Title: Methodology for developing a neural network leaf spring model

Authors: Cor-Jacques Kat; Jennifer L. Johrendt; Pieter Schalk Els

Addresses: Department of Mechanical and Aeronautical Engineering, University of Pretoria, Private Bag x20 Hatfield, 0028, South Africa ' Department Mechanical, Automotive and Materials Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 3P4, Canada ' Department of Mechanical and Aeronautical Engineering, University of Pretoria, Private Bag x20 Hatfield, 0028, South Africa

Abstract: This paper describes the development of a neural network that is able to emulate the vertical force-displacement behaviour of a leaf spring. Special emphasis is placed on aspects that affect the predictive capability of a neural network such as type, structure, inputs and ability to generalise. These aspects are investigated in order to enable the effective use of it to model leaf spring behaviour. The results show that with the correct selection of inputs and network architecture, the neural network's ability to generalise can be improved and also reduce the required training data. The resulting 2-15-1 feed-forward neural network is shown to generalise well and requires minimal data to be trained. Experimental data was used to train and validate the network. The methodology followed is not limited to the application of leaf springs only but should apply to various other applications especially ones with similar nonlinear characteristics.

Keywords: leaf spring modelling; multi-leaf spring; neural networks; generalisation; experimental training data; experimental validation.

DOI: 10.1504/IJVSMT.2017.087971

International Journal of Vehicle Systems Modelling and Testing, 2017 Vol.12 No.1/2, pp.94 - 113

Received: 26 Sep 2016
Accepted: 14 Jun 2017
Published online: 13 Nov 2017 *

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