Advanced Modelling of Frequency Dependent Damper Using Machine Learning Approach for Accurate Prediction of Ride and Handling Performances
Accurate ride and handling prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of damper model. Conventional methods used for modelling complex vehicle components (like bushings and dampers) are often inadequate to represent behaviour over wide frequency ranges and/or different amplitudes.This is difficult in the part of OEMs to model the physics-based model as the damper’s geometry, material and characteristics property is proprietary to part manufacturer. This is also usually difficult to obtain as a typical data acquisition exercise takes lots of time, cost, and effort. This paper aims to address this problem by predicting the damper force accurately at different velocity/ frequency and amplitude of measured data using Artificial Neural Networks (ANN). The predicted damper force histories were found to be quite accurate as the error in ride and handling between the measured and the thus predicted time histories at various locations were found to be less than 15%. This approach is found to be extremely useful in collecting enormous amounts of customer usage data with minimum instrumentation and small sized data loggers. This has given a big fillip to customer usage data collection in the automotive industry, where the size of the loggers has been a constraint in the collection of such data.New modelling methods circumvent these limitations by using laboratory measurements with neural networks. The new methods enable accurate simulation for nonlinear, frequency dependent components, having multiple inputs and outputs, under arbitrary excitation. This paper describes one such method, known as Empirical Dynamics Modelling. Examples are presented for vehicle shock absorbers. Benefits and limitations are discussed, along with requirements for interfacing to a conventional virtual prototyping environment. Results show particularly good correlation between simulation and testing compare with traditional method.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
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Supplemental Notes:
- Abstract reprinted with permission of SAE International.
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Authors:
- Lenka, Visweswara Rao
- Anthonysamy, Baskar
- THANAPATI, Alok Ranjan
- Deshmukh, Chandrakant Ramrao
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Conference:
- WCX SAE World Congress Experience
- Location: Detroit Michigan, United States
- Date: 2023-4-18 to 2023-4-20
- Publication Date: 2023-4-11
Language
- English
Media Info
- Media Type: Web
- Features: References;
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Serial:
- SAE Technical Paper
- Publisher: Society of Automotive Engineers (SAE)
- ISSN: 0148-7191
- EISSN: 2688-3627
- Serial URL: http://papers.sae.org/
Subject/Index Terms
- TRT Terms: Data collection; Handling characteristics; Machine learning; Neural networks; Shock absorbers; Simulation
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01879983
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 2023-01-0672
- Files: TRIS, SAE
- Created Date: Apr 20 2023 9:56AM