Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F23%3A00002544" target="_blank" >RIV/75081431:_____/23:00002544 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2075-1702/11/3/376" target="_blank" >https://www.mdpi.com/2075-1702/11/3/376</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms

Original language description

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%.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20501 - Materials engineering

Project

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Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mathematical machines and systems

ISSN

1028-9763

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

3

Country of publishing house

UA - UKRAINE

Number of pages

18

Pages from-to

1-18

UT code for WoS article

000958805200001

EID of the result in the Scopus database

2-s2.0-85151379090