Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 26 October 2021 / Approved: 27 October 2021 / Online: 27 October 2021 (13:27:03 CEST)

A peer-reviewed article of this Preprint also exists.

El-Sari, B.; Biegler, M.; Rethmeier, M. 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 2021, 11, 1874. El-Sari, B.; Biegler, M.; Rethmeier, M. 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 2021, 11, 1874.

Journal reference: Metals 2021, 11, 1874
DOI: 10.3390/met11111874

Abstract

Resistance spot welding is an established joining process in the production of safety-relevant components in the automotive industry. Therefore, a 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 the 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 is to investigate the extrapolation capability of the multi-layer perceptron model. That means, that the predictive performance of the model will be tested with data that clearly differs 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 trained 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 the 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.

Keywords

Automotive; Resistance Spot Welding; Quality Assurance; Quality Monitoring; Artificial Intelligence

Subject

ENGINEERING, Industrial & Manufacturing Engineering

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