Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105856
Title: In-process endpoint detection of weld seam removal in robotic abrasive belt grinding process
Authors: Pandiyan, Vigneashwara
Tjahjowidodo, Tegoeh
Keywords: Abrasive Belt Grinding
DWT
DRNTU::Engineering::Mechanical engineering
Issue Date: 2017
Source: Pandiyan, V., & Tjahjowidodo, T. (2017). In-process endpoint detection of weld seam removal in robotic abrasive belt grinding process. The International Journal of Advanced Manufacturing Technology, 93(5-8), 1699-1714. doi:10.1007/s00170-017-0646-x
Series/Report no.: The International Journal of Advanced Manufacturing Technology
Abstract: This paper proposes a novel approach for inprocess endpoint detection of weld seam removal during robotic abrasive belt grinding process using discrete wavelet transform (DWT) and support vector machine (SVM). A virtual sensing system is developed consisting of a force sensor, accelerometer sensor and machine learning algorithm. This work also presents the trend of the sensor signature at each stage of weld seam evolution during its removal process. The wavelet decomposition coefficient is used to represent all possible types of transients in vibration and force signals generated during grinding over weld seam. “Daubechies-4” wavelet function was used to extract features from the sensors. An experimental investigation using three different weld profile conditions resulting from the weld seam removal process using abrasive belt grinding was identified. The SVM-based classifier was employed to predict the weld state. The results demonstrate that the developed diagnostic methodology can reliably predict endpoint at which weld seam is removed in real time during compliant abrasive belt grinding.
URI: https://hdl.handle.net/10356/105856
http://hdl.handle.net/10220/48132
ISSN: 0268-3768
DOI: 10.1007/s00170-017-0646-x
Rights: © 2017 Springer-Verlag London Ltd. All rights reserved. This paper was published in The International Journal of Advanced Manufacturing Technology and is made available with permission of Springer-Verlag London Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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