Navigating Evolving Vehicle NVH Challenges with Application of Emerging Technologies in Artificial Intelligence and Machine Learning Altair Engineering India Pvt. Limited

Format:

Book

Conference/Event

Author/Creator:

Miskin, Atul R., author.

Contributor:

Himakuntla, Uma Maheswar

Parmar, Azan

Raj, Sonia

Conference Name:

Noise & Vibration Conference & Exhibition (2025-05-12 : Grand Rapids, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

The multifaceted, fast-paced evolution in the automotive industry includes noise and vibration (NVH) behavior of products for regulatory requirements and ever-increasing customer preferences and expectations for comfort. There is pressing need for automotive engineers to explore new and advanced technologies to achieve a First Time Right' product development approach for NVH design and deliver high-quality products in shorter timeframes. Artificial Intelligence (AI) and Machine Learning (ML) are trending transformative technologies reshaping numerous industries. AI enables machines to replicate human cognitive functions, such as reasoning and decision-making, while ML, a branch of AI, employs algorithms that allow systems to learn and improve from data over time.The purpose of the paper is to show an approach of using machine learning techniques to analyze the impact of variations in structural design parameters on vehicle NVH responses. The study begins by executing the Design of Experiments (DoE) involving systematic variation of connection parameters between different vehicle subsystems employing Latin HyperCube algorithm, a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. The generated designs are leveraged to train multiple machine learning models which are in turn tested against unseen data. The most accurate ML model achieved a remarkable more than 95% accuracy rate using R-squared method. This optimized ML algorithm was further employed to predict performance outcomes at arbitrary input points in space and subsequently validated against traditional Finite Element (FE) based solver (OptiStruct) output data. This framework enhances predictive accuracy and significantly accelerates the analytical workflow, empowering engineers with actionable insights for informed decision-making in structural and acoustic design processes

Notes:

Vendor supplied data

Publisher Number:

2025-01-0130

Access Restriction:

Restricted for use by site license