawrence; Deng, Kailun; Wang, Yizhong; Starr, Andrew; Chen, Zhenmao; Zhao, Yifan
    Laser Thermography manifests superior sensitivity and compatibility to detect cracks and small subsurface defects. However, the existing related systems have limitations on either inspection efficiency or unknown directional cracks due to the utilization of stationary heat sources. This article reports a Fiber-guided Motorised Rotation Laser-line Scanning Thermography (FMRLST) system aiming to rapidly inspect cracks of impact damage with unknown direction in composite laminates. An optical head with fibre delivery integrated with a rotation motor is designed and developed to generate novel scanning heating in a circumferential rotation manner. A FEM model is first proposed to simulate the principle of FMRLST testing and produce thermograms for the development of post-processing methods. A damage enhancement method based on Curvelet Transform is developed to enhance the visualization of thermal features of cracks, and purify the resulting image by suppressing the laser-line heating pattern and cancelling noise. The validation on three composite specimens with different levels of impact damage suggests the developed FMRLST system can extract unknown impact surface cracks efficiently. The remarkable sensitivity and flexibility of FMRLST to arbitrary cracks, along with the miniaturized probe-like inspection unit, present its potential in on-site thermographic inspection, and its design is promising to push the LST towards.
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    Pattern recognition of barely visible impact damage in carbon composites using pulsed thermography
    (IEEE, 2021-12-13) Zhou, Jia; Du, Weixiang; Yang, Lichao; Deng, Kailun; Addepalli, Sri; Zhao, Yifan
    This paper proposes a novel framework to characterise the morphological pattern of Barely Visible Impact Damage using machine learning. Initially, a sequence of image processing methods are introduced to extract the damage contour, which is then described by a Fourier descriptor-based filter. The uncertainty associated with the damage contour under the same impact energy level is then investigated. A variety of geometric features of the contour are extracted to develop an AI model, which effectively groups the tested 100 samples impacted by 5 different impact energy levels with an accuracy of 96%. Predictive polynomial models are finally established to link the impact energy to the three selected features. It is found that the major axis length of the damage has the best prediction performance, with an R2 value up to 0.97. Additionally, impact damage caused by low energy exhibits higher uncertainty than that of high energy, indicating lower predictability.
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    Surface damage in woven carbon composite panels under orthogonal and inclined high-velocity impacts
    (MDPI, 2022-09-26) Marchante Rodriguez, Veronica; Grasso, Marzio; Zhao, Yifan; Liu, Haochen; Deng, Kailun; Roberts, Andrew; Appleby-Thomas, Gareth J.
    The present research is aimed at the study of the failure analysis of composite panels impacted orthogonally at a high velocity and with an angle. Woven carbon-fibre panels with and without external Kevlar layers were impacted at different energy levels between 1.2 and 39.9 J. Sharp and smooth gravels with a mass from 3.1 to 6.7 g were used to investigate the effects of the mass and the contact area on the damage. Optical microscopy and thermography analyses were carried out to identify internal and surface damage. It was identified that sharp impactors created more damage on the impacted face of the panels, while the presence of a Kevlar layer increased the penetration limit and reduced the damage level in the panel at a higher energy.