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System Identification Technique and Neural Networks for Material Lifetime Assessment Application

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Complex System Modelling and Control Through Intelligent Soft Computations

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 319))

Abstract

Modeling of a material lifetime to assess the material useful lifetime during its service in design has been always challenging task. In the present study, a framework of system identification technique based upon nonlinear autoregressive exogenous inputs (NARX) was introduced and presented for material lifetime assessment using neural networks (NN). Using the framework, the task of material lifetime assessment was accomplished in a fashion of one-step ahead prediction with respect to stress level. In addition, by sliding over one-step to one-step of the stress level, the task of prediction dynamically covered all loading spectrum. As a result, material lifetime assessment can be fashioned for a wide spectrum of loading in an efficient manner based upon limited material lifetime data as the basis of the NARX regressor. The multilayer perceptron (MLP)-NARX and radial basis functions NN (RBFNN)-NARX models were developed to predict fatigue lives of composite materials under multiaxial and multivariable loadings. Several multidirectional laminates of polymeric based composites were examined in this study.

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Acknowledgment

The present author would like to thank the Montana State University and A.P. Vassilopoulos and T.P. Philippidis (doi:10.1016/S0142-1123(02)00003-8) for the fatigue database published through the internet. The author also would like to thank to editors and reviewers for their useful suggestions and comments that further improve the presentation of this research work.

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Authors and Affiliations

  1. Department of Materials and Metallurgical Engineering-FTI, Institut Teknologi Sepuluh Nopember Surabaya, Kampus ITS Keputih Sukolilo, 60111, Surabaya, Indonesia

    Mas Irfan P. Hidayat

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  1. Mas Irfan P. Hidayat
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Correspondence to Mas Irfan P. Hidayat .

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Editors and Affiliations

  1. Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom

    Quanmin Zhu

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Hidayat, M.I.P. (2015). System Identification Technique and Neural Networks for Material Lifetime Assessment Application. In: Zhu, Q., Azar, A. (eds) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-12883-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-12883-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12882-5

  • Online ISBN: 978-3-319-12883-2

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