Dynamic resistance signal–based wear monitoring of resistance spot welding electrodes
| Parent link: | International Journal of Advanced Manufacturing Technology.— .— New York: Springer Science+Business Media LLC. Vol. 133.— 2024.— P. 3267-3281 |
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| Corporate Author: | |
| Other Authors: | , , , , , , |
| Summary: | Title screen Resistance spot welding is used extensively in the assembly of the car body in white on the production line. During the continuous welding process, a specific combination of welding process parameters is often used hundreds of times, so the electrodes are prone to wear and need to be replaced from time to time. The aim of this article is to use the dynamic resistance signal to predict the degree of electrode wear and to determine the time for electrode replacement to guarantee weld quality. By analysing the dynamic resistance pattern recorded over the course of the consecutive welding process and using the profile of the electrode tip, it is possible to assess the degree of electrode wear. After a thorough analysis of the gradual evolution of the dynamic resistance and the changes in the electrode tip geometries during the electrode failure process, there was evidence that the dynamic resistance curve group was a reflection of the electrode tip geometries. By establishing a machine learning model to determine the functional correlation between them, electrode wear can be predicted. The test data show that the maximum errors of the test results are 0.28 mm in electrode tip diameter, 1.34 mm2 in electrode tip area and 0.85 mm in electrode tip perimeter, which further illustrates that the dynamic resistance curve is indicative of electrode wear and can be used to detect the progress of electrode wear. Текстовый файл AM_Agreement |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/s00170-024-13993-y |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=674233 |
MARC
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| 200 | 1 | |a Dynamic resistance signal–based wear monitoring of resistance spot welding electrodes |f Dawei Zhao, Nikita Vdonin, Mikhail Slobodyan [et al.] | |
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| 300 | |a Title screen | ||
| 320 | |a References: 69 tit. | ||
| 330 | |a Resistance spot welding is used extensively in the assembly of the car body in white on the production line. During the continuous welding process, a specific combination of welding process parameters is often used hundreds of times, so the electrodes are prone to wear and need to be replaced from time to time. The aim of this article is to use the dynamic resistance signal to predict the degree of electrode wear and to determine the time for electrode replacement to guarantee weld quality. By analysing the dynamic resistance pattern recorded over the course of the consecutive welding process and using the profile of the electrode tip, it is possible to assess the degree of electrode wear. After a thorough analysis of the gradual evolution of the dynamic resistance and the changes in the electrode tip geometries during the electrode failure process, there was evidence that the dynamic resistance curve group was a reflection of the electrode tip geometries. By establishing a machine learning model to determine the functional correlation between them, electrode wear can be predicted. The test data show that the maximum errors of the test results are 0.28 mm in electrode tip diameter, 1.34 mm2 in electrode tip area and 0.85 mm in electrode tip perimeter, which further illustrates that the dynamic resistance curve is indicative of electrode wear and can be used to detect the progress of electrode wear. | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |t International Journal of Advanced Manufacturing Technology |c New York |n Springer Science+Business Media LLC. | |
| 463 | 1 | |t Vol. 133 |v P. 3267-3281 |d 2024 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a Resistance spot welding | |
| 610 | 1 | |a Electrode wear | |
| 610 | 1 | |a Dynamic resistance | |
| 610 | 1 | |a Artifcial neural networks | |
| 701 | 0 | |a Dawei Zhao | |
| 701 | 1 | |a Vdonin |b N. |g Nikita | |
| 701 | 1 | |a Slobodyan |b M. S. |c Specialist in the field of management, specialist in the field of welding production |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1978- |g Mikhail Stepanovich |9 21616 | |
| 701 | 1 | |a Butsykin |b S. E. |g Sergey Eduardovich | |
| 701 | 1 | |a Kiselev |b A. S. |c Specialist in the field of welding production |c Head of the department of Tomsk Polytechnic University, Candidate of technical sciences |f 1955- |g Aleksey Sergeevich |9 18016 | |
| 701 | 1 | |a Gordynets |b A. S. |c specialist in the field of welding production |c assistant of Tomsk Polytechnic University |f 1980- |g Anton Sergeevich |9 18008 | |
| 701 | 1 | |a Wang Yuanxun | |
| 712 | 0 | 2 | |a National Research Tomsk Polytechnic University |9 27197 |4 570 |
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