Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels
Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process paramete...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Metals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4701/11/11/1874 |
| _version_ | 1797509307975073792 |
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| author | Bassel El-Sari Max Biegler Michael Rethmeier |
| author_facet | Bassel El-Sari Max Biegler Michael Rethmeier |
| author_sort | Bassel El-Sari |
| collection | DOAJ |
| description | Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space. |
| first_indexed | 2024-03-10T05:16:50Z |
| format | Article |
| id | doaj.art-789cb2fc227c460bb59592921b2aa342 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T05:16:50Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-789cb2fc227c460bb59592921b2aa3422023-11-23T00:24:55ZengMDPI AGMetals2075-47012021-11-011111187410.3390/met11111874Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength SteelsBassel El-Sari0Max Biegler1Michael Rethmeier2Fraunhofer IPK, Pascalstr. 8-9, 10587 Berlin, GermanyFraunhofer IPK, Pascalstr. 8-9, 10587 Berlin, GermanyFraunhofer IPK, Pascalstr. 8-9, 10587 Berlin, GermanyResistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.https://www.mdpi.com/2075-4701/11/11/1874automotiveresistance spot weldingquality assurancequality monitoringartificial intelligence |
| spellingShingle | Bassel El-Sari Max Biegler Michael Rethmeier Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels Metals automotive resistance spot welding quality assurance quality monitoring artificial intelligence |
| title | Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels |
| title_full | Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels |
| title_fullStr | Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels |
| title_full_unstemmed | Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels |
| title_short | Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels |
| title_sort | investigation of the extrapolation capability of an artificial neural network algorithm in combination with process signals in resistance spot welding of advanced high strength steels |
| topic | automotive resistance spot welding quality assurance quality monitoring artificial intelligence |
| url | https://www.mdpi.com/2075-4701/11/11/1874 |
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