Defect Characteristics and Online Detection Techniques During Manufacturing of FRPs Using Automated Fiber Placement: A Review

Sun, Shouzheng; Han, Zhenyu; Fu, Haipeng; Jin, Hongyu; Dhupia, Jaspreet Singh; Wang, Yang

doi:10.3390/polym12061337

Cited by 53 publications

(24 citation statements)

References 115 publications

(164 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, various sensors are investigated to record the required inspection data. Sun et al (2020) provides a comprehensive overview of the currently available systems and their performances. Accordingly, the Fraunhofer Institute for Integrated Circuits (IIS) investigated polarisation camera based systems (Atkinson et al 2018;Schöberl et al 2016).…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…This might be the key to success of this measurement principle for the AFP inspection [9]. Accordingly, Sun et al [30] summarised several inspection systems and their corresponding properties.…”

Section: Data Recording and Inspection Systemsmentioning

confidence: 99%

Imaging sensor data modelling and evaluation based on optical composite characteristics

Meister

Grundhöfer

Stüve

et al. 2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Automated Fibre Placement is a common manufacturing technique for composite parts in the aero-space industry. Therefore, a visual part inspection is required which often covers up to 50% of the actual production time. Moreover, the inspection quality of this manual step fluctuates significantly. A camera-based automated inline inspection is capable of increasing the inspection efficiency and accuracy. However, the interpretability of the acquired data strongly depends on the sensor configuration and the inspected material. Thus, this paper introduces methods for modelling and assessing an imaging sensor on the example of a composite material reflecting a spot laser to a camera sensor. In this context, the reflection properties of the material are incorporated into a simulation and validated in comparison to real camera images from the experimental setup. The EMVA 1288 sensor model in combination with the Cramér–Rao lower bound indicates a feasible estimability of the beam propagation, but shows limitations in the predictability of the number of incident photons. The laser spot analysis indicated that the laser spot can deviate from an exact oval shape but its peak value is suitable for robust spot identification in an image. The outlined methodology is also adaptable to other imaging sensors, illumination sources and materials. Thus, the findings can be useful for other fields and manufacturing processes.

show abstract

“…Infrared thermography (IRT), an attractive non-destructive assessment technique, is applied widely in the quality assessment of composite structures due to its fast response, wide-range of measurement, and intuitive results [ 1 , 2 , 3 , 4 ]. However, especially in the task of defect assessment, IRT is difficult to achieve the desired inspection results.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography

Liu

et al. 2021

Polymers

View full text Add to dashboard Cite

Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method.

show abstract

Defect Characteristics and Online Detection Techniques During Manufacturing of FRPs Using Automated Fiber Placement: A Review

Cited by 53 publications

References 115 publications

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

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

Imaging sensor data modelling and evaluation based on optical composite characteristics

Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography

Contact Info

Product

Resources

About