利用正弦双曲构成方程和人工神经网络预测 AISI 321 奥氏体不锈钢在动态恢复过程中的流动行为

IF 1.3 4区材料科学 Q3 METALLURGY & METALLURGICAL ENGINEERING

Canadian Metallurgical Quarterly Pub Date : 2024-08-21 DOI:10.1080/00084433.2024.2393026

Mehdi Shaban Ghazani, Akbar Vajd, Keyhan Hosseinnejad

引用次数: 0

摘要

在本研究中，AISI 321 奥氏体不锈钢在 800、850 和 900°C 的温度和 0.001-1 s-1 的应变率范围内进行了热压缩变形。R...

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Prediction of the flow behaviour of AISI 321 austenitic stainless steel during dynamic recovery using sine hyperbolic constitutive equation and artificial neural network

In the present investigation, AISI 321 austenitic stainless steel was subjected to hot compression deformation at temperatures of 800, 850, and 900°C and strain rates in the range of 0.001-1 s-1. R...

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来源期刊

Canadian Metallurgical Quarterly 工程技术-冶金工程

CiteScore

1.90

自引率

11.10%

发文量

审稿时长

6 months

期刊介绍： Canadian Metallurgical Quarterly publishes original contributions on all aspects of metallurgy and materials science, including mineral processing, hydrometallurgy, pyrometallurgy, materials processing, physical metallurgy and the service behaviour of materials. An invaluable resource for international researchers and professionals engaged in interdisciplinary research in metallurgy and materials science.