RETRACTED ARTICLE: Modeling ductile to brittle transition temperature of functionally graded steels by fuzzy logic

Nazari, Ali; Milani, Amir Ali

doi:10.1007/s10853-011-5563-z

Cited by 27 publications

(4 citation statements)

References 20 publications

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“…It needs only a simple controlling method to be set, based on engineering experience. Therefore, it is particularly useful in studying complicated structural control systems (Akkurt et al, 2004;Baykasoglu et al, 2004;Nazari and Milani, 2011). The percentage of water absorption (PWA) of geopolymers can be calculated using the models built with FL.…”

Section: R E T R a Cmentioning

confidence: 99%

RETRACTED: Prediction of Resistance to Water Damage of Geopolymers with Seeded Fly Ash and Rice Husk Bark Ash by Fuzzy Logic

Riahi¹,

Nazari

Ghasemi³

2011

International Journal of Damage Mechanics

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In 2019 SAGE became aware of author misconduct concerning suspected redundant publication of 22 articles published in International Journal of Damage Mechanics and Journal of Composite Materials. SAGE and the journals' Editors immediately launched an investigation and found that the following articles contain significant overlap with previously published articles by at least one of the authors listed on each of the articles below. Therefore, SAGE and the journals' Editors have decided to retract the following articles for reasons of redundant publication.

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Section: R E T R a Cmentioning

confidence: 99%

RETRACTED: Prediction of Resistance to Water Damage of Geopolymers with Seeded Fly Ash and Rice Husk Bark Ash by Fuzzy Logic

Riahi¹,

Nazari

Ghasemi³

2011

International Journal of Damage Mechanics

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“…Several works have addressed the utilization of computer-aided prediction of engineering properties including those done by the authors Riahi, 2010b, 2011a, b;Nazari and Didehvar, 2011;Nazari and Milani, 2011d;Nazari et al, 2011g). Adaptive network-based fuzzy inference system (ANFIS) is the famous hybrid neuro-fuzzy network for modeling the complex systems (Jang, 1993).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Analytical Modeling of Charpy Impact Energy of Functionally Graded Steels by ANFIS

Bohlooli

Nazari

Kaykha

2012

International Journal of Damage Mechanics

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In this study, the Charpy impact energies of ferritic and austenitic functionally graded steels produced by electroslag remelting have been modeled in crack divider configuration by predicting Vickers microhardness profiles of the specimens with adaptive network-based fuzzy inference systems. To produce functionally graded steels, two slices of plain carbon and austenitic stainless steels were spotwelded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. To build the model for graded ferritic and austenitic steels, training, testing, and validation using, respectively, 174 and 120 experimental data were conducted. It was found that the data are valid within the range of examined data. A good fit equation which correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterwards, the Vickers microhardness of each layer in functionally graded steels was related to the Charpy impact energy of the corresponding layer. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.

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“…In addition, formation of polygonal ferrite is depressed with an increase in the austenite grain size and formation of non-polygonal microstructures is promoted 6 . Several works have addressed utilizing of computeraided prediction of engineering properties including those done by the authors [14][15][16][17][18][19] . Adaptive Network-based Fuzzy Inference Systems (ANFIS) is the famous hybrid neuro-fuzzy network for modeling the complex systems 20 .…”

Section: Introductionmentioning

confidence: 99%

Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

Khalaj

Nazari²,

Livary³

2013

Mat. Res.

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The paper presents some results of the research connected with the development of new approach based on the Adaptive Network-based Fuzzy Inference Systems (ANFIS) of predicting the Vickers microhardness of the phase constituents occurring in five steel samples after continuous cooling. The independent variables in the model are chemical compositions, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. To construct these models, 114 different experimental data were gathered from the literature. The data used in the ANFIS model is arranged in a format of twelve input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is Vickers microhardness. In this model, the training and testing results in the ANFIS systems have shown strong potential for prediction of effects of chemical compositions and heat treatments on hardness of microalloyed steels.

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RETRACTED ARTICLE: Modeling ductile to brittle transition temperature of functionally graded steels by fuzzy logic

Cited by 27 publications

References 20 publications

RETRACTED: Prediction of Resistance to Water Damage of Geopolymers with Seeded Fly Ash and Rice Husk Bark Ash by Fuzzy Logic

RETRACTED: Prediction of Resistance to Water Damage of Geopolymers with Seeded Fly Ash and Rice Husk Bark Ash by Fuzzy Logic

RETRACTED: Analytical Modeling of Charpy Impact Energy of Functionally Graded Steels by ANFIS

Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

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