Active Learning Supported Metadata Extraction from NVH Simulations BMW Group

Format:

Book

Conference/Event

Author/Creator:

Luegmair, Marinus, author.

Gröttrup, Sören, author.

Conference Name:

14th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference (2026-06-17 : Graz, Austria)

Language:

English

Subjects (All):

Machine learning.

Noise, Vibration, and Harshness (NVH).

Simulation and modeling.

Local Subjects:

Machine learning.

Noise, Vibration, and Harshness (NVH).

Simulation and modeling.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Simulations can only be searched, reused and leveraged as training data for machine learning methods if suitable metadata are related. Manually obtaining these metadata is time-consuming and requires expert knowledge. Consequently, there often is a lack of metadata and this prohibits the reutilization of simulation data. Therefore, automated frameworks for metadata extraction are essential to obtain metadata information quickly, effortlessly and cost-efficiently. At present, there are no toolboxes for Finite-Element-Simulation data. Nevertheless, machine learning methods are a promising solution for this task. Training classical supervised machine learning methods for metadata generation often faces the lack of labeled data since manual labelling can be very costly. Therefore, rule-based extraction algorithms are used as an alternative for fundamental metadata extraction. For more enhanced tasks they are often not feasible. Active Learning is a suitable technique to overcome this contradiction. Here, as only necessary data points are labelled, it is possible to use machine learning methods for metadata extraction even for simulation models. In this work, the specific advantages and challenges of metadata are shown for Noise-Vibration-Harshness simulations. The focus of this contribution lies on the workflow for active learning applied on Finite-Element-Models, including data preprocessing and first training loops. Additionally, the benefits and challenges of high-level feature engineering on data size and extractor model performance are investigated. Moreover, the results show an extended Active Learning workflow which helps to investigate the given data, enhance the feature engineering and therefore the Machine Learning model quality. Finally, based on these results, recommendations for further development of these techniques are provided

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license