Details
| Original language | English |
|---|---|
| Pages (from-to) | 99-112 |
| Number of pages | 14 |
| Journal | Experimental techniques |
| Volume | 44 |
| Issue number | 1 |
| Early online date | 1 Aug 2019 |
| Publication status | Published - Feb 2020 |
| Externally published | Yes |
Abstract
Resistance spot welding (RSW) is one of the common assembling process of thin metals. Due to the inaccessibility of nugget in inspection process, usually destructive tests are used to assess weld quality which is time consuming and costly. Finding a practical solution for monitoring of RSW would reduce necessity of these tests for quality control. In this study, a new designed algorithm is used for processing of a video recorded by a high speed camera from resistance spot welding process, to determine the displacement of the electrode (DE), which can be easily applied to any similar process. It was found that the diagram of DE can provide appropriate information about the ongoing events during the resistance spot welding process and gives a suitable tool for monitoring and controlling RSW. The effect of environmental conditions on the DE diagram was also investigated. Impact of expanded heat, melting and expulsion on displacement diagram during the process was discussed. The results showed that current and time have similar trend in DE, higher pressure will result in diagram’s compaction. It was also concluded that shunt and contamination have considerable effects on displacement diagram and strengths of weld. Using holders as external constraint can improve weld strength for about 12%. An artificial neural network (ANN)-based program was developed to introduce a relation between displacement diagram and weld quality. In most cases, the predicted values are closed to the experiments. 36 different input modes for welding were investigated in this regard. It is shown that by applying the developed code on DE graph the weld quality can be predicted without need to any destructive test.
Keywords
- Displacement of electrode, Monitoring, Neural network based program, Resistance spot welding, Weld quality
ASJC Scopus subject areas
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
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In: Experimental techniques, Vol. 44, No. 1, 02.2020, p. 99-112.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Monitoring of Resistance Spot Welding Process
AU - Haghshenas, N.
AU - Moshayedi, H.
N1 - Publisher Copyright: © 2019, The Society for Experimental Mechanics, Inc.
PY - 2020/2
Y1 - 2020/2
N2 - Resistance spot welding (RSW) is one of the common assembling process of thin metals. Due to the inaccessibility of nugget in inspection process, usually destructive tests are used to assess weld quality which is time consuming and costly. Finding a practical solution for monitoring of RSW would reduce necessity of these tests for quality control. In this study, a new designed algorithm is used for processing of a video recorded by a high speed camera from resistance spot welding process, to determine the displacement of the electrode (DE), which can be easily applied to any similar process. It was found that the diagram of DE can provide appropriate information about the ongoing events during the resistance spot welding process and gives a suitable tool for monitoring and controlling RSW. The effect of environmental conditions on the DE diagram was also investigated. Impact of expanded heat, melting and expulsion on displacement diagram during the process was discussed. The results showed that current and time have similar trend in DE, higher pressure will result in diagram’s compaction. It was also concluded that shunt and contamination have considerable effects on displacement diagram and strengths of weld. Using holders as external constraint can improve weld strength for about 12%. An artificial neural network (ANN)-based program was developed to introduce a relation between displacement diagram and weld quality. In most cases, the predicted values are closed to the experiments. 36 different input modes for welding were investigated in this regard. It is shown that by applying the developed code on DE graph the weld quality can be predicted without need to any destructive test.
AB - Resistance spot welding (RSW) is one of the common assembling process of thin metals. Due to the inaccessibility of nugget in inspection process, usually destructive tests are used to assess weld quality which is time consuming and costly. Finding a practical solution for monitoring of RSW would reduce necessity of these tests for quality control. In this study, a new designed algorithm is used for processing of a video recorded by a high speed camera from resistance spot welding process, to determine the displacement of the electrode (DE), which can be easily applied to any similar process. It was found that the diagram of DE can provide appropriate information about the ongoing events during the resistance spot welding process and gives a suitable tool for monitoring and controlling RSW. The effect of environmental conditions on the DE diagram was also investigated. Impact of expanded heat, melting and expulsion on displacement diagram during the process was discussed. The results showed that current and time have similar trend in DE, higher pressure will result in diagram’s compaction. It was also concluded that shunt and contamination have considerable effects on displacement diagram and strengths of weld. Using holders as external constraint can improve weld strength for about 12%. An artificial neural network (ANN)-based program was developed to introduce a relation between displacement diagram and weld quality. In most cases, the predicted values are closed to the experiments. 36 different input modes for welding were investigated in this regard. It is shown that by applying the developed code on DE graph the weld quality can be predicted without need to any destructive test.
KW - Displacement of electrode
KW - Monitoring
KW - Neural network based program
KW - Resistance spot welding
KW - Weld quality
UR - http://www.scopus.com/inward/record.url?scp=85077947399&partnerID=8YFLogxK
U2 - 10.1007/s40799-019-00341-z
DO - 10.1007/s40799-019-00341-z
M3 - Article
VL - 44
SP - 99
EP - 112
JO - Experimental techniques
JF - Experimental techniques
SN - 0732-8818
IS - 1
ER -