Work

Machine Learning in Transmission Design

Year: 2025

Type: article

Abstract: In the design process of gear systems, various computational tools are used to optimize the operational behavior. Due to time constraints, the initial stages of development typically assume ideal, deviation-free gear geometries. However, deviations resulting from manufacturing significantly affect performance in later applications. This publication presents a concept to incorporate the influence of manufacturing tolerances during early development phases systematically. Based on an initial set of detailed simulations, a surrogate model is developed and trained. This model can quickly generate a wide range of results, efficiently accounting for the large variance of deviations. The shown approach combines the advantages of detailed simulations with the speed of machine learning models.

Source: Zeitschrift für wirtschaftlichen Fabrikbetrieb

DOI: 10.1515/zwf-2024-0156

Authors: Florian Oberneder, Michael Otto, Karsten Stahl

Institution: Technical University of Munich

Language: English

Topic: Gear and Bearing Dynamics Analysis

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,712