scholarly journals End-to-end defect detection in automated fiber placement based on artificially generated data

2019 ◽  
Author(s):  
Sebastian Zambal ◽  
Christoph Heindl ◽  
Christian Eitzinger ◽  
Josef Scharinger
Keyword(s):  
Defect Detection ◽  
Fiber Placement ◽  
Automated Fiber Placement ◽  
End To End

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

2021 ◽  
Author(s):  
Sebastian Meister ◽  
Mahdieu A. M. Wermes ◽  
Jan Stüve ◽  
Roger M. Groves
Keyword(s):  
Defect Detection ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Fiber Placement ◽  
Detection Systems ◽  
Automated Fiber Placement ◽  
Common Technique ◽  
Manual Testing ◽  
Testing Algorithm ◽  
Laser Line Scan

AbstractThe 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.

Automated Fiber Placement of Composite Wind Tunnel Blades: Process Planning and Manufacturing

2019 ◽  
Author(s):  
Ramy Harik ◽  
Joshua Halbritter ◽  
Dawn Jegley ◽  
Ray Grenoble ◽  
Brian Mason
Keyword(s):  
Wind Tunnel ◽  
Process Planning ◽  
Fiber Placement ◽  
Automated Fiber Placement

scholarly journals Review: Filament Winding and Automated Fiber Placement with In Situ Consolidation for Fiber Reinforced Thermoplastic Polymer Composites

Polymers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1951
Author(s):  
Yi Di Boon ◽  
Sunil Chandrakant Joshi ◽  
Somen Kumar Bhudolia
Keyword(s):  
Processing Parameters ◽  
Filament Winding ◽  
Reference Points ◽  
Thermoplastic Composites ◽  
Manufacturing Processes ◽  
Fiber Reinforced ◽  
Consolidation Process ◽  
Fiber Placement ◽  
Automated Fiber Placement

Fiber reinforced thermoplastic composites are gaining popularity in many industries due to their short consolidation cycles, among other advantages over thermoset-based composites. Computer aided manufacturing processes, such as filament winding and automated fiber placement, have been used conventionally for thermoset-based composites. The automated processes can be adapted to include in situ consolidation for the fabrication of thermoplastic-based composites. In this paper, a detailed literature review on the factors affecting the in situ consolidation process is presented. The models used to study the various aspects of the in situ consolidation process are discussed. The processing parameters that gave good consolidation results in past studies are compiled and highlighted. The parameters can be used as reference points for future studies to further improve the automated manufacturing processes.

Effect of gap on strengths of automated fiber placement manufactured laminates

2021 ◽  
Vol 263 ◽  
pp. 113677
Author(s):  
Hiroshi Suemasu ◽  
Yuichiro Aoki ◽  
Sunao Sugimoto ◽  
Toshiya Nakamura
Keyword(s):  
Fiber Placement ◽  
Automated Fiber Placement

scholarly journals Experimental Investigation on the Influence of Fiber Path Curvature on the Mechanical Properties of Composites

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2602
Author(s):  
Huaqiao Wang ◽  
Jihong Chen ◽  
Zhichao Fan ◽  
Jun Xiao ◽  
Xianfeng Wang
Keyword(s):  
Tensile Strength ◽  
Path Planning ◽  
Composite Laminates ◽  
Bending Strength ◽  
High Strength ◽  
Bending Test ◽  
Fiber Placement ◽  
Automated Fiber Placement ◽  
Fiber Path ◽  
Path Curvature

Automated fiber placement (AFP) has been widely used as an advanced manufacturing technology for large and complex composite parts and the trajectory planning of the laying path is the primary task of AFP technology. Proposed in this paper is an experimental study on the effect of several different path planning placements on the mechanical behavior of laminated materials. The prepreg selected for the experiment was high-strength toughened epoxy resin T300 carbon fiber prepreg UH3033-150. The composite laminates with variable angles were prepared by an eight-tow seven-axis linkage laying machine. After the curing process, the composite laminates were conducted by tensile and bending test separately. The test results show that there exists an optimal planning path among these for which the tensile strength of the laminated specimens decreases slightly by only 3.889%, while the bending strength increases greatly by 16.68%. It can be found that for the specific planning path placement, the bending strength of the composite laminates is significantly improved regardless of the little difference in tensile strength, which shows the importance of path planning and this may be used as a guideline for future AFP process.

Numerical analysis of the temperature profile during the laser-assisted automated fiber placement of CFRP tapes with thermoplastic matrix

2017 ◽  
Vol 31 (12) ◽  
pp. 1563-1586 ◽  
Author(s):  
Andreas Kollmannsberger ◽  
Roland Lichtinger ◽  
Franz Hohenester ◽  
Christoph Ebel ◽  
Klaus Drechsler
Keyword(s):  
Carbon Fiber ◽  
Contact Resistance ◽  
Thermal Contact Resistance ◽  
Thermal Contact ◽  
Fiber Placement ◽  
Composite Layers ◽  
Automated Fiber Placement ◽  
Geometric Boundary ◽  
Laser Heat Source ◽  
Placement Machine

In this study, a thermodynamic model of a laser-assisted automated thermoplastic fiber placement process is developed and validated. The main focus is on modeling the heat transfer into the composite with a laser heat source, the thermal properties of the tape, and the resulting heat distribution in the part, the mold, and the compaction roller. A new integrated analytical method is presented to calculate the energy input of the laser based on the geometric boundary conditions, including first-order reflection and laser shadow. The carbon fiber/polyethersulphone tape is modeled by combining literature properties of carbon fiber and matrix as well as based on experimental data of the tape itself. Also a thermal contact resistance between the tape layers is modeled based on a literature model and own experimental measurements. The created model is discretized and implemented in a 2-D finite difference code. With the help of this simulation, the temperature distribution is calculated during layup. The influence of a possible thermal contact resistance between the composite layers is investigated. Furthermore, an experiment with a thermoplastic fiber placement machine from Advanced Fibre Placement Technology GmbH (AFPT) was conducted in order to evaluate the simulation. The simulation and the experiment show a good agreement and prove that thermal contact resistance between the layers is negligible for the investigation process.

scholarly journals Automated Fiber Placement: A Review of History, Current Technologies, and Future Paths Forward

2021 ◽  
pp. 100182
Author(s):  
Alex Brasington ◽  
Christopher Sacco ◽  
Joshua Halbritter ◽  
Roudy Wehbe ◽  
Ramy Harik
Keyword(s):  
Fiber Placement ◽  
Automated Fiber Placement

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.

A Planar Mechanism with Variable Topology for Automated Fiber Placement

2018 ◽  
Author(s):  
Fei Liu ◽  
Wuxiang Zhang ◽  
Kun Xu ◽  
Huichao Deng ◽  
Xilun Ding
Keyword(s):  
Planar Mechanism ◽  
Fiber Placement ◽  
Automated Fiber Placement ◽  
Variable Topology
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