- Tytuł:
- Innovative stress analysis and machine learning forecasting for semi-trailer truck body durability
- Autorzy:
-
Lyashuk, Oleh
Levkovych, Mykhailo
Stashkiv, Mykola
Pastukh, Oleh
Martyniuk, Volodymyr
Mironov, Dmytro
Rabe, Marcin
Vovk, Yuriy - Powiązania:
- https://bibliotekanauki.pl/articles/27324297.pdf
- Data publikacji:
- 2023
- Wydawca:
- Fundacja Centrum Badań Socjologicznych
- Tematy:
-
transport
energy resources
semi-trailer truck body
static stress
static displacement
CAD model
algorithm
machine learning - Opis:
- This article presents an in-depth analysis of the stress-deformation state (SDS) in the bottom structure of a semi-trailer truck body. Engineering analysis was conducted utilizing the SolidWorks software, focusing on a comprehensive CAD model of the semi-trailer truck body. The study explored variations in SDS parameters resulting from alterations in the geometric parameters of the body bottom elements. The research investigated alterations in static stress and displacement relative to changes in the proportions of the cross-section of the channel while maintaining fixed geometric dimensions of the workpiece, thickness of the workpiece, and the material of the body bottom. Graphical representations were generated to illustrate the variations in static stress, displacement, and safety margin concerning the thickness of the shelf and channel. Additionally, dependencies were derived that correlate static stresses in the channel with the thickness of the channel wall and the thickness of the body bottom sheet. The study results were compiled and summarized, offering valuable insights into the stress-deformation state of the semi-trailer truck body's bottom. Furthermore, machine learning techniques, specifically the RandomForest algorithm, were implemented in a Python environment to predict changes in static stress based on various factors. The model's predictions were validated by comparing predicted static stress values with actual values on a test sample. These findings facilitate efficient selection of appropriately sized elements by predicting static stress values, employing the RandomForest machine learning algorithm.
- Źródło:
-
Journal of Sustainable Development of Transport and Logistics; 2023, 8, 2; 43--57
2520-2979 - Pojawia się w:
- Journal of Sustainable Development of Transport and Logistics
- Dostawca treści:
- Biblioteka Nauki
Artykuł