建立了高强度管材热影响区金属组织形成模型

IF 0.8 4区材料科学 Q4 METALLURGY & METALLURGICAL ENGINEERING

Metallurgist Pub Date : 2025-07-15 DOI:10.1007/s11015-025-01952-1

M. R. Khismatullin, L. A. Efimenko, A. A. Ramus

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引用次数: 0

摘要

本文介绍了一种利用人工神经网络（ann）预测用于生产K60-K70强度等级管道的高强度钢焊缝影响区（WAZ）金属结构相组成的模型的开发结果。该模型由四个子块组成，依次预测决定WAZ金属最终结构相组成的参数，如平均奥氏体晶粒直径、奥氏体分解的临界温度以及定性和定量结构相组成。每个子块都利用已在MATLAB软件环境中作为函数开发、训练和存储的人工神经网络。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Developing a model of metal structure formation in the heat-affected zone of high-strength pipe steels

The article presents the results of developing a model that employs artificial neural networks (ANNs) to predict the structural-phase composition of the weld-affected zone (WAZ) metal in high-strength steels used to produce pipes of K60–K70 strength classes. The model consists of four sub-blocks that sequentially predict parameters determining the final structural-phase composition of the WAZ metal, such as average austenite grain diameter, critical temperatures of austenite decomposition, and both qualitative and quantitative structural-phase compositions. Each sub-block utilizes ANNs that have been developed, trained, and stored as functions in the MATLAB software environment.

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来源期刊

Metallurgist 工程技术-冶金工程

CiteScore

1.50

自引率

44.40%

发文量

151

审稿时长

4-8 weeks

期刊介绍： Metallurgist is the leading Russian journal in metallurgy. Publication started in 1956. Basic topics covered include: State of the art and development of enterprises in ferrous and nonferrous metallurgy and mining; Metallurgy of ferrous, nonferrous, rare, and precious metals; Metallurgical equipment; Automation and control; Protection of labor; Protection of the environment; Resources and energy saving; Quality and certification; History of metallurgy; Inventions (patents).