Prediction of flow and dynamic recrystallization behavior based on three machine learning methods for a novel duplex-phase titanium alloy

被引:2
作者
Zhang, Shuai [1 ,2 ]
Zhang, Haoyu [1 ,2 ]
Wang, Chuan [1 ,3 ]
Zhou, Ge [1 ,2 ]
Cheng, Jun [4 ]
Zhang, Zhongshi [5 ]
Wang, Xiaohu [5 ]
Chen, Lijia [1 ,2 ]
机构
[1] Shenyang Univ Technol, Sch Mat Sci & Engn, Shenyang 110870, Liaoning, Peoples R China
[2] Shenyang Univ Technol, Shenyang Key Lab Adv Struct Mat & Applicat, Shenyang 110870, Liaoning, Peoples R China
[3] Liaoning Univ, Coll Light Ind, Shenyang 110036, Liaoning, Peoples R China
[4] Northwest Inst Nonferrous Met Res, Shaanxi Key Lab Biomed Met Mat, Xian 710016, Shaanxi, Peoples R China
[5] ZS Adv Mat Co Ltd, Donggang 118300, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
Duplex-phase titanium alloy; Hot deformation; Machine learning method; Processing map; Kinetic analysis; HOT DEFORMATION-BEHAVIOR; NEURAL-NETWORK; PROCESSING MAPS; SECONDARY ALPHA; HIGH-STRENGTH; EVOLUTION; WORKING; MICROSTRUCTURE; MECHANISMS; DUCTILITY;
D O I
10.1016/j.intermet.2024.108523
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In this work, the vacuum arc-melting was used to prepare the Ti-10V-5Al-2.5Fe-0.1B alloy. Single-pass isothermal compression experiments were carried out on the alloy in the temperature range of 770-920 degrees C at strain rates of 0.0005-0.5 s-1.-1 . The BP model optimized by the bald eagle search algorithm (BES-BP), the BP model optimized by the sparrow search algorithm (SSA-BP), and the BP model optimized by the gray wolf optimization algorithm (GWO-BP) were developed for high-precision prediction of flow stress. The above models were compared by using the mean square correlation coefficient, root mean square error, and average absolute relative error between the predicted and experimental flow stress. The three prediction accuracy parameters have indicated that the BES-BP model has a higher accuracy for flow stress prediction at the known and the new process parameters. A hot processing map based on the dynamic materials model was developed by using the flow stress predicted in the framework of the BES-BP model, and EBSD analysis was performed as well. The results show that the degree of dynamic recrystallization increases with an increase in the power dissipation factor, and the formation of deformation bands is the main cause of instability. The minimum critical stress for inducing dynamic recrystallization of the alloy was found to be 13.13 MPa at 890 degrees C/0.0005 s-1.-1 . Moreover, the power dissipation factor increases with a decrease in critical stress. In addition, microstructure validation data reveal that the dynamic recrystallization model has a high accuracy for critical stress prediction, confirming that the critical stress increases with a decrease in the dynamic recrystallization fraction.
引用
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页数:15
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共 45 条
[1]   An improved Fuzzy based GWO algorithm for predicting the potential host receptor of COVID-19 infection [J].
Achom, Amika ;
Das, Ranjita ;
Pakray, Partha .
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 151
[2]   Novel meta-heuristic bald eagle search optimisation algorithm [J].
Alsattar, H. A. ;
Zaidan, A. A. ;
Zaidan, B. B. .
ARTIFICIAL INTELLIGENCE REVIEW, 2020, 53 (03) :2237-2264
[3]   A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy [J].
Ashtiani, H. R. Rezaei ;
Shahsavari, P. .
JOURNAL OF ALLOYS AND COMPOUNDS, 2016, 687 :263-273
[4]   Evolution of hot processing map and microstructure of as-forged nickel-based superalloy during hot deformation [J].
Chen, Le ;
Zhang, Bing ;
Yang, Yan ;
Zhao, Tianli ;
Xu, Yi ;
Wang, Qi ;
Zan, Bin ;
Cai, Jun ;
Wang, Kuaishe ;
Chen, Xi .
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, 2023, 24 :7638-7653
[5]   Research on prediction model of geotechnical parameters based on BP neural network [J].
Cui, Kai ;
Jing, Xiang .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (12) :8205-8215
[6]   A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm [J].
Dian S. ;
Zhong J. ;
Guo B. ;
Liu J. ;
Guo R. .
Expert Systems with Applications, 2022, 208
[7]   Coupled effects of deformation and cooling on the evolution of primary and secondary alpha of two-phase Ti-alloys [J].
Fan, X. G. ;
Meng, M. ;
Gao, P. F. ;
Zhan, M. .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2018, 710 :271-279
[8]   Deformation behavior and microstructure evolution during hot working of a coarse-grained Ti-5Al-5Mo-5V-3Cr-1Zr titanium alloy in beta phase field [J].
Fan, X. G. ;
Zhang, Y. ;
Gao, P. F. ;
Lei, Z. N. ;
Zhan, M. .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2017, 694 :24-32
[9]   Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy [J].
Haghdadi, N. ;
Zarei-Hanzaki, A. ;
Khalesian, A. R. ;
Abedi, H. R. .
MATERIALS & DESIGN, 2013, 49 :386-391
[10]   A comparative study of the mechanical properties of high-strength β-titanium alloys [J].
Ivasishin, O. M. ;
Markovsky, P. E. ;
Matviychuk, Yu. V. ;
Semiatin, S. L. ;
Ward, C. H. ;
Fox, S. .
JOURNAL OF ALLOYS AND COMPOUNDS, 2008, 457 (1-2) :296-309