Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars

Wojciech Majewski; Ewa Dostatni; Jacek Diakun; Dariusz Mikołajewski; Izabela Rojek

doi:10.3390/app14156393

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Title

Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars

Authors

Wojciech Majewski (WIM) ^{[ 1 ][ 2.9 ][ DW ]}
Ewa Dostatni (WIM) ^{[ 2 ][ 2.9 ][ P ]}
Jacek Diakun (WIM) ^{[ 2 ][ 2.9 ][ P ]}
Dariusz Mikołajewski
Izabela Rojek

^{[ 1 ]} Wydział Inżynierii Mechanicznej, Politechnika Poznańska | ^{[ 2 ]} Instytut Technologii Materiałów, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | ^{[ DW ]} applied doctorate phd student | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication

2024

Published in

Applied Sciences

Journal year: 2024 | Journal volume: vol. 14 | Journal number: iss. 15

Article type

scientific article

Publication language

english

Keywords

EN

radial tire
tire uniformity
conicity
real data
machine learning
quality control

Abstract

EN This article presents the current state and development directions of the tire industry. One of the main requirements that a tire must meet before it can leave the factory is achieving values of quantities describing uniformity at a defined level. Of particular importance areconicity and the components of the tire with the greatest impact on its value. This research is based on the possibility of using an ANN to meet contemporary challenges faced by tire manufacturers. In order to achieve a satisfactory level of prediction, we compared the use of a multi-layer perceptron and decision trees XGBoost, LightGbmRegression, and FastTreeRegression. Based on data analysis and similar examples from the literature, metrics were selected to evaluate the models’ ability to solve regression problems in relation to the described problem. We selected the best possible solution, standing at the top of the features covered by the criterion analysis. The proposed solutions can be the basis for acquiring new knowledge and contributions in the field of the computational analysis of industrial data in tire production. These solutions are characterized by the required accuracy and efficiency for online work, and they also contribute to the creation of the best fit elements of complex systems (including computational models). The results of this study will contribute to reducing the volume of waste in the tire industry by eliminating defective tire parts in the early stages of the production process.

Date of online publication

23.07.2024

Pages (from - to)

6393-1 - 6393-15

DOI

10.3390/app14156393

URL

https://www.mdpi.com/2076-3417/14/15/6393

Comments

Article Number: 6393

License type

CC BY (attribution alone)