Next Article in Journal
Synergistic Roles of Nb and Mo in the Formation of Oxides on Fe-20Cr-25Ni-Nb Stainless Steels in High-Temperature CO2
Previous Article in Journal
Experimental Study on the Dynamic Behavior of a Cr-Ni-Mo-V Steel under Different Shock Stresses
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Metals
Volume 13
Issue 4
10.3390/met13040664
metals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prediction of Recrystallization Structure of 2A12 Aluminum Alloy Pipe Extrusion Process Based on BP Neural Network

by
Haishun Jiang
1,2,
Rendong Wu
1,2,
Chaolong Yuan
1,2,*,
Wei Jiao
1,2,
Lingling Chen
1,2 and
Xingyou Zhou
1,2
1
Key Laboratory for Advanced Materials Processing Technology, Ministry of Education of China, Beijing 100084, China
2
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Metals 2023, 13(4), 664; https://doi.org/10.3390/met13040664
Submission received: 28 February 2023 / Revised: 25 March 2023 / Accepted: 26 March 2023 / Published: 27 March 2023
Browse Figures
Versions Notes

Abstract

2A12 aluminum alloy is a high-strength aerospace alloy. During its extrusion process, the extrusion process parameters have a great impact on the microstructure evolution of the extruded products. There are three extrusion process parameters controlled in the actual project, which are the initial temperature of billet, the initial temperature of die and the extrusion speed. Combined with a back propagation (BP) neural network and finite element method (FEM) simulation, based on the constitutive equation and recrystallization evolution process of 2A12 aluminum alloy, this paper establishes a prediction model for the grain size of extruded pipe by these three extrusion process parameters. This paper used a 35MN extruding machine for a production verification of 2A12 pipe. The results show that the predicted grain size is 3% smaller than the actual size.
Keywords: 2A12 aluminum alloy; hot extrusion; BP neural network; recrystallization structure; pipe 2A12 aluminum alloy; hot extrusion; BP neural network; recrystallization structure; pipe

Share and Cite

MDPI and ACS Style

Jiang, H.; Wu, R.; Yuan, C.; Jiao, W.; Chen, L.; Zhou, X. Prediction of Recrystallization Structure of 2A12 Aluminum Alloy Pipe Extrusion Process Based on BP Neural Network. Metals 2023, 13, 664. https://doi.org/10.3390/met13040664

AMA Style

Jiang H, Wu R, Yuan C, Jiao W, Chen L, Zhou X. Prediction of Recrystallization Structure of 2A12 Aluminum Alloy Pipe Extrusion Process Based on BP Neural Network. Metals. 2023; 13(4):664. https://doi.org/10.3390/met13040664

Chicago/Turabian Style

Jiang, Haishun, Rendong Wu, Chaolong Yuan, Wei Jiao, Lingling Chen, and Xingyou Zhou. 2023. "Prediction of Recrystallization Structure of 2A12 Aluminum Alloy Pipe Extrusion Process Based on BP Neural Network" Metals 13, no. 4: 664. https://doi.org/10.3390/met13040664

APA Style

Jiang, H., Wu, R., Yuan, C., Jiao, W., Chen, L., & Zhou, X. (2023). Prediction of Recrystallization Structure of 2A12 Aluminum Alloy Pipe Extrusion Process Based on BP Neural Network. Metals, 13(4), 664. https://doi.org/10.3390/met13040664

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop