Performance evaluation of CNN and R-CNN based line by line analysis algorithms for fibre placement defect classification

Meister, Sebastian; Wermes, Mahdieu A. M.

doi:10.1007/s11740-022-01162-7

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…These tasks may include detection of resin race-tracking in molds 135 , flow disturbances 136 , and unfilled zones formation 137 during the filling stage of an LCM process as well as inspection of broken-filaments during fiber production 138 . Novel AI-based methods for the inspection of the Automated Fiber Placement (AFP) process have also been presented by several researchers [139][140][141][142][143] . As part of health monitoring of structures, machine/deep learning models have been used for defect/damage detection [144][145][146][147][148][149][150] , characterization of cracks/delamination [151][152][153] and classification of impact levels 154 .…”

Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 99%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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One of the potential applications of 2D materials is to enhance multi-functionality of structures and components used in aerospace, automotive, civil and defense industries. These multi-functional attributes include sensing, energy storage, EMI shielding and property enhancement. In this article, we have explored the potential of using graphene and its variants as data generating sensory elements in Industry 4.0. We have presented a complete roadmap to cover three emerging technologies i.e. advance materials, artificial intelligence and block-chain technology. The utility of 2D materials such as graphene nanoparticles is yet to be explored as an interface for digitalization of a modern smart factory i.e. “factory-of-the-future”. In this article, we have explored how 2D material enhanced composites can act as an interface between physical and cyber spaces. An overview of employing graphene-based smart embedded sensors at various stages of composites manufacturing processes and their application in real-time structural health monitoring is presented. The technical challenges associated with interfacing graphene-based sensing networks with digital space are discussed. Additionally, an overview of the integration of associated tools such as artificial intelligence, machine learning and block-chain technology with graphene-based devices and structures is also presented.

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Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 99%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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“…The authors used probabilistic graphical models to generate training images and annotations and designed a neural network for image segmentation using an architecture similar to U-Nets, which is suitable for training with few real data. Sebastian Meister et al [46] proposed a defect detection method based on convolutional and recurrent neural networks. In this method, one-dimensional signals are used to analyze the input height distribution of a laser line scanning sensor line by line, which is suitable for classifying images with large defects.…”

Section: Defect Detection In Afpmentioning

confidence: 99%

Research on Defect Detection in Automated Fiber Placement Processes Based on a Multi-Scale Detector

et al. 2022

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Various surface defects in automated fiber placement (AFP) processes affect the forming quality of the components. In addition, defect detection usually requires manual observation with the naked eye, which leads to low production efficiency. Therefore, automatic solutions for defect recognition have high economic potential. In this paper, we propose a multi-scale AFP defect detection algorithm, named the spatial pyramid feature fusion YOLOv5 with channel attention (SPFFY-CA). The spatial pyramid feature fusion YOLOv5 (SPFFY) adopts spatial pyramid dilated convolutions (SPDCs) to fuse the feature maps extracted in different receptive fields, thus integrating multi-scale defect information. For the feature maps obtained from a concatenate function, channel attention (CA) can improve the representation ability of the network and generate more effective features. In addition, the sparsity training and pruning (STP) method is utilized to achieve network slimming, thus ensuring the efficiency and accuracy of defect detection. The experimental results of the PASCAL VOC and our AFP defect datasets demonstrate the effectiveness of our scheme, which achieves superior performance.

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“…To achieve better real-time inspection, Meister and Wermes (2023) evaluate the use of convolutional and recurrent neural network architecture for analyzing laser-scanned surfaces line by line as 1D signals. The different network structures are assessed on both real and synthetic datasets, demonstrating sufficient performance.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in automated fibre placement: learning with data limitations

Ghamisi,

Charter,

et al. 2024

Front. Manuf. Technol.

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Introduction: Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective samples for effective training. However, the scarcity of such labelled data poses a challenge.Methods: To overcome this limitation, we present a comprehensive framework for defect detection and localization in Automated Fibre Placement. Our approach combines unsupervised deep learning and classical computer vision algorithms, eliminating the need for labelled data or manufacturing defect samples. It efficiently detects various surface issues while requiring fewer images of composite parts for training. Our framework employs an innovative sample extraction method leveraging AFP’s inherent symmetry to expand the dataset. By inputting a depth map of the fibre layup surface, we extract local samples aligned with each composite strip (tow).Results: These samples are processed through an autoencoder, trained on normal samples for precise reconstructions, highlighting anomalies through reconstruction errors. Aggregated values form an anomaly map for insightful visualization. The framework employs blob detection on this map to locate manufacturing defects.Discussion: The experimental findings reveal that despite training the autoencoder with a limited number of images, our proposed method exhibits satisfactory detection accuracy and accurately identifies defect locations. Our framework demonstrates comparable performance to existing methods, while also offering the advantage of detecting all types of anomalies without relying on an extensive labelled dataset of defects.

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Performance evaluation of CNN and R-CNN based line by line analysis algorithms for fibre placement defect classification

Cited by 7 publications

References 27 publications

Graphene nanoparticles as data generating digital materials in industry 4.0

Graphene nanoparticles as data generating digital materials in industry 4.0

Research on Defect Detection in Automated Fiber Placement Processes Based on a Multi-Scale Detector

Anomaly detection in automated fibre placement: learning with data limitations

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