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Uncertainty Detection in Sheet Metal Bending Processes with Machine Learning

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Abstract:

The quality and dimensional accuracy of sheet metal components are strongly influenced by various sources of uncertainty, including variations in material properties, tool geometry and process parameters. Determining the specific source responsible for deviations in bending outcomes is usually costly and time-consuming, especially in industrial settings where numerous factors interact. In this study, a machine learning framework that can detect and quantify the impact of uncertainties in both air and bottom bending processes is presented. A dataset comprising forming results such as bending angles, final thickness and measured deviations, is used to train two neural networks metamodels (one for each process) that link input uncertainties to process outcomes. The predictive performance of these models was evaluated using different metrics achieving high predictive accuracy, with coefficients of determination close to 1 for most uncertainty sources in air bending and values above 0.95 for the majority of parameters in bottom bending. These results demonstrate the capability of the methodology to reliably identify dominant sources of uncertainty and support robust process optimization.

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Periodical:

Key Engineering Materials (Volume 1049)

Pages:

55-63

DOI:

https://doi.org/10.4028/p-Nz9UGU

Citation:

Cite this paper

Online since:

April 2026

Authors:

Tomás Parreira*, Daniel Cruz, Armando Marques, Pedro Prates, Marta Oliveira, Abel Santos, Bernardete Ribeiro, André Pereira

Keywords:

Bending Process, Forming Process, Machine Learning, Uncertainty Detection

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Creative Commons CC BY 4.0

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* - Corresponding Author

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