Optimisation of chemical composition of high speed steel with high vanadium content for abrasive wear using an artificial neural network

Materials and Design

Volumn 28, Issue 3, 2007, Pages 1031-1037

  Liujie, Xu ^a,c   Jiandong, Xing ^a   Shizhong, Wei ^b,c   Yongzhen, Zhang ^c   Rui, Long ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b Henan Engineering Research Center for Wear of Material   (China)

^c HENAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Back propogation neural network; Bayesian regularization; Carbon content; High speed steel; Vanadium content

Indexed keywords

CARBON; COMPOSITION; NEURAL NETWORKS; OPTIMIZATION; STATISTICAL METHODS; VANADIUM; WEAR OF MATERIALS;

HIGH SPEED STEEL; WEAR WEIGHT LOSS;

ALLOYS;

ALLOYS; CARBON; COMPOSITION; NEURAL NETWORKS; OPTIMIZATION; STATISTICAL METHODS; VANADIUM; WEAR OF MATERIALS;

EID: 33751321373     PISSN: 02613069     EISSN: 18734197     Source Type: Journal    
DOI: 10.1016/j.matdes.2005.10.015     Document Type: Article

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