한국생산제조학회 학술지 영문 홈페이지

Journal Archive

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 27 , No. 1

[ Article ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 27, No. 1, pp. 41-45
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
ISSN: 2508-5107 (Online)
Print publication date 15 Feb 2018
Received 16 Oct 2017 Revised 7 Dec 2017 Accepted 29 Dec 2017
DOI: https://doi.org/10.7735/ksmte.2018.27.1.41

인공신경망을 이용한 인콜로이 825 합금의 고온 변형 거동 연구
송신형a, * ; 김용배b ; 이승용c

Study on the High Temperature Deformation of Incoloy 825 Alloy using an Artificial Neural Network
Shin-Hyung Songa, * ; Yongbae Kimb ; Seoung-Yong Leec
aDepartment of Mechanical Engineering, Wonkwang University, 460, Iksan-daero, Iksan, Jeonbuk-do, 54538, Korea
bKorea Institute of Industrial Technology, 156, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Korea
cAutomobile Engineering, Seojeong College, 1049-56, Hwahap-ro, Yangju, Gyeonggi-do, 11429, Korea
Correspondence to : *Tel.: +82-63-850-6969, Fax: +82-63-850-6666, E-mail address:57nsongsh@daum.net (Shin-Hyung Song).


Abstract

In this research, a constitutive study of the high-temperature deformation behavior of Incoloy 825 alloy was performed using an artificial neural network (ANN). For the study, a high-temperature compression test on Incoloy 825 was carried out on a Gleeble 3500 system at temperatures ranging from 950-1,150°C and strain rates of 0.2/s and 2/s. After the compression test, the study of the flow stress was conducted for various temperatures and strain rates. The flow stress variation during the deformation of Incoloy 825 was dependent on the deformation temperature and strain rate. The flow stress at various deformation temperatures and strain rates was modeled using the Hollomon-type equation. The constitutive behavior of Incoloy 825 during hot temperature deformation was modeled using an ANN.


Keywords: Incoloy 825, High temperature, Compression test, Artificial neural network

References
1. Park, Y. T., Jeong, Y. H., 2016, Effect of Hot Forging Ratio on the Mechanical Properties in Incoloy 825 Alloy, Journal of the Korean Society for Heat Treatment. 29:6 259-263.
2. Kim, H. B., Lee, C. H., 1997, A Study of Dissimilar Weldability of Incoloy 825 With Mild Steel, Korean Journal of Materials Research 7:2 162-170.
3. Quan, G., Yu, C., 2014, A Comparative Study on Improved Arrhenius-Type and Artificial Neural Network Models to Predict High-Temperature Flow Behaviors in 20MnNiMo Alloy, The Scientific World Journal 2014 108492.
4. Zhao, J., Ding, H., 2014, Modelling of the Hot Deformation Behaviour of a Titanium Alloy Using Constitutive Equations and Artificial Neural Network, Computational Materials Science 92 47-56.
5. Guo, L., Li, B., 2013, Constitutive Relationship Model of TC21 Alloy Based on Artificial Neural Network, Transactions of Nonferrous Metals Society of China 23:6 1761-1765.
6. Duan, Y., Ma, L., 2017, Developed Constitutive Models, Processing Maps and Microstructural Evolution of Pb-Mg-10Al-0.5B Alloy, Materials Characterization 129 353-366.
7. YAN, J., Pan, Q., 2017, Flow Behavior of Al−6.2Zn−0.70Mg− 0.30Mn-0.17Zr Alloy During Hot Compressive Deformation Based on Arrhenius and ANN Models, Transactions of Nonferrous Metals Society of China 27:3 638-647.
8. Babu, A., Mandal, S., 2017, Regression Based Novel Constitutive Analyses to Predict High Temperature Flow Behavior in Super Austenitic Stainless Steel, Materials Science and Engineering: A 703 187-195.