A vision-based system for post-welding quality measurement and defect detection

The measurement of the welding bead and the followed evaluation of the weld quality is an important part in the industrial welding. However, the main difficulty in using visual inspection to evaluate the weld quality is the time taken and labor wasted. In order to solve these problems, an automated...

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Vydané v:International journal of advanced manufacturing technology Ročník 86; číslo 9-12; s. 3007 - 3014
Hlavní autori: Chu, Hui-Hui, Wang, Zong-Yi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.10.2016
Springer Nature B.V
Predmet:
Algorithms
Automatic welding
CAE) and Design
Computer-Aided Engineering (CAD
Engineering
Evaluation
Feature extraction
Image processing
Industrial and Production Engineering
Inspection
Mechanical Engineering
Media Management
Original Article
Production planning
Quality
Quality assessment
Repair & maintenance
Vision systems
Weld bead size
Welded joints
Welding
Weld quality inspection
3D reconstruction
Machine vision
Image processing algorithm
ISSN:0268-3768, 1433-3015
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Popis
Shrnutí:The measurement of the welding bead and the followed evaluation of the weld quality is an important part in the industrial welding. However, the main difficulty in using visual inspection to evaluate the weld quality is the time taken and labor wasted. In order to solve these problems, an automated vision-based system is presented for non-destructive and online post-welding quality inspection. The trouble with respect to vision-based system is the robustness of image processing algorithms. This paper presents a novel image processing method that can automatically extract the weld joint profile and feature points, measure the weld bead size, and detect defects. At the same time, the three-dimensional (3D) profile of the weld surface can be reconstructed with the aim of monitoring the weld quality online. The proposed algorithm is validated through experiment in industrial environment.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-015-8334-1