SCOPUS 정보 검색 플랫폼

Volumn 28, Issue 5, 2007, Pages 1678-1684

Prediction of effect of thermo-mechanical parameters on mechanical properties and anisotropy of aluminum alloy AA3004 using artificial neural network

(2) Forouzan, S a Akbarzadeh, A a

a SHARIF UNIVERSITY OF TECHNOLOGY (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

ANISOTROPY; ANNEALING; BACKPROPAGATION; COLD ROLLING; HOT ROLLING; LEARNING ALGORITHMS; NEURAL NETWORKS; TENSILE STRESS; THERMOMECHANICAL TREATMENT; YIELD STRESS;

ABSOLUTE RELATIVE ERROR; ELONGATION; ULTIMATE TENSION STRESS;

ALUMINUM ALLOYS;

ALUMINUM ALLOYS; ANISOTROPY; ANNEALING; BACKPROPAGATION; COLD ROLLING; HOT ROLLING; LEARNING ALGORITHMS; NEURAL NETWORKS; TENSILE STRESS; THERMOMECHANICAL TREATMENT; YIELD STRESS;

EID: 33845212848 PISSN: 02613069 EISSN: 18734197 Source Type: Journal
DOI: 10.1016/j.matdes.2006.03.016 Document Type: Article

Times cited : (14)

References (6)

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2
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- The effect of preheat treatment on microstructure, recrystallization and texture f strip cast AA3004 alloy
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3
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- Influence of initial structure n hot rolling and recrystallization texture in AA3004 aluminum alloy
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- Ding, Sx.¹ Ren, B.² Morris, Jg.³

4
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- Effect of texture on earing in FCC metals: FEM simulation
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5
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- Prediction of elevated temperature fatigue crack growth rates in Ti-6Al-4V alloy - neural network approach
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- Fotovati, A.¹ Goswami, T.²

6
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- Using neural network to predict parameters in hot working of aluminum alloys
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