Sputter Tracking for the Automatic Monitoring of Industrial Laser-Welding Processes
- 30 April 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 55 (5), 2177-2184
- https://doi.org/10.1109/tie.2008.918637
Abstract
The importance of laser welding in industry increases. Many welds have high-quality demands, and one possibility to satisfy the quality requirements is to monitor the welding process with high-speed cameras. Laser welding is a highly dynamic process; it is therefore challenging to distinguish between normal process fluctuations and abnormal error events in the recorded sequences. This paper investigates a novel classification method to automatically analyze the recorded welding sequences and robustly find the abnormal error events. To our knowledge, it is the first time that a framework to detect and track sputters in welding sequences is proposed and evaluated. To achieve a high usability of the classification algorithm, in the training phase, the user only needs to mark suspicious sequences but does not need to label individual frames within the sequences. The framework is tested on two challenging data sets from real welding processes. The results show that the material particles can be tracked accurately. On a sample data set, the new approach finds all erroneous welds with a small false-positive rate and outperforms previously developed methods.Keywords
This publication has 10 references indexed in Scilit:
- Application of the Extended $k$nn Method to Resistance Spot Welding Process Identification and the Benefits of Process InformationIEEE Transactions on Industrial Electronics, 2007
- Model-Based Real-Time Dynamic Power Factor Measurement in AC Resistance Spot Welding With an Embedded ANNIEEE Industrial Electronics Magazine, 2007
- A Fuzzy-Logic Based Optical Sensor for Online Weld Defect-DetectionIEEE Transactions on Industrial Informatics, 2005
- Einsatz optischer Technologien zur Regelung des Laserstrahlschweißprozesses (Application of Optical Technologies for Closed Loop Control of Laser Beam Welding)at – Automatisierungstechnik, 2005
- Resistance spot welding process identification using an extended knn methodPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Bayesian filtering for location estimationIEEE Pervasive Computing, 2003
- A methodological approach to multisensor classification for innovative laser material processing unitsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Statistical Image Sequence Processing for Temporal Change DetectionLecture Notes in Computer Science, 2002
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002
- Estimation, Control, and the Discrete Kalman FilterApplied Mathematical Sciences, 1989