Simulation and detection of flaws in pre-cured CFRP using laser displacement sensing

Miesen, Nick; Sinke, J.; Groves, Roger M.; Benedictus, Rinze

doi:10.1007/s00170-015-7305-x

Cited by 10 publications

(10 citation statements)

References 17 publications

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“…Furthermore, these previously mentioned defect types are commonly applied as example defects in the related research (Oromiehie et al 2019;Harik et al 2018;Heinecke and Willberg 2019) Additionally, foils as typical foreign bodies in manufacturing processes are considered. They show quite different reflection properties in comparison with layed up fibre material (Potter 2009;Miesen et al 2015).…”

Section: Manufacturing Processmentioning

confidence: 99%

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Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set

et al. 2021

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In the aerospace industry, the Automated Fiber Placement process is an established method for producing composite parts. Nowadays the required visual inspection, subsequent to this process, typically takes up to 50% of the total manufacturing time and the inspection quality strongly depends on the inspector. A Deep Learning based classification of manufacturing defects is a possibility to improve the process efficiency and accuracy. However, these techniques require several hundreds or thousands of training data samples. Acquiring this huge amount of data is difficult and time consuming in a real world manufacturing process. Thus, an approach for augmenting a smaller number of defect images for the training of a neural network classifier is presented. Five traditional methods and eight deep learning approaches are theoretically assessed according to the literature. The selected conditional Deep Convolutional Generative Adversarial Network and Geometrical Transformation techniques are investigated in detail, with regard to the diversity and realism of the synthetic images. Between 22 and 166 laser line scan sensor images per defect class from six common fiber placement inspection cases are utilised for tests. The GAN-Train GAN-Test method was applied for the validation. The studies demonstrated that a conditional Deep Convolutional Generative Adversarial Network combined with a previous Geometrical Transformation is well suited to generate a large realistic data set from less than 50 actual input images. The presented network architecture and the associated training weights can serve as a basis for applying the demonstrated approach to other fibre layup inspection images.

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Section: Manufacturing Processmentioning

confidence: 99%

“…They demonstrated that a LLSS is a suitable system for fabric and preform inspection. Miesen et al (2015) proposed a method for detecting defects with a point laser displacement system. They discussed factors influencing deviations in their research and analysed the accuracy of such systems.…”

Section: Sensor Based Inspection and Data Processingmentioning

confidence: 99%

Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set

et al. 2021

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“…CPT in this application case is about 0.125 mm. For the thickness measure + indicates an increase in thickness and -means a decrease in thickness compared to the deposited fibre material (Miesen et al 2015;Potter 2009). Following, feasible techniques for the recording of these defects are explained.…”

Section: Manufacturing Processmentioning

confidence: 99%

“…They aimed to find and measure wires with a diameter between 0.5 mm and 1 mm, which were incorporated into CFRP laminates. Miesen et al (2015) suggested a method to sense defects with a point measurement laser displacement system. Additionally, they discussed influencing factors for deviations in their research and analysed the accuracy of such a system.…”

Section: Sensors For Data Acquisitionmentioning

confidence: 99%

Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process

et al. 2021

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The aerospace industry has established the Automated Fiber Placement process as a common technique for manufacturing fibre reinforced components. In this process multiple composite tows are placed simultaneously onto a tool. Currently in such processes manual testing requires often up to 50% of the manufacturing duration. Moreover, the accuracy of quality assurance varies significantly with the inspector in charge. Thus, inspection automation provides an effective way to increase efficiency. However, to achieve a proper inspection performance, the segmentation of layup defects need to be examined. In order to improve such defect detection systems, this paper performs a comprehensive ranking of segmentation techniques. Thus, 29 statistical, spectral and structural algorithms from related work were evaluated based on nine substantial criteria as assessed from literature and process requirements. For reasons of determinism and easy technology transferability without the need of much training data, the development of new Machine Learning algorithms is not part of this paper. Afterwards, seven of the most auspicious algorithms were studied experimentally. Therefore, laser line scan sensor depth maps from fibre placement defects were utilised. Furthermore noisy images were generated and applied for testing algorithm robustness. The test data contained five defect categories with 50 samples per class. It was concluded that Adaptive Thresholding and Cell Wise Standard Deviation Thresholding work best yielding detection accuracies mostly $$> 97$$ > 97 %. Noteworthy is that influenced input data can affect the detection results. Feasible algorithms with sensible parameter settings were able to perform reliable defect segmentation for layed material.

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“…Our previous studies [5] presented experimental comparison of the abovementioned four sensors in measuring tilted surfaces. Applications of this sensor can be found in many publications [6][7][8][9][10][11][12][13]. Simpler and thus lowercost configuration of this sensor is also a crucial advantage in industrial applications.…”

Section: Introductionmentioning

confidence: 99%

On the limitation of dual-view triangulation in reducing the measurement error induced by the speckle noise in scanning operations

Ibaraki

Kitagawa

Kimura

et al. 2016

Int J Adv Manuf Technol

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A triangulation-based laser displacement sensor using the diffuse reflection light has a strong practical advantage in its robustness against the tilting of the object surface to the sensor's sensitive direction. On the other hand, it is often subject to higher-frequency noise-like measurement error in scanning operations. This error is caused by the movement of the speckle in the reflected laser beam spot with the scanning operation. This paper first discusses how the dual-view triangulation, using two image sensors aligned symmetrically about the laser beam axis, can reduce the scanning noise in principle. Then, its inherent limitation is discussed. Since different points on the target surface cast the specular reflection to each image sensor, the influence of the sensor's lateral movement cannot be canceled by comparing the reflected spot's displacement on each image sensor. This issue can be observed clearer when measuring a surface of a less random surface profile. It is experimentally validated.

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Simulation and detection of flaws in pre-cured CFRP using laser displacement sensing

Cited by 10 publications

References 17 publications

Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set

Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set

Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process

On the limitation of dual-view triangulation in reducing the measurement error induced by the speckle noise in scanning operations

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