Automated Inspection of Micro Laser Spot Weld Quality using Optical Sensing and Neural Network Techniques

Shao, Jiaqing and Yan, Yong (2006) Automated Inspection of Micro Laser Spot Weld Quality using Optical Sensing and Neural Network Techniques. In: Instrumentation and Measurement Technology Conference, 2006. IMTC 2006. Proceedings of the IEEE. IEEE pp. 606-610. ISBN 0-7803-9359-7. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1109/IMTC.2006.328632

Abstract

This paper presents an approach to the automated inspection of laser spot welding processes using optical sensing and neural network techniques. An optical sensor is used to derive signals covering a spectrum ranging from visible to infrared bands. A set of features extracted from the signals is fed into a neural network to classify the quality of welds. A series of experiments was carried out using a pulsed Nd:YAG laser and a common SMD (surface mounted devices) as a test component. The results obtained show that this approach can be used to inspect the laser welding quality for the microelectronics industry

Item Type: Conference or workshop item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Yiqing Liang
Date Deposited: 15 Aug 2009 10:30
Last Modified: 25 Jun 2014 08:15
Resource URI: http://kar.kent.ac.uk/id/eprint/9112 (The current URI for this page, for reference purposes)
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