Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151209
Title: In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning
Authors: Pandiyan, Vigneashwara
Murugan, Pushparaja
Tjahjowidodo, Tegoeh
Caesarendra, Wahyu
Manyar, Omey Mohan
Then, David Jin Hong
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Pandiyan, V., Murugan, P., Tjahjowidodo, T., Caesarendra, W., Manyar, O. M. & Then, D. J. H. (2019). In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning. Robotics and Computer-Integrated Manufacturing, 57, 477-487. https://dx.doi.org/10.1016/j.rcim.2019.01.006
Project: M-RT1.1 M4061298
Journal: Robotics and Computer-Integrated Manufacturing
Abstract: Transforming the manufacturing environment from manually operated production units to unsupervised robotic machining centres requires a presence of reliable in-process monitoring system. In this paper, we demonstrate a technique for automatic endpoint detection of weld seam removal in a robotic abrasive belt grinding process with the help of a vision system using deep learning. The paper presents the results of the first investigative stage of semantic segmentation of weld seam removal states using encoder-decoder convolutional neural networks (EDCNN). An experimental investigation using four different weld seam states on mild steel work coupon are trained using the VGG-16 network based on encoder-decoder architecture. The results demonstrate the potential of the developed vision based methodology as a tool for endpoint prediction of the weld seam removal in real time during a compliant abrasive belt grinding process. The prediction system based on semantic segmentation is able to monitor weld profile geometry evolution taking into account the varying belt grinding parameters during machining which will allow further process optimisation.
URI: https://hdl.handle.net/10356/151209
ISSN: 0736-5845
DOI: 10.1016/j.rcim.2019.01.006
Rights: © 2019 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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