KADNÁR, Milan, Peter KÁČER, Marta HARNIČÁROVÁ, Jan VALÍČEK, František TÓTH, Marián BUJNA, Milena KUŠNEROVÁ, Rastislav MIKUŠ and Marian BORŽAN. Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms. Mathematical machines and systems. Ukrajina: Institute of Mathematical Machines and Systems of the NAS of Ukraine, 2023, vol. 11, No 3, p. 1-18. ISSN 1028-9763. |
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@article{67883, author = {Kadnár, Milan and Káčer, Peter and Harničárová, Marta and Valíček, Jan and Tóth, František and Bujna, Marián and Kušnerová, Milena and Mikuš, Rastislav and Boržan, Marian}, article_location = {Ukrajina}, article_number = {3}, keywords = {welding; distortion; stamping; model; prediction; neural network}, language = {eng}, issn = {1028-9763}, journal = {Mathematical machines and systems}, title = {Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms}, url = {https://www.mdpi.com/2075-1702/11/3/376}, volume = {11}, year = {2023} }
TY - JOUR ID - 67883 AU - Kadnár, Milan - Káčer, Peter - Harničárová, Marta - Valíček, Jan - Tóth, František - Bujna, Marián - Kušnerová, Milena - Mikuš, Rastislav - Boržan, Marian PY - 2023 TI - Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms JF - Mathematical machines and systems VL - 11 IS - 3 SP - 1-18 EP - 1-18 PB - Institute of Mathematical Machines and Systems of the NAS of Ukraine SN - 10289763 KW - welding KW - distortion KW - stamping KW - model KW - prediction KW - neural network UR - https://www.mdpi.com/2075-1702/11/3/376 N2 - The production of parts by pressing and subsequent welding is commonly used in the automotive industry. The disadvantage of this method of production is that inaccuracies arising during pressing significantly affect the final dimension of the part. However, this can be corrected by the choice of the technological parameters of the following operation—welding. Suitably designed parameters make it possible to partially eliminate inaccuracies arising during pressing and thus increase the overall applicability of this technology. The paper is focused on the upper arm geometry of a car produced in this manner. There have been two neural networks proposed in which the optimal welding parameters are determined based on the stamped dimensions and the desired final dimensions. The Levenberg–Marquardt back-propagation algorithm and the Bayesian regularised back-propagation algorithm were used as the learning algorithm for ANNs in multi-layer feed-forward networks. The outputs obtained from the neural networks were compared with a linear prediction model based on a on the design of experiment methodology. The mean absolute percentage error of the linear regression model on the entire dataset was 3 × 10−3%. A neural network with Levenberg–Marquardt back-propagation learning algorithm had a mean absolute percentage error of 4 × 10−3. Similarly, a neural network with a Bayesian regularised back-propagation learning algorithm had a mean absolute percentage error of 3 × 10−3%. ER -
KADNÁR, Milan, Peter KÁČER, Marta HARNIČÁROVÁ, Jan VALÍČEK, František TÓTH, Marián BUJNA, Milena KUŠNEROVÁ, Rastislav MIKUŠ and Marian BORŽAN. Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms. \textit{Mathematical machines and systems}. Ukrajina: Institute of Mathematical Machines and Systems of the NAS of Ukraine, 2023, vol.~11, No~3, p.~1-18. ISSN~1028-9763.
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