RETRACTED: Modeling ductile to brittle transition temperature of functionally graded steels by ANFIS

Nazari, Ali; Milani, Amir Ali; Khalaj, Gholamreza

doi:10.1016/j.apm.2011.11.032

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…The ductile-brittle transition temperature of steel is influenced by factors such as thermal processing [19,20], metallurgical composition [21], and grain boundary precipitation [22]. For this research, the metallographic structure properties and heat load were closely related in the welding process [23].…”

Section: Influence Of Welding Heat Inputmentioning

confidence: 92%

Research on Influential Mechanism of HAZ Impact Toughness for Shipbuilding Steel with Mg Addition

Sun

Zhu

et al. 2018

Metals

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The welding performance of shipbuilding steel under large heat input could be improved greatly by the addition of Mg to the steel, but the impact toughness of the heat affect zone (HAZ) is not stable. According to the three different thickness steel plates obtained in the industrial experiment, the large heat input welding was carried out by different heat input, and the impact toughness analysis, impact fracture analysis, metallographic microstructure analysis and inclusions analysis were carried out. The results showed that, the HAZ of three kinds of thickness plates induced much intragranular acicular ferrite (IAF); with Mg addition, the inclusion dimension had been reduced effectively, and the IAF-induced ability of the inclusions had also been improved. The difference of HAZ impact toughness with different welding heat input and different impact temperature is significant; considering the influence of welding heat input and metallographic microstructure on the impact toughness of HAZ, the welding heat load had a far greater effect than the metallographic microstructure on ductile–brittle transition temperature. At the same time, if the original metallographic microstructure of steel was coarse, the pinning effect of the inclusions would be reduced significantly, and the microstructure of HAZ would be coarsened and the impact toughness of HAZ would be decreased, so there is a certain matching relationship between the metallographic microstructure and the inclusion dimension.

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Section: Influence Of Welding Heat Inputmentioning

confidence: 92%

Research on Influential Mechanism of HAZ Impact Toughness for Shipbuilding Steel with Mg Addition

Sun

Zhu

et al. 2018

Metals

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“…They made use of multiple artificial neural networks (MANN) and ANFIS to achieve condition monitoring and fault diagnosis of Francis turbine. Other scholars' studies also show that the ANFIS integrates fuzzy inference and neural network learning ability and stronger adaptability in fault diagnosis and damage detection [31][32]. Probabilistic neural network (PNN) uses exponential function instead of the sigmoid activation function to construct the nonlinear decision boundary which is close to the Bayesian optimal decision surface.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Review of the Research Progress of Structural Damage Identification Method Based on Computational Intelligence Techniques

Xiao

2013

AMM

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This paper presents a comprehensive review of computational Intelligence (CI) technology applied in structural damage identification, clarifies the basic principles of computational intelligence techniques, as well as the applicable difficulties that exist in the field of structural damage identification (SDI) from 6 aspects: fuzzy theory, evidence theory, rough set theory, artificial neural networks, support vector machines and evolutionary computation, and then discussed the applicable prospects of computational Intelligence in SDI. It points out that the reasonable cross-fusion of a variety of CI method to specific research object is a necessary means for SDI research. For economy and practicality considerations, CI is suitable for highly integrated complex structural damage identification.

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“…Scientists have used the applications of ANN and ANFIS models successfully in various occasions in the field of Civil Engineering. Nazari et al [3] have used ANFIS effectively in estimating ductile to brittle transition temperature of functionally graded steel. Prasad et al [4] have developed ANFIS productively in predicting the air quality and input optimization to be able to save cost and time.…”

mentioning

confidence: 99%

Predicting the Civil Engineering Characteristics through Soft Computing Models

Keshavarz¹

2017

CERJ

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RETRACTED: Modeling ductile to brittle transition temperature of functionally graded steels by ANFIS

Cited by 10 publications

References 22 publications

Research on Influential Mechanism of HAZ Impact Toughness for Shipbuilding Steel with Mg Addition

Research on Influential Mechanism of HAZ Impact Toughness for Shipbuilding Steel with Mg Addition

A Review of the Research Progress of Structural Damage Identification Method Based on Computational Intelligence Techniques

Predicting the Civil Engineering Characteristics through Soft Computing Models

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