A Machine-Learning Approach to Predict Creep Properties of Cr-Mo Steel with Time-Temperature Parameters
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Machine-Learning Approach to Predict Creep Properties of Cr-Mo Steel with Time-Temperature Parameters
Authors
Keywords
Metallic materials, Machine learning, Time–temperature parameters, Creep property, Life prediction
Journal
Journal of Materials Research and Technology-JMR&T
Volume -, Issue -, Pages -
Publisher
Elsevier BV
Online
2021-05-07
DOI
10.1016/j.jmrt.2021.04.079
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Direct joining of thermoplastic ABS to aluminium alloy 6061-T6 using friction lap welding
- (2020) Mengjia Xu et al. SCIENCE AND TECHNOLOGY OF WELDING AND JOINING
- Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning
- (2020) Yue Liu et al. ACTA MATERIALIA
- Gallium–Boron–Phosphide ($$\hbox {GaBP}_{2}$$): a new III–V semiconductor for photovoltaics
- (2020) Upendra Kumar et al. JOURNAL OF MATERIALS SCIENCE
- Predictions and mechanism analyses of the fatigue strength of steel based on machine learning
- (2020) Feng Yan et al. JOURNAL OF MATERIALS SCIENCE
- Modern data analytics approach to predict creep of high-temperature alloys
- (2019) D. Shin et al. ACTA MATERIALIA
- Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels
- (2019) Amit K. Verma et al. METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE
- Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
- (2018) Zijiang Yang et al. COMPUTATIONAL MATERIALS SCIENCE
- An online tool for predicting fatigue strength of steel alloys based on ensemble data mining
- (2018) Ankit Agrawal et al. INTERNATIONAL JOURNAL OF FATIGUE
- A new sampling method in particle filter based on Pearson correlation coefficient
- (2016) Haomiao Zhou et al. NEUROCOMPUTING
- A predictive machine learning approach for microstructure optimization and materials design
- (2015) Ruoqian Liu et al. Scientific Reports
- Combinatorial screening for new materials in unconstrained composition space with machine learning
- (2014) B. Meredig et al. PHYSICAL REVIEW B
- Cost-Sensitive AdaBoost Algorithm for Ordinal Regression Based on Extreme Learning Machine
- (2014) Annalisa Riccardi et al. IEEE Transactions on Cybernetics
- Materials informatics
- (2012) Krishna Rajan Materials Today
- On the use of Spearman's correlation coefficient for testing ordered alternatives
- (2011) Jeff T. Terpstra et al. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
- Data preprocessing techniques for classification without discrimination
- (2011) Faisal Kamiran et al. KNOWLEDGE AND INFORMATION SYSTEMS
- Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
- (2010) Geoffroy Hautier et al. CHEMISTRY OF MATERIALS
- Support Vector Machines for classification and regression
- (2009) Richard G. Brereton et al. ANALYST
- Time–temperature superposition for foaming kinetics of Al-alloy foams
- (2007) Amkee Kim et al. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
- Effect of tempering temperature on Z-phase formation and creep strength in 9Cr–1Mo–V–Nb–N steel
- (2007) K. Sawada et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Effect of V and Nb on precipitation behavior and mechanical properties of high Cr steel
- (2006) Takashi Onizawa et al. NUCLEAR ENGINEERING AND DESIGN
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search