Resistance Welding Quality Through Artificial Intelligence Techniques
Quality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive qual...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1744 |
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| author | Luis Alonso Domínguez-Molina Edgar Rivas-Araiza Juan Carlos Jauregui-Correa Jose Luis Gonzalez-Cordoba Jesús Carlos Pedraza-Ortega Andras Takacs |
| author_facet | Luis Alonso Domínguez-Molina Edgar Rivas-Araiza Juan Carlos Jauregui-Correa Jose Luis Gonzalez-Cordoba Jesús Carlos Pedraza-Ortega Andras Takacs |
| author_sort | Luis Alonso Domínguez-Molina |
| collection | DOAJ |
| description | Quality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive quality-evaluation methodology. The methodology connects the welding input and during-process parameters with the output visual quality information. A manual resistance spot welding machine was used to monitor and record the process input and output parameters to generate the dataset for training. The welding current, welding time, and electrode pressure data were correlated with the welding spot nugget’s quality, mechanical characteristics, and thermal and visible images. Six machine learning models were trained on visible and thermographic images to classify the weld’s quality and connect the quality characteristics (pull force and welding diameter) and the manufacturing process parameters with the visible and thermographic images of the weld. Finally, a cross-validation method validated the robustness of these models. The results indicate that the welding time and the angle between electrodes are highly influential parameters on the mechanical strength of the joint. Additionally, models using visible images of the welding spot exhibited superior performance compared to thermal images. |
| format | Article |
| id | doaj-art-28e04937acf54aa0b45e1d992ef6eda5 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-28e04937acf54aa0b45e1d992ef6eda52025-08-20T02:43:03ZengMDPI AGSensors1424-82202025-03-01256174410.3390/s25061744Resistance Welding Quality Through Artificial Intelligence TechniquesLuis Alonso Domínguez-Molina0Edgar Rivas-Araiza1Juan Carlos Jauregui-Correa2Jose Luis Gonzalez-Cordoba3Jesús Carlos Pedraza-Ortega4Andras Takacs5Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MéxicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MéxicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MéxicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MéxicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MéxicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MéxicoQuality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive quality-evaluation methodology. The methodology connects the welding input and during-process parameters with the output visual quality information. A manual resistance spot welding machine was used to monitor and record the process input and output parameters to generate the dataset for training. The welding current, welding time, and electrode pressure data were correlated with the welding spot nugget’s quality, mechanical characteristics, and thermal and visible images. Six machine learning models were trained on visible and thermographic images to classify the weld’s quality and connect the quality characteristics (pull force and welding diameter) and the manufacturing process parameters with the visible and thermographic images of the weld. Finally, a cross-validation method validated the robustness of these models. The results indicate that the welding time and the angle between electrodes are highly influential parameters on the mechanical strength of the joint. Additionally, models using visible images of the welding spot exhibited superior performance compared to thermal images.https://www.mdpi.com/1424-8220/25/6/1744resistance spot weldingelectrode forcewelding currentwelding timeconvolutional neural networkthermal images |
| spellingShingle | Luis Alonso Domínguez-Molina Edgar Rivas-Araiza Juan Carlos Jauregui-Correa Jose Luis Gonzalez-Cordoba Jesús Carlos Pedraza-Ortega Andras Takacs Resistance Welding Quality Through Artificial Intelligence Techniques Sensors resistance spot welding electrode force welding current welding time convolutional neural network thermal images |
| title | Resistance Welding Quality Through Artificial Intelligence Techniques |
| title_full | Resistance Welding Quality Through Artificial Intelligence Techniques |
| title_fullStr | Resistance Welding Quality Through Artificial Intelligence Techniques |
| title_full_unstemmed | Resistance Welding Quality Through Artificial Intelligence Techniques |
| title_short | Resistance Welding Quality Through Artificial Intelligence Techniques |
| title_sort | resistance welding quality through artificial intelligence techniques |
| topic | resistance spot welding electrode force welding current welding time convolutional neural network thermal images |
| url | https://www.mdpi.com/1424-8220/25/6/1744 |
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