Automated inspection systems for ultrasonic testing of large aircraft components

1994 ◽  
Vol 27 (3) ◽  
pp. 165
Keyword(s):  
Ultrasonic Testing ◽  
Automated Inspection ◽  
Inspection Systems ◽  
Large Aircraft

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.

Automated Ultrasonic Testing for Mapping and Correlation With ILI Internal Pitting Measurements

2002 ◽  
Author(s):  
Bryan R. Scott ◽  
David K. Jolivette ◽  
Eric M. Sjerve
Keyword(s):  
Ultrasonic Testing ◽  
Ultrasonic Inspection ◽  
Damage Mechanism ◽  
Quantitative Comparison ◽  
Automated Inspection ◽  
Corrosive Environment ◽  
Bottom Part ◽  
Internal Corrosion ◽  
Manual Methods ◽  
Automated Process

This paper presents a methodology for automated ultrasonic inspection of internal corrosion on product pipelines. The automated process replaces the standard manual methods of inspection and uses the ultrasonic ILI tool data to localize the areas of corrosion on the exposed joint. Typically, only the bottom part of the pipe must be inspected due to the predominant damage mechanism being sediment fallout that tends to collect on the bottom of the pipelines producing a corrosive environment. The result is a superior inspection, due to the increased speed, reliability and reproducibility that automated inspection provides. The ultrasonic ILI tool data and the automated ultrasonic data can also be directly compared to each other to provide a quantitative comparison between the two inspection methods. This has shown that the ILI data and the automated ultrasonic data correlated well with regards to the location and overall corrosion dimensions.

A Solution to the Missing-View Problem for Polyhedral Solids

1991 ◽  
Author(s):  
Y. L. Srinivas ◽  
Debasish Dutta
Keyword(s):  
Machine Vision ◽  
Computer Aided Design ◽  
Partial Information ◽  
Connectivity Matrix ◽  
Automated Inspection ◽  
Vertex Connectivity ◽  
Computer Aided ◽  
Inspection Systems ◽  
Aided Design ◽  
The Given

Abstract An algorithm for generating the missing view corresponding to a given pair of orthoghonal views of a polyhedral solid is presented. The solution involves reconstructing the solids from the partial information given and then generating the missing view. The input is a vertex connectivity matrix describing the given views. Reconstruction of solids from incomplete orthographic views will have applications in computer-aided design, machine vision and automated inspection systems.

scholarly journals A Reinforcement Learning Approach to View Planning for Automated Inspection Tasks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2030
Author(s):  
Christian Landgraf ◽  
Bernd Meese ◽  
Michael Pabst ◽  
Georg Martius ◽  
Marco F. Huber
Keyword(s):  
Reinforcement Learning ◽  
Computer Aided Design ◽  
Automated Inspection ◽  
Comprehensive Overview ◽  
View Planning ◽  
Coverage Ratio ◽  
Manual Inspection ◽  
Inspection Systems ◽  
Inspection Tasks ◽  
Aided Design

Manual inspection of workpieces in highly flexible production facilities with small lot sizes is costly and less reliable compared to automated inspection systems. Reinforcement Learning (RL) offers promising, intelligent solutions for robotic inspection and manufacturing tasks. This paper presents an RL-based approach to determine a high-quality set of sensor view poses for arbitrary workpieces based on their 3D computer-aided design (CAD). The framework extends available open-source libraries and provides an interface to the Robot Operating System (ROS) for deploying any supported robot and sensor. The integration into commonly used OpenAI Gym and Baselines leads to an expandable and comparable benchmark for RL algorithms. We give a comprehensive overview of related work in the field of view planning and RL. A comparison of different RL algorithms provides a proof of concept for the framework’s functionality in experimental scenarios. The obtained results exhibit a coverage ratio of up to 0.8 illustrating its potential impact and expandability. The project will be made publicly available along with this article.

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