Skip to main content

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: 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings. IEEE, pp. 606-610. ISBN 0-7803-9359-7. (doi:10.1109/IMTC.2006.328632) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
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: Book section
DOI/Identification number: 10.1109/IMTC.2006.328632
Uncontrolled keywords: inspection; spot welding; optical sensors; neural networks; infrared sensors; infrared spectra; feature extraction; optical pulses; surface emitting lasers; testing
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Yiqing Liang
Date Deposited: 15 Aug 2009 10:30 UTC
Last Modified: 02 Jul 2019 13:23 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/9112 (The current URI for this page, for reference purposes)
  • Depositors only (login required):