Material degradation modeling and failure prediction using microstructure images

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Authors
Si, Wujun
Yang, Qingyu
Wu, Xin
Advisors
Issue Date
2018-11-05
Type
Article
Keywords
Dual-phase advanced high strength steel , Generalized cross-validation , Interval estimation , Penalized least squares estimation , Reliability analysis , Two-point correlation function
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Citation
Wujun Si, Qingyu Yang & Xin Wu (2018) Material Degradation Modeling and Failure Prediction Using Microstructure Images, Technometrics
Abstract

Degradation data, frequently along with low-dimensional covariate information such as scalar-type covariates, are widely used for asset reliability analysis. Recently, many high-dimensional covariates such as functional and image covariates have emerged with advances in sensor technology, containing richer information that can be used for degradation assessment. In this article, motivated by a physical effect that microstructures of dual-phase advanced high strength steel strongly influence steel degradation, we propose a two-stage material degradation model using the material microstructure image as a covariate. In Stage 1, we show that the microstructure image covariate can be reduced to a functional covariate while statistical properties of the image are preserved up to the second order. In Stage 2, a novel functional covariate degradation model is proposed, based on which the time-to-failure distribution in terms of degradation level passages is derived. A penalized least squares estimation method is developed to obtain the closed-form point estimator of model parameters. Analytical inferences on interval estimation of the model parameters, the mean degradation levels, and the distribution of the time-to-failure are also developed. Simulation studies are implemented to validate the developed methods. Physical experiments on dual-phase advanced high strength steel are designed and conducted to demonstrate the proposed model. The results show that a significant improvement is achieved for material failure prediction by using material microstructure images compared with multiple benchmark models.

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Publisher
Taylor & Francis
Journal
Book Title
Series
Technometrics;2018
PubMed ID
DOI
ISSN
0040-1706
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