Abstract
This research proposes the use of Artificial Neural Networks (ANN) to predict the road input for road load data generation for variants of a vehicle as vehicle parameters are modified. This is important to the design engineers while the vehicle variant is still in the initial stages of development, hence no prototypes are available and accurate proving ground data acquisition is not possible. ANNs are, with adequate training, capable of representing the complex relationships between inputs and outputs. This research explores the implementation of the ANN to predict road input for vehicle variants using a quarter vehicle test rig. The training and testing data for this research are collected from a validated quarter vehicle model.
Original language | English |
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Title of host publication | SAE Technical Papers |
Place of Publication | USA |
Publisher | SAE International |
Volume | 2015-April |
Edition | April |
DOIs | |
Publication status | Published - 14 Apr 2015 |
Event | SAE 2015 World Congress and Exhibition - Detroit, United States Duration: 21 Apr 2015 → 23 Apr 2015 |
Conference
Conference | SAE 2015 World Congress and Exhibition |
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Country/Territory | United States |
City | Detroit |
Period | 21/04/15 → 23/04/15 |
Keywords
- Tire
- Modal Analysis
- Finite Element Analysis
ASJC Scopus subject areas
- Automotive Engineering
- Safety, Risk, Reliability and Quality
- Pollution
- Industrial and Manufacturing Engineering