Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Journal of Materials...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Journal of Materials Research and Technology
Article . 2025 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
versions View all 2 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

An artificial neural network constitutive model to predict high temperature flow behaviour in 18Ni(250) maraging steel

Authors: Shucong Xu; Lin Yuan; Debin Shan;

An artificial neural network constitutive model to predict high temperature flow behaviour in 18Ni(250) maraging steel

Abstract

Hot compression experiments and microstructure observation investigations are utilized to analyze the hot deformation behavior and flow characterization of 18Ni(250) maraging steel. Arrhenius model, Strain-Compensated Arrhenius-type (SCA) model, Johnson-Cook (JC) model, Zerilli-Armstrong (ZA) model and ANN model were developed for forecasting the flow characteristics, the prediction of each constitutive model was quantitatively assessed using statistical parameters. To determine the ideal deformation settings, two-dimensional and three-dimensional hot deformation activation energy maps were created, and the affect of deformation parameters on the development of the microstructure was demonstrated. The result shows that the ANN model's coefficient of determination (R2) is 99.679 % and average relative error (ARE) is 2.43 %, indicating that it has a greater prediction accuracy than other constitutive models. The dynamic recovery and flow localization in various deformation areas are examined in conjunction with the activation energy maps, and the optimal hot processing window were achieved. Embed the ANN constitutive model into the expert system to develop the “Forging Forming Force Prediction Module”. This module can calculate the stress and forging forming force in real time according to different deformation conditions, providing a theoretical basis for the selection of forging equipment and the verification of the effectiveness of the process scheme, and improving the intelligent manufacturing level of maraging steel forgings.

Keywords

Artificial neural network, Mining engineering. Metallurgy, Forming force prediction, TN1-997, Activation energy maps, Constitutive model, 18Ni(250) maraging steel

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
gold