Browsing by Author "Du, Weixiang"

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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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    The design and development of the miniaturised active thermography for in-situ inspection of industrial components.
    (Cranfield University, 2021-06) Du, Weixiang; Zhao, Yifan; Addepalli, Pavan
    Nondestructive testing (NDT) is a common and reliable method for the detection of surface and subsurface defects. However, due to the increasing integration and complexity of industrial components and systems, the problem of mismatching of size and volume between the existing inspection unit and the targeted object has limited the applicability of NDT techniques. Especially for geometrically intricate systems, the deployment of NDT devices for in-situ inspection has become a major challenge. Addressing the challenge of inaccessibility and inapplicability, this research proposes a miniaturised active thermography (MAT) system, featured with a small-size and low-cost thermal sensor, and a portable optical heat excitation source. A novel spatial resolution enhancement for a thermogram (SRE4T) system, which includes an infrared (IR) sensor, an XY movement stage and a super-resolution image enhancement method, is also proposed to address the low spatial resolution of the miniaturised sensor without upgrading the sensor. Moreover, dedicated data analysis approaches to evaluate defects are proposed considering the degraded signal quality. Compared with existing non-miniaturised inspection systems, the proposed system is evaluated quantitatively and qualitatively by testing samples with different materials, structures, and a variety of defects. An accessibility test is designed and conducted to evaluate the proposed system’s performance to access geometrically intricate space. The results show that the proposed system can work effectively for the degradation assessment of composite laminates, and also has enhanced accessibility and applicability of deployment for geometrically intricate systems and narrow space targets. It is observed that the data quality for composite materials seems to be more reliable and quantifiable than metal due to the relatively low sample rate of the sensor and the high thermal conductivity of the metal component. The SRE4T system can significantly improve the spatial resolution of miniaturised sensors, although it has not been used for active thermography at the present stage. The current miniaturised IR cameras feature low spatial resolution and low Signal-to-Noise Ratio, which leads to the poor performance of most of the current data analysis methods on these sensors. We propose an effective analytics framework including data processing, image processing and feature extraction to reduce the influence of noise and enhance the detectability of damage.
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    A dissection and enhancement technique for combined damage characterisation in composite laminates using laser-line scanning thermography
    (Elsevier, 2021-05-24) Liu, Haochen; Du, Weixiang; Yazdani Nezhad, Hamed; Starr, Andrew; Zhao, Yifan
    Impact induced combined damage in composite laminates attracts great attention due to its significant degradation of the structural integrity. However, the provision of the quantitative analysis of each damage portion is challenging due to its bare visibility and structural mixture complexity, so-called barely visible impact damage (BVID), which is referred to as inter-laminar delamination, and is inherently coupled with in-plane transverse and matrix damage also known as combined damage. Instead of focusing on one type of damage in most of the existing studies, this paper proposes a decomposition and targeted enhancement technique based on Stationary Wavelet Transform (SWT) for such coupled BVID in composite laminates using laser-line scanning thermography. Firstly, a combined damage model composed of in-plane damage and inter-laminar delamination is established by finite element numerical modelling to predict the thermal response pattern in the laser scanning thermography. Then, a feature separation and targeted enhancement strategy based on SWT in the frequency domain is proposed to improve the contrast of the matrix crack and delamination in combined damage scenarios induced by low-velocity rigid impact via drop-tower tests, meanwhile eliminating noise and suppressing the laser pattern background. The enhanced images of in-plane damage and delamination are furtherly processed by Random Sample Consensus (RANSAC) method and confidence map algorithms to calibrate the damage profile. The proposed technique is validated through inspecting a group of unidirectional carbon fibre-reinforced polymer composite samples, impacted by a variety of energy levels, in fibre-parallel (0°), 45° and orthogonal scanning modes. The results demonstrate that the proposed technique can pertinently isolate, enhance and characterise the inspected in-plane crack and inter-laminates delamination in a flexible manner. The proposed methodology paves the way towards automated infrared thermography data analysis for quantitative dissection of actual combined damage in composite laminates.
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    Estimation of damage thickness in fibre-reinforced composites using pulsed thermography
    (2018-10-31) Sirikham, Adisorn; Zhao, Yifan; Nezhad, Hamed Yazdani; Du, Weixiang; Roy, Rajkumar
    Non-destructive-testing (NDT), including active thermography, has become an inevitable part of composite process and product verification, post-manufacturing. However, there is no reliable NDT technique available to ensure the interlaminar bond integrity during composite laminates integration, bonding or repair where the presence of thin airgaps in the interface of dissimilar polymer composite materials would be detrimental to structural integrity. This paper introduces a novel approach attempting to quantify the damage thickness of composites through a single-side inspection of pulsed thermography. The potential of this method is demonstrated by testing on three specimens with different types of defect, where the Pearson Correlation Coefficients of the thickness estimation for block defects and flat-bottom holes are 0.75 and 0.85, respectively. This approach will considerably enhance the degradation assessment performance of active thermography by extending damage measurement from currently two dimensions to three dimensions, and become an enabling technology on quality assurance of structural integrity.
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    A lightweight temporal attention-based convolution neural network for driver's activity recognition in edge
    (Elsevier, 2023-07-06) Yang, Lichao; Du, Weixiang; Zhao, Yifan
    Low inference latency and accurate response to environment changes play a crucial role in the automated driving system, especially in the current Level 3 automated driving. Achieving the rapid and reliable recognition of driver's non-driving related activities (NDRAs) is important for designing an intelligent takeover strategy that ensures a safe and quick control transition. This paper proposes a novel lightweight temporal attention-based convolutional neural network (LTA-CNN) module dedicated to edge computing platforms, specifically for NDRAs recognition. This module effectively learns spatial and temporal representations at a relatively low computational cost. Its superiority has been demonstrated in an NDRA recognition dataset, achieving 81.01% classification accuracy and an 8.37% increase compared to the best result of the efficient network (MobileNet V3) found in the literature. The inference latency has been evaluated to demonstrate its effectiveness in real applications. The latest NVIDIA Jetson AGX Orin could complete one inference in only 63 ms.
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    A miniaturised active thermography system for in-situ inspections
    (Elsevier, 2020-12-18) Du, Weixiang; Liu, Haochen; Sirikham, Adisorn; Addepalli, Sri; Zhao, Yifan
    With the increase of the functionalisation, integration and complexity of industrial components and systems, deploying Non-Destructive Testing (NDT) devices for ‘in-situ’ inspection has become a major challenge for high-value assets. Due to the mismatching of size and volume between the existing inspection unit and the targeted complex object, inaccessibility and inapplicability have limited the applicability of NDT techniques. To address this challenge, this paper introduces a novel miniaturised active thermography system based on a commercial thermal imaging sensor featured with small size and low cost. Combining with different excitation sources, its detection performance on different types of defect of carbon fibre reinforced polymer (CFRP) is investigated and compared with an existing system. The results show that the proposed system can work with laser and flash effectively for degradation assessment although the detectability is compromised. Such a technique will play a unique role in the in-situ inspection where the space to deploy the device is limited.
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    A miniaturised active thermography system to inspect composite laminates
    (IEEE, 2020-10-13) Du, Weixiang; Liu, Haochen; Zhao, Yitian; Sirikham, Adisorn; Addepalli, Sri; Zhao, Yifan
    With the rapid increase of the integration and complexity of industrial components, the inaccessibility and inapplicability of existing Non-destructive testing devices have become a bottleneck for in-situ inspection of these objects. This paper introduces a miniaturised active thermography system featured with a small size, low resolution and low-cost thermal sensor, where two optional excitation sources including flash and laser are integrated. Dedicated data analysis approaches to evaluate defects are proposed considering the degraded signal quality. Three carbon fibre reinforced polymer laminates with a variety of defects are evaluated quantitatively and qualitatively using the proposed system by comparing with two existing non-miniaturised inspection systems. The results show that the proposed system can work effectively for the degradation assessment of composite laminates. Even with the technical limitations that affect the detectability, for instance, the low pixel resolution, this technique will play an important role to inspect components featured with geometrically intricate space
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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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    The spatial resolution enhancement for a thermogram enabled by controlled sub-pixel movements
    (IEEE, 2019-08-05) Du, Weixiang; Addepalli, Sri; Zhao, Yifan
    The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial resolution of the thermal imager. Using a high-end camera to achieve high spatial resolution can be costly or infeasible due to a high sample rate required. Furthermore, the system miniaturisation becomes an inevitable trend with the continuous development of Internet of Things and their suitability to in-situ inspection scenarios. However, a miniaturised sensor usually suffers a low spatial resolution. Addressing this challenge, the paper reports a novel Spatial Resolution Enhancement for a Thermogram (SRE4T) system to significantly improve the spatial resolution without upgrading the sensor. A high-resolution thermal image is reconstructed by fusing a sequence of low-resolution images with sub-pixel movements. To achieve the best image quality, instead of benefiting from natural movements of existing studies, this paper proposes to use a high-resolution xy translation stage to produce a sequence of controlled sub-pixel movements. The performance of the proposed system was tested on both high-end and low-end thermal imagers. Both visual and quantitative results successfully demonstrated the considerable improvement of the quality of thermal images (up to 30.5% improvement of peak signal to noise ratio). This technique allows improving the measurement accuracy of thermography inspection without upgrading sensors. It also has the potential to be applied on other imaging systems.