Prediction of Dynamic Stiffness of Automotive Components Using Support Vector Methods Tata Technologies

Format:

Book

Conference/Event

Author/Creator:

Paturi, Yuva Venkata Sekhar, author.

Conference Name:

Automotive Technical Papers (2026-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

In the current scenario of EV revolution in the automotive industry, NVH performance of the vehicles is one of the major points of sale to the customers. Auxiliary components play one of the predominant roles in the contribution of noise to overall vehicle interior or exterior sound pressure levels, which impact customer vehicle comfort. CAE prediction of NVH performance of automotive components involves a lot of design iterative processes, large server space utilization, and time-consuming.To reduce cost and time, data-driven technologies like AI algorithms can help CAE engineers because of their high efficiency and high precision. In the current research, a wiper motor mount stiffness prediction algorithm was designed based on the historical data using CAE analysis and AI algorithms, and improved prediction accuracy by tuning the parameters of AI algorithms using grid search methodology.High prediction accuracy of wiper motor mount stiffness has been achieved with the method of support vector machine. CAE engineers can avoid iterative processes by utilizing the optimized design parameters from the prediction results without running full finite element analysis simulations

Notes:

Vendor supplied data

Publisher Number:

2026-01-5016

Access Restriction:

Restricted for use by site license