scholarly journals Optical Material Characterisation of Prepreg CFRP for Improved Composite Inspection

2021 ◽  
Author(s):  
Sebastian Meister ◽  
Jan Stüve ◽  
Roger M. Groves
Keyword(s):  
Fibre Composite ◽  
Great Influence ◽  
Image Data ◽  
Reinforced Plastics ◽  
Optical Inspection ◽  
Data Quality Assessment ◽  
Automated Optical Inspection ◽  
Inspection Systems ◽  
Recorded Data ◽  
Complex Structural

AbstractAutomated fibre layup techniques are often applied for the production of complex structural components. In order to ensure a sufficient component quality, a subsequent visual inspection is necessary, especially in the aerospace industry. The use of automated optical inspection systems can reduce the inspection effort by up to 50 %. Laser line scan sensors, which capture the topology of the surface, are particularly advantageous for this purpose. These sensors project a laser beam at an angle onto the surface and detect its position via a camera. The optical properties of the observed surface potentially have a great influence on the quality of the recorded data. This is especially relevant for dark or highly scattering materials such as Carbon Fiber Reinforced Plastics (CFRP). For this reason, in this study we investigate the optical reflection and transmission properties of the commonly used Hexel HexPly 8552 IM7 prepreg CFRP in detail. Therefore, we utilise a Gonioreflectometer to investigate such optical characteristics of the material with respect to different fibre orientations, illumination directions and detection angles. In this way, specific scattering information of the material in the hemispherical space are recorded. The major novelty of this research are the findings about the scattering behaviour of the fibre composite material which can be used as a more precise input for the methods of image data quality assessment from our previous research and thus is particularly valuable for developers and users of camera based inspection systems for CFRP components.

scholarly journals Optical Inspection Systems for Axisymmetric Parts with Spatial 2D Resolution

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1218
Author(s):  
Aleksandr Kulchitskiy
Keyword(s):  
Control System ◽  
Optical System ◽  
Measurement Accuracy ◽  
Experimental Studies ◽  
Digital Camera ◽  
Digital Cameras ◽  
Bodies Of Revolution ◽  
Optical Inspection ◽  
Inspection Systems ◽  
Projection Error

The article proposes a solution to the problem of increasing the accuracy of determining the main shaping dimensions of axisymmetric parts through a control system that implements the optical method of spatial resolution. The influence of the projection error of a passive optical system for controlling the geometric parameters of bodies of revolution from the image of its sections, obtained by a digital camera with non-telecentric optics, on the measurement accuracy is shown. Analytical dependencies are derived that describe the features of the transmission of measuring information of a system with non-telecentric optics in order to estimate the projection error. On the basis of the obtained dependences, a method for compensating the projection error of the systems for controlling the geometry of the main shaping surfaces of bodies of revolution has been developed, which makes it possible to increase the accuracy of determining dimensions when using digital cameras with a resolution of 5 megapixels or more, equipped with short-focus lenses. The possibility of implementing the proposed technique is confirmed by the results of experimental studies.

scholarly journals Ceramic Cracks Segmentation with Deep Learning

2021 ◽  
Vol 11 (13) ◽  
pp. 6017
Author(s):  
Gerivan Santos Junior ◽  
Janderson Ferreira ◽  
Cristian Millán-Arias ◽  
Ramiro Daniel ◽  
Alberto Casado Junior ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ceramic Plate ◽  
Ceramic Tiles ◽  
Entire Area ◽  
Manual Inspection ◽  
Optical Inspection ◽  
Proposed Model ◽  
Processing Step ◽  
Long Time ◽  
Automated Optical Inspection

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

IMPROVED TOW-END DETECTION FOR FIBER PLACEMENT INSPECTION USING MACHINE LEARNING

2021 ◽  
Author(s):  
ADRIANA W. (AGNES) BLOM-SCHIEBER ◽  
WEI GUO ◽  
EKTA SAMANI ◽  
ASHIS BANERJEE
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Image Data ◽  
Detection Algorithm ◽  
Automated Inspection ◽  
Machine Learning Algorithm ◽  
Fiber Placement ◽  
Inspection Systems ◽  
Automated Fiber Placement ◽  
The University

A machine learning approach to improve the detection of tow ends for automated inspection of fiber-placed composites is presented. Automated inspection systems for automated fiber placement processes have been introduced to reduce the time it takes to inspect plies after they are laid down. The existing system uses image data from ply boundaries and a contrast-based algorithm to locate the tow ends in these images. This system fails to recognize approximately 10% of the tow ends, which are then presented to the operator for manual review, taking up precious time in the production process. An improved tow end detection algorithm based on machine learning is developed through a research project with the Boeing Advanced Research Center (BARC) at the University of Washington. This presentation shows the preprocessing, neural network and post‐processing steps implemented in the algorithm, and the results achieved with the machine learning algorithm. The machine learning algorithm resulted in a 90% reduction in the number of undetected tows compared to the existing system.

Multiscale measurement of air foils with data fusion of three optical inspection systems

2017 ◽  
Vol 17 ◽  
pp. 32-41 ◽  
Author(s):  
Jochen Schlobohm ◽  
Yinan Li ◽  
Andreas Pösch ◽  
Markus Kästner ◽  
Eduard Reithmeier
Keyword(s):  
Data Fusion ◽  
Optical Inspection ◽  
Inspection Systems

Synthesizing images using parameterized models for automated optical inspection (AOI)

2015 ◽  
Vol 82 (5) ◽  
Author(s):  
Max-Gerd Retzlaff ◽  
Josua Stabenow ◽  
Jürgen Beyerer ◽  
Carsten Dachsbacher
Keyword(s):  
Physical Simulation ◽  
Rigid Bodies ◽  
Systematic Evaluation ◽  
Virtual Objects ◽  
Optical Inspection ◽  
Physically Based ◽  
Automated Optical Inspection ◽  
Real Objects ◽  
Physically Based Rendering ◽  
Test Objects

AbstractWhen designing or improving systems for automated optical inspection (AOI), systematic evaluation is an important but costly necessity to achieve and ensure high quality. Computer graphics methods can be used to quickly create large virtual sets of samples of test objects and to simulate image acquisition setups. We use procedural modeling techniques to generate virtual objects with varying appearance and properties, mimicking real objects and sample sets. Physical simulation of rigid bodies is deployed to simulate the placement of virtual objects, and using physically-based rendering techniques we create synthetic images. These are used as input to an AOI system instead of physically acquired images. This enables the development, optimization, and evaluation of the image processing and classification steps of an AOI system independently of a physical realization. We demonstrate this approach for shards of glass, as sorting glass is one challenging practical application for AOI.

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