Research on Equipment Automated Inspection and Acceptance Framework Based on Ontology Modeling

Author(s):  
Zexi Li ◽  
Chen Meng
2018 ◽  
Author(s):  
W.F. Hsieh ◽  
Henry Lin ◽  
Vincent Chen ◽  
Irene Ou ◽  
Y.S. Lou

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.


2010 ◽  
Vol 32 (8) ◽  
pp. 1806-1811 ◽  
Author(s):  
Yan-na Li ◽  
Xiu-quan Qiao ◽  
Xiao-feng Li
Keyword(s):  

2021 ◽  
Vol 170 ◽  
pp. 112517
Author(s):  
S. Jimenez ◽  
D. Bookless ◽  
R. Nath ◽  
W.J. Leong ◽  
J. Kotaniemi ◽  
...  

2020 ◽  
Vol 11 (1) ◽  
pp. 132
Author(s):  
Michael Stamm ◽  
Peter Krüger ◽  
Helge Pfeiffer ◽  
Bernd Köhler ◽  
Johan Reynaert ◽  
...  

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.


2005 ◽  
Vol 4 (1) ◽  
pp. 109 ◽  
Author(s):  
Dragan Djuric ◽  
Dragan Gaševi ◽  
Vladan Devedžic
Keyword(s):  

2021 ◽  
Author(s):  
ADRIANA W. (AGNES) BLOM-SCHIEBER ◽  
WEI GUO ◽  
EKTA SAMANI ◽  
ASHIS BANERJEE

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.


Author(s):  
Chiseung Soh ◽  
Seungtak Lim ◽  
Kihyun Hong ◽  
Young-Yik Rhim

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