Research on anti-interference detection of 3D-printed ceramics surface defects based on deep learning

Chen, Wei; Zou, Bin; Zheng, Qinbing; Huang, Chuanzhen; Li, Lei; Liu, Ji‐Kai

doi:10.1016/j.ceramint.2023.04.081

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Regarding the utilization of 3D data, there have been several studies focusing on employing ML techniques for the detection of defects in industrial components. In their work [45], the authors proposed a deep learning-based approach for identifying defects in 3D-printed objects. Similarly, in [46], a framework based on ML was introduced to detect flaws in 3D objects.…”

Section: Related Workmentioning

confidence: 99%

Exploring Deep Fully-convolutional Neural Networks for Surface Defect Detection in Complex Geometries

García,

Díaz

et al. 2024

Preprint

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In this paper, we propose a machine learning approach for detecting superficial defects in metal surfaces using point cloud data. We compare the performance of two popular deep learning architectures, Multilayer Perceptron Networks (MLPs) and Fully Convolutional Networks (FCNs), with varying feature sets. Our results show that FCNs outperformed MLPs in terms of precision, recall, and f1-score. We found that transfer learning with pre-trained models can improve performance when the amount of available data is limited. Our study highlights the importance of considering the amount and quality of training data in developing machine learning models for defect detection in industrial settings with 3D images.

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Section: Related Workmentioning

confidence: 99%

Exploring Deep Fully-convolutional Neural Networks for Surface Defect Detection in Complex Geometries

García,

Díaz

et al. 2024

Preprint

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“…This model utilizes a parallel architecture with dilated convolutions of different rates to capture multi-scale contextual information and employs feature enhancement and selection modules to reduce confusion. Chen et al [10] introduced a deep learning-based anti-interference defect detection method for ceramic components, addressing misjudgments and defect region segmentation issues caused by interference factors in defect detection. The study utilizes a multi-modal feature fusion network model to locate and identify surface disturbances, then employs a parallel spatial channel attention mechanism to repair pixel disturbances in the region, and finally uses an inception-SSD model to detect ceramic surface defects.…”

Section: Introductionmentioning

confidence: 99%

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Ding,

Zhan,

et al. 2024

Meas. Sci. Technol.

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Surface defects in bearings not only affect the appearance but also impact the service life and performance. Therefore, it is imperative for bearing manufacturers to conduct quality inspections before bearings leave the factory. However, traditional visual inspection methods exhibit shortcomings such as high omission rates, insufficient feature fusion and oversized models when dealing with multiple target defects in bearings. To address these challenges, this paper proposes a surface defect detection method for bearings based on an improved Yolov8 algorithm (G-Yolov8). Firstly, a C3Ghost convolutional module based on the Ghost module is constructed in YOLOv8 to simplify model computational costs. Secondly, a Global Attention Mechanism (GAM) module is designed at the end of the backbone network to increase sensitivity to implicit small target area features and optimize feature extraction efficiency.Subsequently, a deep deformable convolution feature pyramid network (TDSFPN) is constructed by introducing the deformable convolutional networks version 2 (DCNv2) and the lightweight Content-Aware Reassembly of Features upsampling operator (CARAFE) to reduce sampling information loss and improve the fusion of multi-scale target defects. Finally, different attention mechanisms are embedded in the detection network to construct a Multi-Attention Detection Head (MADH) to replace the decoupled head, refining classification and localization tasks, reducing feature confusion, and improving the model's detection accuracy. Experimental results demonstrate that the improved algorithm achieves a 3.5% increase in mean average precision on a self-made small-scale train bearing surface defect dataset, with a 17.3% reduction in model size. This improvement not only enhances accuracy but also addresses the requirement for lightweight deployment in subsequent stages.

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“…Song et al [2] developed a residual network-based structure for detecting surface defects in steel strips, which can effectively segment them. Chen et al [3] used a similar encoding and decoding structure for the segmentation and localization of defects in 3D printed ceramic parts. However, the size of the convolutional kernel in the above method limits the model's ability to perceive the receptive field.…”

Section: Introductionmentioning

confidence: 99%

Cascaded mixed pyramid pooling for surface defect segmentation in solid oxide fuel cell

Wang,

Fu,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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To address the issues of defect leakage and mis-segmentation in the surface defect segmentation of solid oxide fuel cells, we propose a cascaded mixed pyramid pooling segmentation method. In the encoding part, attention-based depth-wise separable convolution is implemented to replace the traditional convolution. This reduces the computational complexity and achieves hierarchical feature extraction. Secondly, cascaded sampling is applied to the last three layers of the encoder to promote multi-level feature fusion, and mixed pyramid pooling consisting of atrous convolution and strip pooling is adopted to achieve feature extraction for long-range band defects. Lastly, an adaptive loss function is constructed to supervise the training process, balancing the relative importance among the losses and improving the learning of defect features by the network. The experiments demonstrate that the proposed method improves segmentation accuracy, as evidenced by better results in mean absolute error, F1 score, intersection over union, and Pratt's figure of merit.

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Research on anti-interference detection of 3D-printed ceramics surface defects based on deep learning

Cited by 8 publications

References 16 publications

Exploring Deep Fully-convolutional Neural Networks for Surface Defect Detection in Complex Geometries

Exploring Deep Fully-convolutional Neural Networks for Surface Defect Detection in Complex Geometries

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Cascaded mixed pyramid pooling for surface defect segmentation in solid oxide fuel cell

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