Automated inspection and monitoring of member deformation in grid structures

2021 ◽  
Author(s):  
Xiao‐Chen Wei ◽  
Jian‐Sheng Fan ◽  
Yu‐Fei Liu ◽  
Jin‐Xun Zhang ◽  
Xiao‐Gang Liu ◽  
...  
Keyword(s):  
Automated Inspection

The Investigation of Fluorine Induced Novel Probe Marker Discoloration

2018 ◽  
Author(s):  
W.F. Hsieh ◽  
Henry Lin ◽  
Vincent Chen ◽  
Irene Ou ◽  
Y.S. Lou
Keyword(s):  
Electric Field ◽  
Dark Field ◽  
Inspection System ◽  
Automated Inspection ◽  
Stress Tests ◽  
Auger Analysis ◽  
Corrosion Film ◽  
Scan Data ◽  
Thickness Variations ◽  
One Year

Abstract This paper describes the investigation of donut-shaped probe marker discolorations found on Al bondpads. Based on SEM/EDS, TEM/EELS, and Auger analysis, the corrosion product is a combination of aluminum, fluorine, and oxygen, implying that the discolorations are due to the presence of fluorine. Highly accelerated stress tests simulating one year of storage in air resulted in no new or worsening discolorations in the affected chips. In order to identify the exact cause of the fluorine-induced corrosion, the authors developed an automated inspection system that scans an entire wafer, recording and quantifying image contrast and brightness variations associated with discolorations. Dark field TEM images reveal thickness variations of up to 5 nm in the corrosion film, and EELS line scan data show the corresponding compositional distributions. The findings indicate that fluorine-containing gases used in upstream processes leave residues behind that are driven in to the Al bondpads by probe-tip forces and activated by the electric field generated during CP testing. The knowledge acquired has proven helpful in managing the problem.

scholarly journals Automated maintenance feasibility testing on the EU DEMO Automated Inspection and Maintenance Test Unit (AIM-TU)

2021 ◽  
Vol 170 ◽  
pp. 112517
Author(s):  
S. Jimenez ◽  
D. Bookless ◽  
R. Nath ◽  
W.J. Leong ◽  
J. Kotaniemi ◽  
...  
Keyword(s):  
Automated Inspection ◽  
Test Unit ◽  
Inspection And Maintenance ◽  
Feasibility Testing ◽  
The Eu

scholarly journals In-Plane Heatwave Thermography as Digital Inspection Technique for Fasteners in Aircraft Fuselage Panels

2020 ◽  
Vol 11 (1) ◽  
pp. 132
Author(s):  
Michael Stamm ◽  
Peter Krüger ◽  
Helge Pfeiffer ◽  
Bernd Köhler ◽  
Johan Reynaert ◽  
...  
Keyword(s):  
Inspection Time ◽  
Scanning Laser ◽  
Aviation Industry ◽  
Automated Inspection ◽  
Comparison Study ◽  
Laser Doppler Vibrometry ◽  
Inspection Method ◽  
Aircraft Fuselage ◽  
Temporal Tracking ◽  
Lock In

The inspection of fasteners in aluminium joints in the aviation industry is a time consuming and costly but mandatory task. Until today, the manual procedure with the bare eye does not allow the temporal tracking of a damaging behavior or the objective comparison between different inspections. A digital inspection method addresses both aspects while resulting in a significant inspection time reduction. The purpose of this work is to develop a digital and automated inspection method based on In-plane Heatwave Thermography and the analysis of the disturbances due to thermal irregularities in the plate-like structure. For this, a comparison study with Ultrasound Lock-in Thermography and Scanning Laser Doppler Vibrometry as well as a benchmarking of all three methods on one serviceable aircraft fuselage panel is performed. The presented data confirm the feasibility to detect and to qualify countersunk rivets and screws in aluminium aircraft fuselage panels with the discussed methods. The results suggest a fully automated inspection procedure which combines the different approaches and a study with more samples to establish thresholds indicating intact and damaged fasteners.

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.

Progressive image stitching algorithm for vision based automated inspection

2016 ◽  
Author(s):  
Muhammad Aliakbar ◽  
Uvais Qidwai ◽  
Mohammad R Jahanshahi ◽  
Sami Masri ◽  
Wei-Min Shen
Keyword(s):  
Image Stitching ◽  
Automated Inspection
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