Joining element design and product variety in manufacturing industries

Eggink, Derk H.D.; Groll, Marco Wilhelm

doi:10.1016/j.procir.2020.05.014

Cited by 11 publications

(20 citation statements)

References 24 publications

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“…with the aid of sensors, algorithms, and simulations, in order to document, and finally, to certify it [14]. In particular, the growing trend of offering highly configurable products is driving the increase in manufacturing efforts [15]. To overcome this rise in complexity, artificial intelligence (AI) methods are suitable tools.…”

Section: Introductionmentioning

confidence: 99%

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

El-Sari

Biegler

Rethmeier

2021

Metals

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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.

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Section: Introductionmentioning

confidence: 99%

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

El-Sari

Biegler

Rethmeier

2021

Metals

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show abstract

“…After the design process, the materials and the geometry of the components as well as the joining design are specified. The individual joining points characterize the joining design, by their technology and parameters, as well as their spatial arrangement [5]. In the subsequent production process, manufacturing inaccuracies as well as scattering in the properties of semi-finished products and sheets result in properties that deviate from the nominal design.…”

Section: Intr Introduction Oductionmentioning

confidence: 99%

Joint point loadings in car bodies – the influence of manufacturing tolerances and scatter in material properties

Martin¹,

Tröster²

2021

Esaform 2021

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“…with the aid of sensors, algorithms and simulations to document it and finally to certify it [14]. Especially, the growing trend of offering highly configurable products, drives the increase of the manufacturing efforts [15]. To overcome this rise of the complexity, artificial intelligence (AI) methods are suitable tools.…”

Section: Introductionmentioning

confidence: 99%

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

El-Sari¹,

Biegler²,

Rethmeier³

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Joining element design and product variety in manufacturing industries

Cited by 11 publications

References 24 publications

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

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

Joint point loadings in car bodies – the influence of manufacturing tolerances and scatter in material properties

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

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