A penalized autologistic regression with application for modeling the microstructure of dual-phase high-strength steel

Abstract

Recently, dual-phase high-strength steel has attracted increasing attention in the automotive industry due to its prominent physical and mechanical properties. Microstructures of dual-phase high-strength steel have a significant effect on the properties of steel, such as wear resistance and strength, so they have an important role in the quality of steel. Therefore, statistical modeling of the microstructures of steel is of great interest. However, most existing methods require many model parameters due to the complex topological forms of microstructures, which make these models suffer from overfitting and high computational time for parameter estimation. To overcome these challenges, a novel statistical model is proposed to characterize microstructures and select the most effective parameters. Furthermore, an efficient parameter estimation method is developed to estimate the model parameters given a microstructure sample. The developed method is based on a penalized pseudo log-likelihood and the accelerated proximal gradient. A simulation study is conducted to verify the developed methods. The proposed methodology is validated by a real-world example of the microstructures of high-strength steel, and the case study shows the superior performance of the developed model compared with existing methods.

Keywords:

• accelerated proximal gradient
• lasso
• maximum likelihood
• microstructure

Acknowledgment

The authors thank the editor and the referees for their valuable comments that helped to improve this article.

Additional information

Funding

This research work was partially supported by the National Science Foundation under Grant CMMI-1404276 to Wayne State University.

Notes on contributors

Mohammad Aminisharifabad

Mohammad Aminisharifabad is a Ph.D. student of Industrial and Systems Engineering at Wayne State University, Detroit, Michigan. He received his master’s degree in Computational Molecular Biology/ Bioinformatics at the Saarland University in 2012. He has a bachelor's degree in Computer Engineering-Software from Yazd University, Yazd, Iran. His major research interests include Data Analytics, Machine Learning, Deep Learning and Reliability.

Qingyu Yang

Qingyu Yang received the B.S. and M.S. degrees in automatic control and intelligent system from the University of Science and Technology of China, Hefei, China, in 2000 and 2003, respectively, and the M.S. degree in statistics and the Ph.D. degree in industrial engineering from the University of Iowa, Iowa City IA, USA, in 2007 and 2008, respectively. Currently, he is an Associate Professor in the Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, USA. His work has been published in Technometrics, IIE Transactions on Quality and Reliability, IEEE Transactions on Reliability, IEEE Transactions on Automation Science, among others. His research interests include data analytics, reliability and quality, and complex system modeling. Dr. Yang is a member of INFORMS and IISE.

Xin Wu

Xin Wu received a B.S. degree in metallurgy from Xi’an Institute of Metallurgy in 1975, an M.S. degree in metal forming from Beijing University of Science and Technology in 1981, and a second M.S. and a Ph.D. degree in materials science and engineering from the University of Michigan -Ann Arbor in 1988 and 1991, respectively. Currently he is an Associate Professor in the Department of Mechanical Engineering at Wayne State University. His research interests include deformation behavior of materials with microstructure evolution and various materials and manufacturing processes. He is a life member of ASME.