Ultrasonic inspection of foundry pieces applying wavelet transform analysis
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Object identification techniques are finding increasing use in many industrial applications. A defect recognition method for foundry pieces in this field is proposed. The system classifies the pieces and selects the apt ones, which will later be machined within the automobile industry. The inspection of the pieces is carried out applying ultrasonic sensing. Due to the ultrasound properties, this type of vision is very appropriate for industrial environments. Starting from the signal reflected from the pieces, the treatment of the data is approached in two significant steps. First, the discrete wavelet transform, DWT, is applied to the analysis of ultrasonic waves for feature extraction. Second, a neural network is used to carry out the discrimination of the foundry pieces. This automated signal classification system obtains great results and the use of the tandem DWT analysis-neural network is shown to be a powerful technique for this type of application.Keywords
This publication has 7 references indexed in Scilit:
- Sensorial system for parts identification and robotic assemblyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A neural network for object recognition through sonar on a mobile robotPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Wavelet transform signal processing for dispersion analysis of ultrasonic signalsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Frequency invariant classification of ultrasonic weld inspection signalsIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 1998
- Ultrasonic sensor for liquid-level inspection in bottlesSensors and Actuators A: Physical, 1997
- Ultrasonic detection in robotic environmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- An ultrasonic visual sensor for three-dimensional object recognition using neural networksIEEE Transactions on Robotics and Automation, 1992