Research on Ultrasonic Positioning and Detection Method Based on MEMS Array Sensor
- DOI
- 10.2991/978-94-6463-058-9_98How to use a DOI?
- Keywords
- MEMS array; Metal member; Defect detection; Ultrasonic positioning; SVM
- ABSTRACT
In order to improve the accuracy of defect location and detection of metal components, an ultrasonic location and detection method based on MEMS array sensor is proposed. Firstly, the micro-electro-mechanical (MEMS) machining technology is analyzed in detail. An ultrasonic detection system based on MEMS array sensor is designed. Then, a SVM based ultrasonic localization and detection method for metal component near surface defects was proposed. The classification and recognition characteristics of SUPPORT vector machine (SVM) were used to accurately locate and detect metal component surface defects. The experimental results show that in the ultrasonic location and detection of metal component defects, the recognition accuracy of SVM is as high as 98.8%, which shows that the use of SVM can effectively improve the accuracy of location and detection, which is feasible and effective.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Mingxu Chen AU - Bingye Zhang AU - Xiaoping Fu AU - Jianrong Ma PY - 2022 DA - 2022/12/27 TI - Research on Ultrasonic Positioning and Detection Method Based on MEMS Array Sensor BT - Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022) PB - Atlantis Press SP - 615 EP - 619 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-058-9_98 DO - 10.2991/978-94-6463-058-9_98 ID - Chen2022 ER -