In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning
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 abra...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/151209 |
| _version_ | 1826114246510903296 |
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| author | Pandiyan, Vigneashwara Murugan, Pushparaja Tjahjowidodo, Tegoeh Caesarendra, Wahyu Manyar, Omey Mohan Then, David Jin Hong |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Pandiyan, Vigneashwara Murugan, Pushparaja Tjahjowidodo, Tegoeh Caesarendra, Wahyu Manyar, Omey Mohan Then, David Jin Hong |
| author_sort | Pandiyan, Vigneashwara |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:36:21Z |
| format | Journal Article |
| id | ntu-10356/151209 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:36:21Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1512092021-06-29T04:45:41Z In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning Pandiyan, Vigneashwara Murugan, Pushparaja Tjahjowidodo, Tegoeh Caesarendra, Wahyu Manyar, Omey Mohan Then, David Jin Hong School of Mechanical and Aerospace Engineering Rolls-Royce@NTU Corporate Lab Engineering::Mechanical engineering Abrasive Belt Grinding Deep Learning 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. National Research Foundation (NRF) This work was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF) (Grant No. M-RT1.1 M4061298) Singapore under the Corp Lab@University Scheme. 2021-06-29T04:45:41Z 2021-06-29T04:45:41Z 2019 Journal Article 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 0736-5845 0000-0003-0074-5101 0000-0002-9784-4204 0000-0002-4420-0894 https://hdl.handle.net/10356/151209 10.1016/j.rcim.2019.01.006 2-s2.0-85060115036 57 477 487 en M-RT1.1 M4061298 Robotics and Computer-Integrated Manufacturing © 2019 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Mechanical engineering Abrasive Belt Grinding Deep Learning Pandiyan, Vigneashwara Murugan, Pushparaja Tjahjowidodo, Tegoeh Caesarendra, Wahyu Manyar, Omey Mohan Then, David Jin Hong In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning |
| title | In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning |
| title_full | In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning |
| title_fullStr | In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning |
| title_full_unstemmed | In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning |
| title_short | In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning |
| title_sort | in process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning |
| topic | Engineering::Mechanical engineering Abrasive Belt Grinding Deep Learning |
| url | https://hdl.handle.net/10356/151209 |
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