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September 10, 2025

A machine learning approach towards automated classification of modal analysis results

Key Points

The developed machine learning model classifies vibration modes effectively, improving accuracy and speed.
High performance classification achieved for three-dimensional vibration modes in truck frame structures.
Exploratory analysis utilized machine learning methods to automate the traditional modal analysis process.
Results may reduce time and errors associated with human inspection in mechanical engineering applications.

Abstract

ABSTRACT: Engineering of lightweight and robust structures is significant in mechanical engineering. Nevertheless, weight optimization of such structures leads to undesirable vibrations. Modal analysis is a common technique used in industry to investigate vibration behaviour. The classification of the mode shapes resulting from the analysis is conducted through human visual inspection, which can be time-consuming and susceptible to error. This paper presents an exploratory study investigating the potential of ML methods to classify three-dimensional vibration modes of truck frame structures. The aim is to evaluate the potential of such an approach to automate the modal analysis process to streamline the development process. As a result, the developed ML model can classify the vibration modes with high performance and additionally demonstrates flexibility regarding changes in geometry topology.

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