Optimization of spring fatigue life prediction model for vehicle ride using hybrid multi-layer perceptron artificial neural networks

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2 Citations (Scopus)

Abstract

In this study, hybrid multi-layer perceptron artificial neural network (HMLP ANN) models were developed to predict the fatigue life of automotive coil springs with high accuracy based on the vertical vibrations of the vehicle and natural frequencies of the vehicle suspension system. The design and development of vehicle suspension systems involve numerous steps from conceptual design to prototyping and testing, including fatigue life evaluation and vehicle ride analysis. Optimizing HMLP ANN models will significantly simplify the design and development process, which forms the motivation of this study. Simulations were conducted on a quarter car model to extract the loading signals using the measured acceleration signals and artificial road profiles as inputs. The fatigue life was predicted based on the Coffin-Manson, Morrow, and Smith-Watson-Topper strain-life models whereas the comfort ride index was assessed according to the ISO 2631-1:1997 standard. Various HMLP ANN models were trained using the Levenberg-Marquardt backpropagation algorithm to determine the optimum architectures. The lowest mean square error (0.0117) is obtained for the Morrow HMLP ANN model with three hidden layers. The coefficient of determination values are more than 0.9559, indicating that there is good fit between the training/testing datasets and the data predicted by the optimum HMLP ANN models. These models were validated using the conservative correlation approach and there is good agreement between the targeted and predicted fatigue life values. It can be concluded that the optimum HMLP ANN models are capable of predicting the fatigue life of automotive coil springs with acceptable accuracy.

Original languageEnglish
Pages (from-to)597-621
Number of pages25
JournalMechanical Systems and Signal Processing
Volume122
DOIs
Publication statusPublished - 1 May 2019

Fingerprint

Multilayer neural networks
Fatigue of materials
Neural networks
Vehicle suspensions
Backpropagation algorithms
Testing
Conceptual design
Mean square error
Vibrations (mechanical)
Natural frequencies
Railroad cars

Keywords

  • Artificial neural network
  • Automotive coil spring
  • Fatigue life
  • Hybrid multi-layer perceptron
  • Strain-life models
  • Vehicle suspension system

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

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title = "Optimization of spring fatigue life prediction model for vehicle ride using hybrid multi-layer perceptron artificial neural networks",
abstract = "In this study, hybrid multi-layer perceptron artificial neural network (HMLP ANN) models were developed to predict the fatigue life of automotive coil springs with high accuracy based on the vertical vibrations of the vehicle and natural frequencies of the vehicle suspension system. The design and development of vehicle suspension systems involve numerous steps from conceptual design to prototyping and testing, including fatigue life evaluation and vehicle ride analysis. Optimizing HMLP ANN models will significantly simplify the design and development process, which forms the motivation of this study. Simulations were conducted on a quarter car model to extract the loading signals using the measured acceleration signals and artificial road profiles as inputs. The fatigue life was predicted based on the Coffin-Manson, Morrow, and Smith-Watson-Topper strain-life models whereas the comfort ride index was assessed according to the ISO 2631-1:1997 standard. Various HMLP ANN models were trained using the Levenberg-Marquardt backpropagation algorithm to determine the optimum architectures. The lowest mean square error (0.0117) is obtained for the Morrow HMLP ANN model with three hidden layers. The coefficient of determination values are more than 0.9559, indicating that there is good fit between the training/testing datasets and the data predicted by the optimum HMLP ANN models. These models were validated using the conservative correlation approach and there is good agreement between the targeted and predicted fatigue life values. It can be concluded that the optimum HMLP ANN models are capable of predicting the fatigue life of automotive coil springs with acceptable accuracy.",
keywords = "Artificial neural network, Automotive coil spring, Fatigue life, Hybrid multi-layer perceptron, Strain-life models, Vehicle suspension system",
author = "Kong, {Y. S.} and Shahrum Abdullah and D. Schramm and Omar, {Mohd. Zaidi} and {Mohamed Haris}, Sallehuddin",
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N2 - In this study, hybrid multi-layer perceptron artificial neural network (HMLP ANN) models were developed to predict the fatigue life of automotive coil springs with high accuracy based on the vertical vibrations of the vehicle and natural frequencies of the vehicle suspension system. The design and development of vehicle suspension systems involve numerous steps from conceptual design to prototyping and testing, including fatigue life evaluation and vehicle ride analysis. Optimizing HMLP ANN models will significantly simplify the design and development process, which forms the motivation of this study. Simulations were conducted on a quarter car model to extract the loading signals using the measured acceleration signals and artificial road profiles as inputs. The fatigue life was predicted based on the Coffin-Manson, Morrow, and Smith-Watson-Topper strain-life models whereas the comfort ride index was assessed according to the ISO 2631-1:1997 standard. Various HMLP ANN models were trained using the Levenberg-Marquardt backpropagation algorithm to determine the optimum architectures. The lowest mean square error (0.0117) is obtained for the Morrow HMLP ANN model with three hidden layers. The coefficient of determination values are more than 0.9559, indicating that there is good fit between the training/testing datasets and the data predicted by the optimum HMLP ANN models. These models were validated using the conservative correlation approach and there is good agreement between the targeted and predicted fatigue life values. It can be concluded that the optimum HMLP ANN models are capable of predicting the fatigue life of automotive coil springs with acceptable accuracy.

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