A distribution-based functional linear model for reliability analysis of advanced high-strength dual-phase steels by utilizing material microstructure images. (2nd September 2017)

• Record Type: Journal Article
• Authors: Si, Wujun; Yang, Qingyu; Wu, Xin
• Is Part Of: IISE transactions. Volume 49:Number 9(2017)
• Page Start: 863
• Page End: 873
• Publication Date: 2017-09-02
• Subjects: Advanced high-strength steel -- dual-phase material -- two-point correlation function -- functional linear regression -- maximum penalized likelihood estimation; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2017.1320599
• Languages: English
• ISSNs: 2472-5854

Abstract

ABSTRACT: The microstructure of a material is known to strongly influence its macroscopic properties, such as strength, hardness, toughness, and wear resistance, which in turn affect material service lifetime. In the reliability literature, most existing research conducts reliability analysis based on either lifetime data or degradation data. However, none of these studies take the information contained in an image of the microstructure of the material into account when conducting reliability analysis. In this article, considering the strong effect on a material's reliability created by its microstructure, we conduct a reliability analysis of an advanced high-strength dual-phase steel by utilizing information about its microstructure. Specifically, the lifetime distribution of the steel, which is assumed to belong to a log-location-scale family, is predicted by utilizing the information contained in images of its microstructure. For the prediction, we propose a novel statistical model called the distribution-based functional linear model, in which the effect of the microstructure on both the location and scale parameters of lifetime distribution is formulated. The proposed model generalizes the existing functional linear regression model. A maximum penalized likelihood method is developed to estimate the model parameters. A simulation study is implemented to illustrate the developed methods. Physical experiments on dual-phase steel are designed and conducted to demonstrate the proposed model. The results show that the proposed model more precisely predicts the lifetime of the steel than existing methods that ignore the information contained in microstructure images.