Loading…
Automatic Welding Seam Tracking and Identification
In the automatic welding process on mid/thick plates, the precision of the welding position has an important effect on welding quality, which mainly relies on the identification of the welding seam. However, due to some possible disturbances in complex unstructured welding environments, e.g., strong...
Saved in:
Published in: | IEEE transactions on industrial electronics (1982) 2017-09, Vol.64 (9), p.7261-7271 |
---|---|
Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c291t-e0f24296e287356afb11bfe5a356530a29f0ea952136f932e7f98fcd398573143 |
---|---|
cites | cdi_FETCH-LOGICAL-c291t-e0f24296e287356afb11bfe5a356530a29f0ea952136f932e7f98fcd398573143 |
container_end_page | 7271 |
container_issue | 9 |
container_start_page | 7261 |
container_title | IEEE transactions on industrial electronics (1982) |
container_volume | 64 |
creator | Xinde Li Xianghui Li Shuzhi Sam Ge Khyam, Mohammad Omar Chaomin Luo |
description | In the automatic welding process on mid/thick plates, the precision of the welding position has an important effect on welding quality, which mainly relies on the identification of the welding seam. However, due to some possible disturbances in complex unstructured welding environments, e.g., strong arc lights, welding splashes, thermal-induced deformations, etc., it is a great challenge to identify the welding seam. In this paper, we propose a robust automatic welding seam identification and tracking method by utilizing structured-light vision. First, after the preprocessing of the welding image, the gray distribution of the laser stripe is tracked and the profile of the welding seam is searched in a small area by using the Kalman filter, with the aim to avoid some disturbances. Second, in order to extract the welding seam profile, a series of centroids obtained by scanning the columns in the rectangular window are fitted using the least-squares method. Third, a character string method is proposed to qualitatively describe the welding seam profile, which might consist of different segment and junction relationship elements. And then, these character strings acquired from the object image are matched with those from the model, so that the position of the welding seam can be determined. Finally, the advantages of the new algorithm are testified and compared through several experiments. |
doi_str_mv | 10.1109/TIE.2017.2694399 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TIE_2017_2694399</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7903643</ieee_id><sourcerecordid>2174430020</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-e0f24296e287356afb11bfe5a356530a29f0ea952136f932e7f98fcd398573143</originalsourceid><addsrcrecordid>eNo9kM9Lw0AQhRdRMFbvgpeA58SZ_ZHNHEupWih4sOJx2SazktomdZMe_O9NafE0PPjeG_iEuEfIEYGeVot5LgFtLgvSiuhCJGiMzYh0eSkSkLbMAHRxLW76fgOA2qBJhJwehm7nh6ZKP3lbN-1X-s5-l66ir76Pybd1uqi5HZrQVCPXtbfiKvhtz3fnOxEfz_PV7DVbvr0sZtNlVknCIWMIUksqWJZWmcKHNeI6sPFjMAq8pADsyUhURSAl2QYqQ1UrKo1VqNVEPJ5297H7OXA_uE13iO340km0WisACSMFJ6qKXd9HDm4fm52Pvw7BHc240Yw7mnFnM2Pl4VRpmPkftwSq0Er9AdBZXNw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2174430020</pqid></control><display><type>article</type><title>Automatic Welding Seam Tracking and Identification</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Xinde Li ; Xianghui Li ; Shuzhi Sam Ge ; Khyam, Mohammad Omar ; Chaomin Luo</creator><creatorcontrib>Xinde Li ; Xianghui Li ; Shuzhi Sam Ge ; Khyam, Mohammad Omar ; Chaomin Luo</creatorcontrib><description>In the automatic welding process on mid/thick plates, the precision of the welding position has an important effect on welding quality, which mainly relies on the identification of the welding seam. However, due to some possible disturbances in complex unstructured welding environments, e.g., strong arc lights, welding splashes, thermal-induced deformations, etc., it is a great challenge to identify the welding seam. In this paper, we propose a robust automatic welding seam identification and tracking method by utilizing structured-light vision. First, after the preprocessing of the welding image, the gray distribution of the laser stripe is tracked and the profile of the welding seam is searched in a small area by using the Kalman filter, with the aim to avoid some disturbances. Second, in order to extract the welding seam profile, a series of centroids obtained by scanning the columns in the rectangular window are fitted using the least-squares method. Third, a character string method is proposed to qualitatively describe the welding seam profile, which might consist of different segment and junction relationship elements. And then, these character strings acquired from the object image are matched with those from the model, so that the position of the welding seam can be determined. Finally, the advantages of the new algorithm are testified and compared through several experiments.</description><identifier>ISSN: 0278-0046</identifier><identifier>EISSN: 1557-9948</identifier><identifier>DOI: 10.1109/TIE.2017.2694399</identifier><identifier>CODEN: ITIED6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Arc welding ; Automatic welding ; Centroids ; Columns (structural) ; Deformation ; Disturbances ; Image acquisition ; Kalman filter ; Kalman filters ; Laser beam welding ; Laser modes ; Least squares method ; qualitative description ; Robots ; Robustness ; Seam tracking ; Sensors ; Strings ; Thick plates ; Welding ; welding seam identification ; welding tracking</subject><ispartof>IEEE transactions on industrial electronics (1982), 2017-09, Vol.64 (9), p.7261-7271</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-e0f24296e287356afb11bfe5a356530a29f0ea952136f932e7f98fcd398573143</citedby><cites>FETCH-LOGICAL-c291t-e0f24296e287356afb11bfe5a356530a29f0ea952136f932e7f98fcd398573143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7903643$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,54771</link.rule.ids></links><search><creatorcontrib>Xinde Li</creatorcontrib><creatorcontrib>Xianghui Li</creatorcontrib><creatorcontrib>Shuzhi Sam Ge</creatorcontrib><creatorcontrib>Khyam, Mohammad Omar</creatorcontrib><creatorcontrib>Chaomin Luo</creatorcontrib><title>Automatic Welding Seam Tracking and Identification</title><title>IEEE transactions on industrial electronics (1982)</title><addtitle>TIE</addtitle><description>In the automatic welding process on mid/thick plates, the precision of the welding position has an important effect on welding quality, which mainly relies on the identification of the welding seam. However, due to some possible disturbances in complex unstructured welding environments, e.g., strong arc lights, welding splashes, thermal-induced deformations, etc., it is a great challenge to identify the welding seam. In this paper, we propose a robust automatic welding seam identification and tracking method by utilizing structured-light vision. First, after the preprocessing of the welding image, the gray distribution of the laser stripe is tracked and the profile of the welding seam is searched in a small area by using the Kalman filter, with the aim to avoid some disturbances. Second, in order to extract the welding seam profile, a series of centroids obtained by scanning the columns in the rectangular window are fitted using the least-squares method. Third, a character string method is proposed to qualitatively describe the welding seam profile, which might consist of different segment and junction relationship elements. And then, these character strings acquired from the object image are matched with those from the model, so that the position of the welding seam can be determined. Finally, the advantages of the new algorithm are testified and compared through several experiments.</description><subject>Arc welding</subject><subject>Automatic welding</subject><subject>Centroids</subject><subject>Columns (structural)</subject><subject>Deformation</subject><subject>Disturbances</subject><subject>Image acquisition</subject><subject>Kalman filter</subject><subject>Kalman filters</subject><subject>Laser beam welding</subject><subject>Laser modes</subject><subject>Least squares method</subject><subject>qualitative description</subject><subject>Robots</subject><subject>Robustness</subject><subject>Seam tracking</subject><subject>Sensors</subject><subject>Strings</subject><subject>Thick plates</subject><subject>Welding</subject><subject>welding seam identification</subject><subject>welding tracking</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNo9kM9Lw0AQhRdRMFbvgpeA58SZ_ZHNHEupWih4sOJx2SazktomdZMe_O9NafE0PPjeG_iEuEfIEYGeVot5LgFtLgvSiuhCJGiMzYh0eSkSkLbMAHRxLW76fgOA2qBJhJwehm7nh6ZKP3lbN-1X-s5-l66ir76Pybd1uqi5HZrQVCPXtbfiKvhtz3fnOxEfz_PV7DVbvr0sZtNlVknCIWMIUksqWJZWmcKHNeI6sPFjMAq8pADsyUhURSAl2QYqQ1UrKo1VqNVEPJ5297H7OXA_uE13iO340km0WisACSMFJ6qKXd9HDm4fm52Pvw7BHc240Yw7mnFnM2Pl4VRpmPkftwSq0Er9AdBZXNw</recordid><startdate>201709</startdate><enddate>201709</enddate><creator>Xinde Li</creator><creator>Xianghui Li</creator><creator>Shuzhi Sam Ge</creator><creator>Khyam, Mohammad Omar</creator><creator>Chaomin Luo</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>201709</creationdate><title>Automatic Welding Seam Tracking and Identification</title><author>Xinde Li ; Xianghui Li ; Shuzhi Sam Ge ; Khyam, Mohammad Omar ; Chaomin Luo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-e0f24296e287356afb11bfe5a356530a29f0ea952136f932e7f98fcd398573143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Arc welding</topic><topic>Automatic welding</topic><topic>Centroids</topic><topic>Columns (structural)</topic><topic>Deformation</topic><topic>Disturbances</topic><topic>Image acquisition</topic><topic>Kalman filter</topic><topic>Kalman filters</topic><topic>Laser beam welding</topic><topic>Laser modes</topic><topic>Least squares method</topic><topic>qualitative description</topic><topic>Robots</topic><topic>Robustness</topic><topic>Seam tracking</topic><topic>Sensors</topic><topic>Strings</topic><topic>Thick plates</topic><topic>Welding</topic><topic>welding seam identification</topic><topic>welding tracking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xinde Li</creatorcontrib><creatorcontrib>Xianghui Li</creatorcontrib><creatorcontrib>Shuzhi Sam Ge</creatorcontrib><creatorcontrib>Khyam, Mohammad Omar</creatorcontrib><creatorcontrib>Chaomin Luo</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on industrial electronics (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xinde Li</au><au>Xianghui Li</au><au>Shuzhi Sam Ge</au><au>Khyam, Mohammad Omar</au><au>Chaomin Luo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Welding Seam Tracking and Identification</atitle><jtitle>IEEE transactions on industrial electronics (1982)</jtitle><stitle>TIE</stitle><date>2017-09</date><risdate>2017</risdate><volume>64</volume><issue>9</issue><spage>7261</spage><epage>7271</epage><pages>7261-7271</pages><issn>0278-0046</issn><eissn>1557-9948</eissn><coden>ITIED6</coden><abstract>In the automatic welding process on mid/thick plates, the precision of the welding position has an important effect on welding quality, which mainly relies on the identification of the welding seam. However, due to some possible disturbances in complex unstructured welding environments, e.g., strong arc lights, welding splashes, thermal-induced deformations, etc., it is a great challenge to identify the welding seam. In this paper, we propose a robust automatic welding seam identification and tracking method by utilizing structured-light vision. First, after the preprocessing of the welding image, the gray distribution of the laser stripe is tracked and the profile of the welding seam is searched in a small area by using the Kalman filter, with the aim to avoid some disturbances. Second, in order to extract the welding seam profile, a series of centroids obtained by scanning the columns in the rectangular window are fitted using the least-squares method. Third, a character string method is proposed to qualitatively describe the welding seam profile, which might consist of different segment and junction relationship elements. And then, these character strings acquired from the object image are matched with those from the model, so that the position of the welding seam can be determined. Finally, the advantages of the new algorithm are testified and compared through several experiments.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIE.2017.2694399</doi><tpages>11</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0278-0046 |
ispartof | IEEE transactions on industrial electronics (1982), 2017-09, Vol.64 (9), p.7261-7271 |
issn | 0278-0046 1557-9948 |
language | eng |
recordid | cdi_crossref_primary_10_1109_TIE_2017_2694399 |
source | IEEE Electronic Library (IEL) Journals |
subjects | Arc welding Automatic welding Centroids Columns (structural) Deformation Disturbances Image acquisition Kalman filter Kalman filters Laser beam welding Laser modes Least squares method qualitative description Robots Robustness Seam tracking Sensors Strings Thick plates Welding welding seam identification welding tracking |
title | Automatic Welding Seam Tracking and Identification |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T03%3A33%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Automatic%20Welding%20Seam%20Tracking%20and%20Identification&rft.jtitle=IEEE%20transactions%20on%20industrial%20electronics%20(1982)&rft.au=Xinde%20Li&rft.date=2017-09&rft.volume=64&rft.issue=9&rft.spage=7261&rft.epage=7271&rft.pages=7261-7271&rft.issn=0278-0046&rft.eissn=1557-9948&rft.coden=ITIED6&rft_id=info:doi/10.1109/TIE.2017.2694399&rft_dat=%3Cproquest_cross%3E2174430020%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c291t-e0f24296e287356afb11bfe5a356530a29f0ea952136f932e7f98fcd398573143%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2174430020&rft_id=info:pmid/&rft_ieee_id=7903643&rfr_iscdi=true |